Abstract
This paper introduces a novel open-source model for assessing urban walkability. The research confronts the limitations of current approaches, which typically analyze walkability factors in isolation. The goal is to develop an integrated framework that simultaneously evaluates accessibility to services within multi-temporal isochrones (5, 10, 15, and 20-minute intervals), topographical barriers like elevation gain, and the quality of urban infrastructure. The core hypothesis is that these diverse factors—topography, access to urban amenities, infrastructure quality, and network connectivity—each contribute with a distinct weight to the final walkability score. The proposed model leverages publicly available data and free APIs, such as OpenStreetMap and elevation data, to ensure its reproducibility and applicability across various urban contexts, with a specific focus on addressing the research gap in Latin American cities. By consolidating these metrics into a single index, the developed tool aims to establish a “universal language” for walkability studies. This results in a more precise analysis that can assist urban planners in identifying areas for improvement, thereby fostering more sustainable, equitable, and pedestrian-oriented cities in line with the “15-minute city” paradigm.
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Urban Walkability, Temporal Isochrones, Machine Learning
