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Assessment of Spatial Accessibility to Residential Care Facilities in 2020 in Guangzhou by Small-Scale Residential Community Data

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Assessment of Spatial Accessibility to Residential Care Facilities in 2020 in Guangzhou by Small-Scale Residential Community Data sustainability Article Assessment of Spatial Accessibility to Residential Care Facilities in 2020 in Guangzhou by Small-Scale Residential Community Data Danni Wang 1,2, Changjian Qiao 3, Sijie Liu 4, Chongyang Wang 2,5,* , Ji Yang 2,5, Yong Li 2,5 and Peng Huang 6 1 Department of Resources and the Urban Planning, Xin Hua College of Sun Yat-Sen University, Guangzhou 510520, China; [email protected] 2 Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China; [email protected] (J.Y.); [email protected] (Y.L.) 3 College of Resources and Environment, Academician Workstation for Urban-Rural Spatial Data Mining, Henan University of Economics and Law, Zhengzhou 450046, China; [email protected] 4 Land and Resources Technology Center of Guangdong Province, Guangzhou 510075, China; [email protected] 5 Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China 6 Shenzhen Municipal Planning and Natural Resources Bureau-Bao’an Management Bureau, Shenzhen 518101, China; [email protected] * Correspondence: [email protected]; Tel.: +86-188-0208-0904 Received: 27 February 2020; Accepted: 12 April 2020; Published: 15 April 2020 Abstract: Population aging has increasingly challenged socio-economic development worldwide, highlighting the significance of relevant research such as accessibility to residential care facilities (RCFs). However, a number of previous studies are carried out only on street (town)-to-district scales, which could cause errors of the accessibility to RCFs for a family. In order to improve the resolution to individual families, we measure and compare the accessibilities to RCFs based on 3494 residential communities and 169 streets of Guangzhou in 2020 through the two-step floating catchment area (2SFCA) method. It was found that the distributions of the elderly and the service-dense blobs of the RCFs show patterns of a three-level spatial distribution, with a characteristic clustering at the center with peripheral dispersion. The resultant accessibility to RCFs in Guangzhou, ranging from 2.5 to 3.45, is generally consistent with the studies focusing on street scales. However, the maximum difference in the accessibility of two residential communities on the same street ranges from less than 0.02 to 0.94 in Guangzhou, indicating large variations. Although the relative errors of the accessibility results based on bi-scale data are relatively low, the cumulative errors can be high, e.g., over 25% in many streets of large cities. Consequently, hundreds of elderly persons per street can be adversely affected by those errors, with six streets over 1000. Therefore, this study focusing on the smaller-scale residential community data may provide more accurate reference to individual households. For the spatial allocation and optimal layout of Guangzhou and similar cities with population aging, we suggest maximizing RCFs in metropolises by taking full advantage of existing residential care facilities with necessary restructuring, improvements, and expansions on service capability. While for less connected cities, we encourage building new RCFs in situ. Keywords: spatial accessibility; residential care facilities; residential communities; population forecast; Guangzhou Sustainability 2020, 12, 3169; doi:10.3390/su12083169 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 3169 2 of 23 1. Introduction Population aging has become an increasingly prominent phenomenon in the world [1]. It brings significant challenges and impacts on many fronts of sustainable development [2,3]. Since the 1990s, many cities of countries have entered an aging society, and the number is continuously increasing. For the third-largest city in China, Guangzhou, reports in 2017 and 2018 from the government showed that the elderly population (aged 60 or older) size is growing at a rate of 5% per year [4,5], with predictions of reaching 1.85 million by 2020, accounting for almost 20% of the total population. The spatial accessibility to public services, such as residential care facilities, hospitals, health services, markets, parks, sport facilities, schools, and banks, is a critical issue in the field of public health, reasonable urban planning and sustainable development [6,7]. It has a significant impact on social equity and justice, especially for an aging society [8]. Therefore, it could find that a number of studies have focused on measuring spatial accessibility to public services worldwide, including cities and other developing regions [9–11]. 2. Literature Review A variety of methods are commonly applied to measuring spatial accessibility, such as interviews, supply-to-demand ratio, distance to the closest facility, kernel density, gravity model and floating catchment area method [2,12–18]. Based on the Gaussian kernel density method, Guagliardo. (2004) gave an example of identifying the areas with a low ratio of primary care physicians to children in Washington [14]. Cheng et al. (2011) used in-depth semi-structured interviews to assess the well-being of elderly residents in residential care facilities (RCFs), and further analyzed the influence of its environment on the elderly activities and health [2]. Bauer et al. (2018) measured spatial general practitioners accessibility by a floating catchment area method and found that general practitioners' accessibility for 25.8% of the population in England was very low, while the mostly high general practitioners accessibility areas represented urban areas [12]. Yu et al. (2018) used a two-step floating catchment area to compare the spatial accessibility of health services within the current and planned network systems in Shenzhen [19]. It was found that the impacts on spatial accessibility of health services had spatial variations, and the most positively and negatively affected places were mainly in the center zone. Through an improved two-step floating catchment area method, Tao et al. (2014) analyzed the spatial accessibility to residential care resources in Beijing [20]. The results showed that the spatial accessibility was higher in the western, northern and northeastern parts of the city than the southern part. Besides, Tao et al. (2018) also measured healthcare accessibility in Shenzhen and found that disparity in transit-mode accessibility is the main reason for the imbalance of healthcare accessibility [17]. It should be noted that those methods of measuring spatial accessibility had both advantages and disadvantages when they were applied to different conditions [3,13,20,21]. In contrast, the floating catchment area may be the most popular and commonly-used method because it takes into account maximum influence factors of spatial accessibility, for instance, the size of supply-side and demand-side, distance and the distance limits [13,14,17,21–23]. In addition, many previous studies about spatial accessibility including the assessment of the accessibility to RCFs were mainly based on the street (town)-to-district scale [3,12,14,17,18,20,21,24–28], especially in cities of China. Streets (towns) are one of the administrative divisions smaller than a district in a city and the minimum statistical unit in Census in China. However, these studies might have in general overlooked the sub-structural differences in a street or town, which could cause errors of accessibility to RCFs for an individual family. For example, in the central city of Guangzhou, the maximum distances of two different families in the same street (town) are about 7.5 km in the Haizhu District, 4.8 km in the Tianhe District, 3.95 km in the Yuexiu District and 3.1 km in the Liwan District. Those distances can be larger for the main and surrounding cities. The interior difference indicates the possibility that one family has to spend significantly more time (20 min to one hour) to Sustainability 2020, 12, 3169 3 of 23 reach RCFs than another family in the same street. Obviously, specific information for a family to access RCFs based only on street scale data is absent. This study intends to measure and compare the difference of spatial accessibilities to RCFs on the residential communities and street scales, and provide more detail and precise information of the accessibility for families in Guangzhou. To achieve the goal, the spatial distribution characteristics of the elderly population in 2020 and RCFs in Guangzhou were analyzed firstly. Then, the accessibilities to RCFs based on residential community data and streets of Guangzhou were calculated and compared by the most commonly-used two-step floating catchment area (2SFCA) method. Finally, this study provides suggestions for the development of RCFs and urban planning in Guangzhou. 3. Dataset and Method 3.1. Study Area Located between longitudes 112.95◦–114.05◦ E and latitudes 22.43◦–23.93◦ N (Figure1), Guangzhou is the political, economic, scientific, educational and cultural center of Guangdong Province, China (http://www.gz.gov.cn/zlgz/index.html). Guangzhou includes 134 streets and 35 towns under the 11 districts (Table1). Table 1. Administrative division, residential community data, residential care facilities (RCFs) and its service intensity-beds, and the elderly population of Guangzhou. RCFs (beds < Streets Residential The Elderly Guangzhou District 100, 100–300, (Towns) Communities Population >300) Yuexiu 18 512 19 (10,8,1) 290,082 Tianhe 21 573 12 (9,1,2) 152,883 The central city Haizhu 18 618 26 (11,11,4) 297,629 Liwan 22 382 30 (11,14,5) 205,478 Baiyun 22 421 31 (4,16,11) 224,552 Huangpu 14 144 10 (2,2,6) 79,940 The main city Panyu 16 562 17 (4,9,4) 155,355 Nansha 9 39 10 (5,4,1) 76,060 The Huadu 10 115 13 (6,5,2) 127,853 surrounding Zengcheng 11 96 11 (3,7,1) 147,673 city Conghua 8 32 10 (9,1,0) 92,495 Total 11 169 3494 189 1,850,000 Note: The elderly population was forecasted for 2020 using a natural growth method (Tao et al., 2015) based on 2010, 2017 and 2018 data.
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