JamaakamaDan/spatial-agent-optimization-kenya

Geospatial optimization pipeline using OSMnx, graph network algorithms, and MCLP to maximize mobile money agent reach across major Kenyan cities. # Spatial Optimization of Urban Financial Agent Networks in Kenya An end-to-end spatial data science pipeline using graph theory, sparse matrix operations, and location-allocation algorithms to optimize mobile money agent locations across major Kenyan cities. ## Executive Summary Mobile money platforms like M-PESA drive financial inclusion in East Africa, yet physical agent networks face spatial coverage gaps and liquidity distribution costs. This project models: 1. **Maximum Coverage Location Problem (MCLP):** Optimal placement of agents within a strict 2,000m pedestrian walking radius. 2. **Minimum Spanning Tree (MST):** Cash-in-transit (CIT) street-level routing connecting agents to minimize operational logistics costs. ## Results Overview | City | Mapped Demand Structures | Optimal Agents Placed | Total Coverage achieved (%) | | :--- | :---: | :---: | :---: | | **Mombasa** | 53,838 | 20 | **99.3%** | | **Kisumu** | 100,171 | 20 | **91.8%** | | **Nakuru** | 101,019 | 20 | **91.5%** | | **Nairobi** | 211,984 | 20 | **86.2%** | ## Key Features & Methodology - **Geospatial Ingestion:** Extracted road network graphs and building footprints via `OSMnx` using a 7.5km spatial radius. - **Sparse Matrix Acceleration:** Handled graph distances ($O(N^2)$ scaling) via `scipy.sparse.csc_matrix` and boundary-constrained Dijkstra ego-graphs. - **Logistics Routing:** Computed exact street-level shortest paths using `NetworkX` graph topologies. - **Interactive Visualizations:** Exported multi-layer HTML dashboards built with `Folium`. ## Quickstart ```bash # Clone the repository git clone github.com cd spatial-agent-optimization-kenya # Install dependencies pip install -r requirements.txt # Run the optimization pipeline python src/pipeline.py