No more detours!

As cities grow more compact, simply having parks nearby isn't enough. It is also important that urban dwellers are able to reach them easily on foot. Traditional planning often relies on static distance measures to judge park access, ignoring actual street layouts, physical barriers, and crowd flows. The Landscape Ecology Group (Dagmar Haase) developed a modeling framework to capture how people move through the streets of Leipzig, Germany, to reach urban green spaces. They integrated data from OpenStreetMap and the Copernicus Urban Atlas to evaluate the space connecting residential buildings to public parks. They introduced two primary indicators: 1) the Detour Index (DI), which points out physical barriers by measuring how much a pedestrian's route deviates from a straight line, and 2) the Local Significance (LS), which is an indicator for the overuse of a space. By testing hypothetical planning scenarios around Leipzig's Lene Voigt Park, such as removing perimeter fences or building high-density housing on smaller green spaces, the framework mapped where street bottlenecks, route inefficiencies and crowding mismatches occur. Learn more about barrier modelling in the Urban science article.

Abstract

In an increasingly urbanized world, ensuring equitable access to urban green spaces (UGS) is essential for human well-being. Previous studies have largely focused on measuring proximity or availability of UGS, often neglecting the role of the walkable environment and the interaction between supply, demand, and movement flows. To address this gap, we develop a novel modeling framework that integrates the Detour Index (DI) and Local Significance (LS) to jointly capture physical barriers and recreational flows within urban street networks. Using openly available data from OpenStreetMap and Urban Atlas, we model the walkable environment in Leipzig, Germany, at a high spatial resolution. The approach enables the identification of inefficient routes, potential barriers, and areas of high use intensity, providing actionable insights for urban planning. By combining network-based accessibility with flow-based indicators, our method advances existing approaches that rely on static distance measures. The analyses of different planning alternatives further demonstrate how changes in urban structure affect accessibility and crowding patterns. The framework is transferable and based on open data, providing a foundation for future research to integrate behavioral factors and richer datasets to further refine accessibility modeling