摘要
兴趣点(POI)对于深化理解地理空间中人类活动和环境特征的作用日益凸显,从大规模空间数据中探测与周围环境显著不同的离群点是增强人地系统认知的重要研究方向。现有离群点挖掘方法应用于POI时存在局部空间分布特征表达及量化不足的缺点,并且其有效性亟待进一步讨论。鉴于此,本文提出了基于局部集聚尺度判定的兴趣点离群分布探测方法。首先,借助Delaunay三角网构建POI的空间邻接关系,并基于交叉K近邻距离和多尺度特征参数判别点群的局部集聚特征尺度;然后,提取满足尺度约束条件的点及其邻接边集;最后,通过边长约束指标剔除不满足条件的局部长边并整合离群簇,完成POI离群探测。根据实际数据的对比实验结果可以得出所提方法泛化能力良好,且可在不破坏POI固有分布特征的前提下有效、稳健地探测离群点。本文进一步开展离群点探测结果的可解释性分析,讨论得出兴趣点离群分布成因与类型占比、空间布局、占地面积及公众认知水平等因素密切相关。本研究为全面把握城市发展动向、资源配置优化、提高城市可持续性及人居生活质量等方面提供新的方法与研究视角。
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.Th
作者
吴鹏
哈斯巴根
秦福莹
WU Peng;Hasibagen;QIN Fuying(College of Resources and Environmental Economics,Inner Mongolia University of Finance and Economics,Hohhot 010070,China;Planning and Geographic Information System Laboratory,Inner Mongolia University of Finance and Economics,Hohhot 010070,China)
出处
《地球信息科学学报》
EI
CSCD
北大核心
2024年第7期1594-1610,共17页
Journal of Geo-information Science
基金
内蒙古自治区自然科学基金项目(2021BS04002、2021MS04006)
内蒙古自治区直属高校基本科研业务费项目(NCYWT23046)
关键词
兴趣点
空间离群点
DELAUNAY三角网
交叉K邻近距离
集聚特征尺度
边长约束指标
可解释性分析
公众认知度
points of interest
spatial outliers
Delaunay triangulation
cross K-nearest distance
aggregation characteristic scale
edge length constraint metric
interpretability analysis
public awareness
