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 [5]. 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 [6]
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 [9] 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 [10], including the disaster preparedness training [11].
One publicly available resource is provided by the USGS in the form of their Prompt Assessment of Global Earthquakes for Response (PAGER [12]) 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 [14], introduced on the previous page, is one such system. Google Crisis Response [15], 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 [16]. 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 [17]. 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 [18]. Scientific Reports 12:16121. (PDF version [19])
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 [20] and Carto [21], and university researchers at the Center for Spatial Data Science [22] 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 [23] 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 [24]). 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 [25], 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 [26] 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 [27] 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 [28] 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 [29] 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. [30] 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 [31] pages and also visit their github site [32] 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 [33]. 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 [34] and shows how a central computer (head node) is orchestrates analysis jobs undertaken by computing nodes.
Distributed computing example (Source: Riga Technical University [34])
Distributed computing is controlled by software systems such as Hadoop [35]. 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 [35]
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? [36])
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 [37].
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 [38]. 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).
Links
[1] https://www.google.org/our-work/crisis-response/
[2] https://www.ready.gov/evacuation
[3] http://wwwsp.dotd.la.gov
[4] https://creativecommons.org/licenses/by-nc-sa/4.0/
[5] http://www.govtech.com/data/How-GIS-Can-Help-Communities-Prepare-for-Disaster.html
[6] https://www.fema.gov/
[7] https://www.fema.gov/sites/default/files/2020-06/apa_planning-for-post-disaster-recovery-next-generation_03-04-2015.pdf
[8] https://youtu.be/q8yGgysHUSE
[9] https://disasteralert.pdc.org/disasteralert/
[10] https://www.pdc.org/solutions/preparedness/
[11] https://www.pdc.org/training-and-exercises/
[12] https://earthquake.usgs.gov/data/pager/
[13] https://earthquake.usgs.gov/earthquakes/search/#%7B%22feed%22%3A%221437493916387%22%2C%22search%22%3A%7B%22id%22%3A%221437493916387%22%2C%22name%22%3A%22Search%20Results%22%2C%22isSearch%22%3Atrue%2C%22params%22%3A%7B%22producttype%22%3A%22losspager%22%2C%22orderby%22%3A%22time%22%7D%7D%2C%22listFormat%22%3A%22losspager%22%2C%22sort%22%3A%22newest%22%2C%22basemap%22%3A%22grayscale%22%2C%22autoUpdate%22%3Afalse%2C%22restrictListToMap%22%3Atrue%2C%22timeZone%22%3A%22utc%22%2C%22mapposition%22%3A%5B%5B-85%2C0%5D%2C%5B85%2C360%5D%5D%2C%22overlays%22%3A%7B%22plates%22%3Atrue%7D%2C%22viewModes%22%3A%7B%22map%22%3Atrue%2C%22list%22%3Atrue%2C%22settings%22%3Atrue%2C%22help%22%3Afalse%7D%7D
[14] http://atlas.pdc.org/atlas/
[15] https://crisisresponse.google/forecasting-and-alerts/
[16] http://noah.up.edu.ph/
[17] https://www.sciencedirect.com/science/article/pii/S1001074216314693
[18] https://www.nature.com/articles/s41598-022-20571-3
[19] https://www.e-education.psu.edu/geog858/sites/www.e-education.psu.edu.geog858/files/Lesson_04/Files/Li%202022%20Location%20intelligence%20reveals%20the%20extent%20timing%20and%20spatial%20variaont%20of%20hurricane%20preparedness.pdf
[20] https://www.esri.com/
[21] https://carto.com/
[22] https://www.uchicago.edu/research/center/center_for_spatial_data_science/
[23] https://carto.com/platform/
[24] https://www.e-education.psu.edu/geog858/sites/www.e-education.psu.edu.geog858/files/Lesson_04/Files/Ebook-Becoming-Spatial-Data-Scientist.pdf
[25] http://esri.com
[26] https://www.esri.com/en-us/arcgis/products/spatial-analytics-data-science/overview
[27] https://www.e-education.psu.edu/geog858/sites/www.e-education.psu.edu.geog858/files/Lesson_04/Files/Machine-Learning-in-ArcGIS.pdf
[28] https://www.esri.com/en-us/arcgis/products/spatial-analytics-data-science/capabilities/real-time-big-data-analytics
[29] https://spatial.uchicago.edu
[30] http://geodacenter.github.io
[31] https://spatial.uchicago.edu/geoda
[32] https://github.com/GeoDaCenter/geoda/
[33] https://www.nvidia.com/en-au/data-center/a100/
[34] https://hpc.rtu.lv/introduction-to-hpc/?lang=en
[35] https://hadoop.apache.org
[36] https://carto.com/what-is-spatial-data-science
[37] https://www.esri.com/en-us/arcgis/products/spatial-analytics-data-science/events
[38] https://spatial-data-science-conference.com/
[39] https://learning.mediaspace.kaltura.com/media/Kaltura+Personal+Capture+Walkthrough+Video/0_x09b7mjb/96200421
[40] http://itld.psu.edu/training/kaltura-quick-start-guide-students
[41] https://student.worldcampus.psu.edu/help-and-support