Abstract
The spatial allocation rationality of the service facilities of residential areas, which is affected by the scope of the population and the capacity of service facilities, is meaningful for harmonious urban development. The growth of the internet, especially Internet map and location-based service (LBS) data, provides micro-scale knowledge about residential areas. The purpose is to characterize the spatial allocation rationality of the service facilities of residential areas from Internet map and LBS data. An Internet map provides exact geographical data (e.g., points of interests (POI)) and stronger route planning analysis capability through an application programming interface (API) (e.g., route planning API). Meanwhile, LBS data collected from mobile equipment afford detailed population distribution values. Firstly, we defined the category system of service facilities and calculated the available service facilities capacity of residential areas (ASFC-RA) through a scrappy algorithm integrated with the modified cumulative opportunity measure model. Secondly, we used Thiessen polygon spatial subdivision to gain the population distribution capacity of residential areas (PDC-RA) from Tencent LBS data at the representative moment. Thirdly, we measured the spatial allocation rationality of service facilities of residential areas (SARSF-RA) by combining ASFC-RA and PDC-RA. In this case, a trial strip census, consisting of serval urban residential areas from Wuxi City, Jiangsu Province, is selected as research area. Residential areas have been grouped within several ranges according to their SARSF-RA values. Different residential areas belong to different groups, even if they are spatially contiguous. Spatial locations and other investigation information coordinate with these differences. Those results show that the method that we proposed can express the micro-spatial allocation rationality of different residential areas dramatically, which provide a new data lens for various researchers and applications, such as urban residential areas planning and service facilities allocation.
Highlights
The provision of amenities/service facilities is one of the issues of concern regarding urban inequalities in social science research [1]
To intuitively show the differences in the SARSF-RA values, we constructed a four-quadrant map and collected a photo of the façade from some typical communities, as shown in Figure 7, which was expected to be more intuitive in terms of explaining the coordination between available service facilities capacity of residential areas (ASFC-RA) and population distribution capacity of residential areas (PDC-RA)
The value of ASFC-RA was the highest, but the value of PDC-RA was in the middle in HuaHuiYuan; it obtained a high degree of rationality
Summary
The provision of amenities/service facilities is one of the issues of concern regarding urban inequalities in social science research [1]. There are two indicators for measuring the accessibility of service facilities: the travel costs and the quality/quantity of opportunities [15]. The pervasive adoption and penetration of new research data, such as Internet map and location-based service (LBS) data, create an upcoming research stream, which retrieves detailed information about local amenities [19]. Representative LBS data include Twitter data and Weibo data, which can be used for dynamic land-use mapping [28] and assessing human travel activity patterns [29]. Based on the above description, Internet map and LBS data that provide micro-scale knowledge about residential areas will be pertinent to SARSF-RA. We will introduce and analyze detail data information and the measurement framework
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