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.
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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.
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, [3] which caused massive loss of life and property. Each group should create a Story Map [4] using ArcGIS Online [5] 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).
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. [12]
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.
Links
[1] https://mapaction.org/how-maps-helped-the-response-to-the-haiti-earthquake/
[2] https://storymaps.arcgis.com/stories/455b4d68f08148b2bc7bbc8492749f55
[3] https://www.npr.org/2021/08/16/1027990749/haiti-earthquake-why-deadly-explainer
[4] https://storymaps.arcgis.com/en/
[5] https://www.arcgis.com/home/index.html
[6] https://gis.harvard.edu/haiti-earthquake-data-portal
[7] https://data.humdata.org/dataset?q=haiti
[8] https://www.maxar.com/open-data
[9] https://youtu.be/2ePf9rue1Ao
[10] https://developer.ibm.com/articles/cc-beginner-guide-machine-learning-ai-cognitive/
[11] https://ehjournal.biomedcentral.com/articles/10.1186/s12940-018-0386-x
[12] https://thenewdaily.com.au/life/tech/2019/09/26/crocspotter-ai-drone/