Visualizing Geospatial Data with Python and BigQuery
Lab Steps
Ready for the real environment experience?
Description
This lab will teach you how to create maps from geospatial data using the GeoJSON extension for JupyterLabs. The first section of the lab walks through building a map of New York City landmarks, building up from a single geospatial point, then creating a feature collection and finally adding points to the existing feature collection. The second section uses Google’s zip code public dataset to convert BigQuery GEOGRAPHY objects to GeoJSON format and map geospatial polygons.
Learning Objectives
Upon completion of this lab you will be able to:
- Use the GeoJSON extension to JupyterLabs to map geospatial data
- Map GeoJSON objects in Jupyter notebooks
- Interact with BigQuery GIS datasets within Jupyter notebooks
- Map geospatial data from BigQuery GIS's
GEOGRAPHYdata type
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.