Multidimensional Urban Vitality on Streets: Spatial Patterns and Influence Factor Identification Using Multisource Urban Data

Abstract

Urban vitality is a key indicator for measuring urban development. This topic has been trending in urban planning and sustainable development, and significant progress has been made in measuring single indicators of urban vitality based on parcel or block units. With the continuous development of smart sensing technology, multisource urban data are becoming increasingly abundant. The application of such data to measure the multidimensional urban vitality of street space, reflecting multiple functions of an urban space, can significantly improve the accuracy of urban vitality analyses and promote the construction of people-oriented healthy cities. In this study, streets were taken as the analysis unit, and multisource data such as the trajectories of taxies and shared bicycles, user reviews and cultural facility points of interest (POIs) in Chengdu, a city in southwestern China, were used to identify spatial patterns of urban vitality on streets across social, economic and cultural dimensions. The correlation between the built environment factors and the multidimensional urban vitality on the street was analyzed using a multiple regression model. The spatial distribution of the different dimensions of urban vitality of the street space in Chengdu varies to a certain extent. It is common for areas with high social vitality to have production and life centers nearby. High economic vitality centers are typically found along busy streets with a high concentration of businesses. Areas with high cultural vitality centers tend to be concentrated on the city’s central streets. Land use, transportation, external environment, population and employment are all closely linked to urban vitality on streets. The crowd counting and POI density have the greatest impact on multidimensional urban vitality. The crowd and the level of service facilities profoundly affect social interaction, trade activities and cultural communication. The goodness of fit (R2) of the regression models for social, economic and cultural vitality are 0.590, 0.423 and 0.409, respectively. Using multisource urban data, our findings can help stakeholders better understand the spatial patterns and influencing factors of multidimensional urban vitality on streets and provide sustainable urban planning and development strategies for the future.

Assessment of Influence Mechanisms of Built Environment on Street Vitality Using Multisource Spatial Data: A Case Study in Qingdao, China

“…Conversely, streets with high social vitality values had both high and low economic vitality values, indicating that, despite high people flows in the street, there was still a low level of economic vitality and a mismatch with social vitality. This finding is consistent with previous studies [7]. Previously, it was believed that areas with high cultural vitality were concentrated in urban centers, but streets with high cultural vitality values did not have high social and economic vitality values.…”

“…The study of street vitality plays a key role in optimizing and improving urban vitality, and also provides a new perspective for the investigation of urban vitality, and can provide important theoretical reference significance for subsequent street planning research. Street vitality is an indicator of the level of urban development and a comprehensive embodiment of the quality of urban development [7]. Jacobs [8] was the first to introduce the concept of street vitality, rethink modern urban planning led by streets, and propose a theory of urban diversity.…”

Spatial Coupling Coordination Evaluation of Mixed Land Use and Urban Vitality in Major Cities in China

“…Following the principles of objectivity, typicality, and data availability, a total of five indicators were selected as the influencing factors of the spatial variation of the level of coupling coordination between mixed land use and urban vitality by using a combination of literature analysis [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ] and expert consultation methods ( Table 5 ). (1) Economic level: economic development determines the concentration of various city resources and factors, which profoundly affects the level of mixed land use and urban vitality, characterized by GDP per capita.…”

Exploring the Relationship between Urban Street Spatial Patterns and Street Vitality: A Case Study of Guiyang, China

“…These attempts to study urban vitality have demonstrated the feasibility of analytical methods that use big data to reflect the characteristics of population activity as a means to obtain an accurate distribution of urban vitality [ 21 ]. They equally prove the validity of geo-information data in the analysis of the spatial pattern and vitality of streets [ 22 ]. However, current studies on urban vitality tend to neglect the interaction of vitality and built-up environment [ 23 ] and few have provided a categorization framework for vitality understanding, which limits the creative value of vitality analysis for urban design [ 24 ].…”