: With the rapid urbanization in China, the emergence of urban diseases has led to the decline of urban quality and urban vitality. Urban vitality provides a new perspective for the study of urban issues. The use of big data provides an accurate and efficient means for the study of urban vitality. Based on the dimensions of population heat map and point of interest (POI) distribution, this study quantitatively evaluated the urban vitality in the central city of Chongqing, and validated the derived vitality values based on the score of street view perception. The results show that the spatial distribution of urban vitality in Chongqing is characterized by multi- center distribution, which is consistent with its multi-center and clustered urban structure. The urban vitality evaluated by three indicators is similar in spatial pattern. It shows that the areas with high urban vitality are mainly located in the inner ring of Chongqing, especially in the main center of Jiefangbei and sub-centers, such as Shapingba, Yangjiaping, Guanyinqiao, and Nanping. The areas with high urban vitality correspond to the sub-centers of Chayuan and Xiyong outside the inner ring. This result is also consistent with the score of street view perception. The study confirms that the evaluation of urban vitality based on big data can compensate for the deficiency of traditional qualitative analysis methods and provide a new way of thinking and perspective for the quantitative study of urban vitality. The study of a mountainous city also addresses the deficiency of the existing studies on mountain city vitality and can help policy making in spatial planning.
[1]
M. Bottero,et al.
Assessing urban quality: a proposal for a MCDA evaluation framework
,
2018,
Ann. Oper. Res..
[2]
Zuo Dong.
Ten relations of territorial planning in the new era
,
2019,
资源科学.
[3]
Ying Long,et al.
Analysis of the Variation in Quality of Street Space in Shrinking Cities Based on Dynamic Street View Picture Recognition: A Case Study of Qiqihar
,
2019,
The Urban Book Series.
[4]
Young-Long Kim,et al.
Seoul's Wi-Fi hotspots: Wi-Fi access points as an indicator of urban vitality
,
2018,
Comput. Environ. Urban Syst..
[5]
Ramesh Raskar,et al.
Deep Learning the City: Quantifying Urban Perception at a Global Scale
,
2016,
ECCV.
[6]
Tong Luyi.
Characterizations of urban sprawl in major Chinese cities
,
2016
.
[7]
Zhang Yao-yu.
Differences in driving-force mechanisms in urban land expansion in China
,
2016
.
[8]
王洋,et al.
广州市多类型商业中心识别与空间模式@@@Identify of the multiple types of commercial center in Guangzhou and its spatial pattern
,
2016
.
[9]
Ramesh Raskar,et al.
Streetscore -- Predicting the Perceived Safety of One Million Streetscapes
,
2014,
2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[10]
J. Rouwendal,et al.
The Impact of Mix Land Use on Residential Property Values
,
2010
.
[11]
John Montgomery,et al.
Making a city: Urbanity, vitality and urban design
,
1998
.