Investigating the Potential of Using POI and Nighttime Light Data to Map Urban Road Safety at the Micro-Level: A Case in Shanghai, China

Abstract

The way in which the occurrence of urban traffic collisions can be conveniently and precisely predicted plays an important role in traffic safety management, which can help ensure urban sustainability. Point of interest (POI) and nighttime light (NTL) data have always been used for characterizing human activities and built environments. By using a district of Shanghai as the study area, this research employed the two types of urban sensing data to map vehicle-pedestrian and vehicle-vehicle collision risks at the micro-level by road type with random forest regression (RFR) models. First, the Network Kernel Density Estimation (NKDE) algorithm was used to generate the traffic collision density surface. Next, by establishing a set of RFR models, the observed density surface was modeled with POI and NTL variables, based on different road types and periods of the day. Finally, the accuracy of the models and the predicted outcomes were analyzed. The results show that the two datasets have great potential for mapping vehicle-pedestrian and vehicle-vehicle collision risks, but they should be carefully utilized for different types of roads and collision types. First, POI and NTL data are not applicable to the modeling of traffic collisions that happen on expressways. Second, the two types of sensing data are quite suitable for estimating the occurrence of traffic collisions on arterial and secondary trunk roads. Third, while the two datasets are capable of predicting vehicle-pedestrian collision risks on branch roads, their ability to predict vehicle safety on branch roads is limited.

Deciphering the Manufacturing Production Space in Global City-Regions of Developing Countries—a Case of Pearl River Delta, China

“…Density index is an index reflecting the intensity of land and space development, which has been used to guide urban spatial planning [43] and sustainable development of urban ecosystem [66,67]. In this study, the density index is the ratio of the production space area of the manufacturing industry to the total area of the urban built-up area, which reflects the development intensity of the manufacturing production space.…”

“…The formation of the spatial pattern of manufacturing production space was the result of the combined effects of multiple factors. Using for reference of scholars' research on manufacturing space reconstruction and the study of its influencing factors [31,70], the quantitative research on industrial production spatial characteristics [43,44] combines with the industry characteristics of the PRD manufacturing industry [17], in this study the analysis framework shown in the Figure 4 was constructed. Furthermore, taking 37 counties (districts) in the PRD as statistical units, the leading factors affecting the spatial pattern of manufacturing production space in the PRD were analyzed by constructing regression model.…”

“…However, the industrial production space includes a large area of mining, electricity, heat, gas and water production and supply, and construction industry, which cannot characterize the spatial state of regional real economic production activities more accurately. Using remote sensing technology and the point of interest (POI) data, a large area of urban ground objects can be accurately extracted [43][44][45][46]. Therefore, using RS and GIS technology to extract spatial information of manufacturing industry, and combining the POI data of manufacturing enterprises to obtain attribute information of manufacturing enterprises, then separating the manufacturing production space from the industrial production space, and exploring the spatial structure and spatial form characteristics of the manufacturing entities in the city-regions need to be strengthened.…”

“…The manufacturing production space, as an important part of city-region function space and contradiction problem of the most prominent areas, the optimal control is extremely urgent [50,51]. Accurately identifying and analysing the true and continuous distribution of manufacturing production space in the city-region is the basis for optimizing the regulation and promoting the intensive, efficient and sustainable development [43,52]. Therefore, the third aim of this paper is to guide the formulation of intensive regulation and control policies of manufacturing production space according to the agglomeration characteristics of manufacturing production space under different spatial scales based on the local development background-not only meeting the national/regional development program, but also promoting the sustainable development of local economy and production space.…”

Towards Healthy Aging: Influence of the Built Environment on Elderly Pedestrian Safety at the Micro-Level

“…The former describes the characteristics of the built environment on the roads such as crossings, traffic lights, sidewalks, and roadside trees that may influence the behavior of the elderly while walking, and the latter is used to delineate features of the community such as bus stops, supermarkets, shopping centers, and clinics that probably affect the daily activities of older people. Despite the limited research on the influence of the built environment on older pedestrians, many studies have focused on the effects of environmental factors on pedestrians of all ages [ 24 , 25 , 26 , 27 ]. This research selected variables by referring to previous studies on pedestrians regardless of age.…”