## 1 Introduction  
The provision of amenities/service facilities is one of the issues of concern regarding urban inequalities in social science research [1]. Public property attributes ensure the equivalence operation of civic social and economic activities [2]. Several guiding documents define service facilities. For example, \"Urban Residential Area Planning and Design Specification (GB50180-93, version 2002)\", which is one of the Chinese urban planning national standards, defines service facilities as those that meet the basic needs of life and are compatible with the size of the population [3]. The policy of shifting the priority of urban construction and public service resources allocation to the suburbs in Shanghai was outlined by a government work report in 2019, which profoundly explained the city government's policy orientation to revitalize the disadvantaged suburbs. The planning target of spatial allocation rationality of service facilities of residential areas (SARSF-RA) is to achieve equivalence between the accessible service facilities of residential areas and the population of residential areas [1; 4; 5]. Through a geographer's lens, SARSF-RA becomes complicated and increasingly microscopic due to the combined influence of natural and human factors, especially in China and other countries experiencing rapid development [6]. Therefore, it is desiderated to adopt new, representative datasets that are good at the micro-spatial scale to calculate the spatial allocation rationality of the service facilities of residential areas. The study of the spatial accessibility of service facilities is a precondition for SARSF-RA and urban evenness measurement [7]. The accessibility of service facilities research continues to receive attention [8; 9]. Previous scholars have mainly focused on a single type of service facilities when examining accessibility, such as public healthcare services [8; 10], urban green space [11], commercial facilities [12], or transit services [13]. These accessibility evaluations can identify the potential shortcomings of typical services and reflect the balance of facilities' spatial layout. However, few comprehensive assessments of the accessibility of various service facilities have been researched [14], and the impact of diversity on the accessibility of service facilities has not been revealed. There are two indicators for measuring the accessibility of service facilities: the travel costs and the quality/quantity of opportunities [15]. The travel costs represent the actual ability to reach the provider through different transportation systems [16], in which travel distance or travel time is considered regular. The quality/quantity of opportunities contains different aspects from different levels, such as population flow from a macro level and population count from the micro level [17]. The traditional measurement for travel costs is mainly based on the Geographical Information System (GIS) analysis of origin-destination matrices, which need valuable fundamental transportation network data [13]. The conventional data source that is used to measure opportunities is often statistical survey data. There are apparent deficiencies for traditional data, such as relying on the intensive investigation, which is costly, time-consuming, and too coarse for detailed mapping and small areas. It is challenging for this situation to meet the requirements of high efficiency and a microcosmic scale [18]. 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].