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支持城市功能街区划分的有序语义聚类算法
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
POI
, Ordered Clustering, Functional Area Division, Mixed Functional Area

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Abstract:

城市中的功能大都分布在沿街道的两侧建筑,表现为线性街区,识别城市街区功能划分的特征可为城市空间结构及资源的全面规划、合理配置、统筹安排等提供帮助。传统线性语义聚类算法可用于划分单功能城市街道区,但城市街区不仅包括单一功能分区,还包括混合功能区。本文提出一种支持城市功能街区划分的有序语义聚类算法,在发现单一功能区的同时,也发现混合区并定义了一种新的度量混合功能区的方法。提出的算法基于层次聚类思想,具体算法分为两阶段,第一阶段为层次树生成,采用凝聚的方法将相邻的相似分段合并,得到层次树;第二阶段为功能区提取,进行单一功能区与混合功能区识别,获取给定街区的线性功能区。在真实数据集上的实验结果表明,所提出的算法可以有效发现混合功能区。
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.

References

[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.
https://doi.org/10.11947/j.AGCS.2019.20180256
[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.
https://doi.org/10.1016/j.compenvurbsys.2014.12.001
[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.
https://doi.org/10.1371/journal.pone.0251988
[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.
https://doi.org/10.1016/j.compenvurbsys.2018.11.008
[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.
https://doi.org/10.1016/j.landurbplan.2020.103908
[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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