Powering Location Data for Good
summary
Location data connects disparate datasets and is 80% of data so it is used here to analyze, predict, and act with ArcGIS Hub, Living Atlas, story maps, dashboards, notebooks and ML. Examples: Johns Hopkins COVID dashboard aggregates cases worldwide; Santa Monica’s web app publishes live business open/close status; a Norfolk story map models inundation showing large areas at risk by 2060–2080; wildfire, air-quality and VA drive-time maps reveal gaps (e.g. some rural VA >90 minutes).
AI-written summary · about
chapters (11)
- 0:00Introduction and welcome
- 1:03Why location data matters
- 3:10Location data and human behavior
- 6:39Location data as a science
- 8:26Spatial problem-solving approach
- 13:16Types of location data
- 18:15COVID-19 data examples
- 26:25Living Atlas and story maps
- 30:21Global environmental tracking
- 34:56Public policy maps and resources
- 37:02Closing and call to action
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