Chapter 6 Spatiotemporal data and real-time GIS
This chapter introduces the concept of spatiotemporal data and the values and challenges of the Internet of Things (IoT) in relation to ArcGIS. Spatiotemporal data can be categorized into groups: Moving (live feeds of airplanes, buses cars etc.) Discrete (criminal incident, earthquakes), Stationary (wind speed and direction measurements at weather stations) and CHange(perimeters of wildfires, flooded areas). In spatiotemporal GIS data, the time value of an event can be a point in time or a duration of time.
Application:
I was really interested in the idea of spatiotemporal data because this chapter was my first time hearing about it. When looking at the Delaware data from GIS 291, I think a good application would be one that is similar to my final from GIS 291, creating a spatiotemporal application that maps emergency incidents. By using available data, the application would display real time emergency incidents as moving or discrete events on a web map. This would include filters by incident type, time of day or severity. It would animate changes over time to identify got spots or trends in emergency services. This application could be deployed as a web app using ArcGIS Enterprise.