Bokeh Maps - Interactive Map for Your Next Web Application
summary
An interactive choropleth is built that visualizes 311 call volumes by neighborhood using pandas and GeoPandas with Dask for large files and Bokeh for callbacks and color mapping, deployed from a Jupyter notebook to Google Cloud. Data cleanup merges tabular rows with polygon geometries for 99 neighborhood councils, handling over a million rows per year and producing a live update via Bokeh column data sources and transform color mappers. The demo runs locally and on cloud but the app is not yet publicly released.
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chapters (24)
- 0:00Introduction
- 1:15Why use interactive maps
- 3:00Drawbacks of interactive maps
- 3:42Case study: 311 call volume map
- 5:49Data preparation and cleaning
- 7:34Handling large datasets with pandas and Dask
- 8:42Reading spatial data with GeoPandas
- 10:09Cleaning and merging geographic data
- 12:58Why Bokeh for interactive maps
- 14:02Handling callbacks in visualizations
- 15:28Converting data for ColumnDataSource
- 17:15Role of the update function
- 19:08Setting up Bokeh figure and tools
- 21:19Drawing polygons with Bokeh patches
- 22:45Color mapping in maps
- 23:57Adding interactive controls
- 25:03Modifying document layout
- 26:49Deploying to Google Cloud
- 28:37Final demo and sharing
- 30:46Finding geographic data
- 32:10Real-time data streaming
- 32:50Choosing features for storytelling
- 34:15Working with zip codes
- 34:56Tracking user engagement
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