Data-Driven City: An Innovative Approach to Urban Area Delineation

Abstract

This study introduces a data-driven, bottom-up approach to urban delineation, integrating feature engineering with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, marking a significant shift from traditional methodologies reliant on simplistic OpenStreetMap (OSM) road node data aggregations. By employing a broad array of OSM categories and refining data selection through feature engineering, our research significantly enhances the precision and relevance of urban clustering. Using Bavaria, Germany, as a case study, we demonstrate that feature engineering effectively reduces noise and mitigates common DBSCAN clustering pitfalls by filtering out irrelevant and autocorrelated data. The method's robustness is validated through a comprehensive assessment involving accuracy metrics, optimal clustering selections based on entropy values, and empirical and theoretical confirmations using nighttime light data and Zipf’s Law, respectively. This study contributes to urban studies by providing a scalable, replicable model that incorporates advanced data processing techniques and multidimensional data sources, supporting improved urban planning and policy-making while effectively delineating urban boundaries in varied settings.