تفکر مکانی برای تغییر و توسعه

Lead Trainer, GIS Analyst

Median Absolute Distance (MAD) — Outlier-Robust Spatial Clustering Tool Median Absolute Distance (MAD)** is a Python-based ArcGIS Pro geoprocessing tool developed to provide an outlier-resistant approach to spatial clustering analysis. The tool replaces traditional mean-based distance measures with median-based calculations, reducing the influence of extreme or misplaced spatial observations commonly found in rapidly urbanizing, informal, and post-conflict environments. The tool calculates median spatial centers, Euclidean distances, and Median Absolute Deviation values to generate global and category-specific clustering buffers. It supports automated analysis by spatial case fields, allowing large datasets to be processed across multiple administrative or thematic groups. The project was tested on **Kabul Municipality data containing approximately 600,000 building features distributed across 27,000 administrative blocks**. The documented analysis demonstrates its ability to process large urban datasets in minutes while identifying differences in spatial concentration and dispersion across the city. Outputs include global and per-category median centroids, MAD buffer polygons, clustering attributes, and an analytical report. A coefficient of variation derived from MAD and median distance provides an additional interpretation of spatial concentration. The project is released as lightweight, open-source software under the MIT License with applications including informal-settlement analysis, infrastructure planning, environmental monitoring, health-related spatial analysis, and commercial geography. **Technologies:** Python, NumPy, ArcGIS Pro, ArcPy, Spatial Analysis **Scale:** 600,000 features / 27,000 groups **Status:** Version 1.0 — Production Ready **License:** MIT Open Source **Repository:** https://github.com/Hoorzhian/MAD-tool-arcgispro.github.io