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An Ordered Semantic Clustering Algorithm Supporting Urban Block Knowledge Graph

DOI: 10.12677/HJDM.2024.141002, PP. 10-19

Keywords: POI,有序聚类,功能区划分,混合功能区
, Ordered Clustering, Functional Area Division, Mixed Functional Area

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The functions in a city are mostly distributed along the buildings on both sides of the street, manifested as linear blocks. Identifying the characteristics of the functional division of urban blocks can provide assistance for the comprehensive planning, rational allocation, and overall arrangement of urban spatial structure and resources. The traditional linear semantic clustering algorithm can be used to divide the single function urban street area, but the city block includes not only the single function area, but also the mixed function areas. This article proposes an ordered semantic clustering algorithm that supports the division of urban functional blocks. While discovering a single functional area, it also discovers mixed areas and defines a new method for measuring mixed functional areas. The proposed algorithm is based on the idea of hierarchical clustering, which is divided into two stages. The first stage is the generation of a hierarchical tree, which uses the aggregation method to merge adjacent similar segments to obtain a hierarchical tree. The second stage involves extracting functional areas, identifying single and mixed functional areas, and obtaining linear functional areas for a given block. The experimental results on real datasets show that the proposed algorithm can effectively discover mixed functional areas.


[1]  邬群勇, 吴祖飞, 张良盼. 出租车GPS轨迹集聚和精细化路网提取[J]. 测绘学报, 2019, 48(4): 10.
[2]  赵莹, 张朝枝, 金钰涵. 基于手机数据可靠性分析的旅游城市功能空间识别研究[J]. 人文地理, 2018, 33(3): 8.
[3]  王俊珏, 叶亚琴, 方芳. 基于核密度与融合数据的城市功能分区研究[J]. 地理与地理信息科学, 2019, 35(3): 7.
[4]  Jiang, S., Alves, A., Rodrigues, F., et al. (2015) Mining Point-of-Interest Data from Social Networks for Urban Land Use Classification and Disaggregation. Computers Environment & Urban Systems, 53, 36-46.
[5]  Wang, Z., Ma, D., Sun, D., et al. (2021) Identification and Analysis of Urban Functional Area in Hangzhou Based on OSM and POI Data. PLOS ONE, 16, e0251988.
[6]  康雨豪, 王玥瑶, 夏竹君, 等. 利用POI数据的武汉城市功能区划分与识别[J]. 测绘地理信息, 2018, 43(1): 5.
[7]  Zhai, W., Bai, X., Shi, Y., et al. (2019) Beyond Word2vec: An Approach for Urban Functional Region Extraction and Identification by Combining Place2vec and POIs. Computers Environment and Urban Systems, 74, 1-12.
[8]  Ran, Z., Zhou, G., Jiamin, W.U., et al. (2019) Study on Spatial Pattern of Consumer Service Industry in Changsha Based on POI Data. World Regional Studies.
[9]  Song, X.P., Richards, D.R., He, P., et al. (2020) Does Geo-Located Social Media Reflect the Visit Frequency of Urban Parks? A City-Wide Analysis Using the Count and Content of Photographs. Landscape and Urban Planning, 203, 103908.
[10]  冯慧芳, 杨文亮. 融合GPS轨迹和POI数据关联规则的城市功能区识别[J]. 测绘科学技术学报, 2020, 37(4): 7.
[11]  陈泽东, 谯博文, 张晶. 基于居民出行特征的北京城市功能区识别与空间交互研究[J]. 地球信息科学学报, 2018, 20(3): 11.
[12]  高苏, 鲍君忠, 王昕, 等. 可解释性有序聚类方法及其应用分析[J]. 计算机应用, 2022, 42(2): 6.
[13]  姚尧, 张亚涛, 关庆锋, 等. 使用时序出租车轨迹识别多层次城市功能结构[J]. 武汉大学学报: 信息科学版, 2019, 44(6): 10.
[14]  苏月同, 徐天捷, 蒲一超, 等. 基于有序样本聚类的城市轨道交通站点差异化高峰时段识别方法[J]. 交通运输工程与信息学报, 2023, 21(2): 123-140.


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