Krenz, K; (2017) Employing volunteered geographic information in space syntax analysis. In: Heitor, Teresa and Serra, Miguel and Silva, João Pinelo and Bacharel, Maria and da Silva, Luisa Cannas, (eds.) Proceedings of 11th International Space Syntax Symposium. (pp. 150.1-150.26). Instituto Superior Técnico, Departamento de Engenharia Civil: Arquitetura e Georrecursos, Portugal.

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Abstract

The application of volunteered geographic information has rapidly increased over the past years. OpenStreetMap (OSM) forms in this context one of the most ambitious and promising projects, providing consistent global coverage of street network information. With a constantly growing number of participants and the implementation of governmental and proprietary based information is a complete coverage of global street networks within reach. The data allows comparative cross-country analyses and any method developed within its framework are transferable to other cases. This makes OSM a powerful and desirable data source for applied network analyses, such as space syntax. However, OSM data does not come without obstacles. Inconsistent representation of space, topological fragmentation and accuracy are just some of the problems that one faces when employing OSM data. In fact, without prior processing and simplification of the network, results differ significantly between case studies. This paper presents a method for OSM data set simplification as well as the theoretical and analytical reasoning behind it. The simplification is done by a series of ArcGIS workflows and algorithms. The outcome of this process is compared to an angular segment analysis (ASA) of a segment model, an Integrated Transport Network (ITN) Ordnance Survey data model and an OSM street network data model. The results show that a simplified version of OSM data is highly comparable to a segmented axial line representation and that such data sets constitute an appropriate alternative for situations where segment maps are not available, such as complex, large-scale regional models and cross-country comparisons. The simplification workflow is transferable to other cases and data sets and helps overcoming common problems while significantly improving computational time needed in the process.

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