As vulnerable road users, elderly pedestrians are more likely to be injured in road crashes due to declining physical and perceptual capabilities. Most previous studies on the influence of the built environment on elderly pedestrian safety focused on intersections or areal units. Using a district of Shanghai as the study area, this research investigated the effects of the built environment at the road segment level with elderly pedestrian collision, taxi tracking point, point of interest, street view image, open street map, land use, housing price, and elderly population datasets. In particular, this research employed both Poisson and geographically weighted Poisson regression (GWPR) models to account for spatial nonstationarity. The Poisson model indicates that green space, sidewalks, and junctions on the roads significantly affected elderly pedestrian safety, and roads around nursing homes, schools, bus stops, metro stations, traditional markets, and supermarkets were hazardous for elderly pedestrians. The results of the GWPR model suggest that the influence of factors varied across the study area. Green space could decrease the risk of elderly pedestrian collisions only in areas without congested environments. Separations need to be installed between roadways and sidewalks to improve elderly road safety.

Global urbanization has brought about a significant transition to rural areas. With the development of remote sensing technologies, land use/land cover (LULC) datasets allow users to analyze the changes in global rural settlements. However, few studies have examined the performances of the LULC datasets in mapping rural settlements. Taking China as the study area, this research selected eight of the latest LULC datasets (ESRI Land Cover, WSF, ESA WorldCover, GHS-BUILT-S2, GISD30, GISA2.0, GLC30, and GAIA) to compare their accuracy for rural settlement detection. Spatial stratified sampling was used for collecting and sampling rural settlements. We conducted omission tests, area comparison, and pixel-based accuracy tests for comparison. The results show that: (1) the performances of the 10 m resolution datasets are better than those of the 30 m resolution datasets in almost all scenarios. (2) the mapping of villages in Western China is a challenge for all datasets. (3) GHS-BUILT-S2 performs the best in almost every scenario, and can allow users to adjust the threshold value for determining a proper range of rural settlement size; ESRI outperforms any other dataset in detecting the existence of rural settlements, but it dramatically overestimates the area of rural settlements. (4) GISD30 is the best among the 30 m resolution datasets, notably in the Pearl River Delta. Finally, we provide useful suggestions on ideal map selection in various regions and scenarios.

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.

Low-density urbanization threatens urban social and ecological sustainability not only directly by excessively encroaching on suburban farmland and ecological space, but may also indirectly do so by undermining the financial basis of sustainable urban development. To address this relationship, this study empirically examines the effect of low-density urbanization on local government debt by using panel data of prefecture-level cities in China from 2006 to 2015. Results show that the scale of local government debt increases significantly with a rise in urban expansion. Furthermore, this study found that low-density urbanization affects local government debt in two ways. First, low-density urban expansion reduces the land output efficiency, which decreases potential fiscal revenue and thus increases local government debt. Second, low-density urban expansion raises the construction and maintenance expenditure of urban infrastructure, which increases the demand for urban construction financing and thus pushes up the scale of debt. The results of the heterogeneous study indicate that low-density urbanization significantly affects local government debt mainly in Central/Western regions, small and medium-sized cities, cities with high fiscal stress and development pressure, and residentially expanding cities. On the contrary, low-density urbanization has no significant effect on the Eastern regions, large cities, cities with low fiscal stress and development pressure, and spatially expanding cities. This study theoretically explored and empirically verified a critical indirect effect of low-density urbanization on urban sustainability by increasing fiscal risks, which is, and will continue to be, a common and vital challenge faced by cities in China and other rapidly urbanizing developing countries.