基于街景数据的建成环境与街道活力时空分析——以深圳福田区为例

doi:10.13249/j.cnki.sgs.2021.09.005

地理科学 ›› 2021, Vol. 41 ›› Issue (9): 1536-1545.doi: 10.13249/j.cnki.sgs.2021.09.005

基于街景数据的建成环境与街道活力时空分析——以深圳福田区为例

司睿¹(), 林姚宇¹^,², 肖作鹏¹^,²^,^*(), 叶宇³

1.哈尔滨工业大学（深圳）建筑学院，广东深圳 518055
2.深圳市城市规划与决策仿真重点实验室，广东深圳 518055
3.同济大学建筑与城市规划学院，上海 200092

收稿日期:2020-06-27 修回日期:2020-12-09 出版日期:2021-09-25 发布日期:2021-11-17
通讯作者: 肖作鹏 E-mail:issirui@foxmail.com;tacxzp@foxmail.com
作者简介:司睿（1992-），女，新疆乌鲁木齐人，博士研究生，主要从事时空行为与城市规划方面的研究。E-mail: issirui@foxmail.com
基金资助:
国家自然科学基金(41571145);哈尔滨工业大学科研创新基金(HIT.NSRIF.2020073);深圳市基础研究(JCYJ20190806144618382)

Spatio-temporal Analysis of Built Environment and Street Vitality Relationship Based on Street-level Imagery: A Case Study of Futian District, Shenzhen

Si Rui¹(), Lin Yaoyu¹^,², Xiao Zuopeng¹^,²^,^*(), Ye Yu³

1. School of Architecture, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, Guangdong, China
2. Shenzhen Key Laboratory of Urban Planning and Decision Simulation, Shenzhen 518055, Guangdong, China
3. College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China

Received:2020-06-27 Revised:2020-12-09 Online:2021-09-25 Published:2021-11-17
Contact: Xiao Zuopeng E-mail:issirui@foxmail.com;tacxzp@foxmail.com
Supported by:
National Natural Science Foundation of China(41571145);Research and Innovation Fund of Harbin Institute of Technology(HIT.NSRIF.2020073);Shenzhen Basic Research Fund(JCYJ20190806144618382)

摘要/Abstract

摘要：

建成环境对街道活力的影响是国内外城市研究的热点议题。然而，对于建成环境要素的测度与评估多偏重于二维建成环境指标，尚未充分挖掘三维建成环境指标。以深圳市福田区为例，采用街景数据、路网数据、POI数据及移动互联网位置服务数据，建立周末分时段模型，探讨商业街道和生活街道活力的时空间分布特征及建成环境对其产生的影响。结果表明：① 商业街道上居民全天活动的峰值时段为18:00~20:00，生活街道上居民全天活动的峰值时段为11:00~13:00。② 深圳市福田区街道活力总体呈现多中心结构，随时间变化显现出明显的空间差异。③ 不同的建成环境指标对街道活力的作用时段与影响程度存在差异。提高功能混合度有利于提升商业街道凌晨、上午和夜间的活力及生活街道午后和傍晚的活力；过宽的相对步行宽度对生活街道活力提升有抑制作用，更安全的步行环境对商业和生活街道夜间活力提升有促进作用；界面连续程度高的商业街道午后及夜间的活力更强，界面多样性丰富的生活街道白天活力更强。

关键词: 街景, 三维建成环境, 街道活力, 热力图, 深圳市

Abstract:

The last decades have witnessed a steady increase in studying urban vitality. However, the information that could depict 3D space has not been included in studies. This research attempts to incorporate street view imagery data to elaborate the spatiotemporal variability of vibrancy in Futian District, Shenzhen. As for the street-level physical environment, attributes are extracted from street view images by deep machine-learning algorithms SegNet. 2D built environment indicators of streets were extracts from the Open Street Map and POI points. A multivariate econometric are framed to examine the association between urban environment and the vitality at the street level of commercial streets and residential streets. The results showed that: 1) The peak hours of activity on commercial streets are 18:00 to 20:00, and the peak hours of activity on living streets are 11:00 to 13:00. 2) Futian District Street Vitality Shows Polycentric Structure, whose distribution had shown obvious spatial differences with time. 3) Variations in the temporal and degree effects of different built environment indicators on street vitality. Increasing the mix of facilities contributes to the morning and nighttime vibrancy of commercial streets, as well as the afternoon vibrancy of living streets. More compact streets have a positive impact on commercial streets’ vitality, and safer pedestrian environments have a positive impact on both living and commercial streets’ vitality. Improving the building continuity will promote the vitality of commercial streets. Higher interface richness increases the vitality of living streets in afternoon and evening. An increase in the richness of the interface will promote the daytime vitality of the residential street.

Key words: street-evel imagery, three-dimensional built environment, street vitality, thermal map, Shenzhen

中图分类号:

K901

司睿, 林姚宇, 肖作鹏, 叶宇. 基于街景数据的建成环境与街道活力时空分析——以深圳福田区为例[J]. 地理科学, 2021, 41(9): 1536-1545.

Si Rui, Lin Yaoyu, Xiao Zuopeng, Ye Yu. Spatio-temporal Analysis of Built Environment and Street Vitality Relationship Based on Street-level Imagery: A Case Study of Futian District, Shenzhen[J]. SCIENTIA GEOGRAPHICA SINICA, 2021, 41(9): 1536-1545.

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变量类型	变量	商业街道		生活街道		说明
变量类型	变量	均值	标准差	均值	标准差	说明
便利性	设施数量/（个/100 m）	9.53	10.14	10.12	5.31	单元内每100 m的设施点数量，反映设施分布
	功能混合度	0.46	0.31	0.52	0.25	单元内POI混合状态，反映土地利用多样性
	最近娱乐休闲设施距离/m	192.40	134.85	186.56	173.40	街道质心至最近娱乐休闲设施的实际距离，反映到达目的地的便捷程度
	最近商业设施距离/m	95.29	148.28	147.49	112.41	街道质心到最近商业设施的实际距离，反映到达目的地的便捷程度
可达性	最近公共交通站点距离/m	230.71	161.73	259.98	179.40	街道质心至最近公交站或地铁站的实际距离，反映公共交通站点的邻近性
可达性	接近度	0.26	0.08	0.25	0.09	对半径内的每个网络链路，计算网络数量并除以到达该网络的距离，然后对半径内的该项求和，反映街道中心性和拓扑整合力
可步行性	相对步行宽度	0.41	0.23	0.43	0.33	单元内步行道占比/马路占比的均值，反映步行空间尺度
可步行性	交通安全设施占比	0.03	0.02	0.02	0.03	单元内栏杆和柱占比总和的均值，反映交通安全程度
舒适性	天空开敞度	0.09	0.05	0.11	0.08	单元内天空占比的均值，反映视觉开敞程度
	建筑连续程度	0.14	0.08	0.11	0.07	单元内建筑占比的标准差，反映街道建筑界面的连续性
	街道高宽比	2.36	2.17	1.90	2.24	单元内建筑占比/（道路占比+人行道占比）的均值，反映空间的紧凑程度
	界面多样性	0.03	0.01	0.03	0.01	单元内可视空间元素种类/道路长度，反映界面丰富性

变量类型	变量	全天	分时段模型
变量类型	变量	全天	0:00~6:00	6:00~11:00	11:00~21:00	21:00~00:00
注：数值为回归系数；^*、^、^* 分别表示在0.01、0.05、0.1的显著性水平下通过检验。
便利性	设施数量	0.028	0.058	0.120	-0.023	0.030
	功能混合度	0.212^**	0.205^**	0.258^**	0.176	0.192^*
	最近娱乐休闲设施距离	0.043	-0.111	0.072	0.085	0.008
	最近商业设施距离	-0.141	-0.095	-0.036	-0.184.	-0.130
可达性	最近公共交通站点距离	-0.294^***	-0.166^*	-0.258^**	-0.323^***	-0.274^***
可达性	接近度	0.216^**	0.172^*	0.228^**	0.202^*	0.225^**
可步行性	相对步行宽度	-0.033	-0.214^**	-0.057	0.047	-0.069
可步行性	交通安全设施占比	0.170^*	0.197^**	0.180^*	0.133	0.235^**
舒适性	天空开敞度	-0.206^*	-0.230^**	-0.198^*	-0.180	-0.199^*
	建筑连续程度	0.184^*	0.113	0.117	0.213^*	0.203^*
	界面多样性	-0.011	-0.029	0.016	-0.020	0.055
	街道高宽比	0.276^**	0.468^***	0.348^***	0.159	0.202^*
截距项		2.855^***	1.424^***	2.534^***	3.957^***	2.449^***
样本量		85	85	85	85	85
调整后的R²		0.650	0.699	0.597	0.556	0.607

变量类型	变量	全天	分时段模型
变量类型	变量	全天	0:00~6:00	6:00~11:00	11:00~21:00	21:00~00:00
注：^*、^、^* 分别表示在0.01、0.05、0.1的显著性水平下通过检验。
便利性	设施数量	0.068	0.170	0.113	0.001	-0.004
	功能混合度	0.156^*	0.082	0.140	0.175^*	0.130
	最近娱乐休闲设施距离	-0.193^*	-0.039	-0.104	-0.248^**	-0.340^***
	最近商业设施距离	-0.378^***	-0.392^***	-0.279^*	-0.351^***	-0.361^***
可达性	最近公共交通站点距离	0.143	0.165	0.118	0.121	0.115
可达性	接近度	0.185^*	0.226^**	0.074	0.187^*	0.157^*
可步行性	相对步行宽度	-0.207^**	-0.087	-0.185^*	-0.231^**	-0.255^***
可步行性	交通安全设施占比	0.064	0.020	0.133	0.028	0.215^**
舒适性	天空开敞度	0.111	-0.018	0.195^*	0.115	0.104
	建筑连续程度	0.004	-0.166	-0.136	0.140	-0.070
	界面多样性	0.213^*	0.074	0.253^*	0.231^*	0.106
	街道高宽比	0.346^**	0.574^***	0.398^**	0.176	0.304^**
截距项		2.560^***	0.892^*	2.663^***	3.581^***	2.672^***
样本量		91	91	91	91	91
调整后的R²		0.593	0.518	0.419	0.559	0.619

基于街景数据的建成环境与街道活力时空分析——以深圳福田区为例

Spatio-temporal Analysis of Built Environment and Street Vitality Relationship Based on Street-level Imagery: A Case Study of Futian District, Shenzhen

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