Geofencing to Organize Your Geospatial Data
Lab Steps
Ready for the real environment experience?
Description
This lab demonstrates how to group geospatial data based on their geographic attributes in BigQuery GIS using Python and Jupyter notebooks. The lab uses spatial joins to combine information from two of Google’s public datasets – the zip codes table and the Chicago crimes table – in order to count the number of crimes in each zip code of Chicago by year. In addition, the lab uses the geopandas package in Python to create a choropleth map showing crime hot spots in Chicago.
Learning Objectives
Upon completion of this lab you will be able to:
- Interact with BigQuery GIS datasets within Jupyter notebooks
- Perform an analysis using spatial joins on
GEOGRAPHYdata - Create a choropleth map using
geopandas
Intended Audience
This lab is intended for:
- GIS engineers
- Data engineers dealing with location-based data
- Developers looking to leverage geospatial information
Prerequisites
You should possess:
- Basic understanding of relational databases and ANSI SQL
- Basic understanding of Python
- Familiarity with BigQuery GIS's
GEOGRAPHYdatatype
Calculated Systems was founded by experts in Hadoop, Google Cloud and AWS. Calculated Systems enables code-free capture, mapping and transformation of data in the cloud based on Apache NiFi, an open source project originally developed within the NSA. Calculated Systems accelerates time to market for new innovations while maintaining data integrity. With cloud automation tools, deep industry expertise, and experience productionalizing workloads development cycles are cut down to a fraction of their normal time. The ability to quickly develop large scale data ingestion and processing decreases the risk companies face in long development cycles. Calculated Systems is one of the industry leaders in Big Data transformation and education of these complex technologies.