Points of Interest(POI),which are rich in semantic information,reflect current situations,and indicate areas of interest,serve as the primary data source in studies related to urban functionalization studies. These studies aim to deepen the understanding of human activities and environmental features within geographical spaces. An important research issue for enhancing the understanding of the human-environment system is detecting outliers,namely elements considerably different from the rest in large-scale spatial data. The detection of POI outliers can be broadly discussed from three perspectives:(1) spatial distribution differences,(2) spatial contextual differences,and (3) variations in the usage frequency of some POI instances and their surrounding points in specific areas due to factors such as special events,changes in urban population behavior,cultural activities,etc.,leading to outliers. This paper focuses on discussing the phenomenon of POI outliers caused by spatial distribution differences. However,current outlier detection methods face with challenges. They fall short of adequately expressing and quantifying POIs' local spatial distribution features. The effectiveness of these methods needs further investigation. Given these considerations,this study proposed a novel approach for detecting POI outliers based on determination of local aggregation scales. Initially,we constructed spatial adjacency relationships of the POIs using Delaunay triangulation. Subsequently,the local aggregation characteristic scales of these points were determined by combining cross K-nearest distances and multi-scale feature parameters. Thereafter,based on the scale constraint,the points and their adjacent edge sets that met the conditions were extracted. Finally,we employed the edge length constraint index to systematically remove local long edges that did not meet the prescribed criteria. This meticulous process ensured the integration of the refined point set,thus facilitating the comprehensive detection of outliers within the POI context. The comparative experimental results,drawn from real-world data,suggested that the proposed method possessed a strong generalization ability. Moreover,it effectively and robustly detected outliers without compromising the inherent distribution characteristics of POI. We also performed an interpretability analysis of outlier detection results. The analysis revealed a close correlation between the causes of outlier distribution and various factors including the proportion of POI types,spatial layout,footprint area,and public awareness level. This study provides novel methodologies and academic perspectives for a comprehensive understanding of urban development trends,optimal resource allocation,and the enhancement of urban sustainability and quality of life.
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