Article How Leisure Venues Are and Why? A Geospatial Perspective in Wuhan, Central China
Yaolin Liu 1,2,3,†, Ying Jing 1,*,†, Enxiang Cai 1, Jiaxing Cui 4, Yang Zhang 1 and Yiyun Chen 1,2,3,*
1 School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China; [email protected] (Y.L.); [email protected] (E.C.); [email protected] (Y.Z.) 2 Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China 3 Collaborative Innovation Center for Geospatial Information Technology, Wuhan University, 129 Luoyu Road, Wuhan 430079, China 4 School of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China; [email protected] * Correspondence: [email protected] (Y.J.); [email protected] (Y.C.); Tel.: +86-159-2737-0373 (Y.J.); +86-15171509047 (Y.C.) † These authors contributed equally to this work.
Received: 9 August 2017; Accepted: 12 October 2017; Published: 17 October 2017
Abstract: Urban leisure venues proffer spatial carriers for citizens’ leisure activities and their functions rely heavily on the spatial configuration, which have largely been ignored. The increasing needs for healthy leisure life and the availability of geospatial open data provide a rising opportunity to fill this gap. To examine the spatial distribution of leisure venues and explore its underlying dominating factors, we adopt geospatial analysis techniques—point pattern analysis and cluster analysis with multi-sources geospatial data in Wuhan, Central China. Results conclude interesting spatial discrepancy based on a three-level clustered pattern of 86520 leisure venues. We find that (1) most clusters are in urban center along the Yangtze River with all 1st-level clusters and plentiful 2nd-level and 3rd-level clusters; (2) There are just sporadic clusters in suburban areas—no 1st-level, merely one 2nd-level and some few 3rd-level ones. Moreover, we demonstrate three underlying dominating factors (i.e., the policy, population and economy) and identify that (1) No systematic (spatial-relevant) leisure policy framework is formed; (2) Population density and commercial centers have positive correlation with the distribution of leisure venues. This study contributes to spatial-relevant leisure policy-making for facilitating healthy leisure life, optimizing leisure space, guiding people-oriented urbanization transition and promoting urban competence.
Keywords: spatial analysis; leisure; urban policy; Wuhan; Central China
1. Introduction With social progress and the arrival of the leisure era, people set about pursuing cultural and psychological contentment [1] for a better quality of life. This indicates the imperative transition of urban governance (i.e., attaching more significance on people-oriented aspect) concerning both China as well as other regions of the world. In terms of a flourishing leisure industry, leisure activities could satisfy these rising needs for citizens [2,3]. In addition, the capacity of leisure venues symbolizes the state of the art for regional development, namely, represents the level of socio-civilization and suggests economic, humanistic and cultural vitality. The boom of leisure industry is promising to bring out big benefits for increasing urban dweller’s well-being and happiness [4,5], rising inhabitants’ quality of life [6], enhancing urban cohesion [7], uplifting urban vitality [8] and promoting social justice through facilitating positive social changes [9–11].
Sustainability 2017, 9, 1865; doi:10.3390/su9101865 www.mdpi.com/journal/sustainability Sustainability 2017, 9, 1865 2 of 20
Given that urban leisure venues proffer spatial carriers for citizens’ leisure activities, leisure is actually a spatial science. Many insightful leisure studies have been done (e.g., on leisure time [12], leisure justice [13,14] or leisure activities [15,16]), however they are normally quantitative researches based on socio-economic statistics data, the spatial aspect is not concerned. Though spatial analysis techniques are commonly applied to a wide range of fields (including land resources and wealth [17], supermarkets and food outlets [18–20], hospitals [21], etc.), few scholars get to spatially analyze leisure activities (e.g., karaoke [22], clubs [23], tourism [24–30], green land [31–33], etc.), the spatiality of leisure issues is still not given sufficient attention. This results in ignoring potential spatial solutions for urban (leisure) issues. This would hinder leisure industry management and relevant policy-making in the light of ignoring to characterize spatiality of leisure activities. In this sense, healthy leisure life on a basis of corresponding urban design gets to become the target of city residents and policy makers. The rapid advance in urban geospatial big/open data (i.e., POIs, social media data, people flow data, etc.) provides us with big opportunities for integrating spatial perspective in leisure studies for facilitating urban spatial development planning mature based on geographical analysis techniques. The increasing attention has been aroused of employing these data for a wide range of urban issues from spatial perspective, involving urban vitality, urban regeneration, social equity and justice, social cohesion and so on. In fact, urban form and urban functions can be visualized in the new data environment, namely, geospatial big/open data in the context of information and communications technology (ICT) development [34]. Visualizing spatial distribution through geospatial data lays a solid foundation for other succeeding spatial analysis. This promisingly contributes to people- oriented policy-making integrating spatial components. Leisure research in China is in the initial stage. Despite of rapid urbanization, China is undergoing the process of ‘New Type Urbanization’. That is, urbanization is shifting from ‘quantity- oriented mode’ with a focus on the construction scale to ‘quality-oriented mode’ with a focal of upgrading intra-city functions and valuing people-oriented principle in China [35]. Leisure industry is manifesting its impact on urban transformation through uplifting citizens’ quality of life [36], health and well-being [37–40], and social cohesion [37]. This corresponds to people-oriented ‘New Type Urbanization’ strategies [41]. Cities are normally enriched in leisure resources. Thus, we aim to demonstrate systematic spatiality of all leisure venues within cities. In this study, the metropolis of Wuhan, Central China is selected to exemplify its leisure venue spatial distribution pattern, as well as the underlying dominating factors through in-depth spatial analysis techniques.
2. Methodology
2.1. Case Study Wuhan, the capital of Hubei Province, lies in the middle reaches of the Yangtze River of Central China and is located in the northeast of Jianghan Plain between 113°41′–115°05′E and 29°58′–31°22′N. Wuhan plays a strategically pivotal role locating in Central China along one of the biggest economic zones—the Yangtze River Economic Zone, with around 10.02 million permanent inhabitants. Besides, Wuhan acts as the traffic hub of nine provinces due to its geographical location and advanced transportation networks. There are 13 districts in the city of Wuhan with a total area of 8494 km2, seven of which make up central urban area with a total area of 678 km2 and the other six districts are suburban areas. Wuhan is also named ‘Three Townships of Wuhan’. This actually refers to the central urban area of Wuhan, consisting of three townships—Hankou Township (i.e., Jiang’an district, Jianghan district and Qiaokou district), Great Wuchang Township (i.e., Hongshan district, Wuchang district and Qingshan district) and Great Hanyang Township (i.e., Hanyang district) around the confluence of the Yangtze River and the Han River. As a typical polycentric city, each township has its own specialty: Great Hankou Township functions as the economic and financial center; Great Wuchang Township plays the role of political, educational and cultural center; and Great Hanyang Township is the industrial center. In Figure 1, the extent of central urban area within the red line Sustainability 2017, 9, 1865 3 of 20
(seven districts) and its suburban areas (6 districts) within the purple line are included with a planning area over 3000 km2, according to the General Urban Plan of Wuhan City (2010–2020).
Figure 1. Case study.
2.2. Data This research mainly involves two types of data sources—geospatial data and statistical data. For geospatial data, we apply the following data: (1) the boundary of the study area, boundaries of districts (administrative division of a city), the boundary of urban central area, geographical features (e.g., the Yangtze River, the Han River, the East Lake, etc.) have been obtained from the First National General Survey of Geographical Conditions during 2013–2015; (2) POIs of leisure venues (illustrated in Table 1) from Baidu Map (similar to Google map) in 2017. For statistical data, population statistic data is acquired from the Six Demographic Census (2010). Furthermore, preliminary data pre- processing has been done. This process refers to leisure venues POIs selection, duplicated data removing and density calculation.
Table 1. Classification of leisure service facilities.
Categories Specific Venues Tourism holiday resorts, urban scenic spots, parks, hotels (above three stars) theatres, libraries, cultural palace, libraries, museum, art gallery, music Culture halls/odium, arboretum, aquariums, zoos, churches stadium, gymnasium, fitness center, fishing venue, golf course, table tennis hall, Sports tennis court, basketball court, badminton court, football court, skating rink, billiards center, turf, angling venue dancing hall, night club, chess and poker club, internet bar, disco-bar, cinemas, the massage bar, public bathhouse, game arcade, amusement parks, shopping Recreation malls, shopping street, franchise store, supermarkets, SPA, barbershop, restaurants, bakery, pubs, cafes, teahouse Sustainability 2017, 9, 1865 4 of 20
In this study, we view leisure venues as whole, and define them as four categories—tourism, culture, sports and recreation. Based on data pre-processing, we select 86520 leisure venues from all POIs of Wuhan. The theoretical framework of this study is to explore leisure domain (with a specific focus on leisure venues) with an initial start on the distribution pattern and dominating factors, from geospatial lens (by spatial visualization and analysis methods), for better understanding the spatiality of leisure venues and facilitating spatially-oriented leisure policy-making, shown in Figure 2.
Spatially-oriented Leisure Policy-making
(e.g., leisure city construction and leisure optimization)
The Spatiality of Leisure Venues
Leisure Domain → Leisure Venues → Distribution Pattern & Dominating factors
Geospatial Lens → Spatial Methods → Spatial Visualization & Analysis Techniques
Figure 2. Theoretical Framework.
2.3. Methods In this research, a series of spatial statistics and analysis techniques are implemented. As one fundamental notion of spatial analysis, point pattern analysis is utilized in this study. It evaluates the pattern, or the distribution, of a set of points on a surface [42]. There are three general patterns: random, uniform and clustered. Cluster analysis seeks to aggregate objects into partitions for forming clusters [43]. Within one cluster, points relate each other more than those in other clusters [44]. Hierarchical clustering is a method of cluster analysis, attempting to create a hierarchy of clusters by grouping points into their specific clusters according to their distances [45]. Specifically, these geospatial analysis techniques in this study include average nearest neighbor (ANN), Ripley’s K function, spatial autocorrelation, spatial interpolation—kernel density estimation (KDE) and standard deviation ellipse (SDE).
2.3.1. Average Nearest Neighbor (ANN) As one basic agglomerative technique, Average Nearest Neighbor (ANN) produces nearest neighbor values according to the mean distance between one feature and its nearest neighbor [46]. Equation (1) depicts the distance from cluster X to cluster Y, namely, D(X, Y). ( , ) =min ( , ), ∈ , ∈ (1) Presuming there are N points in region A, the algorithm (ANN) performs the steps as below: i. Each point itself represents one cluster, namely, each cluster with one single point within. The N × N distance matrix D involves all distance d(i,j). Then figure out it. ii. Define a distance threshold, namely a certain value set manually or expected mean distance of all points given a random pattern.