Data-driven retail decision-making using spatial partitioning and delineation of communities

• Main Authors: TAN, Ming Hui, TAN, Kar Way
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Network centrality; OpenStreetMap; H3; Community detection; Databases and Information Systems; Numerical Analysis and Scientific Computing; Urban Studies and Planning
• Online Access: https://ink.library.smu.edu.sg/sis_research/7199; https://ink.library.smu.edu.sg/context/sis_research/article/8202/viewcontent/Community_Detection_PACIS2022.pdf
• Institution: Singapore Management University

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.