Categorising neighbourhoods using OpenStreetMap POIs: Affinity propagation clustering of 7,213 subdistricts in Thailand

The effective categorisation of neighbourhoods is a critical component of urban planning and development, providing a systematic framework for identifying and addressing the distinct characteristics and needs of different areas. This study utilises data from the open-source platform OpenStreetMap (OSM) to propose a novel approach to neighbourhood categorisation, with a focus on amenities as key elements. Data were collected on 4,121,900 points of interest (POIs) across 7213 subdistricts in Thailand, and the categorisation was conducted using the Affinity Propagation (AP) clustering technique. Through this approach, ten distinct neighbourhood clusters in Thailand were identified, demonstrating the efficacy of integrating OSM data with AP clustering. The findings underscore the necessity for more evidence-based planning policies aimed at enhancing amenities, vibrancy, and overall quality of life in neighbourhoods by promoting innovation and the development of creative districts. Furthermore, the study advocates for the consideration of ecological urbanism as an alternative pathway for neighbourhood development, a concept that has yet to be thoroughly explored in Thailand. • Utilises OpenStreetMap data to categorise neighbourhoods based on amenities. • Presents first nationwide amenity-based categorisation of neighbourhoods. • Clusters 7213 neighbourhood-level subdistricts using Affinity Propagation. • Identifies ten distinct amenity-based neighbourhood clusters across Thailand. • Highlights the absence of ecological urbanism in Thai neighbourhood development pathways.

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Publication Details

Journal
Journal of Urban Management
Published
2024-11-26
DOI
https://doi.org/10.1016/j.jum.2024.11.005
Citations
2
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
Field-Weighted Citation Impact
1.37
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article

Categorising neighbourhoods using OpenStreetMap POIs: Affinity propagation clustering of 7,213 subdistricts in Thailand

Viriya Taecharungroj, Nikos Ntounis
2 citations
Journal of Urban Management
Human Mobility and Location-Based Analysis
1.37
article

Categorising neighbourhoods using OpenStreetMap POIs: Affinity propagation clustering of 7,213 subdistricts in Thailand

Viriya Taecharungroj, Nikos Ntounis
article en
2 citations

Abstract

The effective categorisation of neighbourhoods is a critical component of urban planning and development, providing a systematic framework for identifying and addressing the distinct characteristics and needs of different areas. This study utilises data from the open-source platform OpenStreetMap (OSM) to propose a novel approach to neighbourhood categorisation, with a focus on amenities as key elements. Data were collected on 4,121,900 points of interest (POIs) across 7213 subdistricts in Thailand, and the categorisation was conducted using the Affinity Propagation (AP) clustering technique. Through this approach, ten distinct neighbourhood clusters in Thailand were identified, demonstrating the efficacy of integrating OSM data with AP clustering. The findings underscore the necessity for more evidence-based planning policies aimed at enhancing amenities, vibrancy, and overall quality of life in neighbourhoods by promoting innovation and the development of creative districts. Furthermore, the study advocates for the consideration of ecological urbanism as an alternative pathway for neighbourhood development, a concept that has yet to be thoroughly explored in Thailand. • Utilises OpenStreetMap data to categorise neighbourhoods based on amenities. • Presents first nationwide amenity-based categorisation of neighbourhoods. • Clusters 7213 neighbourhood-level subdistricts using Affinity Propagation. • Identifies ten distinct amenity-based neighbourhood clusters across Thailand. • Highlights the absence of ecological urbanism in Thai neighbourhood development pathways.

Journal of Urban ManagementVol. 14(2)
Manchester Metropolitan University (GB), Mahidol University (TH)
Sustainable cities and communities
Openalex Percentile: Top 16%
Human Mobility and Location-Based Analysis
1.37
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