In this chapter, you learned about how to use geospatial analytics to find insights and answer complex questions about IoT data. The importance of geospatial analysis for geographically distributed IoT devices was discussed. The concept of CRS was introduced along with haversine distance and its limitations.

The world is not a perfect sphere. Methods to adjust for that in order to accurately measure distance was covered. Python functions for geospatial analytics, such as buffer and contains, were discussed, along with some examples.

Storing and processing geospatial data requires some specialized handling. Some geospatial databases and GIS software tools were reviewed. PostGIS spatial functions were also reviewed. We went over some tips for leveraging geospatial analytics in a big data world.

Geospatial analytics offers a huge opportunity to analyze IoT data in new and innovative ways. It can help discover patterns in noisy data. New services can then be created as another way to extract value from your data.