In large Chinese cities, inefficient logistics organization, a rapid increase in freight demand, and the spreading of city logistics space have jointly contributed to the urban problems related to goods movement, such as spatial conflicts, traffic congestion, and air pollution. To address these problems and improve urban sustainability, we proposed a new spatial organization model of supply–demand coordination. We used the data from the Third China Economic Census and online point-of-interest (POI) for China’s four direct-controlled municipalities and 13 sub-provincial cities. We found that: (1) the freight supply and demand in China’s large cities are both spatially decentralized and clustered. However, there is a significant spatial mismatch between freight supply and demand in most of the studied cities. (2) The 17 studied cities can be divided into three types—highly unbalanced, unbalanced, and balanced—in light of the spatial mismatch between freight supply and demand. (3) The capacities of road surface and logistics nodes spatially differ. The supply capacity of the road systems in Beijing, Shanghai, and Guangzhou can only accommodate 18.4%, 35.5%, and 32.2% of the demand, respectively, while the supply capacity of the logistics nodes is more than twice that of the actual demand in these cities. Based on the findings, this paper proposed a differentiated method of demand management in different areas of the cities. To achieve the goals of low-carbon and sustainable development in logistics distribution, policy makers may consider planning urban freight activities along metro lines and intercity rail lines. Thus, this paper will provide a new perspective for understanding the urban freight distribution and management in large Chinese cities.

Nowadays, the application of location‐aware devices, such as global positioning system (GPS)‐enabled mobile phones and personal digital assistants (PDAs) is increasing. Based on this, a new demand for efficiently storing trajectory data has arisen. Trajectory compression reduces the storage space and cost, the cost and time of data transmission by retaining critical trajectory points and effectively eliminating redundant data. Herein, a novel trajectory compression method based on stay points (TCSP) is proposed. By using the road network data and the stay points of GPS trajectory data to compress the GPS trajectory data, this method can greatly improve the ratio and accuracy of compression. Experimental results based on real data sets show that the compression rate of this method can stably reach 8.61%, and the compression effect can be restored to the original trajectory route. Compared with the critical point algorithm and the Douglas–Peucker (DP) algorithm, the TCSP method can reach a better compression effect and compression ratio.

The current 2019 coronavirus disease (COVID-19) pandemic is hitting citizen’s life and health like never before, with its significant loss to human life and a huge economic toll. In this case, the fever clinics (FCs) were still preserved as one of the most effective control measures in China, but this work is based on experience and lacks scientific and effective guidance. Here, we use travel time to link facilities and populations at risk of COVID-19 and identify the dynamic allocation of patients’ medical needs, and then propose the optimized allocation scheme of FCs. We selected Shenzhen, China, to collect geospatial resources of epidemic communities (ECs) and FCs to determine the ECs’ cumulative opportunities of visiting FCs, as well as evaluate the rationality of medical resources in current ECs. Also, we use the Location Set Covering Problem (LSCP) model to optimize the allocation of FCs and evaluate efficiency. Firstly, we divide the current ECs into 3 groups based on travel time and cumulative opportunities of visiting FCs within 30 min: Low-need communities (22.06%), medium-need communities (59.8%), and high-need communities (18.14%) with 0,1–2 and no less than 3 opportunities of visiting FCs. Besides, our work proposes two allocation schemes of fever clinics through the LSCP model. Among which, selecting secondary and above hospitals as an alternative in Scheme 1, will increase the coverage rate of hospitals in medium-need and high-need communities from 59.8% to 80.88%. In Scheme 2, selecting primary and above hospitals as an alternative will increase the coverage rate of hospitals in medium-need and high-need communities to 85.29%, with the average travel time reducing from 22.42 min to 17.94 min. The optimized allocation scheme can achieve two objectives: a. equal access to medical services for different types of communities has improved while reducing the overutilization of high-quality medical resources. b. the travel time for medical treatment in the community has reduced, thus improving medical accessibility. On this basis, during the early screening in prevention and control of the outbreak, the specific suggestions for implementation in developing and less developed countries are made.