Abstract
Transportation networks allow us to model flows of people and resources across geographic space, but the people and resources we wish to model are often not natively tied to our networks. Instead, they can occur as point data (such as store, train station, and domicile locations) and/or grid data (such as socio-economic and aggregate area data). Here we present a set of methods to integrate point data into an augmented transportation network. This method facilitates analyses of temporo-spatial measures (such as accessibility scores) using only efficient breadth first search algorithms. We demonstrate the approach by calculating walkability scores for the train stations within the central Tokyo area.
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Araki, S., Bramson, A. (2021). Connecting the Dots: Integrating Point Location Data into Spatial Network Analyses. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-65351-4_16
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DOI: https://doi.org/10.1007/978-3-030-65351-4_16
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