Using POI Data to Identify the Demand for Pedestrian Crossing Facilities at Mid-Block

Abstract

In Chinese cities, the widespread problem of the low density of the road network has seriously damaged the convenience of pedestrian crossing, resulting in an unfriendly pedestrian experience and restricted development of non-motorized traffic within the city. Only by accurately capturing the crossing needs of pedestrians can we adopt a targeted approach to improve the pedestrian crossing experience. In this paper, the demand and supply are considered synthetically, and a method of using point of interest (POI) data to analyze the demand for pedestrian crossing facilities at the mid-block is proposed. First, we developed the method of calculating the pedestrian crossing demand intensity based on POI data. Secondly, based on the appropriate length threshold and pedestrian crossing demand intensity threshold, a series of road sections with strong demand for pedestrian crossing facilities are identified in the study area. Finally, we use mobile phone data to obtain the intensity of residents’ activity in different areas, and find that the distribution of the areas with more activity is basically the same as that of the target road sections. The result shows that the method proposed in this paper can effectively identify the road sections with strong demand for crossing facilities at mid-block, and can provide support for the improvement of urban non-motorized traffic.

The Mechanism of Safety Culture Construction in Chinese Universities Based on POI Data and Gravity Model

“…China's higher education has stood at a brand-new historical starting point, and the launch of the "double first-class" construction has further promoted the higher education from the scale expansion type extension development to the quality-oriented connotation development. In such a process of transformation and development, colleges and universities can only adapt to this historical opportunity of development through reform and change [4]. The reform of colleges and universities is changing from single reform to comprehensive reform and from incremental reform to stock reform.…”

Spatial Pattern Analysis of Xinjiang Tourism Resources Based on Electronic Map Points of Interest

“…Pei et al [22] made full use of big data thinking-the distribution, change, and mechanism of the study object were revealed by the ideas of correlation, synthesis, and inversion from the rich big footprint data. In terms of spatial distribution characteristics, related studies focused on using the POI data to explore how various service industry facilities are distributed spatially [23]. Dimelli et al [24] studied the economic effects of tourism in the Greek island as a center of research.…”

“…According to Table 2, various categories of tourism resources are distributed in all of the cities in Xinjiang, among which Urumqi has the largest number of tourism resource sites (373), while Bozhou has the smallest number of tourism resource sites (23). Specifically, the architectural landscape has the largest number of resource points, with a total of 1145 resource points, ranking first among the categories of resource points; Urumqi included the most resource points among all cities, having 283 of them.…”

Urban Functional Zone Classification Based on POI Data and Machine Learning

“…Compared with other data types, POI data have the advantages of easy access and wide coverage, and contain rich socioeconomic attributes, providing a new perspective for urban analysis research [16]. The applications of POI in urban studies mainly include functional zone classification [17][18][19], building type identification [20], urban land use monitoring [21][22][23], commercial retail site selection [24], and demand analysis of public service facilities [25]. To classify urban functional zones, some studies use POI data as the main data source to identify urban functional zones through the analysis of spatial distribution characteristics of POI [26,27].…”