Mingxing Hu

Free-floating bike-sharing (FFBS) systems play a valuable role in alleviating traffic congestion and reducing carbon emissions, making them vital to sustainable urban transportation. Although extensive research has investigated the relationship between the built environment and cycling behavior, the adverse effects of air pollution and its interaction with the built environment remain insufficiently understood. In this study, multisource data from Shenzhen are used, and an XGBoost–SHAP model is employed to comprehensively investigate the nonlinear associations among the FFBS trip volume, built environment, and air pollution while considering the spatial heterogeneity in interaction effects. The results indicate that population density, road density, building density, and PM2.5 are the most influential factors. In addition, significant temporal heterogeneity is observed between weekdays and weekends. The effects of the built environment variables and their interactions are more pronounced on weekdays than on weekends. More importantly, an interaction analysis reveals that the positive influence of compact urban development on cycling is conditional: in high-density areas with elevated pollution exposure, the health risks associated with air pollution can offset or even outweigh the mobility benefits of compactness. Overall, this study identifies the complex, spatially heterogeneous mechanisms through which the built environment and air quality jointly shape FFBS usage. These findings provide important evidence for integrating environmental health considerations into compact city planning and offer practical insights for promoting cycling and sustainable urban mobility in high-density cities.

Population migration is an important indicator for measuring the interactions and connections between cities. Analyzing the spatiotemporal distribution pattern of the migration flows between cities is highly important to understanding urban development relationships and regional structures. From a spatiotemporal perspective, we conduct STFlowLISA (Space-Time Flow Local Indicator of Spatial Association) spatiotemporal autocorrelation analysis using population migration data from Hubei Province from 2018 to 2023 and, on this basis, calculate the spatiotemporal hub index and identify spatiotemporal clusters. The research aims to reveal the regional spatial structure shaped by migration flows and compare it with that of existing town system planning to evaluate deviations and provide a decision-making basis for the regional synergistic development of Hubei Province. The key findings include: (1) the population migration flows between Wuhan and its surrounding cities mostly exhibit a spatiotemporal distribution pattern of HH (high-value agglomeration), whereas the long-distance migration flows between eastern and western Hubei mostly follow a pattern of LL (low-value agglomeration), and these urban connections show improvement after the epidemic; (2) the spatiotemporal hubs of Hubei Province demonstrate a “core-periphery” structure with Wuhan as the absolute core, while Xiangyang’s role as a subcenter does not meet planning expectations; and (3) urban spatiotemporal clusters are dense in the east and sparse in the west, with Enshi and Shiyan showing disconnection from the main network, which deviates from the planned polycentric spatial pattern. By examining the spatiotemporal autocorrelation of migration flows, this study enriches the empirical understanding of regional spatial structure in Hubei Province within the framework of classical spatial theory and highlights the necessity of incorporating dynamic flow analysis into regional planning and policy-making.

Fire security is an important part of the urban infrastructure system. In existing quantitative application research, fire stations have always used a maximally covered approach to optimize the layout of urban functions. With this planning, different fire stations face different firefighting pressures (FPs) because of the different distributions of fires. On the basis of FPs, we explored the characteristics of firefighting behavior at fire stations in Nanjing using Moran’s I. We found that a large portion of the stations are in the periphery of high-fire-risk areas, which tend to experience infrequent fires near the stations. These stations organize inter-regional firefighting to reduce the risk of fires in urban areas in general. We attempt to change the maximally covered approach so that it can be used to quantitatively solve the problem of fire planning.

As urban areas serve as spatial carriers for innovation activities, exploring the interaction between innovation and urban spaces has become a research hotspot. Previous studies have primarily focused on urban innovation capacity assessment and measurement, with less attention given to the urban innovation potential and issues such as insufficient granularity of research and weak stability in factor weighting. In this study, an urban spatial innovation potential measurement system was proposed from a demand-side perspective, the structural equation model (SEM) was employed to investigate the direct and indirect influences of factors, and micro-scale empirical analysis was conducted in the main urban area of Suzhou, China. Coupling and coordination with innovation outputs were analyzed. The results revealed that (1) the innovation potential exhibits significant spatial clustering characteristics; (2) there is clear core-periphery spatial differentiation in the innovation potential, demonstrating decreasing spatial diffusion effects with distance from the center; and (3) only 8.18% of the plots in the study area occur at the harmonized innovation coupling stage, indicating that the overall innovation conversion efficiency should be improved. This study aimed to provide a sound basis and practical references for optimizing the layout and improving the quality of urban innovation spaces from a detailed scale perspective, thereby enhancing urban competitiveness.

With the increasing trend of residents and tourists sharing urban spaces, the boundary between leisure spaces and tourism spaces is gradually being blurred. However, few studies have quantified the spatiotemporal correlation patterns of residents’ leisure activities and tourists’ activities. To fill this gap, this paper takes Nanjing as an example to study the temporal and spatial correlation between residents’ leisure activities and tourists’ activities based on mobile phone signal data. First, through kernel density analysis, it is found that there is a spatial overlap between residents’ leisure activities and tourists’ activities. Then, the spatial and temporal correlation patterns of residents’ leisure activities and tourists’ activities are analyzed through the colocation quotient. According to our findings, (1) residents’ leisure activities and tourists’ activities are not spatially correlated, indicating that they are relatively independent in space both during the week and on weekends. (2) On weekday afternoons, the spatial correlation between residents’ and tourists’ leisure activities is strongest. On weekends, the night is when residents’ leisure activities and tourists’ activities are most closely related. (3) The correlation area is mainly distributed in areas near famous scenic spots, as well as public spaces such as parks and squares. Based on the above analysis, this paper aims to study the resident-tourist interaction in the spatial context to provide directions for improving the attractiveness of cities, urban transportation, services, and marketing strategies.

Investigating the impact of the built environment around rail stations on ridership as well as assessing the spatial and temporal variation of that impact is helpful to improving the operational efficiency of urban public transport and guiding station planning and development. Based on 14 days of smart card data in Nanjing, 159 metro stations were taken into consideration in this paper to explain the changing patterns among weekday and weekend ridership. Three types of characteristics were selected as explanatory variables, namely built environment variables, socioeconomic variables and transit service variables. For models, we applied geographically and temporally weighted regression (GTWR) to analyse how the built environment affects ridership from spatial and temporal perspectives and compared its fitting degree in ordinary least squares (OLS) and geographically weighted regression (GWR) models. The results reveal that GTWR performs well in the fitting results and is thus suitable for exploring the association between rail transit ridership and the built environment. In addition, the coefficient of the explanatory variables is influenced by both temporal and spatial factors, with critical importance in the temporal dimension of the analysis of passenger flow. Furthermore, while following the same trend, the impact coefficient, which is mainly affected by commuter traffic, shows stronger temporal heterogeneity during weekdays than weekends. Considering the characteristics of residents' travel activities, the internal relationship between urban space and traffic operation is explored to provide theoretical guidance and suggestions for improving the efficiency of public transport operation and optimizing the layout of urban space.

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.

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.

Geo-information technology plays a critical role in urban planning and management, land resource quantification, natural disaster risk and damage assessment, smart city development, land cover change modeling and touristic flow management. In particular, the development of big data mining and machine learning techniques (including deep learning) in recent years has expanded the potential applications of geo-information technology and promoted innovation in approaches to mining in different fields. In this context, the International Conference on Geo-Information Technology and its Applications (ICGITA 2019) was held in Nanchang, Jiangxi, China, 11–13 October 2019, co-organized by the Key Laboratory of Digital Land and Resources, East China University of Technology, the Institute of Remote Sensing and Digital Earth (RADI) of the Chinese Academy of Sciences (CAS), which was renamed in 2017 the Aerospace Information Research Institute (AIR), CAS, and the Institute of Space and Earth Information Science of the Chinese University of Hong Kong. The outstanding papers presented at this event and some other original articles were collected and published in this Special Issue “Geo-Information Technology and Its Applications” in the International Journal of Geo-Information. This Special Issue consists of 14 high-quality and innovative articles that explore and discuss the typical applications of geo-information technology in the above-mentioned domains.