In this episode of “Through the Human Geography Lens”, hosts Terri Ryan and Eric Rasmussen sit down with Ryan Engstrom, Director of the Data Science Program within the Department of Geography at George Washington University.
00:50 Professor Engstrom’s research interests
01:35 Defining “deprived areas”
Kuffer, Thomson, D. R., Boo, G., Mahabir, R., Grippa, T., Vanhuysse, S., Engstrom, R., Ndugwa, R., Makau, J., Darin, E., de Albuquerque, J. P., & Kabaria, C. (2020). The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries. Remote Sensing (Basel, Switzerland), 12(6), 982–. https://doi.org/10.3390/rs12060982
02:32 Insights from work in Accra, Ghana
03:40 “Do the most vulnerable people live in the worst slums?”
05:30 Using geospatial data to assess the population/environment balance
06:45 Working with census takers.
08:00 Working in the Arctic, particularly in Russia, and the value of open Census data
09:05 Validating data: survey and satellite integration
10:12 Assessing spatial distribution by economic class: surveys often miss the wealthy
12:00 Youth Mappers (open sourced at OpenStreetMap, 300+ chapters, funded by USAID)
15:10 Geography 2050 for elementary and high school support.
https://www.geography2050.org/
15:45 Geospatial tools in use at the university level
17:02 Open-source data models, not just the data: GitHub availability (and it’s working well)
https://github.com/topics/geospatial-analytics
18:35 Major data sources and managers that have appeared over the past few years
https://github.com/sacridini/Awesome-Geospatial
https://www.openstreetmap.org/#map=4/38.01/-95.84
https://earthengine.google.com/
https://aws.amazon.com/?nc2=h_lg
19:25 Example: OSM mapping enhancements in Accra
https://www.openstreetmap.org/node/27565080#map=9/5.3070/0.4971
20:04 Using machine learning for co-variate income analysis in Belize
Hersh, Engstrom, R., & Mann, M. (2021). Open data for algorithms: mapping poverty in Belize using open satellite derived features and machine learning. Information Technology for Development, 27(2), 263–292.
https://doi.org/10.1080/02681102.2020.1811945
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