In my opinion, there is an interesting paradox with geospatial analysis and crisis management - we continually develop new and improved methods for handling disaster situations, but our increasingly complicated societies, economies, and infrastructures increase the challenges associated with disasters. People and their environments are more interconnected than ever, and spatial data science and related technologies are in many cases the most appropriate mechanism for analyzing and rectifying emergency situations.
There are four key phases of emergency management: vulnerability assessment, preparedness, response, and recovery. In subsequent lessons, we will explore each of those topics in detail. Later, we will work together to research and apply methods from spatial data science to emergency management contexts, and we will explore how geospatial perspectives and technologies have been used in a variety of ways in recent disasters.
Each week, you will learn about an emerging technology trend and how it relates to geospatial analysis and crisis management. One of my goals is to make sure you learn about and consider new trends and themes in technology, and imagine how those advances can and will impact Spatial Data Science for Emergency Management in the future. The geospatial planning activities you participate in now should take into account new types of technologies that will be commonplace in the next 5-10 years.
Crowdsourcing approaches for damage assessment have been popular, with micro-tasking platforms like Tomnod used to leverage digital volunteers, as shown here for the 2015 Nepal Earthquake. Volunteers look at images of structures and rate the level of damage.
What You Will Learn
By the successful completion of this lesson, you should be able to:
Lesson 1 is one week in length. To finish this lesson, you must complete the activities listed below.
To Read |
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To Do |
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Please refer to the Course Calendar for specific due dates.
If you have questions about the content or lesson activities, please post them to the General Questions and Discussion forum in Canvas. While you are there, feel free to post your own responses if you, too, are able to help a classmate. If your question is of a personal nature, please email me directly through Canvas.
Before going any further, I'd like you to consider the devastating 2019-20 Black Summer Bushfires in Australia. I focus on event because it has prompted a major National Review of disaster and emergency management arrangements. The Royal Commission into Natural Disaster Arrangements [3] released their final report at the end of last year. Royal Commissions are basically a big investigation that relies on submissions from all kinds of stakeholders, from academics, to frontline workers, to citizens. This process results in a series of recommendations that the government considers. While the bushfires prompted this review, the recommendations take a multi-hazard approach. So, how can you manage fires, smoke, heatwaves, floods, cyclones in a more coordinated way.
I'd like you to look at a few sections of this report and also keep it on hand as we go through the course. It has a lot of information relevant to the topics we cover, albeit we are exploring geospatial dimensions in greater depth. First, have a look at some photos and videos from the Bushfire History Project (below) to get a feel for what happened last year.
Now, look at the download the report from this direct link or go to the Commission's website [4].
Royal Commission into National Natural Disaster Arrangements Report [5]
Royal Commission into National Natural Disaster Arrangements Appendices [6]
Please read the following sections (don't worry if these seem a bit technical given you just started the course):
I'd like you to consider a few questions (nothing to submit now!):
I hope this has provided a concrete and current picture of the complexity of emergency management. I'll refer you back to the Royal Commission report later in the class.
The improvement of the built and social environment in order to reduce, withstand or prevent disaster impacts.
Actions taken prior to a disaster with the intent of ensuring a better event response
Actions taken immediately before, during and after an event to alleviate suffering and prepare for recovery
The rebuilding or improvement of disaster-affected areas
It is generally agreed upon that there are four key stages of emergency management problems.
You can probably imagine a wide array of possible geospatial applications that would make sense for each of these stages of emergency management. Lesson 2 in this class will talk about hazards more generally, and then, in Lessons 3-6, we'll start a deep dive into how geospatial perspectives and technologies can be used in these four stages. In Lessons 7-9, we'll explore scenarios and cover a few case studies to see how geospatial analysis has been used in real-world emergency situations.
Here are brief definitions for each stage of emergency management:
Planning & Mitigation: Evaluation of the potential types of disasters and the development of plans for reducing their probability or their impact on life & resources.
Preparedness: Actions undertaken when mitigation efforts have not prevented or are unable to prevent a disaster from taking place.
Response: Activities that occur in the wake of a disaster that are intended to identify and assist victims and stabilize the overall disaster situation.
Recovery: Actions following a disaster that aim to restore human and environmental systems back to normal.
We will begin our consideration of geospatial approaches and technologies related to emergency management by contrasting four perspectives. On this page, the role of geospatial analysis in the work of the Federal Emergency Management Agency (FEMA) is described. This includes some historical perspective on how FEMA's mission has evolved over the last 10 years or so. Next, we will focus on emergency management related applications developed by Esri, the peak GIS software company globally. Then, for a very different perspective, you will consider what the 'digital humanitarian' community is doing in response to factors like big data, volunteered geographic information (VGI) and social media. Finally, we will consider emergency management in light of cutting-edge technologies. Of course, all of these areas are interrelated, and we will cover much more as the course proceeds! The idea is to start building a context and framework for developing a deep understanding of the topics to come.
First, we will take a look at the Federal Emergency Management Administration [8] (FEMA). If you're not familiar, FEMA is part of the US Department of Homeland Security and is the lead agency for preparing for, responding to and assisting with recovery from major disasters. Have a look at their website if you want to learn more. (As we move through the course, pay attention to the different roles that emergency management organizations play, particularly at local, state and federal levels, and the types of incidents they are responsible for).
For a little context, here's what FEMA Enterprise GIS Services considers its mission with respect to GIS and Emergency Management.
FEMA Enterprise GIS Services
Our primary mission is administration, coordination, collection, and dissemination of geographic information for FEMA and the Emergency Management Community under Emergency Support Function #5 (Information and Planning) of the National Response Framework and in support of the Robert T. Stafford Disaster Relief and Emergency Assistance Act (PL 93-288) as amended. Our current concept of operations includes a full range of GIS services to all FEMA program offices that encompasses sophisticated geospatial analytics through the Mapping and Analysis Center (MAC) and deployable GIS technology through the Deployable Emergency GIS program (DEGS).
Let's dig deeper into this. First, consider this excerpt from FEMA's Mapping and Analysis Center in 2008 [9] (note it is no longer maintained by FEMA and the MAC has evolved into other departments). Make a mental note of the range of functions that they focused on and some of the ways they went about their work. I wanted you to look at this old description, so you can contrast it with what FEMA does now, and more importantly to highlight how much has changed in a short period of time with regard to the ways geospatial products are generated and distributed.
Now, let's jump ahead 10 years! Start with the interesting 2018 presentation slides from Chris Vaughan [10] on GIS @ FEMA Working Smarter Through Data Analytics. Finally, have a look at the 20 September 2017 FEMA Geospatial Coordination Call briefing [11]. This is a summary of 'situation awareness' for the day Hurricane Maria hit landfall in Puerto Rico and is quite comprehensive.
With this historical context, have a look at some of FEMA's current offerings at the FEMA Geospatial Resource Center "Hub" [12]. Notice the dashboard with a summary of current hazard events. Click on a few of the hazards and look at what's available.
Consider these artifacts and reflect on what you see that may have changed in recent years, e.g., increasing focus on analytics and real-time.
For each lesson, I will ask you to read parts of your textbooks and/or selected online materials and articles. As you can see below, I'll try to make it as clear as possible what you're expected to do by always identifying specific reading assignments in a separate box.
You can access the readings right in the course website, and they are also available in Canvas.
For our first set of readings, we will focus on setting the stage for the rest of the lessons this term. First, I'd like you to read the white papers developed by folks at Esri and contrast it with the company’s current software and service offerings. These provide a simple overview of the common terms and topics associated with GIS for Emergency Management, and they show you how the GIS Goliath perceives the role of geospatial tools and methods in the context of Emergency Management. You just read a bit about how FEMA sees the state of affairs, and I think you'll notice some key similarities (and differences) in how the world is viewed from these two perspectives.
Second, I've selected a chapter from a National Academies of Science report written in 2007 that sets a research agenda for GIS in Emergency Management. The specific chapter I've picked for this week focuses on how GIS was or could have been used in a few different disaster scenarios. Unfortunately, these are examples that are still relevant today, over 10 years on.
Finally, you’ll contrast these perspectives with the emerging field of Spatial Data Science. First, you will look at a journal article focused on spatial data science and how it is shaping cartography/visualization. You'll see throughout this course that visualization is an essential part of understanding and addressing problems in emergency management. So, it is useful to explore spatial data science through this lens. The book chapter from Digital Humanitarians introduces how the disaster and humanitarian community is opening up and engaging with big data and volunteered geographic information (VGI) at a remarkable pace.
ESRI White Paper on GIS for Emergency Management, [13] which outlines how Esri sees a role for ArcGIS in Emergency Management (in 2012!). Contrast this with Esri’s current ArcGIS for Disaster Management [14] tools on their website.
These materials present definitions and roles for GIS in the context of Emergency Management. They also reflect the view from a major software vendor in this field. As you peruse these documents, think about which aspects seem software-specific vs. those that appear to be more general to all geospatial applications in emergency management. How would you define roles differently, or broaden some of their definitions?
Chapter 2: Thinking About Worst Cases from Successful Response Starts With a Map: Improving Geospatial Support for Disaster Management. Please visit The National Academies Press [15] and read the chapter online or, you can create an account and download the chapter for free. This chapter is VERY OLD now (2007), but I think it will provide a good background for thinking about different disasters and how geospatial approaches can help us understand what might happen. Reflect on how things may be in 2023. For example, scenario two talks about a hurricane hitting the New York region, and this actually happened with Hurricane Sandy. We will talk about Sandy later in the course.
Robinson, A.C. et al. 2017. Geospatial big data and cartography: research challenges and opportunities for making maps that matter [16]. International Journal of Cartography 3: 32-60.
If you are having trouble accessing the paper through the link above, you can download the PDF directly here [17].
This short web article - Becoming a digital humanitarian, one deployment at a time [18] and Chapter 1: The Rise of Digital Humanitarians from Digital Humanitarians. See the Library Resources menu to read the chapter.
As you read the three different worst-case scenarios, it should be apparent that a key challenge is simply developing a rapid picture of the spatial extent of a disaster. If you assume that a given disaster will disable local EOCs and their accompanying geospatial tools and data, describe at least two ways that emergency managers brought in from afield could quickly assemble data that describes the extent of the disaster. How would folks from the digital humanitarian community approach this problem?
Complete the writing assignment - Details on the next page!
Based on this week's lesson, address the following:
1. As you read the three different worst-case scenarios, it should be apparent that a key challenge is simply developing a rapid picture of the spatial extent of a disaster. If you assume that a given disaster will disable local EOCs and their accompanying geospatial tools and data, describe at least two ways that emergency managers brought in from afield could quickly assemble data that describes the extent of the disaster. How would folks from the digital humanitarian community approach this problem?
2. What do you consider to be the one or two key challenges facing the application of Spatial Data Science to Emergency Management in (a) temporally short, geographically local event(s) versus (b) temporally extended, geographically regional events? Assume your audience is a group of emergency management planners considering incorporating geospatial data and analysis into their operations. They don't have a lot of time, so be direct and succinct in your analysis.
Make clear how your response relates to the readings.
This response should be between 500 - 600 words in length.
This class is writing intensive. I'll be editing and commenting on your written work throughout the term. Penn State's Style for Students [19] guide can be a huge help if it's been a while since you've written for a course like this.
It is important for you to save your files in the following format so that I can match each submission up with the correct student.
L1_assign_firstinitialLastName.doc For example, my file would be named "L1_assign_mBeaty.doc"
Upload your assignment to the Writing Assignment (L1) dropbox. See the Course Calendar for specific due dates.
This assignment is worth 4% of your total course grade and will be graded out of 20 points using the following rubric.
Criteria | Description | Possible Points |
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Content and Impact |
You make strong and logical arguments and provide analytical insights. Ideas are well organized, clearly communicated and relevant to the prompt. All criteria are accurately addressed. Supporting details are shared, elaborated upon and demonstrate understanding. Examples are provided, and your post includes images or other multimedia that support content. | 15 |
Clarity and Mechanics |
Evidence of editing and proofreading is evident. Writing is engaging and well-structured with excellent transitions between sentences and paragraphs. Concepts are integrated in an original manner. | 5 |
A key component of your graduate coursework will be participating in online discussions with classmates. This will occur around two main activities: 1) Discussion of the weekly readings and/or 2) Discussions of the Emerging Theme content.
In this class, you will be expected to be online and participating in online discussions at least 3 days a week. While the times of day and specific days of the week in which you do this work are flexible, you must participate actively and regularly in online discussions in order for you to be successful and fulfill your responsibilities as a member of the class learning community. I would suggest setting aside dedicated time several times a week for participation.
Lessons begin on Wednesday and the discussion will be open for 10 days (even though the rest of the lesson is only scheduled for one week). Your initial post is due on Sunday. You are then required to participate in the conversation on at least two different days by or before the following Thursday.
In addition to being thoughtful about the discussion prompts, I would like to see you asking and answering questions, making suggestions, and sharing examples from your own life. I would like to see this begin to look like a true conversation, so while most of your posts should be about a paragraph in length, some may be shorter and conversational. Remember to contribute to the learning community by being creative in your approaches to topics, being relevant in the presented viewpoints, and attempting to motivate the discussion. And remember, there is value in diverse perspectives, so it is ok to have a constructive and considerate disagreement.
Your contributions will be scored as follows.
Criteria | Points |
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Initial post is submitted on time | 3 |
Follow up posts were made on two different days, 2 days gets full points, less than 2 days gets 0 points. | 6 |
Quality of contributions | 6 |
Total | 15 |
OK, you might think, "Well - isn't GIS already something most people consider high tech?" The answer to that question is a little complicated. In comparison to where we were about 20 years ago, yes, current GIS systems are pretty high tech. In comparison to recent advances in software, interfaces, and the ways in which regular people can participate in the development of data and tools - no, off-the-shelf desktop GIS software isn't so radical anymore.
What I hope to do in this course is to bring in new trends and themes in technology and imagine how those advances can and will impact spatial data science in the future, with particular emphasis on how those technologies fit or could be adapted to support geospatial analysis for emergency management.
Each lesson features an Emerging Theme page that presents a technology and encourages you to envision its potential impact on GIS systems for Emergency Management. I draw upon video lectures, links to live demonstrations, and other multimedia as much as possible to make these modules as engaging as possible.
The themes we will cover this term are:
The following 4:30 minute video, Geospatial - A Golden Thread in the Fourth Industrial Revolution is from the geospatial industry website, Geospatial World. It is a bit sensationalized but does cram in a lot of interesting content about technology and geospatial and provides some viewpoints from industry leaders. I hope it makes you want to learn more about these emerging themes!
On the next page, you'll find your first Emerging Theme assignment. In this assignment, we will examine new types of mobile interfaces and discuss how they could be integrated into future GIS systems for emergency management.
This week, I’d like you to take a look at a few very exciting technology demonstrations that I think are relevant to spatial data science applications for emergency management. These videos show the cutting edge of what is possible with computers, and I think it’s quite reasonable to expect that in the next 5 years or so these things will become quite common in consumer and professional systems.
Throughout this course, we will be considering information from different viewpoints, including industry, government, NGO, and academia.
Let's start with this video about the ways spatial data and technology are being used (to varying effect) to address different aspects of the COVID-19 response.
The next video is from the DHS Science and Technology Directorate’s Next Generation First Responder Program [21] and describe how emerging technology, including geospatial, are being incorporated in first response situations. They both describe a high level of integration amongst technology. Think about how this might fall down in a real emergency situation.
Finally, take a look at the I-React project funded by the European Commission that use augmented reality in an disaster response situation. Visit this website [22] and watch the video below.
NOTE: Respond to this assignment in the Emerging Theme Discussion (L1) forum by the date indicated on the course calendar.
This discussion will be graded out of 15 points.
Please see the Discussion Expectations and Grading page under the Orientation and Course Resources module for details.
This course is built around a term project that will integrate your understanding of geospatial perspectives and technologies with what you have learned about how they can be applied to emergency management. You will select a project topic from one of the options described below and write a report that includes spatial data analysis and visualization approaches. In some cases, this might take the form of a case study, in others, a proposed geospatial system design or a demonstration (e.g., dashboard or app) that illustrates either current trends in applying geospatial thinking to emergency management or what might be possible in the near future.
To a large degree, you will have the freedom to shape the specifics of your term project around the geospatial and/or emergency management contexts in which you are most interested. I hope that this allows you to either focus on a topic related to your day-to-day work or to choose an area that sparks your curiosity. That said, it should be clear who you are writing for and the role you are playing in preparing this report.
Your analysis will be informed by relevant datasets that you find, analyse, and visualize. A key task early on is identifying a suitable dataset and developing some ideas about what you’d like to do with it. I can help with this process and when you develop the abstract for the project, we will meet to discuss your ideas and make sure they are achievable in the time you have. I will also circulate a list of websites where you can search for suitable data. There are many options, so don’t be too concerned about being able to find suitable data.
You can choose from a wide range of tools to conduct your analysis and visualize the results, including Esri products like ArcGIS Pro or ArcGIS Online/Portal. You have access to a wide range of Esri tools through your Penn State Accounts. But don’t forget about other possibilities such as web mapping tools like MapBox or open-source tools like QGIS and R. In this course, we use a range of data and technologies and you can do the same with your projects. Just keep in mind you’ll need to balance the time you have to do the analysis and write your report with the time you have to learn new software.
Each week, you will notice that at least one page of the lesson is dedicated to a goal or assignment associated with your term project. In about half of those Lessons, you need to complete a graded deliverable related to your final project. I've developed a project schedule that is designed to make sure you make steady progress on the term project and that also ensures that we have one full round of draft editing to refine your work. I don't like classes that end with submitting a final project with no chance to do any revisions. That seems silly to me.
My hope is that you end the semester with a product that has utility beyond just meeting the course requirements. It could end up being a use case or a proposal that you share with others in your organization. This has been the case for some students in past offerings of GEOG 858.
Here are some options for your term project. You can choose one of these options, or if you'd like to riff on one of these and take it in a different direction, by all means, do so! These are really just suggestions: I want you to be innovative and surprise me with your good ideas for projects. But I also know that many of you want to know what a good example project might look like, which is why I've listed these options here.
The term project includes the following deliverables that will be assigned to you in future lessons:
Look for details on each deliverable (including specific due dates and grading criteria) in future lessons.
In this lesson, you have received an introduction to some of the major concepts associated with spatial data science for emergency management. You reviewed the four basic stages of emergency management and read some background material that defines common terms to geospatial science and emergency management.
Disasters and emergencies provide a wide range of opportunities for geospatial systems to play an important role, and in future lessons, we will delve into these possibilities in detail.
This week, we covered our first emerging technology theme. I've created a page for every lesson that focuses on a different technology theme that I think is relevant in some way to future geospatial applications for emergency management. When we are concerned about planning future geospatial systems, it is essential to become aware of new technology trends that could significantly impact how systems work in the not-too-distant future.
In the next lesson, we will review the range of hazards and emergencies that may require the use of spatial data science to aid mitigation, preparation, response, and recovery efforts.
You have reached the end of Lesson 1! Double-check the to-do list on the Lesson 1 Checklist page to make sure you have completed all of the activities listed there before you begin Lesson 2.
If you have any questions, please post to the Canvas Discussion Forum called "General Questions" or email the instructor via Canvas conversations (if the question is personal in nature).
Threats to people and their property can take many forms. Many of the situations we concern ourselves with in this course are linked to natural events. But it is also important to consider a wide range of social and economic triggers that could cause emergency management situations. In this lesson, we'll take a look at a variety of disaster types and their associated geographic attributes.
By the end of this lesson, you should be able to:
Lesson 2 is one week in length. To finish this lesson, you must complete the activities listed below.
To Read |
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To Do |
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Please refer to the Course Calendar for specific due dates.
If you have questions about the content or lesson activities, please post them to the General Questions and Discussion forum in Canvas. While you are there, feel free to post your own responses if you, too, are able to help a classmate. If your question is of a personal nature, please email me directly through Canvas.
There is a very wide range of hazards and disasters we must consider when planning and implementing geospatial solutions for emergency management. It is easy to focus on the very large and obvious events - things like hurricanes, earthquakes, and disease epidemics. For many geospatial managers, however, there are day-to-day emergency situations on a local level that deserve plenty of attention: house fires, auto accidents, and violent crimes - just to name a few.
In this lesson, you will consider some of the characteristics of disaster and emergency events in three main ways. On this page, you will explore how different organizations track and provide up to date information on emergencies around the world - A key message that will become evident is that there are many diverse disasters and emergencies occurring at any given time. On the following pages, you will read about some specific hazards and disasters and how they are understood from a geospatial perspective, and you will do a hands-on hazard and damage assessment.
Let's jump in! The Federal Emergency Management Agency (FEMA) keeps a running tab of declared disaster events in the US [25]. You've probably heard of these on the news when the President declares a location a "Federal Disaster Area". In addition to these alerts, FEMA now publishes quite a few interesting summary maps of recent disasters at their GeoPlatform. Please spend some time looking at the various components of the FEMA GeoPlatform [12]. While there, think about what information is provided - DataHubs like this are becoming popular and useful tools for providing external facing data and mapping services. Who is the target audience for this? Is this a potential data source or is it locked down? Some of these pages rely on Esri Story Maps, a tool you will use later in the course. You might want to bookmark this to come back to as we talk about different types of Hazards and Disasters and when new events happen in the United States as we work through this course.
Next, have a quick look at the following presentation prepared for a daily FEMA Geospatial Coordination Conference Call for Hurricane Lane as it passed near Hawaii in the summer of 2018 [26] (You looked at a similar one of these in Lesson 1 focused on Hurricane Maria). These briefings describe the state of Situation Awareness, particularly from a geospatial readiness perspective. We’ll revisit this concept in coming lessons but for now, note the range of actors and their different roles/viewpoints on this event. This is also a much more technical view than what is provided in the GeoPlatform, and you can find some of the data behind this on their GIS portal.
Finally, a complementary example identifying and tracking emergencies and disasters can be found on a map developed by the Emergency and Disaster Information Service (EDIS) to provide information on a wide range of hazards and disasters around the world. Take a look at this application called EventMap [27].
There are other examples like this that we will come across during this course and as part of future lessons, and we will also look at geospatial tools for understanding particular events on a much more detailed level. Next, you will consider some hazards and disasters in greater detail through this week's readings.
Here is a quick recap of how the reading assignments work. For each lesson, I will ask you to read parts of your textbooks, online materials I select, or articles I've found. As you can see below, I'll try to make it as clear as possible what you're expected to do by always identifying specific reading assignments in a separate box.
Part of your class participation grade will be making responses on our discussion board to questions I pose about the readings. Whenever you see a RESPOND prompt, you need to respond to that question as directed. Occasionally, I'll mark items THINK ABOUT when I simply want to direct your thoughts as you read.
You can access most of the readings via the links on this page. Some are also available as files in the Lesson 2 Module of Canvas.
The readings for this week are selected to continue the introduction to spatial data science for emergency management and to hazards and disasters in particular. These are some fundamental concepts you will very likely refer back to as you engage with the course material and develop your own term project. The first reading is from your GIS for Disaster Management textbook and provides a broad background on disaster management and GIS. It also introduces the important point that there are different levels of responsibility for responding to events, some overlapping some distinct. You should think about the role of government (local, state, and federal), non-government organizations, industry and private sector, and the research and education organizations in each phase of emergency management. It is a complex landscape, and this course often considers the ways these actors intersect.
The second reading is a report/handbook developed by the Association of Southeast Asian Nations (ASEAN) on different hazard and disasters, their complex characteristics and how to address them. You will read just a part of the handbook, but it is likely to be of use as you move through the course and encounter different topics you want to learn more about (It could also provide inspiration for your choice of term project topic!).
"GIS for Disaster Management" - Chapter 5, "Disaster Management and Geographic Information Systems" (see Library Resources link in Canvas).
In your view and based on the readings, what are the major challenges in GIS and emergency management for the three major areas of government: local, state, and federal? How do the issues at one level affect those at another? What are the barriers to a cohesive, integrated approach to emergency management across the levels? Finish your evaluation with a couple of sentences about what happens when you overlay a pandemic like what we are experiences with COVID-18. Be critical!
From the 2017 ASEAN report “Specific Hazards: Handbook on Geospatial Decision Support in ASEAN Countries [28]” read the Preface, pages 1 – 13 and then pick one of the chapters on specific hazards (e.g., “Landslide”). The other chapters will be a good reference as you consider other hazards in this class.
What are some of the general principals, approaches, and technologies applicable across the range of hazards considered in this report? Then, think about how these play out for a specific hazard, particularly with regard to:
The 2021 report "Hitting Home: The Compounding Costs of Climate Inaction [29]" by the Climate Council highlights describes how climate change is influencing the timing, frequency, and severity of different disasters. Play attention to what they say about future trajectories, and think about whether we are heading in the right direction with our approaches to emergency management.
This discussion will be graded out of 15 points.
Please see the Discussion Expectations and Grading page under the Orientation and Course Resources module for details.
We are probably all familiar with drones, or UAVs, as they are virtually everywhere these days. UAVs are remote controlled airplanes and helicopters that are capable of providing surveillance and attack capabilities for military and civilian uses (no attack capabilities in the civilian case, unless you mount a potato gun). Their development grew out of the need for airborne reconnaissance on missions that are either too dangerous or too tedious for piloted aircraft. Today, UAVs have evolved to the point that some platforms are small enough to be easily deployed by a small support team and require only a hand launch or a very short runway. They are often referred to more generically as Unmanned Aerial Systems (UAS), as the vehicles themselves are just one piece of the overall puzzle in most geospatial workflows You may also be interested to look at another one of the GIS courses we offer, GEOG 892: Geospatial Applications of Unmanned Aerial Systems (UAS) [30]. It is focused explicitly on how UAVs and GIS working together.
In the emergency management context, UAVs are already used in a variety of ways and new applications continue to emerge.
UAVs are capable of surveying areas very quickly to provide imagery to - or other types of - remotely-sensed data. Satellite data is always valuable and desirable, but satellites cannot always be overhead at the right times on demand. UAVs can be deployed very quickly and can be easily directed toward different areas as the situational picture develops. It is worth noting that UAV footage combined with new approaches to image processing means emergency responders can have high-quality imagery and maps in a matter of hours rather than days.
Click on the image below to see a great example of how UAV footage was used to create a compelling story and reference document about the 2018 Camp Fire in California.
The following 2:28 minute video provides a good illustration of the links between drone mission planning/field operations, image processing, and delivery of products for use in response and recovery activities. Note the time frames involved in this, and how much shorter they are than other traditional aerial or remote sensing efforts. However, the time required to process imagery from those platforms is rapidly shortening as well.
We are all pretty familiar with the use of drones for imagery, but here are a few additional emerging uses for drones in emergency stations. The next video is a bit of a ‘vision’ for drone use and this is followed by a few specific examples of how drones are being used in emergency response.
The following 3:30 minute video: Disaster Response Support with Drones, provides a nice overview.
It’s not just about Amazon delivering goods to your door… Payload drones are increasingly being used in crises as illustrated in the following videos from WeRobotics and Zipline.
Watch WeRobotics Amazon Rainforest Cargo Drones (2:35 minutes)
If you have some time it is worth checking out WeRobotics [32] [32]on their website or YouTube. Patrick Meier, the author of Digital Humanitarians, is also a co-founder of WeRobotics.
Now watch the 4:05 minute video about airdrops of medical supplies to African Villages.
Drones are also able to work together to complete tasks. You may have seen the insane “swarm” drone light display at the opening ceremony [34] of the Winter Olympics in Pyeong Chang in 2018. Other applications are being developed such as the three drones working together to build a rope bridge that can support humans in the following (3:26 minute video).
Building a rope bridge with flying machines (3:26)
Finally, I’d like you to consider how drones are being incorporated with other emerging technology such as artificial intelligence. In the video example below (from Australia!) drones are able to identify swimmers, swimmers in need, sharks, stingrays, and many other things. (2:05 minutes)
We've covered a lot so far in this lesson, and now you will start putting things together through an applied exercise. You will be working with GIS and UAV data to help develop situation awareness for first responders and search and rescue teams approaching an impacted area - NOTE that this will be a common theme throughout this exercise. These teams need to know quickly whether it is safe for them to proceed and what the conditions on the ground might be like. Imagine you are a geospatial professional supporting these efforts with existing GIS data and UAV data coming in from the field in near real-time.
Note: You will be setting up some software and downloading some relatively large datasets. Please do this early in the lesson even if you are unable to begin the exercise right away.
Here is a quick overview of what you will be doing and how it links with what we have learned so far.
See the following pages for more details.
In this section of the exercise, you will work with two types of UAV-derived geospatial products, orthomosaics, and 3D textured mesh datasets. Your goal is to evaluate ways to use these data to support situational awareness for first responders and urban search and rescue teams. For example, think about suggested plans for an evacuation of the area and providing guidance for where search and rescue teams and damage assessment efforts should focus first. Remember to imagine that this is early data coming in from an emergency situation and that you are tasked with quickly providing spatial products for field operations.
Pix4D is used to develop high-resolution imagery products (2D maps and 3D textured mesh images) based on captured images and their associated location information. So, it is a tool that could be used to develop spatial products in a relatively short period of time. Here, I have provided a quick start tutorial using the buildings dataset.
Double click to open the Pix4D project building_1.p4d. If prompted, navigate to the images folder associated with this project.
Explore the Map View. This shows the general study area and the locations of where the UAV images were taken, denoted by the red circles. Go ahead and click on one of the circles to see the corresponding image and parameters.
Open the Processing Options by selecting the button on the lower left of the display. This is where you set up the parameters for processing the raw imagery. Some of the options for outputs include point cloud, 3D mesh, Digital surface model, or Orthomosaic. There is also a panel that shows Resources and Notifications. You can view the resources available on your computer to do the processing.
Look at Processing panel (also at the lower left of the display). This shows the selected options and allows you to launch the model. NOTE: Because Pix4D can be really resource intensive, I have already generated output for you to consider. However, feel free to have a go at running this yourself – it just might take a while.
Now have a look at the processed results and explore some of the options for interrogating the data.
Now you will write a short (400 words + figures) assessment of the situation on the ground as observed in the orthomosaic, keeping in mind your role as a geospatial analyst supporting operations and field teams. Focus mostly on the issues raised when looking at the orthomosaic, as described above, but provide a few insights into the potential advantages of providing 3D products to emergency managers and responders as well.
Submit, along with Part 2, to the GIS and UAV Data Exercise Dropbox.
Later in the course, you will learn about using geoAI and machine learning for rapid, automated assessment of imagery like this. We will also consider how data like this can be delivered more effectively to first responders and others in the field during emergencies.
In 2017, Hurricane Irma had devastating impacts on much of the Caribbean, especially the island nation of Antigua and Barbuda. In fact, nearly all of the buildings on the island of Barbuda were destroyed, and almost the entire population was evacuated to Antigua before or immediately after the storm.
For more context, have a quick look at this reporting from the Guardian, The night Barbuda died: how Hurricane Irma created a Caribbean ghost town [40]. If you have trouble with this link, go to the next page in Canvas.
In this section, you will compare UAV imagery collected soon after the hurricane hit with ‘baseline’ satellite imagery taken before the storm. I want you to contrast the type of information you can get from high-resolution satellite imagery with that from an insanely high-resolution UAV mission. Approach this from the point of view of an emergency manager coordinating search, rescue, and recovery efforts in the immediate aftermath of the event. Also, consider the damage evident in the imagery in support of overall damage assessment and teams entering the field.
The data you will be working with came from a Canada-based group called UAViators [41] and it is distributed on OpenAerialMap [42]. If you are interested in this type of data (for your term project?), the OpenAerialMap website might be a good place to start looking.
Write a short assessment of the situation on the ground, keeping in mind your role as a geospatial analyst supporting operations and field teams.
Submit, along with Part 1, to the GIS and UAV Data Exercise Dropbox.
For each part of this exercise, you'll write a short assessment of the situation on the ground, keeping in mind your role as a geospatial analyst supporting operations and field teams.
Each response should be about 400 words in length. Together, they are worth 50 points.
It is important for you to save your files in the following format so that I can match each submission up with the correct student.
L2_exercise_firstinitialLastName.doc For example, my file would be named "L2_exercise_mBeaty.doc"
Upload your assignment to the Deliverable: GIS and UAV Data Exercise Dropbox (L2). See the Course Syllabus or Calendar for specific due dates.
This will be graded out of 50 points and will count towards the Exercise portion of your grade. I will assess it using the following rubric.
Criteria | Points |
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Content (part 1) You make strong and logical arguments and provide analytical insights. Ideas are well organized, clearly communicated and relevant to the prompt. All criteria are accurately addressed. Supporting details are shared, elaborated upon and demonstrate understanding. Examples are provided, and your essay includes images or other multimedia that support content. |
20 |
Content (part 2) You make strong and logical arguments and provide analytical insights.[j1] Ideas are well organized, clearly communicated and relevant to the prompt. All criteria are accurately addressed. Supporting details are shared, elaborated upon and demonstrate understanding. Examples are provided, and your essay includes images or other multimedia that support content. |
20 |
Clarity and Mechanics (parts 1 & 2) |
10 |
Total | 50 |
In Lesson 1, you were introduced to the term project for this class. This week, you will choose one of the project options and decide what your project will cover. Your abstract should be no longer than 400 words. The goal of this exercise is to pave the way for you to write an exemplary term project, therefore, each section of the abstract will be graded on a satisfactory (1 point)/unsatisfactory (0 points) basis.
Your abstract is worth 6 points and should have the following sections and address these questions:
This week, I would also like you to set up a time to run your ideas by me. I think this will be particularly important with regard to the data component of your project. I can provide you with some feedback on whether the scope of the work seems too big, too small, or just right, and whether I think you’ll be able to get the data you need. This can be a quick discussion or we can take a bit longer if that is helpful.
I know it might be challenging to find a time to meet since we are likely in very different time zones - you probably recall I am based in Melbourne, Australia. That said, there will probably be some overlap where we can set up a voice or video call with Zoom [48]or Skype or communicate via chat. I am fine with getting up early or staying up late to overlap with folks. Have a look at the World Clock Meeting Planner [49] where you can put in your location and my location and see the hours of overlap. Then suggest a time you'd like to talk. I am happy to help with this as well.
Please submit your assignment as a word document to the "Term Project Abstract" dropbox in Canvas. See the Course Calendar in Canvas for specific due dates.
The goal of this exercise is to pave the way for you to write an exemplary term project, therefore, each of the six sections listed above will be graded on a satisfactory (1 point)/unsatisfactory (0 points) basis for a total of 6 points. You will have an opportunity to revise your abstract after receiving my feedback.
This week, you have been introduced to the range of potential hazards that spatial data science for emergency management must be prepared to handle. In your reading assignment, we began to explore some of the key issues associated with supporting emergency management tasks with geospatial tools. Knowing how to design an effective geospatial system for emergency management depends on understanding hazards as much as it depends on understanding the capabilities and limitations of current geospatial technology.
Now that you have a general understanding of the types of hazards relevant to spatial data science for emergency management, we will begin examining the first of the four stages of emergency management in greater detail. In the next lesson, we will explore the role of geospatial perspectives and technologies for Preparedness activities.
You have reached the end of Lesson 2! Double-check the to-do list on the Lesson 2 Overview page to make sure you have completed all of the activities listed there before you begin Lesson 3.
If you have any questions, please post to the Canvas Discussion Forum called "General Questions" or email the instructor via Canvas conversations (if the question is personal in nature).
This week, we focus on the first of the four phases of emergency management - vulnerability assessment and hazard mitigation. We will read about risk mapping and vulnerability assessment using spatial data and GIS. Building on the background knowledge we've gained from previous lessons, each of you will conduct an analysis of Heatwave vulnerability, impacts, and mitigation strategies using social and environmental data. You will also continue making progress on the term project assignment.
The improvement of the built and social environment in order to reduce, withstand, or prevent disaster impacts.
By the successful completion of Lesson 3, you should be able to:
describe the concepts associated with risk mapping and vulnerability assessment,
conduct your own vulnerability assessment using social and environmental spatial data and summarize your findings in a short essay,
conduct background research for your final project,
and discuss the technology trend of volunteered geographic information (VGI).
Lesson 3 is one week in length. To finish this lesson, you must complete the activities listed below.
To Read |
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To Do |
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Please refer to the Calendar in Canvas for specific timeframes and due dates.
If you have questions about the content or lesson activities, please post them to the General Questions and Discussion forum in Canvas. While you are there, feel free to post your own responses if you, too, are able to help a classmate. If your question is of a personal nature, please email me directly through Canvas.
Developing a clear picture of an area's vulnerability to hazards and disasters is a non-trivial task. It's hard to predict exactly what could happen in a disaster situation. However, even a rough estimate can be a huge help to emergency managers and decision makers who can use that information to develop plans for allocating resources and managing recovery operations. By collecting socio-economic and environmental data sources in a GIS, for instance, we can develop risk maps to highlight the potential impact of disasters on people and infrastructure. In this lesson, we will examine some of the specific analytical methods for doing a vulnerability assessment, and we will reflect on the critical issues associated with planning an emergency management GIS system that includes vulnerability assessment as one of its key functions. Most systems for geospatial and emergency management are designed for reaction, not prediction and mitigation, but that’s changing fast.
There is a wide range of relevant questions to consider when conducting a vulnerability assessment, including answers to the following key questions:
Here are a couple of examples of web-based services providing geospatial data on hazards and vulnerability; there are many others!
The first example is a map service developed and maintained by the U.S. Federal Emergency Management Agency (FEMA) that coordinates and conducts a great deal of vulnerability assessment work [50], including flood mapping. FEMA flood maps are used to help set flood insurance rates, among other things. The flood mapping tool [51] shows an overview of ongoing FEMA flood mapping, levee repair, and other flood-related risk assessment and mitigation tasks. Contrast this with recent research reported in this interactive New York Times page - New Data Reveals Hidden Flood Risk Across America [52]. Note: you shoudl be able to view the interactive maps without a subscription, but if you are having trouble, you can view a copy of the article on the following page in Canvas.
The United Nations engages with other entities to develop risk maps for developing countries where they are likely to be involved in future disaster situations. If you check out the map above [53] in greater detail, notice who the collaborators are. They include several NGOs, as well as Munich Re, a major re-insurance player. Interesting, huh?
Keep this Indonesia map in mind, because later in the course you will be considering the 2018 Sulawesi Earthquake and Tsunami in greater detail.
Some private sector firms provide what is known as Address Risk Rating products - in essence, you can look up a specific address and get a report outlining all of the vulnerabilities associated with that location. One of our PSU faculty, Dr. James O'Brien, works for Risk Frontiers [54] in Australia, a firm that works on Address Risk Rating products among others. Here is another example, UNHaRMED [55] model, from the University of Adelaide and the Australian Bushfire and Natural Hazard Cooperative Research Centre.
These examples don't explicitly consider people and the wide range of factors that make some people and places vulnerable while others are not. You will see it is not just a matter of whether you are inside or outside of an impacted area. Geographers have done a lot of work on social vulnerability analysis as part of a rich tradition of Hazards Research.
In this week's hands-on exercise, you will be working with some data related to heatwaves in the USA. Through this work, you will gain an understanding of vulnerability assessment approaches using geospatial data and how they can be used to understand some of the priority areas for action leading up to and during a disaster.
Before conducting the analysis and developing the accompanying short report, I would like you to watch a short overview video on Social Vulnerability Indices (SVI), read a chapter from your textbook on GIS and Disaster Mitigation, and read a journal article on Social Vulnerability to Natural Hazards in Brazil. This material will help you gain an understanding of the human dimensions of vulnerability that I mentioned previously.
Please watch this 3:45 minute video on Social Vulnerability Indices (SVI) from the US Centers for Disease Control and Prevention (CDC).
GIS for Disaster Management - Chapter 8 "Geographic Information Systems and Disaster Mitigation (pp. 233-250)
In this chapter from your textbook, the author goes into some good detail on assessing and modeling risk and vulnerability using GIS, including where to get data to do your own and a few straightforward analysis steps using GIS. It also includes core concepts associated with evaluating mitigation policies as well as the ways in which people can develop social and environmental variables to model risk and resilience.
Loyola Hummell, Cutter, Emrich (2016). Social Vulnerability to Natural Hazards in Brazil. International Journal of Disaster Risk Science, volume 7 (issue 2), 111-122 [56]. This final reading will serve as a rough model for what we will work on next in the hands-on portion of this exercise.
OPTIONAL / FYI - Georgianna Strode et al. (2020). Exploratory Bivariate and Multivariate Geovisualizations of a Social Vulnerabity Index [57].
The last year has seen extreme heatwaves affecting much of the world including in China, India, the USA, and Europe. Heatwaves damage infrastructure, overload power grids, reduce work safety and productivity, and have negative impacts on quality of life in general. Moreover, more deaths and illness are due to heatwaves than any other natural disaster, and in the case of the USA and Australia, more than all other natural disasters combined.
Heatwaves can also compound the impacts of other types of disasters. For example, earlier this year Australia experienced extreme flooding followed by a heatwave. This led to a situation where people were outside cleaning up when temperatures were dangerously high.
Heatwave deaths and illness are generally thought of as being entirely preventable. That is, proper mitigation, preparedness and response activities can be undertaken to minimize or eliminate harm e.g., through effective heatwave warning systems and providing the public with “cool places” to get out of the heat.
There is also a strong emphasis on planning and implementing mitigation and adaptation strategies to reduce vulnerability and promote resilience when heatwaves strike. For example:
Urban planning might focus on “green and blue infrastructure” to reduce Urban Heat Island (UHI) effects;
Social welfare groups (e.g., Red Cross) have “opt-in” systems where vulnerable older people are contacted throughout the heatwave event to make sure they are coping well; and/or
Heat-health outreach to help educate the community about the dangers of extreme heat, how to respond to warning messages, and what steps to take.
Heatwave vulnerability and impacts are not spread evenly across populations and geography. This has a lot to do with differences in socioeconomic factors and characteristics of the built environment e.g., low tree cover, poorly designed housing. Just as there is a geography to vulnerability and impacts, there is also a geography to potential solutions. We often think of these as “spatially targeted interventions”.
In this exercise, you will consider these issues in greater detail and use spatial data and analysis to identify patterns of vulnerability and potential impact along with ways of addressing risk and reducing vulnerability.
In this section, you will take a close look at key characteristics of heatwaves, trends related to climate change, and some of the direct impacts of heat on people. You will produce a few graphs and maps that will be incorporated into your write-up in Part 4.
Understanding Heatwaves (2:05)
From the video, take note of these key characteristics of heatwaves:
Source: Australian Bureau of Meteorology Heatwave Forecast Service [59]
Heatwaves are not defined just based on temperature, rather humidity and longer term trends (acclimatization) need to be incorporated. In the map above based on Excess Heat Factor (EHF), heatwaves are classified into three types, based on intensity. Note these mention potential impacts on people, infrastructure and the environment.
Source: Australian Bureau of Meteorology Heatwave Forecast Service [59]
Heatwave occurrence, duration and severity have all been changing over time. There are several important drivers of heatwaves, including reduction in vegetation cover and more built-up (impervious) surfaces in cities. Climate change is perhaps the strongest factor causing changes in heatwave characteristics (see list above).
Consider the following series of maps showing annual temperature trends in the USA. These patterns are similar to what we are observing globally.
In addition to changes in average temperatures, there have also been changes in the occurrence of extreme events. The following diagram illustrates this, where a shift in mean climate results in more hot and extreme days. As an aside, there is a growing interest in the effects 'chronic heat' on health and wellbeing i.e., hot days but not falling into the 'heatwave' range.
You will finish off this section by conducting some analysis of trends in the key heatwave characteristics (mentioned above) in the USA.
This section focuses on some of the "direct effects" of heatwaves on people, including deaths and illness.
The following image is a warning poster produced by the SA Health to help people recognize the signs of heat exhaustion and heat stroke. Note this is advice aimed at everyone and not targeted to specific groups or places.
Heat-Related Illness Signs, Symptoms And Treatment
Public warning message, such as this, provide simple and consistent information. These are linked to dedicated websites focused on heat related health. Have a quick look at these two examples, again noting that heatwave illness and deaths are, in theory, entirely preventable.
Heatwaves also have significant impacts on critical infrastructure and the environment. We won't go into much detail, but watch this short video from the Today Show, noting the reporting on impacts to critical infrastructure and the environment. If you want to learn more, there is no shortage of news coverage and research literature on these impacts.
Record-Shattering Heat Wave Leads To Deaths Across Britain (2:24)
You will finish off this section by looking at some trends in heat related deaths and illness.
So far we have considered broad trends in heatwave characteristics and direct impacts on people (e.g., heat stroke) and infrastructure. In this section you will consider some of the factors that make a person or community more vulnerable to adverse heatwave impacts. We will be taking a much more granular look at the problem in this and the following sections.
We are going to focus on heatwave vulnerability and impacts from the perspective of human health. This figure illustrates the basic problem - people must maintain their body temperature within a specific range to avoid adverse health outcomes. This is influenced by the ambient environment and characteristics of that person e.g., do they have existing health problems that may compromise their ability to thermo-regulate?
There are also a set of broader factors influencing heat health. The framework illustrated in this figure was used in a recent study to understand heat health vulnerability [69]. This is similar to the CDC Social Vulnerability Index (SVI) you would have seen in one of the previous videos, however the focus here is explicitly on heatwaves.
Generally speaking, indices are designed to help us describe concepts that are not able to be measured directly. For example, "Vulnerability" or "Socioeconomic status" are a multidimensional concept that cannot be measured with a single variable. There are many different indices developed for different purposes. Here you will take a closer look at two of the most widely used approaches.
Have a closer look at the FEMA's National Risk Index website [71]. There are three components to the NRI:
Note that the Social Vulnerability component is based on another index called the Social Vulnerability Index, or SoVI. This is one of the first approaches ever developed for emergency management, and it uses principal components analysis PCA) to reduce a set of variables to one index representing low to high vulnerability.
Similarly, Community Resilience is based on another commonly used approach called Baseline Resilience Indices for Communities (BRIC). BRIC also uses a set of indicators to build up the indices, but it does this through a "standardize and rank approach". To learn more, have a look at the NRI website and/or download the NRI manual [72].
When considering index approaches such as these (and any dataset for that matter), it is important to consult available metadata and "data dictionaries" (e.g.,here is the NRI data dictionary [73]), sometimes referred to as the Data Item List (DIL), that goes with a given dataset. You will find the NRI data in the ArcGIS Pro Project accompanying this lesson. I have mapped the SoVI component of the NRI. Have a look at the NRI attribute table, scan the field names, and look up a few variables in the data dictionary.
Next, have a closer look at the CDC Social Vulnerability Index (SVI) [74]. The SVI takes an approach that is similar to BRIC, where individual variables are used to calculate sub-indices (Themes) and then the overall index. For example, Socioeconomic Status cannot be measured directly, so "below poverty" line, "Unemployed", "Income", and "No High School Diploma" are combined to get at this construct.
Note the table accompanying the SVI feature class includes the underlying data that is used in the index calculations. If you want to "unpack" a given index, you can look at individual variables. For example, you may observe a "vulnerability hotspot" and wish to know why that is the case. SVI let's you drill down to themes and individual variables. In a particular area, you may find that socioeconomic status is the most important theme and poverty and unemployment may be the most important single indicators. You will take a closer look at the CDC SVI, and, as with NRI, it is important to have any metadata and data item list handy (open a copy with this link SVI data dictionary [76]).
When we hear about heatwaves, we often focus on weather as the main driver. However, heatwaves are experienced differently in different places due to variation in microclimate related to the build and natural environment.
In addition to broader UHI effects, there is considerable variation in microclimate (e.g., local temperature) related to features of the built environment. In the following thermal image from Los Angeles in Summer 2018, you can clearly see higher temperatures in more built up areas. Major roads, downtown LA, and the Port of Long Beach stand out as hot spots while areas such as Beverly Hills and Santa Monica along the coast are cooler. Note how you can actually see the "hot" road network grid in the area around Anaheim. This illustrates very fine variation in land surface temperatures. A key point to takeaway, is that local conditions can enhance or ameliorate heatwaves e.g., think about the simple example of standing in an open parking lot versus standing under a tree during a heatwave!
Los Angeles Heat Wave. Source: NASA Jet Propulsion Laboratory [78]
You will finish off this section by continuing to looking heat health vulnerability in Los Angeles County, California. All of the spatial data are provided in the ArcGIS Pro Project, GEOG_858_Lesson_3.aprx.
Deliverables 2
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The final part of your analysis will focus on an assessment of a some heatwave mitigation options. The first focuses on "green infrastructure" and the role of tree canopy cover in urban cooling. You will then look at the distribution of "Cooling Centers" in LA County and the populations they serve. For both cases, you will use spatial data to assess the current situation and provide analysis and advice on what could be done in the future.
ASTER Los Angeles from Space - Source: NASA JPL [82]
Bi-variate map legend for Heat Health Action Index and Percent No Tree Cover. Note "Both High" indicates areas with low tree cover and high heat health vulnerability |
Many cities now have designated "cool places" people can go during heatwaves. In this part of the exercise, you will look at whether officially designated cool places in LA County [83] provide good coverage of the city, are easily accessible, and how they may be improved.
Deliverable 3
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For the final part of this assignment, you will draw upon the analysis and key points developed in Parts 1, 2, and 3 to write a short briefing document (500 words) for planners and emergency managements in Los Angeles County. This should cover:
Deliverable 4
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For this week's Emerging Theme, you will review the materials below and engage in a Canvas discussion with your classmates - see details below.
Spatial data has traditionally been developed by government agencies and businesses who could afford the technical and financial expenditure necessary to digitize spatial information. Recent advances in web mapping and GPS technology make it possible for tech-savvy volunteers to develop their own spatial datasets. This sort of geographic data is frequently called "Volunteered Geographic Information" or VGI for short. The following short (31 seconds) video below shows the dramatic VGI response to the 2010 Haiti Earthquake through additions and corrections to OpenStreetMap data for the country. Haiti had previously been a poorly-mapped place, and there was an immediate need in the aftermath of the disaster to develop a much better base-map to help recovery efforts. This was a watershed event in VGI for disaster/humanitarian response as discussed by Meier in the first chapter of Digital Humanitarians you read in Lesson 1.
OpenStreetMap - Project Haiti [85] from ItoWorld [86] on Vimeo [87].
One of the most effective VGI efforts can be found at OpenStreetMap.org [88]. OpenStreetMap has the goal of developing a basemap of roads, place names, and other common spatial features for the world, based entirely on volunteered contributions. The OpenStreetMap project aims to provide a completely free worldwide geospatial dataset without any legal or technical restrictions on its use. Most popular web mapping resources like Google Maps or Bing Maps tightly constrain how their data can be manipulated, published, or displayed. Quite a few folks take it for granted that these maps are free, but, in fact, they are only free because those companies are providing access to them right now for free. You are not allowed to re-use and re-purpose those resources or download their data yourself, and if Google decided tomorrow to charge you for access to their maps, you would have no recourse to ensure you kept access for free.
Another important trend in VGI is the use of microtasking or ‘micromapping’ campaigns that split up a big task into small chunks that the VGI community can take on. For example, have a look at the this interesting and useful review of microtasksings role in emergency management from the Australian Institute of Disaster Resilience [89]. In some systems, you are presented with tiles from high-resolution imagery and you are asked to search for and tag features like ‘Trash heaps’, ‘Blocked roads’ and ‘Damaged buildings’. It is worth noting that microtasking like this can also be used to train Machine Learning algorithms to detect these same features with high accuracy. You can read more about this in Chapter 6, ‘Artificial Intelligence in the Sky’, of Digital Humanitarians. This is really cutting-edge stuff that is happening now.
Now, I’d like you to consider VGI with “citizens as sensors”. This is where information relevant to the disaster is collected through devices people are carrying around. I am sure you can think of lots of examples of data you could get from smartphones, but I wanted to highlight a project that started in Japan shortly after the Fukushima Daiichi nuclear disaster. The Safecast [90]team developed small devices for radiation, mapping the results which you can see on the web map here [91]. A very recent example of citizens as sensors is the COVIDSafe App [92] being used in Australia. This is a contract tracing app that records all of the people you come in contact with via Bluetooth on mobile phones. The data are encrypted on your phone and only accessed if someone you came in contact with someone who tested positive for COVID-19.
Finally, for a critical perspective, please look at the recent (2018) paper by Billy Tusker Haworth titled, “Implications of Volunteered Geographic Information for Disaster Management and GIScience: A More Complex World of Volunteered Geography [93]".
This discussion will be graded out of 15 points.
Please see the Discussion Expectations and Grading page under the Orientation and Course Resources module for details.
This week, I will be evaluating the abstracts you developed last week. While I do that, I encourage you to spend some time looking for relevant background information that will help you develop your project. This should include identifying some of the data sources you will need. I am happy to provide suggestions of places to look, but have a go on your own first!
To get started, you could:
Each project will have quite specific needs, so you will need to think of the additional information you will need to write your report.
Some Suggestions...
You do not need to turn anything in for your Term Project this week, but you really should get cracking on your background research. Don't let this time slide by without making some progress on that effort.
If you have questions about how to proceed - you can ask those in the General Questions Discussion in Canvas. It's great if you're able to help a classmate, too, so don't be shy.
In this lesson, we have learned about the first stage of emergency management - vulnerability assessment and hazard mitigation. We focused attention on how geospatial data and tools can be used to conduct risk mapping analyses to identify places where populations and critical infrastructure are vulnerable to disasters.
An effective vulnerability assessment requires answers to the following questions (among others, of course):
When developing geospatial system for emergency management, one must consider the analytical tools and data sources necessary to answer these questions. Often, decision makers need information on potential human and financial losses to make their case for resources to mitigate against disasters.
In the next lesson, we will shift focus toward situations in which a disaster is imminent and geospatial analysis is called upon to help prepare for potential impacts. Even in the best case scenarios, there is often very little warning (and sometimes no warning at all) prior to a disaster, so there is a serious need for efficient and effective geospatial systems to evacuate citizens and stage response resources.
You have reached the end of Lesson 3! Double-check the to-do list on the Lesson 3 Overview page to make sure you have completed all of the activities listed there before you begin Lesson 4.
We are shifting our focus now from vulnerability assessment and hazard mitigation to the next stage of emergency management, preparedness. One way you can think of this phase is that it involves activities to address shortcomings in planning aimed at reducing vulnerability and mitigating hazards. Preparedness is about what you need to be able to do when the worst happens - being ready to respond and promote recovery.
In this lesson, you will read about ways in which geospatial analysis can be used to target intervention and evacuation efforts to reduce the impact of forecast disasters. You'll respond to one of the readings with a written critique. This week, the emerging theme discussion focuses on Humanitarian Logistics and Supply Chains. Finally, for your term project, you will develop a detailed outline to help guide your progress.
At the successful completion of Lesson 4, students should be able to:
Lesson 4 is one week in length. To finish this lesson, you must complete the activities listed below.
To Read |
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To Do |
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Please refer to the Course Calendar for specific due dates.
If you have questions about the content or lesson activities, please post them to the General Questions and Discussion forum in Canvas. While you are there, feel free to post your own responses if you, too, are able to help a classmate. If your question is of a personal nature, please email me directly through Canvas.
Actions taken prior to a disaster with the intent of ensuring a better event response
...is worth a pound of cure, right? Often disaster situations do not present themselves with substantial warning. Some events, like earthquakes or terror attacks, occur with little or no advanced warning. Other events, like hurricanes or tsunamis, may allow for some substantial amount of time (ranging from an hour or two in the case of a tsunami to several days in the case of a hurricane) to prepare for the initial impact. No matter what the type of event, there are ways we can prepare by taking advantage of geospatial capabilities.
In lesson, we will explore geospatial enabled preparedness in several ways. On this page you will contrast different scenario-based activities - one focused on large scale disasters and another on a more localized emergency. Then you will consider some of the science behind forecasting and modeling potential emergencies, and the geospatial technologies that are being used to develop the capacity ahead of time for situation awareness when disasters do strike. Finally, you will once more contrast large and small scale preparedness activities and the role of geospatial data and analysis by looking at Humanitarian and Disaster Logistics and models for improving building evacuation. So the idea is to think about preparedness as a set of activities with multiple dimensions (spatial and temporal scales) and geospatial analysis as a key tool for managing this complexity.
A highly regarded method for preparing for disasters involves the use of scenarios to conduct realistic exercises to simulate a crisis situation. Using the examples below, contrast live training exercises on small-scale (such as Active Attacker Situations) with those developed by FEMA for large-scale earthquake scenarios. For disasters that provide no advanced warning, using scenarios may be the only way to really prepare in advance. We'll go in-depth on designing scenarios later on in Lesson 7, but for now, read this short article from GovTech about how GIS can help communities prepare for disasters [101]. How effective do you think these activities would be? Could the community be engaged more actively? How do you think things have changed since the GovTech essay?
FEMA has developed a wide range of training exercises to aid in disaster preparedness and response. I'd like you to consider the following materials they developed for a catastrophic earthquake in Southern California. Here is their description of this resource.
"Emergency planners use HAZUS-MH to provide realistic catastrophic planning exercises. Over the last several years, FEMA has supported the development of a suite of "priority maps" to support our Federal Response Plan (FRP) partners in preparing for, and responding and recovering from a catastrophic earthquake. A suite of ten priority maps that illustrate the region of strong ground shaking, direct and induced damage, as well as estimated social impacts were developed to provide information for FRP partners within a few hours of an earthquake event. By using the priority maps in regular planning exercises, the FRP partners will become familiar with the map information produced within a few hours of a damaging earthquake." Credit: FEMA [102]
Here is an example of one of the exercise's realistic maps showing casualties. Other realistic geospatial products and other material are produced and presented to participants during the course of the exercise to help prepare emergency managers for real events. When reviewing these materials, do a quick thought experiment and think about all of the different groups involved in a disaster like this. Think about the agencies and organizations involved and the level of coordination required at local, state, and federal levels. We'll consider these issues as we move on through the course.
Preparedness scenario exercises are not just undertaken for large scale, catastrophic events but are increasingly being used in response to local events. One of the clearest examples of this, unfortunately, is the increasing prevalence of active shooter or active attacker drills. These range from training for police to more detailed and realistic exercises involving first responders along with real civilians (including students and teachers) and perpetrators played by actors.
I'd like you to have a look at two example videos. The first one is a news report on a very realistic drill being conducted at a Colorado school. This video provides a pretty good behind the scenes view of how elaborate this training can be. The second item to look at is a more educational-type video produced by Penn State for Students, Faculty, and Staff to help them know what to do during an Active Attacker situation.
Warning! These videos depict simulated active shooter scenario that some people might find distressing. If you prefer not to watch the video, please reach out to the instructor for alternative media.
Video: Police Practice Active Shooting Drill at Colorado High School (8:03 minutes)
Video: Run, Hide, Fight - Surviving an Active Attacker (6:42 minutes)
GIS and other geospatial technologies can support a key element of disaster preparation through computational simulation and modeling. A wide array of specialized modeling software extensions for ArcGIS and other GIS platforms are available. This software enables users to tweak disaster parameters and simulate damage patterns due to storms, earthquakes, disease outbreaks, and fires (think back to InaSAFE from the previous lesson). With the rise of cloud computing, near-real-time data streams, and big data analytics, much of this happens at a fast pace including analysis well before the event up to the start of the event itself. For example, thinking about the preparations for Hurricane Florence and how often decisions on pre-deploying assets changed as new information became available to the managers. This will become clearer when we consider disaster and humanitarian logistics later in this lesson.
The output of these models can be viewed in static maps or interactive web tools. Some real-time modeling capabilities exist for emergency managers to test various parameters and visualize their potential impact, but few of these systems are available for free to the general public (very unfortunate!). The Pacific Disaster Center [105] in Hawaii does quite a lot of work on modeling and visualizing model outputs for disaster scenarios. Have a closer look at this site and some of the tools and apps PDC offers [106], including the disaster preparedness training [107].
One publicly available resource is provided by the USGS in the form of their Prompt Assessment of Global Earthquakes for Response (PAGER [108]) system. PAGER provides rapid reporting on the potential impacts of recent earthquakes on human life and structures in easy-to-consume reports and maps.
You may want to refer back to some of these resources (and find others!) as potential sources of data for your term project and the case study assignments coming later in this course.
A rapidly growing part of preparedness is the development of geospatial tools, data analytics, and visualizations that can be put into place ahead of a disaster. This includes making sure existing datasets, like roads and other infrastructure, demographics, and critical facilities are ready to use. Increasingly, these efforts involve the use of real-time or near real-time information from data feeds including Internet of Things (IoT) devices, reports from field crews, streaming model outputs, and others. We will focus on this in greater detail in Lesson 5 and again later when we consider the emerging technology of IoT. This diverse range of information is often summarized using maps and emergency management dashboards. Below, we'll consider some interesting examples of these trends.
Let's start with something very familiar, Google Maps! While many sophisticated methods for modeling disaster impacts aren't yet publicly available in web tools, there are in fact a very large range of options for free platforms used to evaluate and monitor a situation in progress. The Pacific Disaster Center's Global Hazards Atlas [110], introduced on the previous page, is one such system. Google Crisis Response [111], also mentioned earlier, is another example and is more readily available and usable by the responders and the general public alike.
This next example is from the PDC Global Hazard Atlas and shows the position and projected path of a tropical cyclone bearing down on Japan. Note that as with the Google map, there are a lot of other layers that can be examined to gauge likely impact and help make decisions about where resources might need to be pre-positioned. Another way this data can be used is for future planning and mapping of disaster prone areas (think back to the FEMA Southern California Earthquake example). Finally, and you will see this more in the following video, these maps can help emergency managers evaluate the potential for disasters to interact. For example, some areas may be vulnerable to a cyclone and may also have a critical facility like a power station. GIS 101 but very powerful nonetheless.
Finally, check out the impressive Nationwide Operational Assessment of Hazards (NOAH) program from the Philippines [112]. This is a good example of the trend toward multi-hazard approaches to emergency management, rather than focusing on a single hazard type. This site has a lot of functionality including the ability to map the likely impact of different hazards based on historical data. After viewing this short video, take some time to click on a few of the buttons and see what you can learn. For example, display volcano hazards alongside critical facilities to see if there are places particularly at risk.
Video: How Project NOAH helped avert potential disasters (2:14 minutes)
For more on NOAH, have a look at this journal article: Disseminating near-real-time hazards information and flood maps in the Philippines through Web-GIS [113]. This link takes you to the abstract. To see the entire document, see e-Reserves under Library Resources in Canvas.
The readings this week continue our focus on preparedness. You will read a chapter in your textbook that covers some of the broader issues around GIS and disaster preparedness, continuing some of the themes we've been covering. Next, you will consider a journal article that takes a (very) deep dive into emergency building evacuation modeling. This paper is challenging but has a lot of useful information even if the technical bits are too much!
I like to remind students that, as you read, it is important to read critically and not necessarily accept what you read at face value, even if it appears in a peer-reviewed journal. Many of the course assignments are aimed at helping you build the skills to assess published reports on geospatial technology objectively and critically. There are multiple perspectives from which to critically assess what you read. No papers can cover all issues and no author is all-knowing; thus, it is likely that you know something relevant that the author does not (or that he/she did not consider relevant, but that is relevant from your perspective). Methods of data processing and analysis that might be acceptable in one discipline may be at odds with established methods in another discipline, so you will find disagreement among authors about what methods are “right.” People make mistakes (in their original conceptualization of a problem, in carrying out work, and in interpreting the results) – and your practical experience and/or solid grounding in geospatial analysis may give you special insight to identify these mistakes. In many cases, the authors may have limited practical knowledge, thus, they may completely ignore issues that are critical in a real world context.
From "GIS for Disaster Management": Chapter 6 - "Geographic Information Systems and Disaster Planning and Preparedness". See Library Resources in Canvas for the electronic version.
These chapters focuses on the various ways preparation can be characterized in the context of GIS, as well as some of the key methods by which geospatial tools can be used to support near-term preparation when we know a disaster is about to strike.
What are some of the specific ways in which preparedness is different from mitigation? You might consider this from the perspective presented by text author or (more interestingly) from the perspective of a GIS manager in a state Emergency Operations Center, from the perspective of a local regional government deciding whether to invest in GIS, or from the point of view of a citizen who expects service from their government. How might GIS activities to support preparedness differ for different kinds of emergencies – what are examples of different kinds of emergencies in which preparedness activities would differ?
Bo Li and Ali Mostafavi 2022. Location intelligence reveals the extent, timing, and spatial variation of hurricane preparedness [114]. Scientific Reports 12:16121. (PDF version [115])
This paper examines preparedness for hurricanes based on geospatial data and anlaysis
Are there other data and technologies that could be brought to bear on the problem of disaster prepartedness? How might the authors’ work be applied in other emergency situations e.g., fire, flood? Note, you will provide a written critique of this article following on the live discussion - details to follow!
This discussion will be graded out of 15 points - pretty easy this week! Just show up and share your thoughts.
For this week’s Emerging Theme topic, we are going to take a step back from emergency management and focus on spatial data science (SDS) in general. I want to emphasize that SDS (and terms like Big Data or Machine Learning) can mean several different things.
On the one hand, it is how we talk about GIS and geospatial science in the age of large data sets (e.g., imagery and otherwise), enhanced computing power, and networked data and services. A lot of traditional GIS workflows are described in (spatial) data science terms. For example, variants of regression analysis and hotspot analysis are referred to as machine learning and cluster detection, respectively. This is all fine, but SDS is also the integration of big data, high performance computing, and programming of machine learning/AI algorithms to conduct analysis in some fundamentally different ways from traditional GIS/geospatial analysis. You will explore and discuss some this complexity in this Emerging Theme Discussion.
To set the stage, I'd like you to have a look at few perspectives on spatial data science, and where it is heading, from two geospatial industry leaders, Esri [116] and Carto [117], and university researchers at the Center for Spatial Data Science [118] at the University of Chicago.
When considering SDS as a set of activities, we can identify several interrelated parts. These are listed here with some examples of common associated tasks (not exhaustive):
Visit Carto's Technology Stack Overview [119] page to see a similar list. Take note of the Data ingestion and Management & Analysis steps. Are you familiar with the technologies listed there? Pick a couple e.g., PostGIS, Python SDK, ELT, PostgresSQL that you are not familiar with and look them up. Gaining a general familiarity with the various parts of SDS is a good first step.
Finally, Carto have produced a useful free e-book on Becoming a Spatial Data Scientist (download the PDF here [120]). Read the first chapter and have quick look at the rest of the book. This may be a good resource for you going forward as it lists many of the tools you can use for analytics projects.
You are probably aware that the dominant player in the GIS space is Esri [121], the developer of ArcGIS Pro amongst many other offerings. In addition to desktop software, they offer server and cloud based services that allow for big data analytics at scale.
Visit the Esri Spatial Analysis and Data Science [122] page. Note the components of SDS they outline and a few of the tools on offer. I'd like you to take a closer Machine Learning and AI & Big Data Analytics.
Artificial Intelligence is a somewhat generic term for a class of techniques including machine learning and deep learning. On a basic level, AI is all about developing algorithms that can "learn", or can be "trained", to recognize patterns in datasets and then predict likely behavior. For example, algorithms have been written to identify and differentiate sharks from swimmers in real-time UAV camera feeds over beaches in Australia. Post hurricane damage assessment is also commonly done by AI these days, often with the help of volunteers training the algorithms e.g., looking at single buildings and decided on a damage class.
Artificial intelligence, machine learning and deep learning. Source: Esri
Read this short article on Machine Learning in ArcGIS [123] by Esri Spatial Analyst Lauren Bennent. What are some of the key issues she cites about using ML and GIS? What stands out as being different from what you can do with Desktop GIS alone? Do you think you can get started with ML using ArcGIS Pro? What constraints might you run up against?
One way SDS is different from traditional GIS workflows is the ability to deal with large volumes of data including collection and cleaning, storage, analysis and visualization. Analysis of real-time (or near real-time) data is a rapidly growing area for geospatial science and emergency management applications in particular. Have a look at the following video and website [124] to see a geo-analytics workflow using Esri.
The geospatial industry are making great advances in SDS and delivering data and tools to a wide audience, however research groups at universities have been at the cutting edge of developments in (spatial) data science for many years. This includes work in computer science, high performance computing, mathematics, statistics, geography, human-computer interaction, amongst others.
One research group that has been very influential across these areas is Professor Luc Anselin's Center for Spatial Data Science [125] at the University of Chicago. Have a look at a few of the research projects this center has undertaken in recent years. What similarities or differences do you see compared to the problems described in the Carto or Esri sites, or that you have usually thought about in the context of GIS problems?
Screenshot of multiple linked displays from analysis with GeoDA
One of this group's most widely used products is the GeoDA software. [126] This program has a lot of basic GIS functionality but is also loaded with easy to use advanced spatial analysis tools. This is a desktop application, but many of the tools can be used by coding with Python and R, thus making the tools scalable with data and hardware needs.
Look at the GeoDA [127] pages and also visit their github site [128] which hosts software and training materials. Be sure to scroll down this page to view the desktop spatial analysis program GeoDA. As mentioned, they are also actively developing R libraries. Why would they focus on both?
Note that you can download and use GeoDA for free (and it works on multiple platforms). It might be worth considering as part of your projects?
As mentioned previously, SDS goes beyond desktop GIS and requires the use of a range of computing resources and programming tools to manage different analysis steps.
What about the hardware required for Spatial Data Science. In many ways it is all about scalability. You may be able to accomplish many tasks with desktop software like ArcGIS Pro, but for bigger and more complex analysis you may need to rely on enterprise solutions or high performance computing.
NVIDIA A100 Tensor Core GPU (Source: NVIDIA)
Have a very quick look at this fact sheet for the NVIDIA A100 Tensor Core GPU [129]. This type of hardware is designed for for AI, data analytics and high performance computing in server/cloud applications.
Hardware like this is used in distributed computing where tasks to be split up and conquered by a stack or cluster of processors. The figure below is from the Riga Technical University [130] and shows how a central computer (head node) is orchestrates analysis jobs undertaken by computing nodes.
Distributed computing example (Source: Riga Technical University [130])
Distributed computing is controlled by software systems such as Hadoop [131]. Here is a description from the developers website of what Hadoop does:
The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. - Source Hadoop [131]
I'd like to end this section by showing you a useful diagram produced by Carto (again!). It is meant to show the relationships amongst data science tools and geospatial analysis.
Python and R are the main languages used for data manipulation and analysis in much of SDS. The two languages overlap in functionalist but also offer different capabilities (R is good for some things / Python excels at others). This highlights that you need to be somewhat pragmatic and use whatever tool will work best. The tools hanging off the R and Python circles refer to specific packages e.g., ArcPy is the site package used by Esri for accessing ArcGIS functionality. SQL is the main language for querying and managing databases. Finally, the platforms area refers to the many ways you can interact with the data and run analyses. Are you familiar with any of these? The Carto book recommended above provides some practical help on how to set some of these up for your own analysis.
Data Science tools for spatial analysis (Source: Carto - What is Spatial Data Science? [132])
We will come back to the topics of GeoAI and real-time analytics later in the course, but in the meantime Esri and Carto offer many free resources on SDS (some listed above) and this includes free seminars and training materials. Have a look at this page listing current resources and upcoming events - Spatial Data Science Events, Videos, Webinars and Courses [133].
The growing interest in spatial data science has spawned several conferences that bring together scientists and analysts in the public and private sectors. I encourage you to take a look at the Spatial Data Science Conference website [134]. You can register and attend online for free this year.
This discussion will be graded out of 15 points.
Please see the Discussion Expectations and Grading page under the Orientation and Course Resources module for details.
This week, you need to compile and submit an outline for your term project paper. By now, you've received my feedback on your project abstract, and you had time last week to collect some background information.
A good outline will help you complete your term project as efficiently as possible. I like working with an outline, because then I know the gaps that I need to fill. It's also an excellent way of narrowing what your paper will cover given a specific word count constraint.
Your outline should include:
The outline should reflect the limitations you have on word count (no more than 3000 words) for the final product (you won't be able to have dozens of sections covering every possible topic).
I like to add short statements for the key ideas I will cover in each subsection; that way I know exactly what I must cover to complete the paper, but I'll leave it up to you to decide how much detail your outline includes beyond section and subsection headings.
For your term project, you must include the sections/headings provided in the table below. These are the major items I will be looking for. You can create subheadings as you see fit.
Section | Description |
Introduction | The introduction meaningfully engages the target audience/reader and clearly presents the central argument along with its substantive, technical and applied contexts. |
Background and Supporting Research | The paper is well researched and contains references to peer-reviewed articles, government documents and industry reports that relate to the arguments in a logical manner. References are correctly cited. |
Analysis and Interpretations |
The design and implementation of a methodology was appropriately used to address the central arguments of your topic. Critical, relevant and consistent connections are made between evidence and central arguments. Includes appropriate use of maps, graphics, and tables. Analytical insights are sound and show a deep understanding of the issues. Depending on your selected topic, this may involve describing the steps taken for data analysis and mapping (NOTE –step by step instructions can be put into an appendix and will not count against word limits – disc |
Conclusion |
Excellent summary of topic and central arguments with concluding statements that impacts the target audience/reader. |
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Submit your assignment as a word document or PDF to the Term Project: Outline dropbox in Canvas.
Save your files in the following format: L4_tp_firstinitialLastName.doc.
See our Canvas Course Calendar for specific due dates.
The goal of this exercise is to pave the way for you to write an exemplary term project; therefore, each section will be graded on a satisfactory (1 point)/unsatisfactory (0 points) basis. You need to address the following criteria:
The outline is worth 5% of your total course grade and will be graded out of 45 points.
The ability to synthesize technical information into a concise package that is appropriate for a broad audience is a skill that is hard to hone and yet highly sought after in the workplace. This assignment provides you an opportunity to do just that. I would like you to create a short (5 - 7 minute) recorded presentation about your term project proposal. The presentation will be shared with your classmates.
You're almost done! The last step is to add your video to our Term Project Presentation gallery so everyone can see!
1. Click the Media Gallery link in the course navigation on the left side of the page.
2. Click the + Add Media button in the upper right of the page.
3. Select the video you would like to add by checking the checkbox to the left of the video.
4. Click Publish in the upper right of the page.
5. Let me know you've uploaded your video, and I'll approve it for the Media Gallery.
NOTE: The video will not appear in the Media Gallery until I approve it.
Go to the Media Gallery in Canvas and view your peers' presentations. Please provide comments and feedback to your peers.
The chapter from your book is matched with a journal paper that focused on GIS for emergency management situations that include preparedness components. Your written deliverable for this week’s lesson (beyond what you wrote for the class participation section) is to produce a brief (no more than 400 words) critical assessment of the paper by Lochhead and Hedley. The critical assessment should begin with a one-two sentence summary of the authors’ goals in the project reported. Then, in 2-4 paragraphs, discuss the strengths and weaknesses of the work reported. Consider the following issues:
Please name your document using the following as an example: L4_assign1_firstinitialLastName.doc
Submit your assignment to the Lesson 4 Writing Assignment (L4) Dropbox. See the Course Calendar for specific due dates.
For this assignment, I will assign grades with the following rubric. It is worth 4% of your total course grade and will be graded out of 20 points.
Criteria | Description | Possible Points |
---|---|---|
Content and Impact | You make strong and logical arguments and provide analytical insights. Ideas are well organized, clearly communicated and relevant to the prompt. All criteria are accurately addressed. Supporting details are shared, elaborated upon and demonstrate understanding. Examples are provided, and your post includes images or other multimedia that support content. | 15 |
Clarity and Mechanics | Evidence of editing and proofreading are evident. Writing is engaging and well-structured with excellent transitions between sentences and paragraphs. Concepts are integrated in an original manner. | 5 |
This week, we focused on how GIS can be used to prepare for a disaster. Different disasters present different types of opportunities for preparation - some, like terror attacks or earthquakes, provide little or no warning time at all. Others, like hurricanes or other severe storms, may offer a window of opportunity where geospatial data and tools can be used to coordinate evacuations and other types of preparation efforts (sandbagging levees, for example).
One way to prepare for disasters that offer little or no warning is to develop spatial computational models of disaster impacts and use a GIS to run simulations of hypothetical emergency situations. In this lesson, we looked at how the USGS uses PAGER to quickly estimate damage from earthquakes. When planning a geospatial system for emergency management, it may be very useful to allocate time and resources toward disaster modeling efforts to simulate situations that present very little advanced warning.
In the next lesson, we will shift our attention to the response phase of emergency management. In the time immediately following a disaster, GIS and other geospatial technologies will be called upon to develop a situational picture and to allocate first responder resources. In Lesson 5, we will delve into a wide variety of challenges that are associated with disaster response.
You have reached the end of Lesson 4! Double-check the to-do list on the Lesson 4 Overview page to make sure you have completed all of the activities listed there before you begin Lesson 5.
If you have any questions, please post to the Canvas Discussion Forum called "General Questions" or email the instructor via Canvas conversations (if the question is personal in nature).
In this lesson, we will focus on how geospatial perspectives and technology are used in response to emergency situations. Geospatial analysis has tremendous potential for aiding disaster response, but as you will learn, it is not easy to quickly translate geospatial data into actionable information when lives are at stake. Responders need to know where to go and how to get there, and emergency managers need to understand and react to a changing situational picture.
Actions taken immediately before, during and after an event to alleviate suffering and prepare for recovery
By the successful completion of this lesson, you should be able to:
Lesson 5 is one week in length. To finish this lesson, you must complete the activities listed below.
To Read |
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To Do |
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Please refer to the Course Calendar for specific due dates.
If you have questions about the content or lesson activities, please post them to the General Questions and Discussion forum in Canvas. While you are there, feel free to post your own responses if you, too, are able to help a classmate. If your question is of a personal nature, please email me directly through Canvas.
In the wake of a serious disaster, geospatial analysts along with other emergency managers are expected to provide a wide array of information with short deadlines for a variety of important tasks. First, it is essential for everyone involved to have a clear sense of the current situation (situation awareness) and to receive updates on the situational picture as time progresses. This can be a serious challenge because often a disaster can impact the types of data that are available. Also, consider the extreme case of the EOC for New York on 9/11 which was located at 7 World Trade Center, and its state-of-the-art equipment and data were destroyed as a result of the attacks.
You can read more here on how GIS resources were developed on an ad hoc basis during the 9/11 crisis [140]. It is interesting to contrast these activities with what is possible now, over 20 years later! In particular, think about the crowdsourcing and crisis mapping efforts you read about in the Digital Humanitarians chapters.
Any geospatial plan for responding to an emergency or crisis should consider several key questions:
Even contemporary web-based geospatial systems present possible challenges in a real crisis situation. While cloud-hosted solutions can help avoid the risks associated with data storage in a single EOC, many disasters make Internet access difficult or impossible. We will consider this issue in more detail later.
The most pressing need facing geospatial managers during the immediate aftermath of a disaster is to estimate the impact of the disaster on the local population to determine where first responders should focus their rescue efforts. This problem requires an awareness of the scale and scope of the disaster as well as the ability to know where response resources are located, what their capabilities are, and what routes are available for them to take to those who need their help.
As an example, consider the May 22, 2011 tornado that went through Joplin, Missouri. The map above shows locations of key facilities and estimated building damage levels. Developing an understanding of the scope of damage during and immediately following an event is a key goal for geospatial analysis. Spatial data on the location and functions of key facilities can be developed as part of mitigation and preparedness.
Later in this lesson, we will consider these issues in greater detail when we look at (near) real-time mapping and spatial analytics. The data and tools to support emergency management are changing rapidly and are much more advanced than they were just a few years ago.
On the next page, you'll find your reading assignment for this week, where we'll delve deeper into how GIS and other spatial tools are used during response activities, including a focus on the limitations of GIS systems in response situations.
The readings for this week focus on the fourth component of emergency management, response. You will read an overview chapter from your textbook, review a situation awareness briefing from FEMA during Hurricane Maria, and a book chapter on emergency management communications technology.
"GIS for Disaster Management" - Chapter 7 - "Disaster Management and Geographic Information Systems"
Previous readings have focused on how GIS can be used to avoid disasters, mitigate the consequences of events that may happen in the future, and prepare (in those cases where there is warning) for a disastrous event that is likely to happen (e.g., a hurricane that exists and has a predicted track and severity). These chapters focuses on how GIS can help in situations where an emergency/crisis is unfolding and shows how a well-reasoned, timely response can make a difference in the consequences of a disaster. Have a look at the entire chapter but focus on pages 192 - 203 – Geographical Aspects of Situational Awareness.
What are the key inter-agency coordination issues that should be considered to make a GIS-based response effort successful? How might recent advances in location-based services change the ways in which emergency management professionals and the public interact through geospatial information and technologies to respond to a disaster?
Situation awareness described in FEMA Geospatial Coordination Updates on two days during Hurricane Maria. The PDF's are located in Module 5 of Canvas.
These slide decks supported one of the daily coordination briefings that FEMA ran in the days up to and following Hurricane Maria. They provide a nice summary of what different FEMA teams and allied agencies are doing. It is meant to provide an overall summary of the current situation. Contrast these "hard copy" products with newer web based tools in the FEMA Geospatial Resource Center. [145]
Take note of the range of information and resources contributing to situation awareness for this event. Does anything seem to be missing? How well do you think the work of this group may support other parts of the emergency response e.g., search and rescue teams?
Norris, C. 2018. Chapter 2 - Computer Networks and Emergency Management from Technology and Emergency Management found in Module 5 of Canvas.
Finally, I’d like you to think a bit more about communication systems during disasters. This book chapter provides some interesting, albeit a bit basic, information on communication systems and then describes some of the ways they are impacted by disasters and how they can be restored during a disaster. It is good background given how much geospatial technologies depend on reliable ICT.
You might also be interested in this late 2017 news article on What Happens to the Internet After a Disaster? [146]
What are some of the specific geospatial challenges to communication and IT systems disruptions during emergencies? Does this make geospatial approaches vulnerable and potentially ineffective? Are there new ways for restoring these resources in a hurry?
This discussion will be graded out of 15 points.
Please see the Discussion Expectations and Grading page under the Orientation and Course Resources module for details.
This week’s emerging theme focuses on the topic of looking at disasters and maintaining situation awareness in (near) real time. To complete this section, you will need to:
For this exercise, we are going to focus primarily on the Solutions for Emergency Management [147] apps that have been developed by Esri and are based on their suite of ArcGIS technologies, especially cloud and web-based GIS services. Also, see Esri’s Disaster Response Program [148] website for the help the company offers during emergencies. There are other systems out there, some that leverage major industry platforms and some that are developed in-house from open-source software (e.g., recall InaSAFE [149], PDC [150], and even Google Crisis Response [151]).
Regardless of the system being used, they have similar goals such as providing:
Before developing your own situation awareness app, have a look at the two videos and the optional reading below. The first (short) video is an overview of a case study where these approaches are used by the California Office of Emergency Services (Cal OES). The second (longer) video provides more detailed examples about incorporating live data feeds into a situational awareness app and dashboard using Esri tools. These concepts are further discussed in the optional reading from The ArcGIS Book.
I know this is a long video. If you don't have time to watch the entire video, please have a look at the first 10-15 minutes or so.
Chapter 9: Mapping The Internet Of Things [153] from The ArcGIS Book
As a companion to the two videos, you might want to have a look at this book chapter from Esri. At least keep it in mind as a resource as you work through the rest of the exercise.
There is no Emerging Theme Discussion this week.
The next page will provide you with the details of the Exercise and writing assignment that are due this week.
For the analysis part of this exercise, you will consider the applications of (near) real-time geospatial tools for emergency management in greater detail. You should critically evaluate what is already out there and what is required for an effective decision support system utilizing dynamic and real-time spatial data. Once again, you are assuming the role of a geospatial analyst that is planning for and responding to emergency situations.
This assignment is worth 50 points toward the exercise portion of your course grade.
This assignment will be grading using the following rubric.
Criteria | Description | Possible Points |
---|---|---|
Part 1 – Critique of existing systems and design advice |
You make strong and logical arguments and provide analytical insights. Ideas are well organized, clearly communicated and relevant to the prompt. All criteria are accurately addressed. Supporting details are shared, elaborated upon and demonstrate understanding. Examples are provided, and your essay includes images or other multimedia that support content. | 20 |
Part 2 – Needs assessment and dashboard demo | You make strong and logical arguments and provide analytical insights. Ideas are well organized, clearly communicated and relevant to the prompt. All criteria are accurately addressed. Supporting details are shared, elaborated upon and demonstrate understanding. Examples are provided, and your essay includes images or other multimedia that support content. | 20 |
Overall - Clarity and Mechanics |
Evidence of editing and proofreading are evident. Writing is engaging and well-structured with excellent transitions between sentences and paragraphs. Concepts are integrated in an original manner. Mechanics (word limits and other requirements) were met. |
10 |
This week, you should be making significant progress on the first draft of your term project. Your goal should be to make the first draft as high quality as possible, with the idea that doing so will mean you have less work ahead of you to complete your second (and final) draft.
I have designed the timing of this assignment so that I have time to read your full drafts, offer feedback and editing suggestions, and return them to you with enough time left in the course so you can revise your work before submitting a final version.
Here are my expectations for your first draft:
Criteria | Description | Possible Points |
---|---|---|
Introduction | The introduction meaningfully engages the target audience/reader and clearly presents the central argument along with its substantive, technical and applied contexts. | 15 |
Background and Supporting Research |
The paper is well researched and contains references to peer-reviewed articles, government documents and industry reports that relate to the arguments in a logical manner. |
30 |
Analysis and Interpretations |
The design and implementation of a methodology was appropriately used to address the central arguments of your topic. Critical, relevant and consistent connections are made between evidence and central arguments. Includes appropriate use of maps, graphics, and tables. Analytical insights are sound and show a deep understanding of the issues. Depending on your selected topic, this may involve describing the steps taken for data analysis and mapping (NOTE –step by step instructions can be put into an appendix and will not count against word limits – discuss this with the instructor). |
30 |
Conclusion |
Excellent summary of topic and central arguments with concluding statements that impacts the target audience/reader. 10 | 10 |
Writing | There is evidence of editing and proofreading. Writing is engaging and well-structured with excellent transitions between sentences and paragraphs. Writing is polished and professional. Concepts are integrated in an original manner. | 15 |
This week, you should complete at least the first half of your term project draft. You will have all of this week and next week to complete your first draft, and since you have other assignments in this course, I recommend you manage your time accordingly.
If you're like me and you have trouble getting started on writing assignments, consider this piece of advice I heard from a colleague about completing a Ph.D. dissertation:
"Every day, set aside a writing task for yourself that is so small that you cannot possibly fail to complete it."
When I was writing my dissertation, I set little goals for myself every day that mirrored this advice. For me, it worked best to set a specific word count that I had to achieve every day. For you, there may be better ways to motivate yourself, so your mileage may vary.
Remember, if you have any questions while you are working on your first draft, just send me an email or leave a post on the Questions and Comments Discussion in Canvas.
Effective response to a disaster depends on quickly synthesizing actionable information and disseminating that information to responders in the field. Geospatial data systems and analyses are frequently used to assemble the "big picture" in a disaster. Among other things, it is essential for geospatial systems to help decision makers understand where first responder resources are located and where help is needed.
This week, we also focused attention on another challenge for geospatial systems in response situations. Quite often, a significant disaster will destroy the infrastructure that had been designed to support emergency management. For example, we learned about how an ad hoc system was developed in New York after 9/11. One way of avoiding this kind of problem is to distribute the emergency management geospatial system through a local network or via the Internet where it can be accessed from multiple entry points. This type of approach makes it less important that all emergency management personnel are in the same place.
Up to this point, we have covered mitigation, preparedness, and response topics for emergency management GIS. In the next lesson, we will move on to the final stage of emergency management and explore how geospatial data and analysis is used in longer-term recovery efforts to rebuild disaster areas.
You have reached the end of Lesson 5! Double-check the to-do list on the Lesson 5 Overview page to make sure you have completed all of the activities listed there before you begin Lesson 6.
This week, we will focus on how geospatial approaches and technologies can support the final phase of emergency management - recovery. After response efforts have ended, recovery efforts can begin in earnest. GIS and related geospatial tools can be used to plan near-term infrastructure repairs and to identify candidate organizations and communities to receive long-term aid and assistance through grants and infrastructure projects. Recovery projects frequently involve close interaction with disaster victims who want to rebuild and return to 'life as usual." This poses challenges and opportunities for geospatial practitioners and those who consume information from geospatial analyses. We will discuss these topics and others throughout this lesson.
The rebuilding or improvement of disaster-affected areas
By the successful completion of this lesson, you should be able to:
Lesson 6 is one week in length. To finish this lesson, you must complete the activities listed below.
To Read |
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To Do |
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Please refer to the Course Calendar for specific due dates.
If you have questions about the content or lesson activities, please post them to the General Questions and Discussion forum in Canvas. While you are there, feel free to post your own responses if you, too, are able to help a classmate. If your question is of a personal nature, please email me directly through Canvas.
Recovery is a difficult process and involves the coordination of a range of actors with different levels of decision-making power and resources. This is illustrated by the US Federal Emergency Management Agency’s National Disaster Recovery Framework (NDRF) [156] which seeks to address and find a way to operate given this complexity:
The National Disaster Recovery Framework is a guide that enables effective recovery support to disaster-impacted States, Tribes, Territorial and local jurisdictions. It provides a flexible structure that enables disaster recovery managers to operate in a unified and collaborative manner. It also focuses on how best to restore, redevelop and revitalize the health, social, economic, natural and environmental fabric of the community and build a more resilient Nation.
The framework document includes some nice visualizations of disaster recovery as a process that plays out over time (and space!). Take a moment to consider this diagram and the listed examples of activities in the short, intermediate, and long term. Who are some of the actors responsible for undertaking these activities? Where do spatial data and analysis come in?
The next image, again from FEMA, seeks to illustrate how recovery efforts are related to one another but also play out differently in different contexts. For example, recovery in Puerto Rico from Hurricane Maria has, in many ways, been a slower process than what has happened over the last year in Texas and Florida. Resources can be stretched thin when so many events occur around the same time (Note that in addition to the hurricanes, there were also major wildfires in the Western USA occurring around the same time in 2017). Moreover, some places never fully recover as they are repeatedly subject to disaster events. We will explore the recovery process in the next section on Hurricane Sandy, and you will also consider a few case studies in later lessons on events in Nepal and Indonesia.
The 2019-20 Bushfire season was catastrophic for much of Australia and National Bushfire Recovery Agency [159] was created to coordinate the recovery effort. Take some time to look at this site and think about the range of activities underway and who they are targeted for.
As we have seen, the boundary between response and recovery is a fuzzy one as is that between recovery and mitigation. As we have talked about earlier, it is useful to think of the stages of emergency/crisis management as a circle with each stage blending into the next. The roles of geospatial approaches and technologies can be conceptualized as occupying (often overlapping) positions along this circle.
This overlap in functions was never more apparent than with the 2012 Sandy Hurricane disaster hitting the eastern seaboard. In the midst of efforts to rescue people stranded by floodwaters, politicians and others began discussing how or whether to rebuild--opening what will probably be a long dialogue about the potential to rebuild in a way that is more resistant to similar events in the future and comparing the economic and other costs of this option with suggestions to not rebuild at all or to relocate parts of the city. Similarly, while repairs and rescue efforts continued, work began in some parts of the city to start on recovery – with spatial tasks ranging from figuring out where displaced individuals were, to assessing damage in regions of the city to determine whose insurance claims of disaster relief requests to process first, to re-establishing utilities, fixing roads, and other efforts to establish the infrastructure required to carry out whatever recovery efforts were decided upon.
This video, Inside the Megastorm (54:38 minutes), produced by PBS NOVA does a nice job of describing the storm and why it was so damaging. It also helps illustrate the many ways that spatial data analysis was used to aid in the response and immediate recovery. Note how different groups used geospatial data and analysis e.g, first responders, search and rescue, utilities, transportation. I know this is a long video, but it is worth a look, especially if you are not very familiar with what happened.
To get a sense for just how complex and challenging recovery can be, have a look at this report from the Guardian, Hurricane Sandy, five years later: 'No one was ready for what happened after' [161] and contrast it with some of the materials on the FEMA site, Sandy Five Years Later [162]. Do you notice a difference in tone and emphasis of these two sources? Unfortunately, there are pretty much always winners and losers during recovery efforts.
Finally, here are two critical perspectives on recovery I’d like you to pay close attention to. This essay, A Tale of Two Recoveries: 5 Lessons from Hurricanes Katrina and Sandy [163], is written by geographers Susan Cutter and Christophe Emrich of the Hazards and Vulnerability Research Institute [164] at the University of South Carolina. Susan Cutter is an influential scholar working in this space, and you are likely to come across her research center’s work (in fact, consider having a quick search in Google Scholar and/or visit the HVRI website). The second article, As Storms Keep Coming - FEMA Spends Billions in ‘Cycle’ of Damage and Repair [165], also focuses on some important and difficult issues around recovery, in particular, the back-and-forth between disasters and recovery effort. This echoes some of the messages in the Cutter article, and I’d like you to think about how the 5 Lessons relate to this analysis of FEMA spending. Although the article focuses on FEMA, the story is much the same for other countries trying to manage the complexity of recovery efforts, and we will see this later in the Sulawesi earthquake and tsunami case study.
As we've seen, disasters have a highly variable impact spatially and temporally. There are also strong spatial differences in social, economic, and environmental characteristics that shape both impact and recovery. The map below illustrates this issue by mapping housing damage against median income for part of Long Island in the wake of Hurricane Sandy. This is probably not too surprising given what you learned in Lesson 3 about hazards and social vulnerability. You can really see this play out during a big event like Sandy. This map comes from a piece in The Conversation, Storms hit poorer people harder, from Superstorm Sandy to Hurricane Maria [166], by Professor Chris Sellers at Stony Brook University.
The optional Business of Disaster video (54:47) continues to explore many of the themes around the complexities of disaster recovery. The video was produced by PBS FRONTLINE and NPR in 2017 and focuses specifically on the (ongoing) recovery from Hurricane Sandy.
As you read the course materials and other resources this week, think about strategies that are needed to develop geospatial data and analysis as general capability through which governments and other organizations can address the full range of emergency management challenges. Consider, in particular, what strategies are needed to make the process of using GIS and related technologies to support each stage of emergency management seamless - so that it is practical for emergency management teams to move quickly from the planning to the recovery stage as an event happens and to move among response, recovery, and planning-mitigation tasks as needed.
Also consider one common constraint - quite often the provisioning given to GIS systems to support emergency management is focused on preparedness and response phases. It's a lot harder to convince people to invest in new systems to support long-term recovery efforts. As we continue to face many and nearly simultaneous disasters, investing in recovery this way may become more and more urgent.
Finally, much of what we have considered has focused on events impacting the USA in particular. Moving forward we will explore these issues in other places, including Nepal and Sulawesi, Indonesia. These are places with perhaps more limited resources and geospatial infrastructure and often involve the international community and organizations playing a much stronger role. So think critically about the role of geospatial analysis and what is essential versus what is in development and may roll out eventually. In terms of recovery, how does it play out in these different countries and who is leading recovery efforts?
The readings for this week focus on the final component of emergency management, recovery. You will read a chapter in your text and two papers that address different approaches for using spatial analysis to understand patterns of recovery after major disasters. They all touch on the challenges of using geospatial analysis to help communities and organizations cope with events having geographically distributed impacts. Such events can range from relatively localized chemical spills affecting a small drainage basin, through major events impacting hundreds of thousands of people and with substantial financial impacts (such as 9/11, the 2011 Japan Earthquake, or Hurricane Florence).
Chapter 8 – Geographic Information Systems and Disaster Recovery from Geographic Information Systems (GIS) for Disaster Management
This chapter from your textbook provides another overview of how GIS is used in disaster recovery. Note how it contrasts how different types of events require different types of geospatial tools. It also provides some good descriptions of where GIS response could be improved and ways that a long-term recovery infrastructure could be promoted.
As you read this chapter, consider the following: How is the use of geospatial for recovery likely to differ for different kinds of events? What recovery-related geospatial issues does your text not cover that ended up being important in the years subsequent to a disaster like, say, Hurricane Sandy? When is recovery over?
Schumann et al. 2020. Wildfire recovery as a “hot moment” for creating fire-adapted communities. Internaional Journal of Disaster Risk Reduction 42:101354. (Available on next page in Canvas)
This paper pulls together many of the topics we have consdiered so far. The authors suggest that "the period following a destructive wildfire may provide a “hot moment” for community adaptation. Drawing from literature on natural hazard vulnerability, disaster recovery, and wildfire ecology, this paper proposes a linked social-ecological model of community recovery and adaptation after disaster".
Both the journal articles focus on very different ways of using spatial data and GIS analysis to explore longer-term recovery from different types of disasters. What advantages or disadvantages do you see in both approaches? Pick one or two to share with your classmates, and try to link your points to other ideas we have covered so far in the course.
This discussion will be graded out of 15 points - pretty easy this week! Just show up and share your thoughts.
This week’s emerging theme is focused on an age-old problem, how to get things from point A to point B. The importance of logistics and supply chains in emergency management cannot be overstated. It is a topic that intersects all phases of emergency management but is perhaps most important in the preparation stage. In this section, I’ll provide a bit of background, then you will look at a few videos, agency presentations, and short readings. We’ll end with a consideration of cutting-edge trends in the field that are having or have the potential for big impacts. Finally, you will take what you've learned into a discussion forum and bounce ideas off your classmates. I have also provided some links to optional reading if you want to learn more about this topic.
We will begin with some quick background information about disaster and humanitarian logistics. The first video provides an overview description of what humanitarian logistics is all about. Then there are two videos that show what this looks like on the ground during some recent disasters. Finally, a news article explores what can go wrong if one part of the supply chain is disrupted - blue tarps!
Watch: The Logistics Cluster in 2:30 Minutes
For further reading (optional):
Watch: Crowley and FEMA Accelerate Relief Aid to Puerto Rico (1:18 minutes)
Watch: Logistics On Location: Supporting Hurricane Maria (2:56 minutes)
Read: Puerto Rico: urgently needed tarps delayed by failed $30m FEMA Contract [171] from The Guardian
Next, let's consider some of the ways geospatial approaches are used in humanitarian logistics and supply chains. Start by reviewing what Esri is doing by watching the following short video, visiting and reviewing the Logistics Planning Website [172] and trying out the Logistics Planning App [173]. After you finish looking at these resources, contrast this work with what Google is doing in this space by looking at their Google Maps Platform: Transportation Website [174].
Watch: Watch this 3:33 minute demonstration video and then explore the live app on the Esri website
We’ll end with a consideration of some of the cutting-edge trends in logistics R&D and practice that are having or have the potential for big impacts on humanitarian and disaster operations.
DHL is one of the major world logistics companies and they produce an annual tech trend assessment for their industry. I'd like you to consider how they characterize the current state of the art and how that is changing rapidly due to developments in technology and operational models. Start with the video and then move on to the report linked below.
Watch: DHL Data Analytics video (1:27 minutes)
This short video and industry report were developed by the commercial logistics company DHL. It provides some useful insights into where the industry is heading and how new technologies are shaking things up. When you watch this, think about what you read and watched previously and consider how these ideas may or may not match up, particularly in the emergency management context.
DHL Data Analytics (click image)
Review: Logistics Trend Radar Industry Report [177] (also found in Lesson 4 of Canvas)
This discussion will be graded out of 15 points.
Please see the Discussion Expectations and Grading page under the Orientation and Course Resources module for details.
This week, you need to finish the first draft of your term project. Your goal should be to make the first draft as high quality as possible, with the idea that doing so will mean you have less work to complete the second (and final) draft.
I have designed the timing of this assignment so that I have time to read your full drafts, offer feedback and editing suggestions, and return them to you with enough time left in the course to revise your work before submitting a final version.
Here are my expectations for your first draft:
Submit your assignment to the Lesson 6 Term Project First Draft dropbox. See the Course Calendar for specific due dates.
The first draft of your term project is worth 10% of your final course grade and is graded out of 100 points. For this assignment, I will assign grades with the following rubric:
Criteria | Description | Possible Points |
---|---|---|
Introduction | The introduction meaningfully engages the target audience/reader and clearly presents the central argument along with its substantive, technical and applied contexts. | 15 |
Background and Supporting Research |
The paper is well researched and contains references to peer-reviewed articles, government documents and industry reports that relate to the arguments in a logical manner. |
30 |
Analysis and Interpretations |
The design and implementation of a methodology was appropriately used to address the central arguments of your topic. Critical, relevant and consistent connections are made between evidence and central arguments. Includes appropriate use of maps, graphics, and tables. Analytical insights are sound and show a deep understanding of the issues. Depending on your selected topic, this may involve describing the steps taken for data analysis and mapping (NOTE –step by step instructions can be put into an appendix and will not count against word limits – discuss this with the instructor). |
30 |
Conclusion |
Excellent summary of topic and central arguments with concluding statements that impacts the target audience/reader. | 10 |
Writing | There is evidence of editing and proofreading. Writing is engaging and well-structured with excellent transitions between sentences and paragraphs. Writing is polished and professional. Concepts are integrated in an original manner. | 15 |
This week, we moved to the final phase of emergency management - recovery. Recovery from a disaster can take a very long time (many would argue that we are still working on the aftermath of Sandy, for example), and there are a wide range of roles that geospatial perspectives and technologies can play in the recovery process. For example, GIS and mapping may be called upon to identify areas for redevelopment projects or to recalibrate vulnerability models to help predict future disaster impacts.
Talk of recovery plans may begin quite early following a disaster. We learned that during Sandy there were efforts to begin talking about the rebuilding process during the response phase of the disaster. A key challenge that geospatial systems for emergency management must face will be rapidly changing priorities.
Now that we have identified and discussed all four stages of the emergency management process, we will shift focus in the next lesson toward the use of scenarios to plan geospatial systems for emergency management. You've had a bit of experience with these already in your vulnerability assessment work in Lesson 3. Scenarios can be incredibly useful tools to help predict what technology and capabilities a GIS system will need to have to handle all phases of emergency management.
You have reached the end of Lesson 6! Double-check the to-do list on the Lesson 6 Overview page to make sure you have completed all of the activities listed there before you begin Lesson 7.
In this lesson, we will rely on our new knowledge of geospatial perspectives and technology as it is used in the four stages of emergency management to develop scenarios that can be used to inform the design of new geospatial systems for emergency management. Scenarios are a key creative mechanism for evaluating system designs against the likely impacts and outcomes from a hypothetical disaster situation. You will learn about scenarios and then develop your own.
By the successful completion of this lesson, you should be able to:
Lesson 7 is one week in length. To finish this lesson, you must complete the activities listed below.
To Read |
|
---|---|
To Do |
|
Please refer to the Course Calendar for specific due dates.
If you have questions about the content or lesson activities, please post them to the General Questions and Discussion forum in Canvas. While you are there, feel free to post your own responses if you, too, are able to help a classmate. If your question is of a personal nature, please email me directly through Canvas.
Besides researching previous disasters and geospatially-enabled emergency management technology, an excellent way to forecast what is needed in a future geospatial system is to develop scenarios. You've already had some experience with this in Lesson 3 where you explored potential disasters for Texas using the InaSAFE plugin for QGIS.
In a disaster management setting, scenarios are realistic stories that describe what would happen to people, infrastructure, and the natural environment with a given set of disaster conditions. Often, scenarios are developed as part of a hazard assessment process where they can be used to predict the possible effects on a place, given different types of hazard situations. Scenarios are also used to create training simulations to test preparedness measures and response plans. This latter purpose is particularly relevant for this class; we need to use scenarios to evaluate the extent to which our geospatial infrastructure and analytical capabilities will actually hold up during a disaster situation.
Note that in geospatial system design activities, scenarios can end up being quite formal in terms of their structure. For further reading on the essence of scenario-based design, check out this section from Geospatial System Analysis and Design: GEOG 583 [179] – NOTE: you don’t have to read all of this! Just have a quick look.
A great way to understand scenarios is to read a few yourself. The US Department of Homeland Security and FEMA have prepared many different scenarios and training materials that you can review. Please click on the "Emergency Planning Exercises For Your Organization" link on Emergency Planning Exercises [180] page to see some examples. In particular, I would like to explore a few of these exercises. Please pay particular attention to the roles that participants play, the rules to follow, the scenario itself, and the prompts used to keep things moving. What are participants expected to do and get out of this? Focus on these (but feel free to explore others):
“Developed by the Office of External Affairs and the FEMA's National Exercise Division, this exercise is based on a combination of U.S. communities experiencing critical power failure during a severe weather event. Designed to help the private sector identify ways to prepare for, respond to, and recover from such a disaster.”
“Prepare to respond to and recover from a Category 5 hurricane. Based on the National Planning Scenario for a major hurricane, this exercise was developed by the Office of External Affairs together with FEMA's National Exercise Division to prepare the private sector for catastrophic damage caused by major flooding, tornado, and other natural disasters.”
“Based on the National Planning Scenario for a chlorine tank explosion, this exercise is designed to help the private sector improve Organizational Continuity, Preparedness, and Resiliency in the event of an emergency, to respond to, recover and restore operations.”
You’ll also note that scenarios often go hand-in-hand with Tabletop exercises. Tabletop exercises are simulated response activities. Usually, these are held in an extremely generic hotel ballroom with stakeholders of all types hunkered down on their laptops. An exercise begins with a scenario description, and then a moderator provides additional information during the response activities to throw things into further chaos and test the limits of what people are prepared for. Some of the FEMA materials include videos to simulate news reporting, although they need to be about forty times more hyperbolic to match the 24/7 news channel intensity these days.
The 2:06 minute video below provides an interesting example of a tabletop exercise conducted by the local government in Waco, Texas in 2017. Pretty low-tech, but you get the idea of what can be accomplished. More focused exercises can subsequently be run with specific stakeholders, e.g., geospatial teams.
This week we will do something a little different for our writing assignment. I would like you to work in groups (see the Lesson 7 announcement for group assignments) to develop one of three different scenarios that focus on leverage points for the use of geospatial applications and technology. You will have the opportunity to imagine a realistic situation and propose different roles for geospatial approaches during the disaster. Each group will use Esri Story Maps [181] to present the content and analysis you have developed.
Group 1 - TBD
Group 2 - TBD
Group 3 - TBD
Group 4 - TBD
Develop a scenario about a cyber attack that impacts the power grid in the Southeast US. Split your scenario into five 6-hour long time periods, starting with the time period 6 hours prior to the attack.
Develop a scenario about a major indiustrial accident (e.g.,chemical spill but you can choose something else) in an industrial site within a major urban area. Split your scenario into five 6-hour long time periods, starting with the time period 6 hours prior to the accident.
You will notice that I have not provided detailed specifics on certain aspects of each disaster, such as how fast the wildfires are spreading, or how effective the cyber-attack was at interrupting the power supply. I encourage you to fill in those gaps in your writing and to imagine plausible answers to those sorts of questions.
I know group work can be challenging if everyone doesn't do their part. As part of the assessment you will be asked to provide confidential feedback to me about how things went with your team mates. If you are having problems during the week, please let me know as well.
For this group exercise, you will be organizing your content into an Esri Story Map. For those of you not familiar with Story Maps, they are a great way of presenting a narrative that includes a mix of text, maps, and other multimedia. They can be a powerful way of conveying a lot of complex material in a “guided tour” format. You can view many examples on the Story Maps homepage [181]. The Story Map related to Hurricane Harvey [182] is particularly nice and you saw one on the 2018 Camp Fire in Lesson 2.
There are a number of ways to get started. You can collaboratively edit in your Story Map directly or you can create a “Storyboard” in a program like PowerPoint or Word and then migrate content. I recommend you develop a storyboard. This will help you organize your content and make sure you have what you need to make the story map. As mentioned in earlier lessons, you can manage your collaboration in the course OneDrive folder.
Here are some resources to get you started.
Before you get started, I recommend that you decide on a division of labor for this work. For example, one person might want to be responsible for developing the overall narrative in consultation with the group, another person might want to take on the analysis/mapping task and another may want to focus on migrating everything into the Story Map.
Since you are working in small groups, collaboration should be fairly straightforward, I suggest the following -
For this week's exercise, please submit the following items to the Lesson 7 Group Scenario Exercise Dropbox in Canvas. See our Course Calendar in Canvas for specific due dates.
I'll assign grades by group. This project is worth 4% of your total course grade and will be graded out of 20 points using the following rubric.
Criteria | Description of Criteria | Possible Points |
---|---|---|
Content and Impact | Your group makes strong and logical arguments and provides analytical insights. Ideas are well organized, clearly communicated and relevant. All criteria are accurately addressed. Supporting details are shared, elaborated upon and demonstrate understanding. Examples are provided, and your StoryMap includes geospatial products, images or other multimedia that support content. | 14 |
Clarity and Mechanics | Your StoryMap shows evidence of editing and careful proofreading. Writing is engaging and well-structured with excellent transitions between sections and visual content. Concepts are integrated in an original manner. | 4 |
Team member evaluation | Confidential comments are provided on your experience working in your assigned group. | 2 |
Total Points | - | 20 |
We have already talked a lot about volunteered geographic information (VGI) and other types of citizen involvement in emergency management, but here we will consider how social media data, like Twitter, Instagram, and Facebook, are being used informally and more formally through big (spatial) data analytics. These are potentially rich data sources but are still a bit difficult to use and have some specific problems around getting accurate and meaningful location information.
To get this section started, have a look at this NBC Nightly News Story (2:01 minutes) about how search-and-rescue used social media to help locate people needing rescue.
In the recent past, there have been significant advances in automated tools for extracting place information from news articles and other text media. This led to a wave of map mashups that allowed for news stories to be browsed using a map. Since those earlier efforts, social media data sources have become ubiquitous, and while similar methods can be used to extract and represent places mentioned in social media reports like Tweets, there are also a lot of challenges we have yet to overcome to make these datasets truly useful in a crisis situation. Moreover, as we saw in Lesson 5, the rise of real-time geospatial systems means we need to be able to locate and understand the content of social media in near real-time, and this is still challenging!
The use of social media in disaster response really took off in the mid-2000s with the efforts of digital humanitarians like Patrick Meier, the author of your textbooks (also see Chapter 3 – Crowd Computing Social Media in Digital Humanitarians for more). In the following short video (1:31 minutes), Patrick explains how this works, particularly how we can teach machines to understand and classify tweets into actionable information.
A few of the challenges associated with mapping information from social media are:
Here at Penn State, we've been engaged in research to develop new tools for foraging through and visualizing geographic information coming from social media reports.
The SensePlace2 project harvests tweets that include disaster-related keywords. From these tweets, we then extract place names and geocode them (along with other named entities, such as people, organizations, and resources). Please have a look at the following 3 minute video, SensePlace2: Visual Analytics and Big Data for Spatiotemporal Sensemaking.
If you want to learn more, check out this 2017 journal article on the project: SensePlace3: a geovisual framework to analyze place–time–attribute information in social media, Cartography and Geographic Information Science [186]
Because so many social media sources now feature API access to their data feeds, new map mashups are now possible that can integrate multiple forms of social media with other geospatial data. Keep in mind that the quality of these vary considerably. For example, some tools just use the location feature that some (very few, it turns out [187]) enable on their devices when they use Twitter.
OPTIONAL
You may be interested in this study that was published just this year (2022!) on using tweets and retweets to understand information diffusion during disasters. This is just FYI, but it is worth a skim now.
Jinwen Xu & Yi Qiang (2022) Analysing Information Diffusion in Natural Hazards using Retweets - a Case Study of 2018 Winter Storm Diego [188], Annals of GIS, 28:2, 213-227, DOI: 10.1080/19475683.2021.1954086
This discussion will be graded out of 15 points.
Please see the Discussion Expectations and Grading page under the Orientation and Course Resources module for details.
This week, I'll be reading each of your term project drafts and providing my feedback. So, for now, you can relax a little about your project and focus on the rest of this lesson.
This week, we have explored how scenarios can be used to predict and plan for how GIS can be used for emergency management situations. Scenarios are stories developed around a hypothetical disaster situation, and they are used quite commonly in planning activities as a way to predict what will happen in a real situation.
You have worked with your classmates in this lesson to develop your own scenarios to see for yourself how scenario-based planning works. I hope you found it valuable to attempt this task with your colleagues. Most people who are charged with the task of planning a GIS for emergency management will not be working on that task alone, so the challenges posed by group work in this situation are quite relevant.
Scenarios are not easy to pin down. There are no universal rules as to what they should or should not include, and there are no automated tools available yet that can generate them. Scenario writing requires the synthesis of multiple types of knowledge, and, ultimately, it demands a fair bit of creativity on behalf of the author(s).
Next, we will apply what we have learned so far about the dimensions of emergency management and ways to plan GIS systems to support emergency management tasks. In the next lesson, we will work together on a case study research project to understand how GIS was used in the mitigation, preparation, response, and recovery from a recent disaster.
You have reached the end of Lesson 7! Double-check the to-do list on the Lesson 7 Overview page to make sure you have completed all of the activities listed there before you begin Lesson 8.
This week, we will tackle our second collaborative project. This collaborative assignment is designed to pull together what you have learned so far in this class and apply it toward researching and critiquing the use of geospatial approaches and technology in a recent disaster. You will work in teams to gather and condense information to explain and critique how GIS was used in a real crisis situation - the 2021 Haiti Earthquake.
By the successful completion of this lesson, you should be able to:
Lesson 8 is one week in length. To finish this lesson, you must complete the activities listed below.
To Read |
|
---|---|
To Do |
|
Please refer to the Course Calendar for specific due dates.
If you have questions about the content or lesson activities, please post them to the General Questions and Discussion forum in Canvas. While you are there, feel free to post your own responses if you, too, are able to help a classmate. If your question is of a personal nature, please email me directly through Canvas.
This week, I would like you to work together to research key phases of emergency management, and the use of geospatial analysis to support them for the 2021 Haiti Earthquake. Each group will be assigned to evaluate one or two phases of Emergency Management:
Group 1 - TBD - Topic: Preparedness following the 2010 Earthquake (focus on period from 2016 to 2021)
Group 2 - TBD - Topic: Response / Relief in the immediate aftermath of 2021 Earthquake
Topics not covered this term:
Group # - TBD - Topic: Recovery between 2010 and 2021 Earthquakes
Group # - TBD - Topic: Mitigation/Preparedness for next major earthquake
You will be researching the 2021 Haiti Earthquake, [191] which caused massive loss of life and property. Each group should create a Story Map [181] using ArcGIS Online [183] for their phase of emergency management according to the following criteria:
One Section identifying the key stakeholders and their needs.
2-3 Sections about how geospatial was used during this phase. At a minimum, answer the following questions. !hat worked and what did not work? Did geospatially-oriented social media play a role? If so, how?
Include at least three pictures and/or videos (more = better) and link when appropriate to external sources that back up what you are reporting.
Together, I'd like you to create at least one analytical product using one or more of the datasets I've linked to here (or others you find on your own).
Maxxar Open Data Program [194]
This analytical product should include a map and supporting graphics/text such that it can stand on its own if it were distributed widely. It's up to your group what you would like to highlight - there are dozens of datasets out there now and a wide range of possible stories you might try to tell using them. Creativity is encouraged!
Since you are working in small groups, collaboration should be fairly straightforward, I suggest the following -
For this week's exercise, please submit the following items to the Lesson 8 Group Haiti Earthquake Analysis Dropbox in Canvas. See our Course Calendar in Canvas for specific due dates.
I'll assign grades by group. It is worth 4% of your total course grade and will be graded out of 20 points using the following rubric.
Criteria | Description of Criteria | Possible Points |
---|---|---|
Content and Impact | Your group makes strong and logical arguments and provides analytical insights. Ideas are well organized, clearly communicated and relevant. All criteria are accurately addressed. Supporting details are shared, elaborated upon and demonstrate understanding. Examples are provided, and your StoryMap includes geospatial products, images or other multimedia that support content. | 14 |
Clarity and Mechanics | Your StoryMap shows evidence of editing and careful proofreading. Writing is engaging and well-structured with excellent transitions between sections and visual content. Concepts are integrated in an original manner. | 4 |
Team member evaluation | Confidential comments are provided on your experience working in your assigned group. | 2 |
Total Points | - | 20 |
We hear a lot about artificial intelligence (AI) these days, and indeed AI is a rapidly expanding field finding applications in many aspects of our lives. Emergency management and geospatial applications are no exception. So, what is AI and GeoAI and how are they being applied to the phases of emergency management?
Artificial intelligence refers to a range of approaches and applications whereby computers are trained to simulate intelligent human behavior and act in autonomous or semi-autonomous ways. AI systems are also able to process and learn from vast amounts of data that are difficult if not impossible for humans to readily understand.
If you want to learn more about AI or don’t feel like you have a very good understanding of what it is all about, have a look at these resources (optional):
What about geospatial artificial intelligence (geoAI)? I’d like you to have a look at two videos that illustrate some of the current characteristics of geoAI and where it is heading. The first video is a presentation on machine learning and the prediction of road accidents from a recent Esri conference (8:50 minutes). It provides a good overview of geoAI and an interesting application to illustrate what’s currently possible with one of the main GIS software systems.
The second video is an interview with Nigel Clifford the CEO Ordinance Survey, the UK’s national geospatial agency where he talks about the future of AI and geoAI in particular (4:35 minutes).
If you want to learn more about geoAI, have a look at this recent paper by Trang VoPham et al from 2018. At the very least, it will be a good resource if you come back to the topic of geoAI in the future.
Finally, I’d like you to consider a few examples of AI and geoAI applied to recent emergency management problems. The first is a presentation from Robert Munro, CTO Figure Eight, at the recent CogX AI conference (18:47 minutes). While not specifically focused on geoAI, geospatial problems figure through most of his presentation. Contrast this perspective with what we saw from Esri in the earlier video. The second video is a quick recap of the surf rescue video you saw in the UAV exercise in Lesson 2.
Artificial Intelligence Processing at the Edge: The Little Ripper Lifesaver UAV (2:05 minutes)
---> Breaking news: Here is an update on Little Ripper and its cousin CrocSpotter. [198]
Finally, I’d like to refer you back to the Digital Humanitarians textbook. As you know, this book provides a contrasting perspective to many of the government and private sector perspectives we consider. This is also the case with geoAI. If you have the time or want to come back to it later, I recommend Chapter 6: Artificial intelligence in the Sky.
This discussion will be graded out of 15 points.
Please see the Discussion Expectations and Grading page under the Orientation and Course Resources module for details.
By now, you should have received my feedback on the first draft you submitted in Lesson 6. You now have until the end of Lesson 10 to submit your final draft. Please dedicate some time this week toward incorporating some of the suggestions I've provided. If you are unclear about any of my comments or suggestions, get in touch so I can clarify things for you.
This week, you took on the task of developing a case study analysis of the 2021 Haiti earthquake, focusing specifically on the role of geospatial analysis during emergency management phases. This was presented via a Story Map that can be shared with others in an interesting and accessible format. We also discussed the AI and how it is being developed in the geospatial realm as geoAI. I like that this class juxtaposes a consideration of the practical needs of emergency management now with cutting edge trends that are changing things very quickly. I didn't ask about this earlier, but can you envisage ways geoAI (or other emerging themes) could be brought to bear if the Nepal event were to happen now? As we have seen, what seemed to be impossible or cutting edge a few years ago are mainstays today.
Next week, you will consider another case study about an event that occurred late in 2018, the Sulawesi earthquake and tsunami. This event has so many interesting dimensions, many of which we have been studying and discussing in this class, including logistics, social vulnerability, multiple hazards, national and international responses, and even civil unrest! For the final Emerging Theme, we will revisit some of the real-time GIS advances covered in Lesson 5 in greater detail by discussing the Internet of Things (IoT).
You have reached the end of Lesson 8! Double-check the to-do list on the Lesson 8 Overview page to make sure you have completed all of the activities listed there before you begin Lesson 9.
This week, we will learn about how geospatial approaches and technologies were used to respond and recover from the 9.28.2018 Sulawesi [200] Earthquake and Tsunami in Indonesia [201]. This disaster required a large-scale response from many entities including response organizations around the world. The geography of the affected area made it very difficult to reach victims and assess the damage - posing a variety of challenges to geospatial analysts that we will explore this week.
By the successful completion of this lesson, you should be able to:
Lesson 9 is one week in length. To finish this lesson, you must complete the activities listed below.
To Read |
|
---|---|
To Do |
|
Please refer to the Course Calendar for specific due dates.
If you have questions about the content or lesson activities, please post them to the General Questions and Discussion forum in Canvas. While you are there, feel free to post your own responses if you, too, are able to help a classmate. If your question is of a personal nature, please email me directly through Canvas.
On September 28, 2018, at 6:02 PM local time, a 7.5 magnitude earthquake struck in central Sulawesi, Indonesia. The quake triggered a tsunami with a maximum height of 4-7 meters (13-23 feet) causing massive destruction. There was also widespread destruction due to soil liquefaction, landslides, flooding, and aftershocks (see below). There have been over 2,100 confirmed dead, 10,679 injured, and over 5000 still missing. There was mass disruption to transportation links which delayed response and recovery efforts.
The earthquake occurred in a part of Indonesia that is diverse both in terms of human settlement patterns and environmental factors. It contains relatively large settlements such as Palu (population 335,297 in 2010) as well as small and remote rural settlements. The triple impact of the earthquake, tsunami and land movement made it extremely difficult to locate people in need and deliver food and medical assistance. The recovery effort has also been challenging.
To get a sense for what this all looked like on the ground, please consider the following:
Now that we have a better understanding of what happened in Sulawesi during and right after the earthquake and tsunami, I'd like you to read about three topics related to Indonesia’s disaster vulnerability and preparedness. These touch on themes we've considered earlier, e.g., social vulnerability, planning and preparedness, and emergency communications.
Social vulnerability to natural hazards in Indonesia: driving factors and policy implications (2014) in the Journal of Natural Hazards. You can find this article on the following page in Canvas.
The first reading is a journal article about measuring and mapping social vulnerability in Indonesia and how this can be used to inform policy (you first encountered way back in Lesson 3), As you read this, think about how it fits or contrasts with what you learned about who was impacted the most by the 2018 event.
Chapter 9: Spatial Planning, Disaster Risk Reduction, and Climate Change Adaptation Integration in Indonesia: Progress, Challenges, and Approach (2017) in the recent book Disaster Risk Reduction in Indonesia. You can find this book in the Penn State Library [95] and/or on the following page in Canvas.
The second reading focuses on the role of spatial planning in Disaster Risk Reduction (DRR) efforts in the context of current vulnerabilities and changing vulnerability with climate change. There are two key points I'd like you to take from this reading. The first is the concept of Disaster Risk Reduction, and the second is the idea that the current hazard and risk profile of a given area is not fixed and may be exacerbated by factors such as climate change or rapid urbanization.
Finally, have a look at the following online resources about tsunami warning systems.
The final readings take a different direction and discuss tsunami warning systems, how they are meant to work and what happen during the 2018 Sulawesi event.
How might you use geospatial technology in new ways to facilitate disaster warnings? These articles deal with tsunami warning systems, but how might this work with other types of emergencies such as other large-scale events or small-scale events like an active attacker incident? Finally, what are some of the issues associated with providing early warning to everyone versus just to first responders and emergency managers?
This week’s emerging theme topic, digital twin, brings together most of the emerging themes (and other content) you have learned about over the last few weeks.
The basic idea behind a digital twin is to build a virtual version of a real world system by integrating a wide range of datasets and models. The twin allows you to examine the way the system works and to see the effects of potential changes. They may also incorporate machine learning are are able to learn and change over time as new information is added.
For example, a digital twin of an aircraft engine allows engineers to understand maintenance needs and performance issues under real world and modelled conditions. For example, Rolls-Royce feeds inflight sensor and instrument data via satellite link its digital twin.
Rolls-Royce UltraFan TurboFan - Source: Rolls-Royce [209]
Read this short article on Rolls-Royce’s IntellgentEngine program: How Digital Twin technology can enhance Aviation [209]
You may hear digital twin talked about in the context of the “multiverse”. This language is a bit trendy, but the basic point is that a digital twin provides a way of creating / testing out new ideas or looking at problems in different (endless??) ways. A basic example might be modelling the potential impact of different road intersection options on pedestrian safety. On a much broader scale, and in an emergency management context, a digital twin may be used to understand the cascading impacts of major flooding in an urban area. Impacts that may not be obvious using traditional GIS or statistical analysis.
WATCH
TAKE A QUICK LOOK / KEEP FOR REFERENCE
Have a quick look at these two websites that provide some detailed information about Digital Twin from the point of view of two software developers in this space.
Take note of how familiar geospatial and data science methods and technologies are used in the context of a Digital Twin.
Now, look at this short video and have a play with the New South Wales Digital Twin
New South Wales Digital Twin
Source: New South Wales Digital Twin [213]
Now spend a few minutes exploring the data sets and tools on the New South Wales Digital Twin web portal [214]
End your exploration with this short article about how the NSW Digital Twin to inform emergency planning this bushfire season [215].
Climate Resilience Demonstrator
The following video and interactive app were created as part of The Digital Twin (DT) Hub [216] by the Centre for Digital Britain at the University of Cambridge. It will probably make you think about the scenario development group project you completed a couple of weeks ago. Start by watching the video and then move on to the interactive app.
Now, work through the interactive app [218].
TAKE A QUICK LOOK / KEEP FOR REFERENCE
If you are interested in taking a deeper dive into the topic of Digital Twin, you may want to look at the follow recent journal papers. They provide nice reviews of the history of DT and their applications in disaster and emergency managment. No need to read these carefully - Just skim / have a look at tables and figures. Note PDF versions are on the following page in Canvas.
Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management
Chao Fan, Cheng Zhang, Alex Yahja, Ali Mostafavi 2021. International Journal of Information Management, Volume 56
Abstract: This paper presents a vision for a Disaster City Digital Twin paradigm that can: (i) enable interdisciplinary convergence in the field of crisis informatics and information and communication technology (ICT) in disaster management; (ii) integrate artificial intelligence (AI) algorithms and approaches to improve situation assessment, decision making, and coordination among various stakeholders; and (iii) enable increased visibility into network dynamics of complex disaster management and humanitarian actions. The number of humanitarian relief actions is growing due to the increased frequency of natural and man-made crises. Various streams of research across different disciplines have focused on ICT and AI solutions for enhancing disaster management processes. However, most of the existing research is fragmented without a common vision towards a converging paradigm. Recognizing this, this paper presents the Disaster City Digital Twin as a unifying paradigm. The four main components of the proposed Digital Twin paradigm include: multi-data sensing for data collection, data integration and analytics, multi-actor game-theoretic decision making, and dynamic network analysis. For each component, the current state of the art related to AI methods and approaches are examined and gaps are identified.
Keywords: Digital twin; Machine learning; Information flow; Disaster management
Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities
Yu, D., He, Z. 2022. Nat Hazards 112, 1–36 (2022).
Natural hazards, which have the potential to cause catastrophic damage and loss to infrastructure, have increased significantly in recent decades. Thus, the construction demand for disaster prevention and mitigation for infrastructure (DPMI) systems is increasing. Many studies have applied intelligence technologies to solve key aspects of infrastructure, such as design, construction, disaster prevention and mitigation, and rescue and recovery; however, systematic construction is still lacking. Digital twin (DT) is one of the most promising technologies for multi-stage management which has great potential to solve the above challenges. This paper initially puts forward a scientific concept, in which DT drives the construction of intelligent disaster prevention and mitigation for infrastructure (IDPMI) systematically. To begin with, a scientific review of DT and IDPMI is performed, where the development of DT is summarized and a DT-based life cycle of infrastructures is defined. In addition, the intelligence technologies used in disaster management are key reviewed and their relative merits are illustrated. Furthermore, the development and technical feasibility of DT-driven IDPMI are illustrated by reviewing the relevant practice of DT in infrastructure. In conclusion, a scientific framework of DT-IDPMI is programmed, which not only provides some guidance for the deep integration between DT and IDPMI but also identifies the challenges that inspire the professional community to advance these techniques to address them in future research.
RESPOND
This discussion will be graded out of 15 points.
Please see the Discussion Expectations and Grading page under the Orientation and Course Resources module for details.
I'd like you to conducted a quick analysis and write a short report synthesising what you have learned about the Sulawesi Earthquake and Tsunami.
To begin with, you will work with satellite image pairs taken before and immediately following the 2018 Sulawesi event. The imagery is from Palu and the surrounding area. Imagine you are helping assess the damage on critical infrastructure (rather than population issues or housing) for the purposes of early recovery/clean-up.
I would like you to identify three areas demonstrating three different types of disaster impacts, e.g., landslide area. Present these as side by side image pairs. You can identify these areas manually through visual examination, refer to crowdsourcing maps where damage has already been identified, or even conduct your own image classification and change detection analysis.
After you assess the satellite images and have your image pairs, look online for an on-the-ground photo showing what these areas might look like up close, and then provide short captions for each image.
There will be a lot of obvious damage to things like buildings, so, brownie points for having one of your three image deal with more unusual (but significant) impacts.
You will need to:
Finally, draw upon your findings and the reading you have done to answer the following questions in a 300-400 report:
Deliverables
Post the images, short descriptions (1-2 sentences max), and your short repor in the Lesson 9 Research Assignment Dropbox in Canvas.
This assignment is worth 5% of your total grade and will be graded out of 10 points.
Criteria | Description of Criteria | Possible Points |
---|---|---|
Content and Impact |
Three disaster impact areas are identified and before and after images are provided along with a short description of each. This should be from the point of view of responding to the disaster and draw upon what you have learned in the course. For example, damaged bridges might be important to identify because of their importance to humanitarian logistics and tools like payload drones might be needed to help people isolated right after the event. |
8 |
Clarity and Mechanics | Writing is engaging and well-structured. Concepts are integrated in an original manner. | 2 |
At this point, you should be well on your way toward finishing your final term project paper. If you have already finished, consider having a colleague at work (or someone else you know who understands geospatial approaches and technology) read your final draft and offer feedback. This is a great way to check for spelling and grammatical errors, and it's also a great way to find out how well you are at communicating your ideas.
As always, if you run into trouble and need some help, please email me.
If I were you, I'd also have a look ahead at the video presentation component of the final project, which you may want to begin preparing now. It's due during Lesson 10.
This week, we have explored the 9.28.2018 Sulawesi Earthquake and Tsunami and the many ways in which geospatial approaches were used to respond and recover from this disaster. The magnitude of the disaster means that for the next several years, GIS and related technologies will continue to have a role in the long-term recovery of the region, and we can already see in subsequent disasters (like the many USA storms in 2018) that the expectations for GIS outputs continue to evolve at a rapid pace.
Next, we will begin the final lesson for this course. We will devote our attention to the term projects you have been working on throughout the semester. You will submit your final term project assignment materials and participate in a mini-conference to share your findings with your classmates.
You have reached the end of Lesson 9! Double-check the to-do list on the Lesson 9 Overview page to make sure you have completed all of the activities listed there before you begin Lesson 10.
At the successful completion of Lesson 10, you should be able to:
Lesson 10 is one week in length. To finish this lesson, you must complete the activities listed below.
To Read |
|
---|---|
To Do |
|
Please refer to the Calendar in Canvas for specific timeframes and due dates.
If you have questions about the content or lesson activities, please post them to the General Questions and Discussion forum in Canvas. While you are there, feel free to post your own responses if you, too, are able to help a classmate. If your question is of a personal nature, please email me directly through Canvas.
This week provides you with your last opportunity to finish revising your term project. Make sure you have addressed the issues I pointed out in your first draft. If you have questions about your edits, reach out to me quickly so I have time to get back to you before the due date.
Once you have made it through the edits, consider the following ideas for enhancing your final project:
Please submit your assignment to the "Term Project Final Draft" dropbox in Canvas. See the Course Calendar in Canvas for specific due dates.
It should be clear who you are writing for and the role you are playing in preparing this report
The final text is no longer than 3000 words (not including references or an appendix where you can outline your methodology in greater detail)
Includes images and graphics where relevant
Cites sources using a consistent citation format
Applies consistent formatting across the sections of your paper (hint: use MS word styles)
Presents clear and organized arguments to support your project goals
Matches the spirit and goals associated with the project option you have chosen
Your final term project is worth 15% of your final course grade and will be graded out of 100 points using the following rubric.
Criteria | Description | Possible Points |
---|---|---|
Introduction |
The introduction meaningfully engages the target audience/reader and clearly presents the central argument along with its substantive, technical and applied contexts. |
15 |
Background and Supporting Research |
The paper is well researched and contains references to peer-reviewed articles, government documents and industry reports that relate to the arguments in a logical manner. References are correctly cited. |
30 |
Analysis and Interpretations |
Design and implement a methodology to address the central arguments of your topic. Critical, relevant and consistent connections are made between evidence and central arguments. Includes appropriate use of maps, graphics, and tables. Analytical insights area sound and shows a deep understanding of the issues. Depending on your selected topic, this may involve describing the steps taken for data analysis and mapping (NOTE –step by step instructions can be put into an appendix and now count against word limits – discuss this with the instructor). |
30 |
Conclusion | Excellent summary of topic and central arguments with concluding statements that impacts target audience/reader. | 10 |
Writing | There is evidence of editing and proofreading. Writing is engaging and well-structured with excellent transitions between sentences and paragraphs. Writing is polished and professional. Concepts are integrated in an original manner. | 15 |
Total | -- | 100 |
The ability to synthesize technical information into a concise package that is appropriate for a broad audience is a skill that is hard to hone and yet highly sought after in the workplace. This assignment provides you an opportunity to do just that. I would like you to create a short (5 - 7 minute) recorded presentation about your term project. The presentation will be shared with your classmates.
You may choose your own screen recording software, or record your screen-cast from within Canvas. Here is a link to instructions on how to use Kaltura Capture [135] to record within Canvas. Note: Kaltura Capture is accessed in Canvas by clicking on My Media in the Canvas menu and "Add new". If you do not use Kaltura Capture, you will need to upload your own video file to My Media [136] using these instructions.
Record your screen while you give your five to seven-minute slideshow (make sure the slides are visible and the audio is clear - using a headset microphone is normally the best way to ensure decent audio quality).
Need more help? Contact the World Campus Helpdesk [137] for assistance.
Directions for creating, submitting, and sharing your presentation can be found with the dropbox.
Go to the Media Gallery in Canvas and view your peers' presentations. Please provide comments and feedback to your peers in the "Lesson 10 Term Project Presentation Discussion" forum in Canvas. I will leave the course open for several weeks so you are able to view your classmate's work.
See the Course Calendar in Canvas for specific due dates.
The Term Project Presentation is worth 5% of your total course grade and will be graded out of 50 points using the following rubric.
Criteria | Description | Possible Points |
---|---|---|
Content and Impact |
The recording provides a concise presentation of the term project that is appropriate for a broad audience. You make strong and logical arguments and provide analytical insights. Ideas are well organized, clearly communicated and relevant. | 30 |
Flow, Pacing, and organization |
Presentation is well organized. Flow from part to part is seamless. Presentation is well organized and uses media that is appropriate, supportive of content, balanced and well considered. | 10 |
Clarity and Mechanics |
Slides: show evidence of editing and careful proofreading, graphics are engaging and appropriate, and concepts are integrated in an original manner. Verbal delivery: The audio recording is free of distractions. You are poised, easy to understand (clear articulation, proper volume, steady rate, etc.), exhibit enthusiasm, confidence, and comfort with the topic. Length: Meets requirements. |
10 |
Total | - | 50 |
This week, you have finished work on your term project and shared your findings with your classmates. I hope you have found this experience to be intellectually stimulating. Throughout the course, I have tried to balance multiple learning objectives, and I appreciate your patience with me as I have refined things a little as we progressed through the material. Every class I teach is different from the last instance, as I like to keep things as fresh as possible.
I think it's clear that geospatial approaches and technologies can be shaped in a wide range of ways to fit various types of emergency management tasks. Emerging technology trends like volunteered geographic information and geoAI will allow future GIS systems for emergency management to be flexible and responsive to dynamic and complicated crisis situations. Now that you have completed this course, you should have the ability to plan new GIS systems that take into account the real-world constraints of a disaster scenario and blend together off-the-shelf GIS tools with creative solutions that leverage new technologies and data sources. I wish you the best of luck in your future work! Please stay in touch and let me know how you're doing.
I will work to quickly evaluate your final project materials and post your grades. In the meantime, please complete the course evaluation that you are sent automatically and provide honest, constructive feedback for the material as well as my performance. Your feedback makes it possible for future students in this course to benefit from your experiences.
Links
[1] https://coastal.er.usgs.gov/hurricanes/sandy/lidar/newjersey.php
[2] https://www.e-education.psu.edu/geog858/sites/www.e-education.psu.edu.geog858/files/Lesson_01/files/Zheng%202021%20-%20Rapid%20DA%20with%20ML.pdf
[3] https://naturaldisaster.royalcommission.gov.au/
[4] https://naturaldisaster.royalcommission.gov.au/publications/html-report
[5] https://www.e-education.psu.edu/geog858/sites/www.e-education.psu.edu.geog858/files/Lesson_01/Images/Royal%20Commission%20into%20National%20Natural%20Disaster%20Arrangements%20-%20Report%20%20%5Baccessible%5D.pdf
[6] https://www.e-education.psu.edu/geog858/sites/www.e-education.psu.edu.geog858/files/Lesson_01/Images/Royal%20Commission%20into%20National%20Natural%20Disaster%20Arrangements%20-%20Appendices%20%20%5Baccessible%5D.pdf
[7] https://creativecommons.org/licenses/by-nc-sa/4.0/
[8] http://www.fema.gov
[9] https://www.e-education.psu.edu/geog858/sites/www.e-education.psu.edu.geog858/files/Lesson_01/files/FEMA%20Mapping%20and%20Analysis%20Center.pdf
[10] https://www.urisa.org/clientuploads/directory/Documents/Committees/Professional%20Education/FEMA_URISA_31JUL18.pdf
[11] https://www.e-education.psu.edu/geog858/sites/www.e-education.psu.edu.geog858/files/Lesson_05/Files/Maria_GeoUpdate_20170920.pdf
[12] https://gis-fema.hub.arcgis.com/
[13] http://www.esri.com/library/whitepapers/pdfs/arcgis-for-emergency-management.pdf
[14] https://www.arcgis.com/apps/solutions/index.html?domain=Emergency%20Management&gallery=true&industry=Public%20Safety&sortField=relevance&sortOrder=desc#home
[15] https://www.nap.edu/catalog/11793/successful-response-starts-with-a-map-improving-geospatial-support-for
[16] https://www.tandfonline.com/doi/full/10.1080/23729333.2016.1278151
[17] https://www.e-education.psu.edu/geog858/sites/www.e-education.psu.edu.geog858/files/Lesson_01/files/Geospatial%20big%20data%20and%20cartography%20research%20challenges%20and%20opportunities%20for%20making%20maps%20that%20matter.pdf
[18] https://reliefweb.int/blogpost/becoming-digital-humanitarian-one-deployment-time
[19] https://www.e-education.psu.edu/styleforstudents/
[20] https://www.youtube.com/watch?v=RPevhwumqD0
[21] https://www.dhs.gov/science-and-technology/ngfr
[22] https://www.joinpad.net/2019/06/27/ireact-project/
[23] https://www.digitalglobe.com/opendata
[24] https://www.abc.net.au/news/2018-10-03/why-the-indonesia-quake-and-tsunami-were-so-destructive/10330420
[25] http://www.fema.gov/disasters
[26] https://www.e-education.psu.edu/geog858/sites/www.e-education.psu.edu.geog858/files/Lesson_02/Files/Lane_GeoUpdate_20180825.pdf
[27] https://rsoe-edis.org/eventMap
[28] https://www.unescap.org/publications/specific-hazards-handbook-geospatial-decision-support-asean-countries-0
[29] https://www.e-education.psu.edu/geog858/sites/www.e-education.psu.edu.geog858/files/Lesson_01/Images/hitting-home-report-V7-210122.pdf
[30] https://www.e-education.psu.edu/geog892/
[31] http://buttecountygis.maps.arcgis.com/apps/MapSeries/index.html?appid=af7e5bb3960a48c096ed910c640a30b3
[32] https://werobotics.org/
[33] https://techcrunch.com/
[34] https://www.youtube.com/watch?v=yRMUNptyTag
[35] https://www.flickr.com/people/47407357@N07
[36] https://commons.wikimedia.org/wiki/Flickr
[37] https://www.flickr.com/photos/47407357@N07/5475356514
[38] https://creativecommons.org/licenses/by/2.0
[39] https://commons.wikimedia.org/w/index.php?curid=14437334
[40] https://www.theguardian.com/global-development/2017/nov/20/the-night-barbuda-died-how-hurricane-irma-created-a-caribbean-ghost-town
[41] http://uaviators.org/
[42] http://openaerialmap.org/
[43] https://data.humdata.org/
[44] https://www.openstreetmap.org/
[45] http://ghdx.healthdata.org/record/antigua-and-barbuda-population-and-housing-census-2011-2012
[46] https://caribbean.eclac.org/countries/antigua-and-barbuda
[47] https://openaerialmap.org
[48] https://cmm.psu.edu/zoom/
[49] https://www.timeanddate.com/worldclock/meeting.html
[50] https://www.fema.gov/flood-maps/tools-resources/risk-map
[51] https://msc.fema.gov/portal/home
[52] https://www.nytimes.com/interactive/2020/06/29/climate/hidden-flood-risk-maps.html
[53] http://www.preventionweb.net/files/3794_ochaidnhazardv4110606.pdf
[54] https://riskfrontiers.com/
[55] https://www.bnhcrc.com.au/resources/poster/3704
[56] https://link.springer.com/article/10.1007/s13753-016-0090-9
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