This chapter introduced several new topics, including the tweepy module used to monitor live Twitter data feeds and the use of the Windows Task Scheduler to automate the process of monitoring Twitter activity. Although only about 2% of tweets include location information, we can still get a good understanding of the spatial patterns of social media when monitoring large events over an extended period of time. In this chapter, the live tweets were written to a local feature class and then mapped to the Hot Spot Analysis tool found in the Spatial Statistics Tools toolbox.
In the next chapter, you'll learn how to use Python to extract the geographic coordinates from smartphone photos, reverse geocode the coordinates to retrieve the nearest address, and create an ArcGIS Online application to display the results.