Ekoloji 28(107): 995-1006 (2019) Spatial Accessibility of Urban Green Space Based on Multiple Research Scales: A Case Study of Futian District, Shenzhen Quanyi Zheng 1, Xiaolong Zhao 1*, Mengxiao Jin 2 1 School of Architecture, Harbin Institute of Technology; Key Laboratory Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, CHINA 2 School of Environment, Harbin Institute of Technology, CHINA * Corresponding author: [email protected] Abstract Urban green space is an indispensable public infrastructure for residents. In China, traditional green space planning overemphasizes quantitative indicators and ignores spatial layout. The spatial accessibility assessment of green space makes up for the traditional deficiencies. Traditional accessibility studies used single data and a single method. It fails to fully consider the actual needs of residents, which results in calculation errors. Few scholars used different precision data to evaluate the same green space from multiple scales. Based on multi-source data, this paper uses the big data visualization and 2SFCA improved model to evaluate the accessibility of forty-four UGSs in Futian District of Shenzhen from the street and building scales. The results show that the calculation results of the two methods are similar; 60% of the streets in Futian District have good accessibility, but the internal differences are obvious; the research scale and data accuracy affect the calculation results significantly; the calculation results can describe the service capabilities and scope of UGSs. The results are expected to help planners improve the spatial layout of UGSs and optimize the allocation of UPGS resources. Keywords: accessibility, network car data, 2SFCA improve model, multi-scale, data accuracy, supply and demand perspective Zheng Q, Zhao X, Jin M (2019) Spatial Accessibility of Urban Green Space Based on Multiple Research Scales: A Case Study of Futian District, Shenzhen. Ekoloji 28(107): 995-1006. INTRODUCTION quantitative characteristics of UGSs, reflecting the Green Infrastructure (GI) is an indispensable public relationship between the total green spaces and the infrastructure for cities and an important tool for population (Sun et al. 2012). Due to the failure to fully achieving sustainable urban development (Gu et al. consider the actual needs of the residents, the spatial 2017). GI includes public green space, parks, layout of UGSs was not taken seriously. Accessibility is allotments, green corridors, street trees, urban forests, an important factor in UGS planning, and it is an roofs, and vertical greening (Cameron et al. 2012). important indicator for evaluating the comprehensive Urban Green Space (UGS) is an important type of GI, strength of cities. It is usually represented by the level of which are a key element of sustainable urban planning services of the UGSs’ spatial distribution (Wendel et al. and can bring a variety of well-being to urban life 2011). The spatial accessibility of UGSs is balanced (Viniece et al. 2016). UGS can also improve social between supply and demand when the population and equity by providing and maintaining public spaces and the spatial distribution of UGSs are coordinated (Tan natural environment for social activities (Wolch et al. and Samsudin 2017). Residents can conveniently and 2014). In this paper, UGSs are defined as public park equally access the resources and services of UGSs is an green spaces and other green spaces, which are managed important part of urban planning and management. by the government for free for residents. In China, Different understandings and expressions bring traditional green space planning over-emphasizes about a variety of accessibility assessment methods. quantitative indicators such as the number of green Nicholls (2001) considered the spatial location of the spaces per capita. It is regarded as a planning basis by UGS and used the buffer method to calculate the urban planners and policy makers (Kong and Nakagoshi accessibility; Yin et al. (2008) included population 2006). However, the indicator can only evaluate the © Foundation Environmental Protection & Research-FEPR Received: 18 Mar 2018 / Accepted: 2 Nov 2018 Zheng et al. distribution factors and used the proximity distance in the research phenomenon. In addition, the method to calculate green space accessibility. Oh and appropriate data is specific to the problem, and the same Jeong (2007) used network analysis to calculate park scale is not the same for different application purposes accessibility in Seoul. Zhou and Guo (2003) used the (Li and Zhuang 2002). Therefore, for a certain purpose gravity model to calculate the accessibility of UGSs of research, there is a suitable geographic data scale; for considering the actual needs of residents. Luo and Wang a given data in the rasterization process, there is also a (2003) studied the accessibility of public facilities in suitable grid size (Li and Zhou 2003). Chicago with the two-step floating catchment area Considering the data requirements, model (2SFCA) method that considered supply and demand. construction complexity and computational cost of Kong et al. (2017) evaluated the accessibility of urban different accessibility calculation methods, many public facilities by calculating the pick-up and drop-off scholars (Ling and Zeng 2014, Rosa 2014, Zhang et al. points in the public facility buffer from the taxi 2011) use a single method to evaluate urban green space trajectory data. Different approaches describe different accessibility. However, due to the single-precision data aspects of accessibility, each of which has advantages and a single spatial scale, the study does not consider the and disadvantages, but none of them can cover all effects of scale effects and data accuracy on the results, information about accessibility of UGSs (Wang et al. which results in the fact that the results cannot 2013). objectively describe the actual accessibility. The gap in Spatial scale have a significant impact on accessibility current research and literature calls for quantitative studies (Yin et al. 2008), which is often expressed in assessment of the spatial accessibility of UGSs in the terms of gram or granularity (Qi and Wu 1996). Spatial same study area from multiple scales and multiple granularity refers to the feature length, area or volume precision data. Different precision data and methods are represented by the smallest identifiable unit. In many selected for different research scales to investigate the research fields, the research results will be changed after existing UGSs layout and service status of the city. The the research entities are divided by units with the same findings of this study are expected to help decision granularity and different shapes. This phenomenon is makers and planners rationally and scientifically called the scale effect. It refers to the phenomenon that optimize and allocate UGSs. Our specific objectives the analysis result also changes when the spatial data is were to: (1) calculate the percentage of pick-up and aggregated to change its amplitude, granularity and drop-off pionts from the network car data in the UGSs’ direction (Sun et al. 2007). In ecology, Qi and Wu buffer zone of Futian District in Shenzhen. The spatial (1996) study the impact of scale changes on the use of accessibility of UGSs at street scale is assessed by spatial autocorrelation methods to analyze landscape rasterizing and visualizing the results of the calculations; patterns. It shows that the spatial autocorrelation (2) and use the improved 2SFCA model, which is coefficient is sensitive to changes in area units. At introduced an impedance function and time impedance, different scales, the degree of autocorrelation of a and combine multi-source data of Futian District in variable in the same landscape is quite different. In the Shenzhen, such as POI (point of interest), road field of remote sensing, Su and Li (2001) believe that network, electronic map, building and statistical the pixel size affects image features. It is found that the yearbook, to calculate the spatial accessibility of UGSs local variance of the image varies with its spatial at building scale. resolution, and when the pixel size is close to the size of the target individual in the scene, the local variance STUDY AREA AND DATA reaches the peak. Some scholars have conducted related Study Area research in the fields of hydrology and soil science Shenzhen is located in the southern part of (Skop 2013, Wang et al. 2003). Guangdong Province, China. It is south to Hong Kong and west to Macau across the sea, and it is one of the In addition, data accuracy has an impact on most representative high-density cities. Futian District accessibility research (Yin et al. 2008). Li et al. found is located in the central city of Shenzhen. It contains ten that the data accuracy should not be too low or too high. streets with a total area of 78.8 km2 and about 1.5 million Too low precision cannot show the spatial distribution people. The per capita UGS is about 22.52 m2, which is rules of research phenomena that need to be displayed much higher than the 10-15 m2 recommended by the at a certain scale; Too high precision does not reflect World Health Organization (WHO). As shown in Fig. what should be displayed at a particular level, and 1, considering the influence of the boundary effect on excessive detail masks the law that should be expressed 996 Ekoloji 28(107): 995-1006 (2019) Spatial Accessibility of Urban Green Space Based on Multiple Research Scales: A Case Study of Futian … Fig. 1. Location of study area: Futian District in the city of Shenzhen, Guangdong Province, China the study area, the residential area and green space from the Gaode map platform. However, the POI has outside the boundary have a significant impact on the no area and volume, and it is necessary to link the area boundary, so the 2000 m buffer outside the study area data in the electronic map data with it.