基于城市空间大数据的重庆主城区居住小区活力评价

doi:10.3969/j.issn.1004-9479.2025.08.20230837

摘要/Abstract

摘要：

在“以人为本”的城市发展导向下，识别居住小区的活力空间分布特征，制定有效的提升措施，营造富有活力的居住小区，对于满足居民“美好生活需求”具有重要意义。因此，研究从物质环境、邻里环境和人口集聚3个维度构建了居住小区活力的评价体系，并以重庆主城区为例，融合三维建筑模型、人口热力图、兴趣点（POI）等城市空间大数据，在精细尺度上识别其居住小区活力的分布特征。结果表明：重庆居住小区活力呈现多中心、组团式分布格局；小区活力从核心区域向外围逐步降低，活力较高的区域分布在主/副中心及城市组团，而新兴区域的小区活力较低；POI密度、人口热力、容积率等因素对居住小区活力的贡献较大；重庆小区活力的分布受山地地形显著影响。本研究构建的多维度居住小区活力的综合评价体系，融合精细尺度上的城市空间大数据，具有较好的普适性。同时，对居住小区活力的低值区域的有效识别，可为小区活力营造和品质提升提供参考。

关键词: 活力评价, 居住小区, 城市空间大数据, 山地城市, 重庆市

Abstract:

Under the guidance of "people-oriented" urban development, it is of great significance to identify the spatial distribution of community vitality and formulate measures for vibrant communities, further meeting residents' "needs of a better life". Therefore, to evaluate community vitality, this study constructed a comprehensive framework from the dimensions of physical environment, neighborhood environment, and population agglomeration. Then, this study integrated building models, population heatmaps, POI, and other urban spatial big data to quantify the distribution of community vitality at a fine scale. The results show that community vitality in Chongqing presents a polycentric and cluster pattern and the vitality decreases from the core area to the surrounding area with a gradient. In addition, vibrant communities concentrate in the main/sub-centers and urban clusters, whereas the newly built-up cluster exerts only low vitality. Factors such as POI density, population, and plot ratio are the main contributors to community vitality. Further, community vitality in Chongqing is influenced by the complex landform in mountainous terrain. The comprehensive framework based on multiple dimensions and the utilization of urban spatial big data has good generalizability. More importantly, the effective identification of areas with low community vitality provides a reference for targeted measures for vitality enhancement and quality improvement strategies.

Key words: vitality assessment, residential neighbourhood, urban spatial big data, mountainous city, Chongqing main urban area

明雨佳, 刘勇. 基于城市空间大数据的重庆主城区居住小区活力评价[J]. 世界地理研究, 2025, 34(8): 128-138.

Yujia MING, Yong LIU. Vitality assessment of residential neighborhoods in Chongqing main urban area based on urban spatial big data[J]. World Regional Studies, 2025, 34(8): 128-138.

图/表 6

图1 研究区居住小区示意图

Fig.1 Location of the communities in the study area

表1 居住小区活力评价指标

Tab.1 Proposed index for assessing the vitality of communities

维度	指标	计算公式	方法
物质环境	边界形状指数	$0.25 P / A$	P为小区边界周长；A为小区占地面积
	建筑密度	$∑ i = 0 n S i / S a$	S_i 为小区建筑基底面积；S_a 为小区占地面积
	容积率	$∑ i = 0 n S i L I / S a$	S_i 为小区建筑基底面积；S_a 为小区占地面积；L_i 为小区建筑楼层数
邻里环境	地铁可达性	$m i n L s$	L_s 为小区到地铁站的距离
	公交站点数量	—	800m缓冲区内、10~15分钟步行半径内的公交站点的数量
	设施密度	$N / A$	N为800m缓冲区内POI数量；A为缓冲区面积
	设施多样性	$- ∑ j = 1 n P j I n P j$	n为POI的类别数，P_j 为800m缓冲区内某类POI数量占比
人口集聚	户数	—	小区内的住户数量
	人口热力	—	800m缓冲区内的人口热力值

表1 居住小区活力评价指标

Tab.1 Proposed index for assessing the vitality of communities

维度	指标	计算公式	方法
物质环境	边界形状指数	$0.25 P / A$	P为小区边界周长；A为小区占地面积
	建筑密度	$∑ i = 0 n S i / S a$	S_i 为小区建筑基底面积；S_a 为小区占地面积
	容积率	$∑ i = 0 n S i L I / S a$	S_i 为小区建筑基底面积；S_a 为小区占地面积；L_i 为小区建筑楼层数
邻里环境	地铁可达性	$m i n L s$	L_s 为小区到地铁站的距离
	公交站点数量	—	800m缓冲区内、10~15分钟步行半径内的公交站点的数量
	设施密度	$N / A$	N为800m缓冲区内POI数量；A为缓冲区面积
	设施多样性	$- ∑ j = 1 n P j I n P j$	n为POI的类别数，P_j 为800m缓冲区内某类POI数量占比
人口集聚	户数	—	小区内的住户数量
	人口热力	—	800m缓冲区内的人口热力值

图2 居住小区活力指数的聚类结果

Fig.2 Clustered results of vitality index of communities

图3 居住小区活力样本集的最佳投影方向

Fig.3 Optimal projection direction of sample sets of vitality of communities

图4 居住小区活力主要贡献因子的空间分布

Fig.4 Distribution pattern of contributing factors of vitality of communities

图5 居住小区活力与高程和坡度的交叉分析

Fig.5 Cross analysis of vitality by elevation and slope

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