Accessibility research of healthcare facilities is developing towards multiple transportation modes (MTM), which are influenced by residential transportation choices and preferences. Due to differences in travel impact factors such as traffic conditions, origin location, distance to the destination, and economic cost, residents’ daily travel presents different residential transportation mode choices (RTMC). The purpose of our study was to measure the spatial accessibility of healthcare facilities based on MTM considering RTMC (MTM-RTMC). We selected the gravity two-step floating catchment area method (G2SFCA) as a fundamental model. Through the single transportation mode (STM), MTM, and MTM-RTMC, three aspects used to illustrate and redesign the G2SFCA, we obtained the MTM-RTMC G2SFCA model that integrates RTMC probabilities and the travel friction coefficient. We selected Nanjing as the experimental area, used route planning data of four modes (including driving, walking, public transportation, and bicycling) from a web mapping platform, and applied the three models to pediatric clinic services to measure accessibility. The results show that the MTM-RTMC mechanism is to make up for the traditional estimation of accessibility, which loses sight of the influence of residential transportation choices. The MTM-RTMC mechanism that provides a more realistic and reliable way can generalize to major accessibility models and offers preferable guidance for policymakers.

No 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.

Abstract:Extrusion is widely used to construct models in 3D cadasters. However, the basic extrusion approach only supports relatively simple conditions, and a 3D cadastral data model that supports extruded 3D models that are associated with their corresponding footprints is not available. In this paper, we present a new extrusion approach based on non-overlapping footprints (EABNOF) that supports relatively complex 3D situations. In EABNOF, overlaps between overlapping footprints of the input data are removed, which also involves splitting extrusion intervals and handling the associated cadastral objects of footprints. The newly generated non-overlapping footprints are extruded to generate primitives. To construct geometric models and topologies for cadastral objects, three judgment criteria are proposed to identify and remove redundancies from these primitives, and then primitives of the same 3D spatial unit or topological feature are merged. Considering the feasibility of using EABNOF for current cadastral data, we design a data model that associates 3D cadastral data with the footprints of 2D cadasters. We examine two types of structures on Pozi Street to verify EABNOF: a building complex and property objects. The results demonstrate that EABNOF can construct geometric models and topologies in 3D cadasters. EABNOF is based on the footprints of 2D cadastral data, and thus is particularly suited to areas with 2D cadastral data to establish 3D cadasters with low costs.