Identifying Traffic Accident Hotspots in Recife Using K-Means Clustering: An Analysis of Legal Implications for Algorithmic Governance

This project investigates the application of unsupervised machine learning for urban traffic accident analysis, a key aspect of modern 'smart city' governance and public safety. Focusing on a dataset of over a thousand traffic incidents with victims from Recife, Brazil, collected in 2016, the study employs the K-Means clustering algorithm to identify geographical accident hotspots. The methodology encompasses rigorous data preprocessing of geographical coordinates (longitude and latitude) to ensure data quality. The Elbow Method is subsequently utilized to determine the optimal number of clusters, which in this context is identified as four. The primary objective is to demonstrate the technical efficacy of K-Means in partitioning accident data into distinct, high-concentration

geographical zones. Further analysis involves characterizing each identified hotspot by dominant incident types, such as 'collision,' and average victim counts per incident. This project offers practical experience in implementing data-driven approaches for urban safety, illustrating how machine learning can delineate critical areas for intervention. It also provides a foundation for understanding the implications of such algorithmic governance, particularly when initial findings suggest homogeneity in incident characteristics across spatially separated hotspots, challenging assumptions about the need for highly tailored, localized policy responses.

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