The rapid expansion of cities brings in new challenges for the urban firefighting security, while the increasing fire frequency poses serious threats to the life, property, and safety of individuals living in cities. Firefighting in cities is a challenging task, and the optimal spatial arrangement of fire stations is critical to firefighting security. However, existing researches lack any consideration of the negative effects of the spatial randomness of fire outbreaks and delayed response time due to traffic jams upon the site selection. Based on the set cover location model integrated with the spatiotemporal big data, this paper combines the fire outbreak point with the traffic situation. The presented site selection strategy manages to ensure the arrival of the firefighting task force at random simulated fire outbreak points within the required time, under the constraints of the actual city planning and traffic situation. Taking Nanjing city as an example, this paper collects multi-source big data for the comprehensive analysis, including the full data of the fire outbreak history from June 2014 to June 2018, the traffic jam data based on the Amap, and the investigation data of the firefighting facilities in Nanjing. The regularity behind fire outbreaks is analyzed, the factors related to fire risks are identified, and the risk score is calculated. The previous fire outbreak points are put through the clustering analysis, the spatial distribution probability at points in each cluster is calculated according to the clustering score, and the random fire outbreak points are generated via the Monte Carlo simulation. Meanwhile, the objective emergency response time is set as five minutes. The average vehicle speed for each road in the urban area is calculated, and the actual traffic network model is built to compute the travel time from massive randomly-distributed simulated fire points. The problem is solved by making the travel time for all simulated demand points below five minutes. At last, the site selection result based on our model is adjusted and validated, according to the planned land use. The presented method incorporates the view of the spatiotemporal big data and provides a new idea and technical method for the modification and efficiency improvement of the fire station site selection model, contributing to a service cover ratio increase from 58% to 90%.

Economic expansion has caused increasingly serious land resource problems, and the decoupling of urban industrial land expansion from economic development has become a big topic for intensive development. The current research has mainly concerned industrial land efficiency, a single, static indicator, compared to a decoupling model, which takes into account two variables and gives a full expression of the spatio-temporal dynamic characteristics. However, little attention has been paid to the relationship between industrial land expansion and economic development in China from the perspective of decoupling. Based on a combination of Tapio‘s decoupling model and spatial analysis methods, this paper investigates the decoupling relationship between industrial land expansion and economic development in Chinese cities from 2010 to 2019. On that basis, we divided the study area into three policy zones and made differentiated policy recommendations. In addition, based on the decoupling model, we obtained the decoupling indices of the cities and grouped the cities into eight decoupling types. After the spatial autocorrelation analysis, we further verified the spillover effect of decoupling with the results of urban spatial differentiation. This paper draws the following conclusions: (1) Urban industrial land expansion and economic development exhibit marked and increasingly significant spatial heterogeneity and agglomeration. (2) Industry and economy are in weak decoupling in most cities, but there are a growing number of cities in negative decoupling. (3) Decoupled cities are shifting from the southeast coast to the middle and lower reaches of the Yellow River and Yangtze River, while negatively decoupled cities keep spreading from northeast and south China to their periphery, with clear signs of re-coupling. (4) It is necessary to develop urban industrial land supply and supervision policies according to local actuality and to implement differentiated control of industrial land for cities and industrial sectors with different decoupling types. To some extent, this paper reveals the evolution dynamics, performances, and strategies of industrial land, providing a decision basis for industrial land management policies and industrial planning in China and other countries at similar stages.

Time allocation is closely related to life quality and is a potential indicator of urban space utilization and sociospatial differentiation. However, existing time allocation studies focus on how time is allocated to various activities but pay less attention to where individuals allocate their time. In the context of China’s transformation, this study examines the differences in time allocation in different urban spaces between low- and non-low-income groups based on two methods, descriptive statistics and social area analysis. The results show that low-income participants’ daily activities (especially work) are highly dependent on the central city area. However, they are at a disadvantage in accessing the central city area. Nevertheless, non-low-income individuals have diversified activity spaces and can better choose locations according to the purpose of activities and make fuller use of various types of urban areas. This study indicates that there are social differences in time allocation and urban space utilization among different income groups. The results obtained with regression models reveal that in addition to income, activity characteristics and built environment characteristics are significant factors affecting the differences. Social policies should support the equitable distribution of urban resources for different social groups, especially for vulnerable groups who live in affordable housing.