This technical talk looks at how Open Source tools (predominantly Python and Redis) can be used to index and query public geospatial data sets to add location awareness to web and mobile applications.
Over 60 bike share systems, including the sharing systems in San Francisco, Boston, and New York City, provide real-time GBFS data feeds that can be used to get station information, bike availability and user alerts.
We show how Python (with a variety of open source packages) can be used to build a feed parser and how Redis can be used to build a geospatial index of the station locations. We demonstrate how apps can use the geospatial index query commands in Redis to find and display nearby locations matching a variety of attributes.
GBFS data feeds are used to provide real-world examples and sample code, but the techniques taught are applicable to a wide range of geospatial data indexing problems.