02-20, 11:45–12:10 (US/Pacific), Prerecorded Talks
Many cities in the United States provide data as a part of the oversight initiatives and federal compliance. However, datasets often present information in incomplete or hard-to-compare formats.
This talk will break down how to compile data from the San Diego Police Department into pandas dataframes and use geopandas, eland, and Elasticsearch to enrich the data with approximate GeoPoints so that it can be visualized in Kibana or a public plotting platform
After discovering that many major cities in the United States have open data projects that allow for a level of transparency. I was happy to discover that my own city had data available but quickly realized that the data had many issues that would make it difficult to analyze as-is.
In this talk, I will show how I was able to consolidate "Calls for Service" to the San Diego Police Department since 2015. This was done by importing the city-provided CSV files into Pandas DataFrames and making modifications to standardize data.
I will also convert approximate location data to a geo_point using Public geoJSON data and the filter capabilities in Elasticsearch.
Lastly, I will show how you can bulk upload the data into and out of Elasticsearch via eland so that it can be visualized in Kibana or a public graphing platform and how I automated this process to check for updates.
Jay is a Developer Advocate at Elastic, based in San Diego, Ca. A multipotentialite, Jay enjoys finding unique ways to merge his fascination with productivity, automation, and development to create tools and content to serve the community of tech content creators.