Commercial Building IQ Engine Accelerator

Overview

This repository contains a Solutions Accelerator designed to teach developers how to build a serverless site-selection engine using Wherobots and Apache Sedona.

By following this guide, you will build a pipeline that:

1. Ingests Overture Maps building footprints and Foursquare retail points of interest.
2. Performs a spatial join to link retail tenants to specific buildings.
3. Aggregates data to calculate a "Commercial Intensity" score for every building.
4. Exports the result as a PMTiles archive for serverless 3D visualization.

Repository Structure

• notebooks/: Contains the consolidated Jupyter notebook for data processing.
• app/: Contains the source code for the frontend MapLibre application.
• docs/: Detailed step-by-step guides for each phase of the project.

Prerequisites

Before starting, ensure you have:

1. Wherobots Cloud Account: A free account is sufficient to run the analysis.
2. Python Environment: If running locally, you need Python 3.9+.
3. AWS Account (Optional): Required only if you wish to host your own tile artifacts. A public URL is provided for the visualization step if you do not have AWS access.

Quick Start Guide

This accelerator is broken down into three distinct steps. Please follow the documentation files in order:

Step 1: Data Processing Run the spatial analysis pipeline to generate your building metrics. > Go to Step 1 Documentation

Step 2: Cloud Storage Setup (Optional) Configure AWS S3 to host your map tiles. This covers IAM permissions and CORS policies required for browser access. > Go to Step 2 Documentation

Step 3: Application Development Build the interactive 3D map using MapLibre GL JS to visualize your results. > Go to Step 3 Documentation

Data Sources

This project uses open data available natively in the Wherobots Catalog:

• Overture Maps Foundation: Building footprints (Theme: Buildings).
• Foursquare: Points of Interest (Table: Places).

License

This project is licensed under the Apache 2.0 License.