城市活力与建成环境的非线性关系和阈值效应研究——以广州市中心城区为例

doi:10.18306/dlkxjz.2023.01.007

Abstract

Abstract:

The relationship between urban vitality and the built environment has always been a subject of intense research in urban geography and planning. Traditionally, global regression techniques have been mostly developed to analyze their quantitative relationship based on a linear consumption. However, the results from existing studies are rather mixed, indicating that their relationship may not be global. Therefore, it is necessary to explore the local characteristics of the associations between urban vitality and the built environment. Based on a collection of multi-source datasets including Baidu Heat Map data, building data, road network data, and point of interest data in central Guangzhou City—including Liwan, Yuexiu, Tianhe and Haizhu districts, this study applied the gradient boosting decision tree model to unveil the nonlinear associations of built environment characteristics (including intensity, function, and accessibility) with urban vitality and threshold effects. The differences in the impacts of the built environment between daytime and nighttime on weekdays have also been examined. The results show that: 1) During both the daytime and the nighttime, floor area ratio contributes the greatest to urban vitality, followed by the intensity of recreation and public transport facilities. Compared to diversity, reasonable development intensity, concentration of leisure and office facilities, and public transport oriented development have larger collective contributions to urban vitality. 2) Although differences exist between daytime and nighttime, all built environment variables have nonlinear associations with urban vitality. The threshold value and gradient of key built environment variables are recognized in the nonlinear shapes of associations. Urban planners and local governments are recommended to meticulously disentangle the complicated built environment associations to make informed and targeted interventions for fostering and maintaining urban vitality.

Key words: urban vitality, built environment, nonlinear associations, threshold effect, machine learning, Baidu Heat Map

WANG Chenggang, WANG Bo, WANG Qizhi, LEI Yaqin. Nonlinear associations between urban vitality and built environment factors and threshold effects: A case study of central Guangzhou City[J].PROGRESS IN GEOGRAPHY, 2023, 42(1): 79-88.

Figures/Tables 7

Fig.1

Tab.1

Variable definition and descriptive statistics

变量		描述	平均值	标准差	最小值	最大值
城市活力		百度热力指数	381.30	79.33	36.28	531.75
建设强度	建筑密度	建筑基底面积/用地面积	0.43	0.19	0	1.00
建设强度	容积率	建筑总面积/用地面积	2.46	1.40	0	9.41
功能性质	居住设施密度	居住设施POI密度(个/km²)	22.82	25.22	0	193.00
	办公设施密度	办公设施POI密度(个/km²)	112.48	120.26	0	1232.00
	休闲设施密度	休闲设施POI密度(个/km²)	141.18	160.45	0	1026.00
	功能混合度	$Z i = 全域第 i 类 P O I 数量 (个) 全域 3 类 P O I 总数量 (个))$ $X i = 方格网第 i 类 P O I 数量 (个) 方格网 3 类 P O I 总数量 (个)$ $A i = X i Z i$ ， $P i = A i ∑ i = 1 n A i 功能混合度 = - ∑ i = 1 n P i l n P i l n n, n = 3$	0.56	0.20	0.11	1.00
交通可达性	路网密度	道路长度/用地面积(km/km²)	0.94	1.58	0	11.50
	公交密度	公交线路数量×公交站点数量/用地面积(个/km²)	78.82	69.69	0	431.69
	地铁可达性	网格中心到最近地铁站的直线距离(km)	0.62	0.44	0.32	1.20

Tab.1

Fig.2

Tab.2

Fig.3

Fig.4

Fig.5

References 38

[1]	Jacobs J. The death and life of great American cities[M]. New York, USA: Random House, 1961.
[2]	Whyte W H. The social life of small urban spaces[M]. Washington D C, USA: Conservation Foundation, 1980.
[3]	Lynch K. A theory of good city form[M]. Cambridge, USA: The MIT Press, 1982.
[4]	Gehl J. Life between buildings: Using public space[M]. Washington D C, USA: Island Press, 1987.
[5]	Montgomery J. Making a city: Urbanity, vitality and urban design[J]. Journal of Urban Design, 2007, 3(1): 93-116. doi: 10.1080/13574809808724418
[6]	Wu C, Ye X Y, Ren F, et al. Check-in behaviour and spatio-temporal vibrancy: An exploratory analysis in Shenzhen, China[J]. Cities, 2018, 77: 104-116. doi: 10.1016/j.cities.2018.01.017
[7]	Glaeser E. Cities, productivity, and quality of life[J]. Science, 2011, 333: 592-594. doi: 10.1126/science.1209264 pmid: 21798941
[8]	塔娜, 曾屿恬, 朱秋宇, 等. 基于大数据的上海中心城区建成环境与城市活力关系分析[J]. 地理科学, 2020, 40(1): 60-68. doi: 10.13249/j.cnki.sgs.2020.01.008
	[Ta Na, Zeng Yutian, Zhu Qiuyu, et al. Relationship between built environment and urban vitality in Shanghai downtown area based on big data. Scientia Geographica Sinica, 2020, 40(1): 60-68. ] doi: 10.13249/j.cnki.sgs.2020.01.008
[9]	王波, 雷雅钦, 汪成刚, 等. 建成环境对城市活力影响的时空异质性研究: 基于大数据的分析[J]. 地理科学, 2022, 42(2): 274-283. doi: 10.13249/j.cnki.sgs.2022.02.009
	[Wang Bo, Lei Yaqin, Wang Chenggang, et al. The spatio-temporal impacts of the built environment on urban vitality: A study based on big data. Scientia Geographica Sinica, 2022, 42(2): 274-283. ] doi: 10.13249/j.cnki.sgs.2022.02.009
[10]	Wang B, Lei Y, Xue D, et al. Elaborating spatiotemporal associations between the built environment and urban vibrancy: A case of Guangzhou City, China[J]. Chinese Geographical Science, 2022, 32(3): 480-492. doi: 10.1007/s11769-022-1272-6
[11]	邹思聪, 张姗琪, 甄峰. 基于居民时空行为的社区日常活动空间测度及活力影响因素研究: 以南京市沙洲、南苑街道为例[J]. 地理科学进展, 2021, 40(4): 580-596. doi: 10.18306/dlkxjz.2021.04.004
	[Zou Sicong, Zhang Shanqi, Zhen Feng. Measurement of community daily activity space and influencing factors of vitality based on residents' spatiotemporal behavior: Taking Shazhou and Nanyuan streets in Nanjing as examples. Progress in Geography, 2021, 40(4): 580-596. ] doi: 10.18306/dlkxjz.2021.04.004
[12]	Yue Y, Zhuang Y, Yeh A G O, et al. Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy[J]. International Journal of Geographical Information Science, 2017, 31(4): 658-675. doi: 10.1080/13658816.2016.1220561
[13]	Huang B, Zhou Y L, Li Z G, et al. Evaluating and characterizing urban vibrancy using spatial big data: Shanghai as a case study[J]. Environment and Planning B: Urban Analytics and City Science, 2019, 47(9): 1543-1559. doi: 10.1177/2399808319828730
[14]	王波, 甄峰, 张浩. 基于签到数据的城市活动时空间动态变化及区划研究[J]. 地理科学, 2015, 35(2): 151-160. doi: 10.13249/j.cnki.sgs.2015.02.151
	[Wang Bo, Zhen Feng, Zhang Hao. The dynamic changes of urban space-time activity and activity zoning based on check-in data in Sina web. Scientia Geographica Sinica, 2015, 35(2): 151-160. ] doi: 10.13249/j.cnki.sgs.2015.02.151
[15]	Loo B P Y, Wang B. Progress of e-development in China since 1998[J]. Telecommunications Policy, 2017, 41(9): 731-742. doi: 10.1016/j.telpol.2017.03.001
[16]	甄峰, 王波. “大数据”热潮下人文地理学研究的再思考[J]. 地理研究, 2015, 34(5): 803-811. doi: 10.11821/dlyj201505001
	[Zhen Feng, Wang Bo. Rethinking human geography in the age of big data. Geographical Research, 2015, 34(5): 803-811. ] doi: 10.11821/dlyj201505001
[17]	Delclòs-Alió X, Miralles-Guasch C. Looking at Barcelona through Jane Jacobs's eyes: Mapping the basic conditions for urban vitality in a Mediterranean conurbation[J]. Land Use Policy, 2018, 75: 505-517. doi: 10.1016/j.landusepol.2018.04.026
[18]	Wu J Y, Ta N, Song Y, et al. Urban form breeds neighborhood vibrancy: A case study using a GPS-based activity survey in suburban Beijing[J]. Cities, 2018, 74: 100-108. doi: 10.1016/j.cities.2017.11.008
[19]	钮心毅, 吴莞姝, 李萌. 基于LBS定位数据的建成环境对街道活力的影响及其时空特征研究[J]. 国际城市规划, 2019, 34(1): 28-37.
	[Niu Xinyi, Wu Wanshu, Li Meng. Influence of built environment on street vitality and its spatiotemporal characteristics based on LBS positioning data. Urban Planning International, 2019, 34(1): 28-37. ] doi: 10.22217/upi.2018.495
[20]	Jin X B, Long Y, Sun W, et al. Evaluating cities' vitality and identifying ghost cities in China with emerging geographical data[J]. Cities, 2017, 63: 98-109. doi: 10.1016/j.cities.2017.01.002
[21]	王波, 甄峰, 张姗琪, 等. 空气污染对城市活力的影响及其建成环境异质性: 基于大数据的分析[J]. 地理研究, 2021, 40(7): 1935-1948. doi: 10.11821/dlyj020200685
	[Wang Bo, Zhen Feng, Zhang Shanqi, et al. The impact of air pollution on urban vibrancy and its built environment heterogeneity: An empirical analysis based on big data. Geographical Research, 2021, 40(7): 1935-1948. ] doi: 10.11821/dlyj020200685
[22]	Wang B, Zhen F, Wei Z C, et al. A theoretical framework and methodology for urban activity spatial structure in e-society: Empirical evidence for Nanjing City, China[J]. Chinese Geographical Science, 2015, 25(6): 672-683. doi: 10.1007/s11769-015-0751-4
[23]	Long Y, Huang C C. Does block size matter? The impact of urban design on economic vitality for Chinese cities[J]. Environment and Planning B: Urban Analytics and City Science, 2017, 46(3): 406-422. doi: 10.1177/2399808317715640
[24]	王伟强, 马晓娇. 基于多源数据的滨水公共空间活力评价研究: 以黄浦江滨水区为例[J]. 城市规划学刊, 2020(1): 48-56.
	[Wang Weiqiang, Ma Xiaojiao. Vitality assessment of waterfront public space based on multi-source data: A case study of the Huangpu River waterfront. Urban Planning Forum, 2020(1): 48-56. ]
[25]	Ye Y, Li D, Liu X J. How block density and typology affect urban vitality: An exploratory analysis in Shenzhen, China[J]. Urban Geography, 2017, 39(4): 631-652. doi: 10.1080/02723638.2017.1381536
[26]	Xia C, Yeh A G O, Zhang A. Analyzing spatial relationships between urban land use intensity and urban vitality at street block level: A case study of five Chinese megacities[J]. Landscape and Urban Planning, 2020, 193:103669. doi: 10.1016/j.landurbplan.2019.103669. doi: 10.1016/j.landurbplan.2019.103669
[27]	Kim Y L. Seoul's Wi-Fi hotspots: Wi-Fi access points as an indicator of urban vitality[J]. Computers, Environment and Urban Systems, 2018, 72: 13-24. doi: 10.1016/j.compenvurbsys.2018.06.004
[28]	Xiao L Z, Lo S, Zhou J P, et al. Predicting vibrancy of metro station areas considering spatial relationships through graph convolutional neural networks: The case of Shenzhen, China[J]. Environment and Planning B: Urban Analytics and City Science, 2021, 48(8): 2363-2384. doi: 10.1177/2399808320977866
[29]	Yang J W, Cao J, Zhou Y F. Elaborating non-linear associations and synergies of subway access and land uses with urban vitality in Shenzhen[J]. Transportation Research Part A: Policy and Practice, 2021, 144: 74-88. doi: 10.1016/j.tra.2020.11.014
[30]	吴志强, 叶锺楠. 基于百度地图热力图的城市空间结构研究: 以上海中心城区为例[J]. 城市规划, 2016, 40(4): 33-40.
	[Wu Zhiqiang, Ye Zhongnan. Research on urban spatial structure based on Baidu heat map: A case study on the central city of Shanghai. City Planning Review, 2016, 40(4): 33-40. ]
[31]	赵鹏军, 罗佳, 胡昊宇. 基于大数据的生活圈范围与服务设施空间匹配研究: 以北京为例[J]. 地理科学进展, 2021, 40(4): 541-553. doi: 10.18306/dlkxjz.2021.04.001
	[Zhao Pengjun, Luo Jia, Hu Hao-yu. Spatial match between residents' daily life circle and public service facilities using big data analytics: A case of Beijing. Progress in Geography, 2021, 40(4): 541-553. ] doi: 10.18306/dlkxjz.2021.04.001
[32]	钟炜菁, 王德. 上海市中心城区夜间活力的空间特征研究[J]. 城市规划, 2019, 43(6): 97-106, 114.
	[Zhong Weijing, Wang De. A study on the spatial characteristics of nighttime vitality in the city center of Shanghai. City Planning Review, 2019, 43(6): 97-106, 114. ]
[33]	司睿, 林姚宇, 肖作鹏, 等. 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. doi: 10.13249/j.cnki.sgs.2021.09.005
	[Si Rui, Lin Yaoyu, Xiao Zuopeng, et al. Spatio-temporal analysis of built environment and street vitality relationship based on street-level imagery: A case study of Futian District, Shenzhen. Scientia Geographica Sinica, 2021, 41(9): 1536-1545. ] doi: 10.13249/j.cnki.sgs.2021.09.005
[34]	韩寒. 深圳市轨道交通结构与商业活力空间关联分析[J]. 经济地理, 2021, 41(3): 86-96.
	[Han Han. Spatial correlation between rail transit structure and commercial vitality in Shenzhen. Economic Geography, 2021, 41(3): 86-96. ] doi: 10.2307/141859
[35]	仝照民, 安睿, 刘耀林. 建成环境对居民通勤方式选择的影响: 以武汉市城中村为例[J]. 地理科学进展, 2021, 40(12): 2048-2060. doi: 10.18306/dlkxjz.2021.12.006
	[Tong Zhaomin, An Rui, Liu Yaolin. Impact of the built environment on residents' commuting mode choices: A case study of urban village in Wuhan City. Progress in Geography, 2021, 40(12): 2048-2060. ] doi: 10.18306/dlkxjz.2021.12.006
[36]	张琳, 周素红, 关美宝, 等. 城市公交车微环境对乘客舒适度的非线性影响[J]. 地理科学进展, 2021, 40(6): 967-979. doi: 10.18306/dlkxjz.2021.06.007
	[Zhang Lin, Zhou Suhong, Kwan Mei-Po, et al. Nonlinear effects of bus micro-environments on passengers' comfort. Progress in Geography, 2021, 40(6): 967-979. ]
[37]	Liu J, Wang B, Xiao L. Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach[J]. Journal of Transport Geography, 2021, 92:103034. doi: 10.1016/j.jtrangeo.2021.103034. doi: 10.1016/j.jtrangeo.2021.103034
[38]	Friedman J H. Greedy function approximation: A gradient boosting machine[J]. Annals of Statistics, 2001, 29(5): 1189-1232. doi: 10.1214/aos/1013203450

变量		工作日白天		工作日夜间
变量		重要性/%	排名	重要性/%	排名
建设强度	建筑密度	3.79	7	5.36	4
建设强度	容积率	33.29	1	34.79	1
功能性质	休闲设施密度	24.16	2	30.38	2
	居住设施密度	0.22	9	0.10	9
	办公设施密度	6.16	4	0.57	8
	功能混合度	2.61	8	2.18	7
交通可达性	路网密度	4.35	6	3.91	6
	公交密度	20.75	3	18.63	3
	地铁可达性	5.37	5	4.08	5

Nonlinear associations between urban vitality and built environment factors and threshold effects: A case study of central Guangzhou City

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