Portraying the spatial dynamics of urban vibrancy using multisource urban big data

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Portraying the spatial dynamics of urban vibrancy using multisource urban big data Wei Tua,b,c,**, Tingting Zhua,b,c, Jizhe Xiaa,b,c,*, Yulun Zhoud, Yani Laic, Jincheng Jianga,b, Qingquan Lia,b,c a Guangdong Key Laboratory of Urban Informatics, Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, and Research Institute of Smart Cities, Shenzhen University, Shenzhen 518060, China b Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China c Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China d Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong

A R T I C LE I N FO

A B S T R A C T

Keywords: Urban vibrancy Geographically weighted regression mobile phone data Social media Points-of-interest Big data

Understanding urban vibrancy aids policy-making to foster urban space and therefore has long been a goal of urban studies. Recently, the emerging urban big data and urban analytic methods have enabled us to portray citywide vibrancy. From the social sensing perspective, this study presents a comprehensive and comparative framework to cross-validate urban vibrancy and uncover associated spatial effects. Spatial patterns of urban vibrancy indicated by multisource urban sensing data (points-of-interest, social media check-ins, and mobile phone records) were investigated. A comprehensive urban vibrancy metric was formed by adaptively weighting these metrics. The association between urban vibrancy and demographic, economic, and built environmental factors was revealed with global regression models and local regression models. An empirical experiment was conducted in Shenzhen. The results demonstrate that four urban vibrancy metrics are all higher in the special economic zone (SEZ) and lower in non-SEZs but with different degrees of spatial aggregation. The influences of employment and road density on all vibrancy metrics are significant and positive. However, the effects of metro stations, land use mix, building footprints, and distance to district center depend on the vibrancy indicator and location. These findings unravel the commonalities and differences in urban vibrancy metrics derived from multisource urban big data and the hidden spatial dynamics of the influences of associated factors. They further suggest that urban policies should be proposed to foster vibrancy in Shenzhen therefore benefit social wellbeing and urban development in the long term. They also provide valuable insights into the reliability of urban big data-driven urban studies.

1. Introduction The concept of urban vibrancy dates back to 1961, when Jacobs (1961) first described it in The Death and Life of Great American Cities as follows: “liveliness and variety attract more liveliness; deadness and monotony repel life”. Jane Jacobs further emphasized that safer and more vibrant streets were those attracting many people engaging in activities (either commercial or residential activities). In general, urban vibrancy describes the attraction, diversity, and prosperity of urban places resulting from human activities and interactions (Chhetri, Stimson, & Western, 2006; Montgomery, 1998). Vibrant urban spaces

support diverse human activities and facilitate social communication and interactions, thereby benefiting long-term sustainable development (Sulis, Manley, Zhong, & Batty, 2018; Walks, 2011). In particular, vibrancy improves people’s subjective feelings of urban spaces, which is crucial for the well-being of urbanites (Pinquart & Sörensen, 2000; Xu, Belyi, Bojic, & Ratti, 2017). Hence, understanding urban vibrancy is necessary for urban governors and urban planners. Urban vibrancy has attracted scholarly attention from multiple disciplines, such as urban planning, geographical information sciences (GIS), and social sciences. In brief, two main issues have been raised: portraying urban vibrancy and exploring its determinants (Wu, Ye, Ren,

⁎

Corresponding author at: Guangdong Key Laboratory of Urban Informatics, Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, and Research Institute of Smart Cities, Shenzhen University, Shenzhen 518060, China. ⁎⁎ Co-corresponding author. E-mail addresses: [email protected] (W. Tu), [email protected] (J. Xia). https://doi.org/10.1016/j.compenvurbsys.2019.101428 Received 11 May 2019; Received in revised form 22 October 2019; Accepted 23 October 2019 0198-9715/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Wei Tu, et al., Computers, Environment and Urban Systems, https://doi.org/10.1016/j.compenvurbsys.2019.101428

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well align with each other because of potential biases. For example, one park in the urban center may have sparse POIs but high-density geotagged social media contents. The park will be indicated to have different degrees of vibrancy. Accordingly, multisource urban big data will confuse the understanding of urban vibrancy and mislead the following policy-making. Multisource urban big data raise the first question: do multisource urban big data reveal the same spatial pattern of urban vibrancy? Furthermore, when exploring spatial dynamics of the influences on urban vibrancy, the second question appears: if spatial distributions of the vibrancy are not well matched, are the effects of determinants on different sourced urban vibrancies consistent? These two questions highlight the necessity of a comprehensive and comparative study to investigate urban vibrancy using multisource urban big data. Different urban vibrancy metrics should be developed and carefully compared to uncover their commonality and differences. The effects of associated demographic, socioeconomic, and built environmental factors on the urban vibrancy metrics need to be further distinguished. Previous studies relied on one single source urban data to portray urban vibrancy (Yue et al., 2017; Wu, Ye et al., 2018, Wu, Ta et al., 2018); therefore, they lack a multifaceted view. This study presents a comprehensive and comparative framework to portray urban vibrancy and uncover spatial dynamics of the effects on urban vibrancy. Three simple urban vibrancy metrics were derived from multisource urban big data, including POIs, social media check-ins, and mobile phone records. Moreover, a comprehensive urban vibrancy index was formed by adaptively weighting these metrics. The effects of related demographic, socioeconomic, and built environmental factors were revealed with both ordinary least squares (OLS) and geographical weighted autoregressive regression (GWAR) models. Spatial dynamics of the influences of significant associated factors were mapped and compared to examine the consistency of the results. The empirical study in Shenzhen, China, suggests that different urban vibrancy metrics globally agree with each other but differ in several local areas. Social media check-in-based urban vibrancy is more aggregated than those derived from POIs and mobile phone records. Local regression analysis with GWAR illustrates that employment and road density have significant and positive effects on vibrancy. The effects of metro stations, land use mix, building footprints, and distance to district center, however, depend on the urban vibrancy metrics and location. These results also bring critical insights for policy-making to foster urban vibrancy and benefit the involved urban planning and urban governance communities. The remainder of this article is organized as follows: Section 2 reviews the related literature of urban vibrancy. Section 3 introduces the study area and datasets used. Section 4 describes the urban vibrancy measures, independent variables, and global and local regression models. Section 5 reports and analyzes the results. Section 6 discusses the obtained results and the implications. The last section concludes this study and provides outlooks for future work.

& Du, 2018; Wu, Ta, Song, Lin, & Chai, 2018; Ye, Li, & Liu, 2018). Field survey is an effective method for capturing urban vibrancy as such surveys directly investigate human activities, interactions, and living experiences (Azmi & Karim, 2012). However, field surveys are extremely costly and time-consuming and of limited sample size; therefore, it is not easy to cover a wide area. With the increase in available urban datasets, points-of-interest (Joosten & Nes, 2005), housing prices (Nicodemus, 2013), and land use are regarded as proxies for human activities and interactions from a long-term view; therefore, they have been successfully adopted to represent urban vibrancy indirectly. Another research stream is devoted to uncovering the determinants of vibrancy. A set of models including multivariate regression and binomial regression has been developed. Previous studies have reported the close relationship between human activities and demographic and socioeconomic factors, i.e., population, employment, and income (Xu, Belyi, Bojic, & Ratti, 2018; Yue, Zhuang, Yeh, Xie, & Ma, 2017). The built environment (e.g., land use, buildings, and transportation networks) is also widely accepted as having significant effects on vibrancy (Azmi & Karim, 2012; Chhetri et al., 2006). These studies provide valuable implications to cultivating urban vibrancy. For example, small street blocks and good street façades would support more diverse human activities and thus breed vibrant streets and neighborhoods. However, these studies ignore spatial heterogeneity and omit spatial dynamics of the influences on urban vibrancy (Wu, Ye et al., 2018; Wu, Ta et al., 2018). In the era of big data, multisource urban big data are emerging, such as mobile phone data (Jiang, Li, Tu, Shaw, & Yue, 2019; Sulis et al., 2018; Tu, Hu et al., 2018, 2017), global positioning system (GPS) trajectories (Zhou, Wang, Li, Yue, & Tu, 2017), and social media data (Jendryke, Balz, McClure, & Liao, 2017; Kim, Kojima, & Ogawa, 2016; Liu et al., 2017; Martí, Serrano-Estrada, & Nolasco-Cirugeda, 2018; Luo et al., 2016). These new datasets contain rich human activities and interaction information in the city (Shaw, Tsou, & Ye, 2016; Xu et al., 2018; Yuan, 2018). In particular, mobile phone data and social media data may highly penetrate the total population; therefore, from a social sensing perspective, they are able to capture massive human activities, rhythms, and preferences (Liu et al., 2015). These meaningful urban big data enable us to portray invisible landscapes of urban space (Ratti, Frenchman, Pulselli, & Williams, 2006). They also bring valuable insights for quantifying vibrancy. Recently, De Nadai et al. (2016) derived a vibrancy metric from the density of mobile phone internet records and explored the vibrancy of six Italian cities. Using massive mobile phone data, Yue et al. (2017) quantified the vibrancy in Shenzhen based on the density of mobile phone users and revealed the effect of mixed land use. Wu, Ye et al. (2018) took social media check-in data as the proxies of urban vibrancy and explored the spatio-temporal distribution characteristics of vibrancy and the spatio-temporal relationships with the influential factors. These advanced studies further deepen the understanding of urban vibrancy. Multisource urban big data are able to generate multifaceted images of vibrancy (Zhu, Khan, Kats, Bamne, & Sobolevsky, 2018; Tu, Cao, Yue, Zhou, & Li, 2018; Zhang, Xu, Tu, & Ratti, 2018; Huang et al., 2019). For example, POIs generally reflect long-term human activities as they are the places where human activities happen. Social media check-ins are recognized as the weighted POIs by considering the preference of locations and social media users. Previous studies have demonstrated that most social media check-ins are produced by young users at popular destinations (Longley, Adnan, & Lansley, 2015; Martí et al., 2018). Mobile phone data generated by most urban residents capture daily human activities with high temporal resolution. However, although urban big data provide unprecedented opportunities for urban studies, they are also accompanied by potential biases in penetrated population, spatial coverage, or sampling methods, etc., which may cause misunderstandings by urban researchers and urban planners (Jiang et al., 2019; Zhao et al., 2019). The vibrancies derived from multisource urban big data might not

2. Literature review 2.1. Urban vibrancy and urban big data analytics From the micro perspective, Jane Jacobs (1961) first introduced the concept of vibrancy as safer and more vibrant streets and neighborhoods that are able to attract many people engaging in commercial or residential activities. Jane emphasized that vibrant urban spaces would encourage interactions between people. Following this line, Montgomery (1998) noted that a lively urban area should be an open space breeding high-density human activities. Chhetri et al. (2006) further suggested that urban vibrancy is the external manifestation of the interactions between urban residents and their surrounding entities. Above all, although the concept of vibrancy is always evolving, human activities and interactions denote the kernel and thus stimulate many urban vibrancy studies. 2

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Quantifying vibrancy has been of great interest to urban studies. Traditional surveying and interviewing approaches question many urban residents to examine urban vibrancy. For instance, Filion and Hammond (2003) measured neighborhood vibrancy by interviewing residents on how they utilize their surrounding environments in Waterloo. Zarin, Niroomandm, and Heidari (2015) invited urban dwellers in Tehran to score community welfare, public health, and public green space to characterize vibrancy with geographic information system (GIS) tools. Although these methods provide detailed and reliable human activity information that includes gender, age, work, activity type, and duration, they are costly and cover several streets or neighborhoods in a city (Azmi & Karim, 2012; Wu, Ta et al., 2018). Diverse urban datasets have been recognized as reasonable proxies of vibrancy, including POIs (Joosten & Nes, 2005), housing prices (Nicodemus, 2013), and land use. For example, Walks (2011) investigated the changes of six neighborhoods in New York and revealed the thriving and declines using a field survey and POI. These urban datasets are the consequences of long-term human activities and are thus accepted to represent vibrancy indirectly. Recently, alternative urban big data, such as mobile phone data, GPS trajectories, smart-card data, and social media data, provide significant advantages that enable us to capture the dynamic interactions between residents and urban space (Shaw et al., 2016). Compared with traditional field surveys and urban datasets, they offer several advantages for urban vibrancy: (1) high penetration, (2) wide spatial coverage, and (3) rich information on human activities, such as the location, time, and semantic contexts. These valuable big data have inspired new approaches to measuring vibrancy. For example, JacobsCrisioni, Rietveld, Koomen, and Tranos (2014) used the density of mobile phone users as a proxy for human activities and examined the effect of urban form on vibrancy in Amsterdam. De Nadai et al. (2016) derived an urban vibrancy metric from long-term mobile phone internet records and explored the vibrancy of six Italian cities. These advanced studies further widen the road to portraying urban vibrancy. In addition, data on social media, such as Twitter and Weibo, have also been employed to derive urban vibrancy measures (Hasan, Zhan, & Ukkusuri, 2013). For example, Wu, Ye et al. (2018) took hourly social media check-in data as the urban vibrancy indicator and first explored the spatio-temporal distribution characteristics of vibrancy and complex relationships with the influential factors. These new urban datasets are able to produce citywide images of vibrancy but raise new questions: considering different characteristics, do multisource urban big data portray different landscapes of urban vibrancy? Do they share the same spatial patterns? Using POIs, social media check-ins, and mobile phone records, we provide the first answers to these questions by taking Shenzhen as a case study.

Built environment, like land use, roads, metros, buildings, and location, also influence urban vibrancy (De Nadai et al., 2016; Sulis et al., 2018; Ye et al., 2018; Zhou et al., 2019). Mixed land use, referring to a combination of residential, commercial, cultural, institutional, or industrial uses, significantly affects human activities and the indicated urban vibrancy. Many studies have also found that mixed land use will cultivate urban vibrancy (Jacobs, 1961, 1969; Rodenburg, Vreeker, & Nijkamp, 2003; Chris et al., 2014; Tranos & Nijkamp, 2015). However, several studies that contradict these findings have also been reported. For example, Yue et al. (2017) and Wu, Ye et al. (2018) found that mixed land use has a negative effect on neighborhood vibrancy in highdensity megacities. Local built environments, such as roads, metro stations, buildings, and public facilities, significantly affect urban vibrancy (Wu et al., 2016). For instance, Mehta (2007) demonstrated that weather, public space, walking lanes, shadows, street connectivity, pedestrian flow, density and landscapes have significant influences on street vibrancy. Li et al. (2016) reported that increasing road density will encourage human travel and fixed activities, thus cultivating vibrancy in the future. Ye et al. (2018) reported that building typology and density are associated with significant positive effects on urban vitality as indicated by small business caterings. Recently, several studies have been conducted to explore the vibrancy in Shenzhen. Yue et al. (2017) adopted mobile phone positioning records as the proxy for vibrancy and revealed the influences of the population, employment, and mixed land use at the neighborhood level. Wu, Ye et al. (2018) quantified urban vibrancy with hourly social media check-ins and revealed important influences of POIs, road density, and neighborhood locations using geographically and temporally weighted regression. These studies have provided one portrait of the vibrancy in Shenzhen using one single dataset. However, they have emphasized the influences of different factors and used different zoning methods. Hence, they leave gaps for future comprehensive studies in the era of big data. The urban vibrancy measured by multisource urban big data and the spatial dynamics of the determinants of urban vibrancy should be carefully cross-validated to distinguish their similarities and differences, which will produce a comprehensive understanding of urban vibrancy by taking advantage of big data. We used three representative types of alternative urban big data, POIs, social media check-ins, and mobile phone records, to portray the vibrancy in Shenzhen and to compare the influences of the associated demographic, socioeconomic, and built environmental factors. This study fills the gaps left by previous studies and deepens the understanding of urban vibrancy in high-density megacities.

2.2. Urban vibrancy and demographic, socioeconomic, and built environment

This study focuses on Shenzhen, China, a megacity located on the east side of the Pearl River Delta, north of Hong Kong. Since its founding in 1979, when China implemented its policy of reform and opening up, Shenzhen has grown from a coastal village to a megacity with a population of 15 million, covering an area of approximately 1996 km2. Shenzhen has ten administrative districts (Fig. 1). The four inner-city districts in the south, Nanshan, Futian, Luohu, and Yantian, constitute the early special economic zone (SEZ) with high-technology enterprises, business and financial districts, universities, government centers, and railway stations. The remaining six districts on the outskirts in the north and east are non-SEZs and contain many factories, parks, water bodies, and nature reserves. Notably, the SEZ expanded to the northern six districts in 2010, and the development of Shenzhen has entered a new phase. The built-up area of Shenzhen increased from 61.6 km2 in 1990 to 661 km2 in 2012. This fast urbanization process has also introduced great challenges to the even and sustainable development. For example, many neighborhoods with urban villages and old industrial parcels lost their attractiveness because of their crumbling infrastructures and the low quality of living experiences. This

3. Study area and dataset

Revealing associated factors of urban vibrancy has attracted much attention for formulating policies to foster vibrancy. The literature suggests that urban vibrancy has a close relationship with demographic, socioeconomic and built environmental factors (Walks, 2011; Yue et al., 2017; Wu, Ta et al., 2018). As human beings perform their daily activities, such as housing, work, shopping, and sports, demographic and socioeconomic factors are believed to fundamentally influence urban vibrancy (Walks, 2011; Zarin et al., 2015; Li, Wang, Wang, & Wu, 2016; Wu, Ye et al., 2018; Ye et al., 2018). In general, high-density urban residents will produce high vibrancy in streets or neighborhoods. Highincome urban residents also perform more daily activities; therefore they contribute more to vibrancy. Aggregated human characteristics including gender, age, race, education, and income have been evident with significant effects on human activities and consequent vibrancy (Axhausen, Zimmermann, Schönfelder, Rindsfüser, & Haupt, 2002; Malizia & Motoyama, 2016). 3

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Fig. 1. Study area: Shenzhen, China.

and to compare their spatial patterns and the influences of their determinants. Complementary data sources include demographic vector data (e.g., population, employment, and income) and data on land use, building footprints, road networks, and metro stations. Data for the first two were provided by the urban planning bureau of Shenzhen. The road network and metro station data were crawled from Open Street Map (OSM) at the end of 2014. It is believed that the data quality of the OSM for major cities in China is good, and this dataset has been used to measure the built environment (Ye et al., 2018; Zeng et al., 2017). These auxiliary datasets were employed to calculate the independent variables. Following Yue et al. (2017) and Wu, Ta et al. (2018), we leveraged traffic analysis zones (TAZs) as the basic unit to explore the spatial variation of urban vibrancy in Shenzhen. A TAZ is a type of basic geographic unit that shares similar demographic and socioeconomic characteristics, and it has been widely accepted in urban studies and transportation research (Rodrigue, 2017). In total, there are 491 TAZs in Shenzhen, as displayed in Fig. 2. The TAZs are produced by the Transport Committee of Shenzhen Municipality for travel surveys. The areas of the TAZs vary from 0.5 to 46.2 km2. Using multisource urban big data, we portray spatial variations of urban vibrancy and cross-validate spatial dynamics of the effects of demographic, socioeconomic, and built environmental factors.

situation emphasizes the necessity of portraying urban vibrancy and examining the spatial dynamics of its determinants for policy-making that will foster vibrancy in the future. We used the three urban datasets below to measure neighborhood vibrancy:

• The POI dataset. The POI data used were crawled from the Dianping

•

•

website (one of largest online-to-offline living service companies in China) and the AMap online web map service (one of China’s largest online map service providers) in 2015. It contains many categories, such as residential communities, enterprises, factories, shopping facilities, restaurants, service facilities, schools, and recreation areas. Duplicate POIs were deleted. Ultimately, 168,775 POIs remained. We acknowledge that this POI dataset does not account for all activity places of urban residents. POIs have proven useful in portraying urban vitality in studies (Gibson et al., 2012; Yuan et al., 2013). Social media check-ins. The check-in data were crawled from Sina Weibo (the largest microblogging website in China) in 2014. Sina Weibo had over 431 million monthly active users in 2018. Following the approach of Longley et al. (2015), data filters were applied to alleviate biases in the social media check-in data: (1) users with more than 1000 records in a year were not used as they may belong to machine accounts; and (2) users with less than three records in a year were omitted as they may be tourists with highly personal preferences. Ultimately, 798,789 check-in records were used to examine neighborhood vibrancy in Shenzhen, China. Mobile phone positioning records. This dataset was provided by a major mobile communication service company in Shenzhen and includes a sequential positioning record of 9.8 million mobile phone users (approximately 65.3% of the total population, 15.4 million) on a workday in March 2012. The spatial coverage of this mobile phone dataset was generated by 5846 cell towers, and following the approach of Tu et al. (2017), cell tower coverage is defined by Voronoi tessellation.

4. Methodology We present a data-driven comparative framework to investigate urban vibrancy and explore the effects of demographic, socioeconomic, and built environmental factors. As shown in Fig. 3, the presented framework contains four steps: (1) measuring the vibrancy using POIs, social media check-ins, and mobile phone positioning records, (2) calculating the potential association factors, (3) modeling the relationship with global and local regression, and (4) evaluating the significantly associated factors and mapping the spatial dynamics of their effects on urban vibrancy. The details of these steps are described below.

We acknowledge that the time periods of the three datasets are different. However, previous studies have suggested the stability of human activities behind massive mobile phone data (Song, Qu, Blumm, & Barabási, 2010). It is reasonable to infer that human activities in Shenzhen did not change much from 2012 to 2015. On the other hand, Shenzhen experienced rapid urban expansion from 1978 to 2010; after 2010, the urbanization in Shenzhen entered a relative stable stage (Fei & Zhao, 2019). Thus, in the short interval from 2012 to 2015, the urban vibrancy derived from POIs and social media check-ins are also stable. Above all, it is acceptable to derive urban vibrancy using these datasets

4.1. Urban vibrancy indicators Using the aforementioned three urban datasets, three urban vibrancy metrics and a comprehensive vibrancy index are derived as outlined in the following. 4.1.1. Density of POI POIs have been widely used as proxies for neighborhood vibrancy because human activities happen at POIs, such as residential buildings, 4

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Fig. 2. Traffic analysis zones in Shenzhen.

POIs. To some degree, social media check-ins can be viewed as weighted POIs according to the popularity of POIs. Considering preferences, check-ins at POIs on social network websites are recognized as another reasonable proxy for vibrancy (Wu, Ye et al., 2018; Ye et al., 2018). We calculated the check-in density in a TAZ, dci, and normalized it to vci, to quantify the degree of vibrancy.

factories, schools, shops, and parks (Gao, Janowicz, & Couclelis, 2017; Zhang et al., 2019). Here, we denote the vibrancy of a TAZ as the POI density, dpoi. To compare with other vibrancy metrics, we normalize the POI derived vibrancy to, vpoi, in the range [0, 1] with Min-Max normalization, as estimated by Eq. (1), where v denotes the vibrancy derived from POIs, dpoi, in a TAZ; dmax and dmin are the maximum and the minimum of the POI density, respectively.

vpoi

v − d min = d max − dmin

4.1.3. Density of mobile phone records Mobile phone data are another effective proxy for human activities and interactions because of the high penetration and easy-to-carry characteristics of mobile phones. Following the approach of Ye et al. (2018), the density of mobile phone positioning records at a TAZ, dmpr, is summed to reflect the vibrancy. Considering different spatial layouts of cell tower service areas and TAZs, the records in a cell tower area across multiple TAZs are assigned according to overlap areas, as

(1)

4.1.2. Density of social media check-ins Human activities at places may be uneven because of the different preferences of individuals and the characteristics (i.e., type, size) of

Fig. 3. The data-driven comparative framework for examining neighborhood vibrancy. 5

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and the ground space index (GSI) (Berghauser Pont & Haupt, 2009; Ye et al., 2018), are calculated. The FSI denotes the building density of a TAZ, as estimated by Eq. (6), while the GSI represents the relationship between the built area and the nonbuilt area, as estimated by Eq. (7), where Fi, Bi, and Ai are the gross floor area, gross building footprint, and gross area, respectively.

estimated by Eq. (2), where i and j denote a cell tower and a TAZ, respectively, Dj is the mobile phone record count of cell tower i, Dij is the estimated mobile phone records generated by cell tower i at TAZ j, Si and Sj represent the cell tower area and the TAZ, respectively, and Si ∩ Sj is the overlapping area. We also normalized dmpr to vmpr to obtain the comparable vibrancy

Dij = Dj

Si ∩ Sj Sj

(2)

FSIi = Fi/ Ai

(6)

GSIi = Bi / Ai

(7)

Independent variables are listed in Appendix Table 1(in Supplementary material). Multicollinearity (Wheeler & Tiefelsdorf, 2005) occurs when two or more independent variables are moderately or highly correlated, which can result in an incorrect interpretation of a regression analysis. We employed the variance inflation factor to detect any possible multicollinearity of the independent variables. Finally, lower intercorrelated independent variables are selected for future global and local regression analysis.

4.1.4. Comprehensive vibrancy index The single source urban data provide one facet of the measure of the vibrancy; therefore, it may be biased at some places. Integrating multisource urban datasets to map vibrancy is a promising approach (Sulies et al., 2018; Zhou et al., 2019). To obtain a comprehensive vibrancy illustration, we integrated the above three vibrancy metrics with the Entropy-weight approach (Zou, Yun, & Sun, 2006), which is an objective weighting method considering the distribution of values. The Shannon entropy of one type of vibrancy, Hk, is measured as Eq. (3), where vki is the vibrancy in a TAZ, i; n is the total count of TAZs.

4.3. Global and local regression models

n

Hk = −∑ vki ln (vki ) 1

Both global regression and local regression are employed to investigate the associated factors of urban vibrancy. The global regression is conducted by the OLS regression model, which feasibly discloses the relationship between the dependent variables and independent variables (Nachtsheim and Chris, 2004). The OLS formula is given by Eq. (8), where y denotes the dependent variable, x j is the jth independent variable, βj is the corresponding estimated coefficient, and ε denotes the residual

(3)

The comprehensive urban vibrancy index, vc, is calculated as Eq. (4), where vk is one type of vibrancy.

vc =

∑k ={poi,ci, mpr }

Hk vk ∑ Hk

(4)

m

4.2. Associated factors

y = β0 +

∑ βj xj + ε j=1

It has been reported that the three factor categories, i.e., demographic, socioeconomic, and built environment, have significant influences on urban vibrancy (Walks, 2011; Zarin et al., 2015; Li et al., 2016; Yue et al., 2017; Wu, Ye et al., 2018; Wu, Ta et al., 2018; Ye et al., 2018). After having examined the related literature, we select common studied factors as independent variables. In term of demographics and socioeconomics, we consider the population density, the employment density, the highly educated population density, and the average annual income in a TAZ. Regarding built environment, we take land use, transportation, and buildings into account. Land use mix measures the diversity of land use in an area. The influences of mixed land use on vibrancy have long been observed (Jacobs-Crisioni et al., 2014). There are several metrics to measure land use, such as Richness, the Shannon entropy, and the Simpson diversity index (Song, Merlin, & Rodriguez, 2013; Yue et al., 2017). In this study, the Shannon entropy is calculated to represent the land use mix, as estimated by Eq. (9), where pi is the area of the ith type of land use, including residential, commercial, industrial, governmental, or open areas.

Four OLS models are built to untangle the association between urban vibrancy and its determinants. The dependent variables are the four metrics of vibrancy, while the independent variables are the potential factors described above. Geographic variables often exhibit spatial autocorrelation, which affects the accuracy and uncertainty of the parameter estimates in regression models. Recent advances in spatial statistics provide several effective approaches to remedy the problem by including spatial autocorrelation in the classic OLS model, i.e., autoregressive operators (Griffith 1996; Lichstein, Simons, Shriner, & Franzreb, 2002; Gollini, Lu, Charlton, Brunsdon, & Harris, 2015), Eigenvector spatial filter (Anselin, 1988; Chun & Griffith, 2011; Griffith, 2000; Helbich & Griffith, 2016; Griffith & Chun, 2019), interpoint distance matrix eigenfunctions, etc. In particular, autoregressive operators take the influence of neighbors into consideration, thus alleviating the effect of spatial auto-correlation in the OLS. Generally, the spatial autoregressive model (SAM) is defined by Eq. (9), where y is a spatial variable, W is the spatial weight matrix, ρ is the estimated coefficient, and ε is random noise.

c

D = −∑ pi ln (pi ) 1

(8)

m

(5)

y = ρWy + β0 +

∑ βj xj + ε j=1

Flexible transportation will encourage human travel and activities. The road density and the road cross density in a TAZ are calculated to represent the spatial coverage of the transportation network. The road density is obtained by dividing the road length by the TAZ area. The road cross density is equal to the number of road crosses per square kilometer. Taking transportation supply into account, the bus station density and the metro station density are also selected as independent variables. TAZ location is also important for attracting human activities and interactions (Wu, Ye et al., 2018). The distances to the central business district (CBD) at Futian and to the district center are calculated. Using the building dataset, two popular measures, the floor space index (FSI)

(9)

Nonstationarity is another common phenomenon in geographic distribution (Fotheringham, Charlton, & Brunsdon, 1998). In other words, the relationship among geographical variables changes with location. The OLS model neglects the spatial impacts of variables; therefore, it cannot reveal the spatial dynamics of the relationship between spatial variables (Demšar, Fotheringham, & Charlton, 2008; Nakaya, Fotheringham, Charlton, & Brunsdon, 2009). In the spatial effects hypothesis, the GWR model is used to explore the spatial heterogeneity underlying the spatial effects. Therefore, we combine the GWR and autoregressive regression as the geographic weighted autoregressive (GWAR) model to investigate the influences on urban 6

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Table 1 Description of global regression and local regression models. Name

Model

Dependent variables

Independent variables

M1 M2

OLS OLS

M3

OLS

Demographic and socioeconomic: the population, employment, the highly educated population, and the average annual income Built environment: the Shannon entropy of land use, road density, road cross density, bus station density, metro station density, floor space index, ground space index, distance to CBD, and distance to district center

M4

OLS

POI derived vibrancy, vpoi Social media check-ins derived vibrancy, vpoi Mobile phone records derived vibrancy, vmpr Comprehensive vibrancy index, vc

M5 M6

GWAR GWAR

M7

GWAR

Demographic and socioeconomic: the population, employment, the highly educated population, and the average annual income. Built environment: the Shannon entropy of land use, road density, road cross density, bus station density, metro station density, floor space index, ground space index, distance to CBD, and distance to district center

M8

GWAR

POI derived vibrancy, vpoi Social media check-ins derived vibrancy, vpoi Mobile phone records derived vibrancy, vmpr Comprehensive vibrancy index, vc

indicated as having a median level of vibrancy. Finally, Fig. 4d illustrates that the comprehensive vibrancy index comprising three metrics shows a similar pattern, especially in the original non-SEZ areas. Although there are different characteristics of multisource urban big data, those derived urban vibrancy metrics match each other globally. The results further demonstrate the polycentric structure of vibrant neighborhoods, which is generally in line with the urban growth of Shenzhen. Several local vibrancy centers are perceived in the SEZ and in western Shenzhen. For instance, Futian-Luohu has the highest vibrancy, and Nanshan has the second highest vibrancy. Moran’s I test (Moran, 1950) was used to evaluate the spatial heterogeneity of vibrancy; Table 2 reports the results. Spatial autocorrelation can be observed for all z-values above 20, which corroborates the significance of the spatial patterns of neighborhood vibrancy in Fig. 4. This finding verifies the general consistency derived from the vibrancy metrics using multi-source data. However, there are also differences among these vibrancy metrics. For example, social media check-in-derived vibrancies are the most spatially aggregated in the urban center, while the vibrancies indicated by mobile phone records are slightly scattered. Both POIs and mobile phone records suggest additional local vibrancy centers at Baoan and Longhua, which cannot be observed from social media check-ins. These results suggest the necessity of this comparative study using multi-source urban data. The correlations among the four vibrancy metrics are illustrated in Fig. 5. As proxies for human activities and interactions, these four indicators are highly correlated. All single-source data-derived vibrancies are highly correlated with the comprehensive index, with Pearson correlation coefficients exceeding 0.92. Other neighborhood vibrancy pairs also have median Pearson correlation coefficients greater than 0.8. These correlations again suggest that vibrancy indicators share similarities. Taken together, both the spatial distributions of the derived vibrancy and their correlations indicate invisible commonalities and differences of the suggested vibrancy in a megacity. These findings emphasize the quantification of urban vibrancy using multisource urban big data and call for a regression analysis to explore the effects of associated factors for future policy-making.

vibrancy. GWAR takes the location (u) into account, as estimated by Eq. (10), where ρ(u) is the coefficient of the dependent variable and βk (u ) is the coefficient of the independent variable x ik at location u

yi = ρ (u) Wy + β0 (u) +

m

∑k =1 βk (u) xik + ε (u)

i = 1, 2, …, n

(10)

GWR can be viewed as a locally weighted least squares regression model where the weights associate pairs of geographic data (Lu, Wang, Ge, & Harris, 2018; Mcmillen, 2002). The weights are generally modeled by fixed or adaptive kernel functions, such as the distance threshold, the inverse distance weights, the Gaussian kernel function, and the bisquare function. In this study, the Gaussian kernel function is used to model geographic weights. The best kernel bandwidth is set according to the corrected Akaike information criterion (AIC). Similar to global regression, we built four GWAR models with all urban vibrancy metrics. The details of the OLS and GWAR models are presented in Table 1. GWModel R Package (Lu, Harris, Charlton, & Brunsdon, 2014) was employed to conduct the global and local regression analysis. 4.4. Significant associated factors evaluation The OLS and GWAR model results are summarized to investigate the significant associated factors of urban vibrancy. Common determinants of all vibrancy metrics are identified. Factors influencing some urban vibrancy indicators are also examined. Considering spatial heterogeneity, the effects on urban vibrancy are mapped. Spatial dynamics of the influences are compared and discussed. 5. Results and analysis 5.1. Spatial patterns of urban vibrancy Spatial patterns of urban vibrancy in Shenzhen, as indicated by three urban big data sets, are displayed in Fig. 4. All of the patterns demonstrate that most neighborhoods with high vibrancy are located in the SEZ areas in southern Shenzhen. In terms of POIs, Fig. 4a shows that neighborhoods in the southern Luohu District have the highest vibrancy. 18 TAZs covering 16.9 km2 have vibrancies exceeding 0.25. Futian and Nanshan also contain 22 and 14 TAZs, respectively, with vibrancies exceeding 0.25. Only a few highly vibrant TAZs are scattered in the non-SEZs to the west, north, and east. Regarding social media check-ins, Fig. 4b demonstrates that the most vibrant TAZs are also located in Luohu. Highly vibrant TAZs appear at Futian and Nanshan. However, the remaining non-SEZ areas, especially TAZs in southwestern Shenzhen, are indicated as having a low level of vibrancy, which is quite different from those indicated by the POIs. Regarding mobile phone records, the most vibrant TAZs are shown to be in the Luohu District. The distribution of vibrancy is similar to those derived from the POIs; however, several TAZs in the Longgang District are

5.2. Results of the global regression models The OLS models (Models 1–4) were estimated to explore the global relationship between neighborhood vibrancy and demographic, socioeconomic and built environment. The results are reported in Table 3. Employment, the land use mix, road density, metro station density, building footprints (GSI), and distance to district center are significantly associated with neighborhood vibrancy in Shenzhen. All of them are significant at the 0.05 confidence level. The AIC and AICc suggest a good association between neighborhood vibrancy and the aforementioned six factors. In general, the reported factors explain 69.7%, 49.3%, 65.5%, and 65.8% of the variations in POI-based (Model 7

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Fig. 4. Spatial distribution of neighborhood vibrancy in Shenzhen. (a) POIs. (b) Social media check-ins. (c) Mobile phone records. (d) Comprehensive vibrancy index. Blank TAZs have a zero density.

influences of the land use mix and building footprints and therefore highlight the necessity of local regression analysis.

1), check-in-based (Model 2), mobile phone record-based (Model 3), and comprehensive (Model 4) vibrancy metrics, respectively. Although the multisource urban big data-based vibrancy indicators exhibit differences (as shown in Fig. 4), the global regression results suggest that employment, road density, and metro station are always significantly associated with derived vibrancy. This finding agrees with previous studies (Ye et al., 2018, 2018). For example, Jacobs-Crisioni et al. (2014) reported a significant association between mobile phone data-based vibrancy and metro stations in Amsterdam, the Netherlands. Moreover, these factors have positive influences on fostering neighborhood vibrancy. In general, improving employment, roads, and the metro system will attract people to perform more activities and interactions, thus stimulating vibrancy (Fig. 6). The remaining three factors, including land use mix, building footprints, and distance to district center has influences on some vibrancy metrics. Both land use mix and building footprints have important effects on POI-based neighborhood vibrancy. However, land use mix is not significantly associated with check-in-based vibrancy, in contrast to the other three metrics. This finding partially implies the potential biases of social media check-ins (Longley et al., 2015). Building footprints are also significantly associated with check-in-based vibrancy, which is consistent with the result of Ye et al. (2018). Distances to district center are highly correlated with urban vibrancy, except for that derived from POIs. These results demonstrate that the associations between neighborhood vibrancy and related factors differentiate when using different urban big data as proxies for neighborhood vibrancy. The corresponding coefficients further suggest that both land use mix and building footprints are negatively associated with the corresponding neighborhood vibrancy in Shenzhen, which is not consistent with common knowledge (Jacobs, 1961). These findings imply complex

5.3. Results of the local regression models Compared to OLS models, GWAR deals with issues of spatial autocorrelation and nonstationarity and therefore further reveals the spatial dynamics of influences on urban vibrancy. Table 4 reports the estimated coefficients of the independent variables and fitting degree by the GWAR models. The adjusted R2 suggests that 76.6%, 63.7%, 75.0%, and 75.7% of the variations in POI-based, check-in-based, mobile phone record-based, and comprehensive vibrancies, respectively, can be explained by the associated factors. By including the geographical weighted component and the auto-regressive operator, these results are 6.9%, 14.6%, 9.5%, and 9.9.4% higher than those of these corresponding OLS models. The AICc values in the four GWAR models are lower than those of the above OLS models. These outcomes verify the better explanatory power of the GWAR models. The coefficient of the spatial lag term indicates the influence of near neighborhoods. The results of the four GWAR models demonstrate that a vibrant neighborhood will have a positive impact on its neighbors. That is due to the fact that a vibrant neighborhood will attract more human activities and interaction, which will bring more people to near neighborhoods. From a long-term perspective, it will increase the vibrancy of these neighborhoods. Fig. 7 further displays the spatial distribution of the influences of neighborhood vibrancy. The influences of the POI-based, social media check-in-based, and comprehensive vibrancy show a south-north pattern, while mobile phone record-based vibrancy exhibits a sandwich pattern, with the highest impact in the center and the lowest impact in the east and the west. This may be due to spatial differences in the indicated vibrancy metrics as shown in

Table 2 Moran’s I test of urban vibrancy. Vibrancy metrics

Global Moran’s I index

z-value

p-value

POI derived vibrancy, vpoi Social media check-ins derived vibrancy, vci Mobile phone records derived vibrancy, vmpr Comprehensive vibrancy index, vc

0.48 0.50 0.45 0.44

22.10 23.47 21.05 22.50

0.001 0.001 0.001 0.001

8

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Fig. 5. Cross-correlations of the vibrancy metrics.

Fig. 1(in Supplementary material)). Increasing jobs in these neighborhoods will attract more POIs to be built in the future and more people to perform daily activities there. However, the influences of employment on mobile phone record-based vibrancy display a different south-north “sandwich” pattern: There are higher effects in southern and northern Shenzhen but lower effects in central Shenzhen. This result apparently indicates that increasing employment will result in a smaller improvement in neighborhood vibrancy in central Shenzhen. It may result from the slightly scattered distribution of mobile phone record-based neighborhood vibrancy, as shown in Fig. 4c. Evidently, an increase in road density will promote neighborhood vibrancy in Shenzhen, regardless of vibrancy metrics, similar to those of employment in Fig. 7. This finding generally agrees with those reported by Wu, Ye et al. (2018); Tu, Hu et al., 2018 and Ye et al. (2018). In general, the transportation system provides flexible travel service, thus increasing opportunities for human activity and interactions (Rodrigue, 2017). Building more roads will improve the accessibility of shopping centers, parks, and public facilities, thus advancing human activities and interactions in the long term. These figures further demonstrate that the effects of road density have similar spatial dynamics. In general, the influence of road density is higher in southern Shenzhen but lower in northern Shenzhen. The highest influence appears in Luohu. One unit increase in road density will induce a 0.91% improvement in neighborhood vibrancy. On the other hand, these results imply that enough road infrastructures have been built for urban residents in northern Shenzhen. In fact, considering the population, northern Shenzhen has a higher density of roads (see Appendix Fig. 1(in Supplementary material)) (Fig. 8).

Fig. 4. The results describe spatial dynamics of the associated factors’ influences on neighborhood vibrancy. Although the mean of the coefficients is similar to that reported by the OLS models, the range of coefficients suggests that the amplitude of these influences varies from place to place throughout the entire city. In contrast to the OLS results, land use mix, metro station, building footprints, and distance to district center have both positive and negative effects on neighborhood vibrancy, depending on the location. Hence, GWAR models are necessary to reveal spatial dynamics of the effects of the associated factors. Employment is the only demographic factor significantly associated with the four vibrancy metrics in Shenzhen. Fig. 7 shows the spatial distribution of the coefficients of employment, demonstrating positive influences on vibrancy, regardless of the vibrancy proxy and location. The reason is that the population is a basal determinant of human activities and interactions. As Shenzhen has rapidly urbanized over the past forty years, many industrial parks with factories have been built in the SEZ and expanded to non-SEZs in the outskirt districts of the city (Wu, Ye et al., 2018). More than 50% of the population are workers who contribute the most activities and interactions in the city. Consequently, from the citywide view, employment, as opposed to population, has significant effects on urban vibrancy. The results further illustrate the spatial variations of the employment’s influences. The impact on both POI-based and social media check-in-based vibrancy metrics exhibits an east-west pattern: high influence in eastern Shenzhen and low influence in western Shenzhen. In fact, eastern Shenzhen (including Yantian, Pinshan, Dapeng, and eastern Longgang) have the lowest employment density (Appendix 9

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Table 3 The results for global regression. OLS models

Coefficient

t-statistic

Standard deviation

Semipartial correlation

AIC

AICc

R2

Adjusted R2

Model 1: POIs, vpoi Constant Employment Land use mix Road density Metro station density GSI

　 −0.021 0.048 −0.065 0.009 0.066 −0.112

　 　 13.26** −2.75* 11.62** 6.97** −1.90*

　 0.013 0.004 0.024 0.001 0.010 0.059

　 　 0.523 −0.126 0.474 0.307 0.088

−1031.9

−1031.7

0.700

0.697

Model 2: Social media check-ins, vci　 Constant −0.028 Employment 0.022 Road density 0.007 Metro station density 0.073 GSI −0.270

　 　 6.31** 9.09** 7.72** −4.98**

　 0.011 0.004 0.001 0.009 0.054

　 　 0.280 0.387 0.336 −0.225

−1038.3

−1038.2

0.498

0.493

Model 3: mobile phone records, vmpr　 Constant −0.006 Employment 0.037 Road density 0.005 Metro station density 0.049 Distance to district center −0.003

　 0.008 0.003 0.001 0.007 0.001

　

−1277.3

−1277.1

0.658

0.655

　 14.10** 7.59** 6.88** −3.67**

−1201.8

−1201.5

0.663

0.658

11.16** −2.23* 10.21** 8.23** −2.52* −2.09*

0.013 0.003 0.020 0.001 0.008 0.050 0.001

Model 4: comprehensive vibrancy index, vc Constant −0.003 Employment 0.034 Land use mix −0.044 Road density 0.007 Metro station density 0.065 GSI −0.125 Distance to district center −0.002

0.546 0.331 0.303 −0.167

0.460 −0.103 0.428 0.356 −0.116 −0.097

* significant at the 0.05 level, ** significant at the 0.001 level.

humans to travel and perform more daily activities and interactions, thus increasing the vibrancy in these neighborhoods. Shenzhen opened a metro line connecting western Shenzhen to the urban center at Futian and Luohu at the end of 2015, which fostered neighborhood vibrancy in these areas. The negative effects of metro stations on neighborhood vibrancy appear at Longhua (Fig. 9a and c) and Longgang (Fig. 9a only). These different effect patterns remind us to pay attention to new metro lines in non-SEZs. A new metro line may attract people in these

Fig. 9 shows the influences of metro stations. It demonstrates that the effects of metro stations on neighborhood vibrancy depend on the metrics and location. POI-based and mobile phone record-based vibrancies are positively or negatively affected by metro stations, while social media check-in-based and comprehensive vibrancy metrics are always positively associated. The highest positive effect of metro stations appears in western Shenzhen, which has a high-density population but does not have metro service. Building metro stations will encourage

Fig. 6. The coefficient of the spatial lag term. 10

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Table 4 The results for local regression. GWAR models

Min

Max

Mean

Standard deviation

AIC

AICc

R2

Adjusted R2

Model 5: POIs, vpoi Constant Spatial lag term Employment Land use mix Road density Metro station density

　 −0.1728 0.1676 0.0280 −0.0739 0.0031 −0.0092

　 −0.0093 0.6131 0.0485 0.0485 0.0090 0.0929

　 −0.0680 0.4057 0.0383 −0.0325 0.0068 0.0353

　 0.0496 0.1294 0.0045 0.0266 0.0019 0.0226

−1174.5

−1143.7

0.783

0.766

Model 6: Social media check-ins, vci　 Constant −0.0857 Spatial lag term 0.4413 Employment 0.0073 Road density 0.0011 Metro station density 0.0336 GSI −0.1693

　 −0.0449 0.6722 0.0124 0.0035 0.0395 −0.0535

　 0.0235 0.0490 0.0028 0.0011 0.0031 0.0487

−1209.6

−1191.7

0.652

0.637

−0.0105 0.7265 0.0183 0.0053 0.0445 0.0060

Model 7: mobile phone records, vmpr　　 Constant −0.1180 Spatial lag term 0.1858 Employment 0.0192 Road density 0.0006 Metro station density −0.0800 Distance to district center −0.0031

　 −0.0357 0.4051 0.0295 0.0035 0.0149 −0.001

　 0.0324 0.1190 0.0044 0.0014 0.0299 0.0010

−1449.7 　

−1412.2 　

0.771 　

0.750

−0.0063 0.6357 0.0393 0.0059 0.0541 0.0007

　 　 　

　 　 　

　 　 　

Model 8: comprehensive vibrancy, vc Constant −0.1260 Spatial lag term 0.2639 Employment 0.0183 Land use mix −0.0351 Road density 0.0018 Metro station density 0.0052

−1383.0

−1353.4

0.774

−0.0077 0.6289 0.0285 0.0167 0.0072 0.0533

−0.0504 0.4783 0.0241 −0.0177 0.0045 0.0339

0.0368 0.1252 0.0026 0.0110 0.0017 0.0111

0.757

Land use, building footprints, and distance to district center are associated with one or two types of vibrancy metrics. Land use has different influences on POI-based and comprehensive vibrancy, as shown in Fig. 7. The negative effects on POI-based neighborhood vibrancy almost cover the entire city except Shekou in southwestern Nanshan, where this effect is compromised by the impact of other factors. This finding is different from the common perception that fine-

neighborhoods to travel to the SEZs in the south. Consequently, a new metro line may reduce neighborhood vibrancy. As we observed differences in the influences on neighborhood vibrancy, this finding also verifies the necessity of a multifaceted view of the spatial dynamic of neighborhood vibrancy using multisource urban data. Although urban sensed data is considered big, a single source of urban data may produce biased results.

Fig. 7. The coefficient of employment on neighborhood vibrancy. 11

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Fig. 8. The coefficient of road density on neighborhood vibrancy.

Fig. 9. The coefficient of metro station on neighborhood vibrancy.

et al. (2014) pointed out, mixed and high-density land use will increase neighborhood vibrancy, and thus the land use configuration should be considered in combination with density to foster neighborhood vibrancy in mega-cities. Fig. 11 illustrates the effect of building footprints on social media check-in-based vibrancy. Similar to the influences in Fig. 10, positive effects of building footprints occur in southwestern Shenzhen, Nanshan, because of the spatial function configuration of Shenzhen. Nanshan is a high-technology and industrial district with fewer social media checkins. Although these areas already have high-density building footprints, more buildings can still attract additional human activities and

grained land functional space will generate sufficient interactions and activities to improve vibrancy (Jacobs, 1961; Montgomery, 1998). This discrepancy possibly results from two aspects: (1) The influence of employment masks that of land use. Yue et al. (2017) also reported that neighborhood vibrancy is “primarily a function of the socioeconomic characteristics and secondarily a function of the land use configuration”. (2) The land use layout in Shenzhen is polarized. In general, those highly vibrant neighborhoods in the urban center and several subcenters have low degrees of mixed land use, while the neighborhoods with low level of vibrancy at the urban borders have a higher land use mix (see Appendix Fig. 1(in Supplementary material)). As Jacobs-Crisioni 12

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Fig. 10. The coefficient of the land use mix on POI-based vibrancy.

measure directly. Quantifying city-wide vibrancy has been limited by available urban datasets. Using three sources of urban big data, the citywide vibrancy in Shenzhen was portrayed: neighborhoods with high vibrancy in the south, neighborhoods with lower vibrancy in the north, and a polycentric structure of urban vibrancy. These spatial dynamics of vibrancy are in line with the urban growth process of Shenzhen since its foundation in 1979. They also indicate the uneven distribution of urban vibrancy; therefore, future policies need to breed vibrancy, especially in north Shenzhen, which will benefit well-being in this city. More vibrant urban neighborhoods will improve the living experience of low-income people in north Shenzhen. Moreover, vibrant streets will attract more human activities and interactions, which will stimulate the prosperity of restaurants in the long term. Furthermore, the identified effects suggest the following approaches:

interactions, thus increasing neighborhood vibrancy. For the remaining neighborhoods, increasing building footprints in these areas will not induce a natural improvement in vibrancy. For example, for the highly developed urban center of Shenzhen, building more man-made infrastructure to attract human activities and interactions in these districts is not easy. In eastern Shenzhen, more buildings will not improve vibrancy in these neighborhoods. The influences of distance to district center on mobile phone-based vibrancy are illustrated in Fig. 12. It shows that the location of neighborhoods has both positive and negative effects on vibrancy. The locations of most neighborhoods exhibit a negative effect on vibrancy, which indicates that neighborhoods far from the district center naturally have low vibrancy, consistent with a geographical decay function. However, a few neighborhoods at the borders of Shenzhen show a positive impact, which suggests the limitation of this decay effect. Above all, the results reveal that the influences on vibrancy can be differentiated by the type of urban big data used and location. Furthermore, the spatial distribution of the influences on neighborhood vibrancy also change according to the derived vibrancy metric. This finding validates the multifaceted view of the spatial dynamics of the effects on urban vibrancy using multisource urban data. These findings provide useful insights for policy-making to foster urban vibrancy and future big data-driven urban studies.

(1) Employment. The effects on all vibrancy metrics in Fig. 6 demonstrate that increasing employment will improve all vibrancy metrics in Shenzhen. This finding inspires us to recognize that although Shenzhen is transforming from a manufacturing city to an innovative and high-technology city, attracting more workers in the city is still useful for fostering vibrancy, whether in SEZs or nonSEZs. (2) Transportation. The influences of roads and metro stations suggest that building more roads and metros will improve accessibility and thus breed vibrancy in the future, except in part of north Shenzhen, as shown in Fig. 9c. Accessibility is an important issue for attracting human activities and interactions in a neighborhood. High-density roads will provide flexible travel and activity opportunities for urban residents. Moreover, a wide-coverage metro system

6. Discussion 6.1. Policies for fostering urban vibrancy Urban vibrancy is a relatively subjective concept that is difficult to

Fig. 11. The coefficient of the building footprint area on check-in-based vibrancy. 13

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Fig. 12. The coefficient of distance to district center on mobile phone-based vibrancy.

integrated to produce a comprehensive understanding of urban space. The results in Section 5.3 further reveal that some associated factors’ effects on urban vibrancy depend on the vibrancy metric and location. Thus, policy-making to foster urban vibrancy is complex. When making urban policies based on the results of one single source of urban big data, urban governors should be aware of potential conflicts. For example, Fig. 10 suggests building footprints have both positive and negative effects on vibrancy in some TAZs along the border between Futian and Nanshan district. The relationships verified by all urban big data sources are more reliable than those for which these sources conflict.

facilitates travels in the city, thereby cultivating vibrancy. (3) Buildings. Fig. 11 indicates that increasing building footprints may be an effective approach to breed vibrancy in southwestern Shenzhen. However, this conclusion is not supported by social media check-in-based vibrancy and mobile phone records-based vibrancy. We should be careful in increasing the total building footprint to avoid threatening the current natural ecosystem. Although mixed land use has been reported to foster urban vibrancy, this effect is not guaranteed because of the complex urban spatial structures in Shenzhen. The POI-based vibrancy and comprehensive vibrancy metric in most TAZs are negatively influenced by mixed land use. Thus, from the city-wide view, increasing the mix of land use would not naturally improve vibrancy in Shenzhen.

7. Conclusion Fostering urban vibrancy not only satisfies diverse daily human demands but also encourages social communication and interactions in public urban spaces. Quantifying vibrancy and revealing associated factors will provide useful insights into breeding urban vibrancy. Previous studies used one sourced urban dataset (i.e., POIs, social media data) to measure urban vibrancy, lacking the multifaceted view. From the social sensing perspective, this study developed a big datadriven comprehensive and comparative framework to portray urban vibrancy and its association with demographic, socioeconomic, and built environment. Multisource urban big data were employed to derive urban vibrancy metrics. Global regression and local regression models were built to cross-validate the associated factors of urban vibrancy and the spatial dynamics of their effects. The similarities and differences of different vibrancy metrics and the influences of their determinants were examined to produce multifaceted portraits of urban dynamics. The results suggest that neighborhood vibrancy in Shenzhen exhibits a SEZ and non-SEZ pattern: higher vibrancy in the SEZ at the south and lower vibrancy in the non-SEZs at the north; the polycentric structure is observed from the spatial distribution of four vibrancy metrics. These spatial patterns are subject to the urban datasets used and which is in line with the urban growth of Shenzhen. Differences between neighborhood vibrancy do indeed exist; social media check-inbased vibrancy is the most spatially aggregated. The profiling results revealed that employment and road density have significant and positive effects on the four vibrancy metrics. The influences of employment display an east-west pattern based on POIs and a sandwich pattern based on mobile phone records. Mapping the effect of road density demonstrates a south-north pattern: a higher effect in the north and a lower effect in the south. However, land use mix, metro station density, the GSI, and distance to district center do not

6.2. Multisource big data and urban studies Emerging urban sensed data (i.e., social media data, mobile phone records, GPS trajectories, etc.) enable us to sense urban dynamics with large sample sizes, high penetration, and rich semantics (Liu et al., 2015; Shaw et al., 2016; Yuan, 2018; Zhang et al., 2018). In particular, multisource urban big data provides multi-faceted views on urban space. This study employed POIs, social media check-ins, and mobile phone records to produce multiple portraits of urban vibrancy and further cross-validated the spatial dynamics of their associated factors to deepen the understanding of urban dynamics. However, every coin has two sides. Although urban big data provides useful approaches to sensing urban space, the multiple illustrations it provides may confuse researchers and urban planners (Zhang et al., 2018; Zhu et al., 2018). This empirical study in Shenzhen demonstrates that different urban vibrancy metrics do not completely match each other because of their different generation and collection processes. This divergence reminds us of the need to consider the reliability of alternative urban big data sources when conducting big data-driven urban studies. In general, urban big data are generated and collected without guidelines or regulations; therefore they may be subject to biases, such as uneven population penetration (Martí et al., 2018), different sampling methods (Jiang et al., 2019), or partial spatial coverage (McKenzie, Janowicz, Gao, & Gong, 2015). Accordingly, one single source of urban big data may reveal inaccurate spatial dynamics of one urban concept, i.e., urban vibrancy, especially in low-density urban areas. This highlights the need to be aware of the inherent characteristics of urban big data when using them to investigate urban dynamics. In the future, multi-source urban big data should be 14

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influence all proxies for neighborhood vibrancy. The influences of the land use mix and building footprints depend on the derived neighborhood vibrancy and location. The models with POIs and mobile phone records indicate that metro stations have both positive and negative influences on neighborhood vibrancy, while the model with social media check-ins suggests only positive effects. These findings provide a comprehensive understanding of urban vibrancy using a multisource urban dataset, thereby verifying the reliability of urban big data in sensing urban space. This study makes the following contributions: (1) Urban vibrancy is measured using multisource urban big data from the social sensing perspective, resulting in a comprehensive and multi-facet portrait of citywide urban vibrancy. (2) A comparative framework is developed to cross-validate the spatial dynamics of urban vibrancy and associated demographic, socioeconomic, and built environmental factors. Their commonalities and differences are identified. The proposed comparative framework has significant potential for evaluating other urban concepts, such as urban resilience and urban innovation. (3) The effects of associated factors on urban vibrancy can be used to evaluate the vitality of neighborhoods and identify appropriate design interventions in an evidence-based manner. This study still leaves room for future explorations. First, urban vibrancy survey data have not been included because of the lack of available data. We plan to collect field survey datasets on urban vibrancy in typical neighborhoods in the future and to include them in the comprehensive framework presented. Second, the modifiable areal unit problem (MAUP) (Liu et al., 2015; Openshaw, 1983) is an important issue in geostatistical analysis. However, it is not investigated in this article because the demographic and socioeconomic data provided do not support multiscale analysis. Combining future urban surveys, we will investigate changes in the spatial patterns of urban vibrancy and the influences of their determinants on different spatial scales.

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