2.1 Study area
Shenzhen is located in the Pearl River Delta region of southern China, close to Hong Kong. It was the first city to open up city as part of China’s reform policy and is one of the core cities in the Guangdong-Hong Kong-Macao Greater Bay Area. Statistics from 2017 show that Shenzhen has an administrative area of 1997.27 km2, including 975.5 km2 for construction, with a service population of 20 million people. It is the city with the smallest area and largest population among all metropolises in China.
Shenzhen was established as a city in 1980, starting from the Shenzhen Special Economic Zone (SEZ) which was around 327.5 km2, and commonly referred to as “the Inside Shenzhen”. The SEZ has carried out top-down urban construction as part of the overall urban planning, forming a sparse and orderly banded-cluster structure. Each cluster has mixed land-use and, street networks and is bordered by natural elements such as rivers and mountains. Meanwhile, regarding “the Outside Shenzhen” around SEZ, the remaining land, covering more than 1,600 km2, was built up following a dis-ordered bottom-up way, with rapid urban sprawl along external traffic corridors. Due to the lack of planning management early on, street networks and public services are relatively poorer than those in SEZ. As a result, two distinct urban forms have been shaped: clustered and sprawled. As shows in Figure 1, these two kind of urban forms provided two typical high-density built environment samples for this research.
2.2 Research design
Based on the comprehensive understanding of diversity, this research defined PAD as an integrated characteristic of PA's richness in terms of PA participants, PA type, and PA occurrence time. We hypothesized that, the built environment supports PAD by four progressive steps, as shows in Figure 2: accessibility for people to do PA, a utilitarian environment to improve the possibilities of PA, inclusiveness to support the co-action of various PAs, and landscape attractiveness to gather various PAs. At the same time, we hypothesized that the effects of the built environment to support PAD were different between clustered and sprawled high-density urban forms.
2.3 Data resources
2.3.1 PA data
In this study, the PA data were collected from Codoon, one of China's most popular self-tracking applications. With Codoon, people can track PA information (including the type of activity, time, speed, duration, routes, etc.), and upload, share, and compare their workouts on social network platforms, which forms a natural VGI data pool. Considering that PA mostly occurred in April and July, 4-day data from April and 4-day data from July 2015 were selected for this study, including two workdays and two weekends. Routes which started or finished in residential areas were chosen, to identify the social class of PA participants by housing price. There were 735 data, including 194 walking, 486 jogging, and 55 cycling data.
2.3.2 Built environment data
The built environment data included the following: the Shenzhen Land-Use Sur-vey (2014), in which land was classified to nine types (residential land, commercial land, government, and institutional land, industrial land, warehouse land, street land, infrastructure land, parklands, and other lands), street network from the Open Street Map, including five types (motorway, primary, secondary, branch, and others); bus stop data from Baidu Map Point of Interest (2012); greenway networks from the Shenzhen Greenway Map (2013); sports facilities and scenic spots from the Scott Map (2016); and house prices from Home Link, which is one of the most popular real estate transaction platforms in China.
2.4 Measurements
2.4.1 Measurements Unit
In order to measure the characteristics of PA diversity and the impact of the built environment on it, a 500m2 grid [31] was used to present the characteristics of the PA diversity and to explore the influence of the built environment on it [34, 35]. By using the Fishnet Tool in ArcGIS, land in Shenzhen was divided into grids, and those without PA were excluded, which left 2049 grids.
2.4.2 Dependent variable
Based on the integrative definition, PAD was measured as the sum of PA participant diversity (PAPD), PA type diversity (PATD), and PA occurrence time diversity (PAOD). An entropy index was used as a calculation tool for diversity [36], which varied between 0 and 1 (0 for maximum specialization, 1 for maximum diversification). PAPD was defined as the mixture of participants of lower-income, low-income, middle-income, and high-income classes [37]; PATD was the richness of different activities including walking, jogging, and cycling; PAOD was used to measure the diverse occurrence times from morning to night. The measures of PAD are shown in Table 1.
Table 1. Measures of PAD.
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)
2.4.3 Independent variables
According to the hypothesis, built environment were measured from four aspects: accessibility, utility, inclusiveness and landscape attractiveness. Accessibility contained street network density [15-17, 25], bus stop station density [12], and service facility density [13, 14], which can represent the accessibility of potential destinations in a city. Utility contained population density [22, 38, 39], the proportion of commercial land [40], and the land use mixture [24-26, 41], which can directly reflect the utility of a built environment. Inclusiveness contained greenway network density [23] and sport facility density [41], which were built to support diverse PAs. Landscape attractiveness contained green land ratio [29] and scenic spot density [4, 42]. Indicators and measures are shown in Table 2.
Table 2. The Variables and measures of built environment.
Character
|
Variable
|
Measure
|
Accessibility
|
Street network density
|
The total length of street per unit area
|
Bus station density
|
Number of bus stops per unit area
|
Service facility density
|
Number of service facilities per unit area
|
Utility
|
Population density
|
The ratio of residential building area to total building area per unit area
|
The proportion of commercial land
|
The ratio of commercial land to total land per unit area
|
Land use mixture
|
An entropy index was used to calculate the land-use mixture, which varied between 0 and 1 (0 for maximum specialization, 1 for maximum diversification)
|
Inclusiveness
|
Greenway network density
|
Length of greenway network per unit area
|
Sport facility density
|
Number of sport facilities per unit area
|
Landscape attractiveness
|
Green space ratio
|
The proportion of green space per unit area
|
Scenic spot density
|
Number of scenic spots per unit area
|
2.4.4 Dependent variable
Considering that PA is affected by seasons and holidays, two control variables were selected: air temperature (1 for 28 ℃ and below, 0 for above 28℃), and weekend or not (1 for weekend, 0 for weekday) [43]. PA data obtained from the Internet did not contain individual attributes, so personal factors are not discussed in this paper.
2.5 Data merging and statistical analysis
The PA and built environment data were overlaid in ArcGIS 10 with the Shenzhen local coordinate system to form the database. SPSS was used for statistical and regression analyses to establish multivariate linear regression models to explore the association between PAD and the built environment. Three models were built up for overall, clustered and sprawled urban area separately (Table 3). Collinearity diagnostics among the independent and control variables were performed before modeling, and the result showed no strong associations among the variables (VIF <10).
Table 3. Regression models and the hypothesized effect.
|
Model 1
Overall
|
Model 2
Clustered area
|
Model 3
Sprawled area
|
Dependent variables
|
PAD
|
Regression models
|
Multivariate linear regression model
|
Number of units
|
2049
|
Hypothesized effect
|
|
Independent variables
|
Accessibility
|
Street network density
|
Positive
|
Positive
|
Positive
|
Bus station density
|
Positive
|
Positive
|
Positive
|
Service facility density
|
Positive
|
Positive
|
Positive
|
Utility
|
Population density
|
Positive
|
Positive
|
Positive
|
The proportion of commercial land
|
Positive
|
Positive
|
Positive
|
Land use mixture
|
Positive
|
Positive
|
Positive
|
Inclusiveness
|
Greenway network density
|
Positive
|
Positive
|
Positive
|
Sports facility density
|
Positive
|
Positive
|
Positive
|
Landscape attractiveness
|
Green space ratio
|
Positive
|
Positive
|
Positive
|
Scenic spot density
|
Positive
|
Positive
|
Positive
|
Control variables
|
Temprature≤28
|
Positive
|
Positive
|
Positive
|
Weekend
|
Positive
|
Positive
|
Positive
|