Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2022

Abstract

Urbanisation is resulting in rapid growth in road networks within cities. The evolution of road networks can be indicative of a city's economic growth and it is a field of research gaining prominence in recent years. This paper proposes a framework for spatial partition of large scale road networks that produces appropriately sized geospatial units in order to identify the type of community they serve. To this end, we have developed a three-stage procedure which first partitions the road network using Louvain method, followed by outlining the boundary of each partition using Uber H3 grids before classifying each partition using K-means clustering. Experimental results in Da Nang, Vietnam, show that the proposed method can partition and classify a large scale road network into various community types.

Keywords

Network centrality, OpenStreetMap, H3, Community detection

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Urban Studies and Planning

Research Areas

Data Science and Engineering

Publication

Pacific Asia Conference on Information Systems PACIS 2022: Virtual, July 5-9: Proceedings

First Page

1

Last Page

15

Publisher

AIS

City or Country

Atlanta

Additional URL

https://aisel.aisnet.org/pacis2022/117/

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