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<p>Accurate delineation of urban spatial extent is essential for planning, yet conventional methods based on land cover and satellite imagery are often time-consuming and may lag behind urban changes. This study explores how urban functional area can be delineated using Points of Interest (POI) data and Kernel Density Estimation (KDE), offering an activity-based alternative to morphology-based approaches. Using Pekanbaru as the study area, a metropolitan city in Indonesia, the method incorporates spatial autocorrelation to weight POIs and generate a KDE surface. The resulting delineation is compared to Sentinel-2-derived built area using the STEP Similarity Index and Jaccard Index. STEP results indicate strong thematic (0.96) and positional (0.97) similarity, with low shape and edge values, showing that POI-based KDE captures activity intensity rather than physical form. The Jaccard Index (0.64) confirms a moderate spatial overlap. While satellite data reflects built structures, KDE highlights zones of concentrated human activity, supporting its utility for planning applications. Future work should advance POI temporal filtering, KDE threshold calibration, and functional zone mapping, enabling integration into multi-scale spatial planning. This study contributes a scalable, data-driven method for delineating urban extent using openly available activity-based data.</p>

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Assessing Urban functional area delineation: POI data and kde analysis in pekanbaru

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月7日

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  • Zahra Witsqa Maghfira,
  • Ridwan Sutriadi,
  • Ahmad Baikuni Perdana

摘要

Accurate delineation of urban spatial extent is essential for planning, yet conventional methods based on land cover and satellite imagery are often time-consuming and may lag behind urban changes. This study explores how urban functional area can be delineated using Points of Interest (POI) data and Kernel Density Estimation (KDE), offering an activity-based alternative to morphology-based approaches. Using Pekanbaru as the study area, a metropolitan city in Indonesia, the method incorporates spatial autocorrelation to weight POIs and generate a KDE surface. The resulting delineation is compared to Sentinel-2-derived built area using the STEP Similarity Index and Jaccard Index. STEP results indicate strong thematic (0.96) and positional (0.97) similarity, with low shape and edge values, showing that POI-based KDE captures activity intensity rather than physical form. The Jaccard Index (0.64) confirms a moderate spatial overlap. While satellite data reflects built structures, KDE highlights zones of concentrated human activity, supporting its utility for planning applications. Future work should advance POI temporal filtering, KDE threshold calibration, and functional zone mapping, enabling integration into multi-scale spatial planning. This study contributes a scalable, data-driven method for delineating urban extent using openly available activity-based data.