When planning intra-urban control measures for epidemics with significant societal impact, it is essential to consider the spatiotemporal orderliness of interventions, thus mitigating the disruption to daily life. For instance, improving intervention consistency among highly interacted sub-regions and avoid frequent and significant changes of interventions over time can be effective. However, existing studies on optimizing epidemic control overlooked the need for spatiotemporal consistency and stability of the interventions, potentially impacting their practicality and public compliance. To fill this gap, this study systematically conceptualized and quantified spatiotemporal orderliness for intra-urban epidemic control. A deep-reinforcement-learning (DRL) framework integrating the spatiotemporal orderliness was proposed to optimize the interventions across sub-regions over time. Taking Shenzhen, China as a study area, we solve a joint control plan for 74 sub-regions based on a meta-population SEIR epidemic model with a real-world intra-urban mobility network. The results demonstrate that the proposed model can effectively generate tailored dynamic interventions for sub-regions, significantly improving spatiotemporal orderliness. Furthermore, the effectiveness and generalizability of proposed model are demonstrated under different urban structures and transmissibility of respiratory viruses. Overall, this study provides a DRL-based tool for planning intra-urban epidemic control measures with enhanced spatiotemporal orderliness, potentially aiding future epidemic preparedness.

Multi-city human mobility prediction is critical for GIScience and urban computing. However, pronounced spatiotemporal heterogeneity and training instability on large-scale, multi-city datasets restrict model generalization. To address these challenges, we propose the Human Mobility Predictor (HuMob Predictor), a novel framework that enhances multi-city mobility prediction. Our model introduces a multi-level encoding module that captures cross-city heterogeneity and within-city spatial relationships. City encodings capture macro-level characteristics unique to each city, while absolute spatial encodings preserve geographic proximity across grid cells. By combining these encodings, HuMob Predictor learns universal mobility representations while retaining city-specific features. To stabilize training across cities, we adopt an incremental training strategy that gradually increases prediction difficulty, significantly improving convergence and cross-city generalization. Experiments on the GISCUP 2025 multi-city datasets demonstrate that HuMob Predictor achieves superior performance in individual mobility prediction.

Individual trajectory data play a pivotal role in various application fields, such as urban planning, traffic control, and epidemic simulation. Despite the diverse means for data collection in current times, the real-world trajectory data in practical application remains severely limited due to concerns over personal privacy. In this study, we designed a Spatiotemporal-knowledge enhanced multi-TAsk GEnerative adversarial network (GAN), named STAGE, to generate synthetic trajectories that statistically resemble the real data without recycling personal information. In STAGE, we designed a multi-task generator with three stages of spatio-temporal generation tasks, i.e. activity-sequence generation task, township-level trajectory generation task, and neighborhood-level trajectory generation task, with the last one as the main task while the other two as auxiliary tasks. Meanwhile, we designed a spatial consistency loss in the adversarial training process to assess the spatial consistency of generated trajectories at different spatial scales. Experiment results show that compared to the baselines, trajectories generated by our method have closer data distributions to the real ones. We argued that the designs of spatiotemporal-knowledge enhanced generation tasks and training loss benefit the spatiotemporal generation processes, which help reproduce the temporal patterns of human daily activities and spatial distribution of human movements.

Understanding and simulating individual human mobility within intra-urban areas are essential for urban planning, transportation, public health, and other related fields. Currently, Random Walk-based and Exploration and Preferential Return (EPR)-based models are the primary mechanistic approaches used to model individual human mobility. However, these models often rely on assumptions and parameters derived from studies conducted at larger spatial scales, such as the national level. This results in inaccuracies when applied to urban contexts because individual mobility at the intra-urban scale exhibits unique spatiotemporal characteristics. Through a systematic analysis of three consecutive trajectory datasets and three check-in trajectory datasets collected from six cities worldwide, we identified two key insights into intra-urban mobility: (1) individuals’ waiting time distributions follow a log-normal function with universal parameters, rather than the gamma, power-law, or exponential functions commonly used in current studies, and (2) individuals’ spatial choice behavior is better captured by the Universal Opportunity (UO) model with universal parameters, rather than the frequently used gravity models. Based on these findings, we proposed Urban-EPR, a universal model specifically designed to simulate intra-urban individual mobility. Additionally, we compared seven mainstream mechanistic models, demonstrating the superior performance of Urban-EPR and providing a benchmark for intra-urban mobility research.

Accurate epidemic modeling requires detailed transmission networks, which are often approximated using population flow, but representing these networks is typically complex and computationally demanding. Traditional simplification methods rely on static indicators, ignoring the dynamic nature of human mobility and disease transmission. To address this, we introduce a novel method that simplifies population flow networks using geographic adjacency constraints and a dynamic transmission model. This method dynamically compares transmission characteristics to ensure critical human dynamics and transmission features are retained. We tested our method on Shenzhen’s 1 km grid-level network, showing it outperforms conventional approaches and higher-level spatial units like sub-districts. Our findings indicate that while overall bias in epidemic simulations remains low as network simplification increases, incorporating spatial factors leads to an increase in bias but reduces uncertainty. Notably, simulation errors rise sharply as the simplification rate nears complete simplification. Additionally, diseases with higher transmission rates exhibit larger simulation biases during network simplification. Our method significantly improves the accuracy of epidemic simulations while reducing computational complexity. The results have important implications for public health strategies, emphasizing the need for models that balance detail and feasibility to improve epidemic preparedness and response amid evolving human dynamics.

Understanding human mobility patterns is essential for various applications, from urban planning to public safety. The individual trajectory such as mobile phone location data, while rich in spatio-temporal information, often lacks semantic detail, limiting its utility for in-depth mobility analysis. Existing methods can infer basic routine activity sequences from this data, lacking depth in understanding complex human behaviors and users’ characteristics. Additionally, they struggle with the dependency on hard-to-obtain auxiliary datasets like travel surveys. To address these limitations, this paper defines trajectory semantic inference through three key dimensions: user occupation category, activity sequence, and trajectory description, and proposes the Trajectory Semantic Inference with Large Language Models (TSI-LLM) framework to leverage LLMs infer trajectory semantics comprehensively and deeply. We adopt spatio-temporal attributes enhanced data formatting (STFormat) and design a context-inclusive prompt, enabling LLMs to more effectively interpret and infer the semantics of trajectory data. Experimental validation on real-world trajectory datasets demonstrates the efficacy of TSI-LLM in deciphering complex human mobility patterns. This study explores the potential of LLMs in enhancing the semantic analysis of trajectory data, paving the way for more sophisticated and accessible human mobility research.

Human mobility data play a crucial role in many fields such as infectious diseases, transportation, and public safety. Although the development of Information and Communication Technologies (ICTs) has made it easy to collect individual-level positioning records, raw individual trajectory data are still limited in availability and usability due to privacy issues. Developing models to generate synthetic trajectories that are statistically close to the real data is a promising solution. This study proposed a novel trajectory generation method called Act2Loc (Activity to Location), which combined machine learning and mechanistic models. First, an activity-sequence generation model was constructed based on machine learning models (i.e. K-medoids and Transformer) to generate individual activity sequences aligning with human activity patterns. Then, a spatial-location selection model was proposed based on mechanistic models (e.g. Universal Opportunity model) to explicitly determine the specific locations of the activities in each generated sequence. Experimental results showed that compared to baselines based on purely machine learning or mechanistic models, Act2Loc can better reproduce the spatio-temporal characteristics of the real data, with additional advantage of low data requirements for training, proving its potential for generating synthetic trajectories in practice. This research offers new insights on knowledge-guided GeoAI models for human mobility.

Mobile phone location data have been extensively used to understand human mobility patterns through the employment of mobility indicators. The temporal sampling interval (TSI), which is measured by the temporal interval between consecutive records, determines how well such data can describe human activities and influence the values of human mobility indicators. However, systematic investigations of how the TSI affects human mobility indicators remain scarce, and characterizing those relationships is a fundamental research question for many related studies. This study uses a mobile phone location dataset containing 19,370 intensively sampled individual trajectories (TSI < 5 minutes) to systematically assess the impacts of the TSI on four typical mobility indicators that describe human mobility patterns from different aspects, which are movement entropy, radius of gyration, eccentricity, and daily travel frequency. We find that different TSIs have complex impacts on the values of different mobility indicators. Specifically, (1) coarser TSIs tend to underestimate the values of the four selected indicators with different degrees; (2) the degrees of underestimation vary significantly among users for eccentricity and daily travel frequency but exhibit high inter-user consistency for radius of gyration and movement entropy. The above findings can help better understand the variations among human mobility studies.

Understanding the stability of urban flows is critical for urban transportation, urban planning and public health. However, few studies have measured the stability of aggregate human convergence or divergence patterns. We propose a spatiotemporal model for assessing the stability of human convergence and divergence patterns. A mobile phone location data set obtained from Shenzhen, China, was used to assess the stability of daily human convergence and divergence patterns at three different spatial scales, i.e. points (cell phone towers), lines (bus lines) and areas (traffic analysis zones [TAZs]). Our analysis results demonstrated that the proposed model can identify points and bus lines with time-dependent variations in stability, which is useful for delineating TAZs for transportation planning, or adjusting bus timetables and routes to meet the needs of bus riders. Comparisons of the results obtained from the proposed model and the widely used entropy measure indicated that the proposed model is suitable for assessing the differences in stability for various types of spatial analysis units, e.g. cell phone towers. Therefore, the proposed model is a useful alternative approach of measuring spatiotemporal stability of aggregate human convergence and divergence patterns, which can be derived from the space–time trajectories of moving objects.

In recent years, call detail records (CDRs) have been widely used in human mobility research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement. However, can we fully trust CDRs given the uneven distribution of people’s phone communication activities in space and time? In this article, we investigate this issue using a mobile phone location dataset collected from over one million subscribers in Shanghai, China. It includes CDRs (~27%) plus other cellphone-related logs (e.g., tower pings, cellular handovers) generated in a workday. We extract all CDRs into a separate dataset in order to compare human mobility patterns derived from CDRs vs. from the complete dataset. From an individual perspective, the effectiveness of CDRs in estimating three frequently used mobility indicators is evaluated. We find that CDRs tend to underestimate the total travel distance and the movement entropy, while they can provide a good estimate to the radius of gyration. In addition, we observe that the level of deviation is related to the ratio of CDRs in an individual’s trajectory. From a collective perspective, we compare the outcomes of these two datasets in terms of the distance decay effect and urban community detection. The major differences are closely related to the habit of mobile phone usage in space and time. We believe that the event-triggered nature of CDRs does introduce a certain degree of bias in human mobility research and we suggest that researchers use caution to interpret results derived from CDR data.

Exploring the evolution of people’s social interactions along with their changing physical locations can help to achieve a better understanding of the processes that generate the relationships between physical distance and social interactions, which can benefit broad fields of study related to social networks. However, few studies have examined the evolving relationships between physical movements and social closeness evolution. This is partially related to the shortage of longitudinal data in both physical locations and social interactions and the lack of an exploratory analysis environment capable of effectively investigating such a process over space and time. With the increasing availability of sociospatiotemporal data in recent years, it is now feasible to examine the relationships between physical separation and social interactions at the individual level in a space–time context. This research was intended to offer a spatiotemporal exploratory analysis approach to address this challenge. The first step was to propose the concept of a social closeness space–time path, which is an extension of the space–time path concept in time geography, to represent evolving human relationships in a social closeness space. A space–time geographical information system (GIS) prototype was then designed to support the representation and analysis of space–time paths in both physical and social closeness spaces. Finally, the effectiveness of the proposed concept and design in gaining insight into the impact of physical migration on online social closeness was demonstrated through an empirical study. The contributions of this study include an extension of the time–geographic framework from physical space to social closeness space, the development of a multirepresentation approach in a GIS to integrate an individual’s space–time paths in both physical and social closeness spaces, and an exploratory analysis of the evolving relationships between physical separation and social closeness over time.

Complex spatial control problems can be computationally intensive. Timely response in urgent spatial control situations such as wildfire control poses great challenges for the efficient solving of spatial control problems. Web-based and service-oriented architectures of integrating geographic information system (GIS) clients and parallel computing resources have been suggested as an effective paradigm to solve computationally intensive spatial problems. Such real-time coupling framework is highly dependent upon interactivity and on-demand availability of dedicated parallel computing resources appropriate for the problem. We present an approach to enhancing the efficiency of solving spatial control problems while offering another coupling framework of integrating computing resources from desktop GIS and parallel computing environments to alleviate such dependency. Specifically, a model knowledge database is developed to bridge the gap between desktop GIS models and parallel computing resources. Desktop GIS models can iteratively improve themselves by steering rules retrieved from the model knowledge database. To examine its effectiveness, we applied the framework to a wildfire control case. Simulation results show dramatic reduction in computation time of the improved desktop GIS model, and indicate that desktop GIS models enhanced by model knowledge databases can be useful in providing timely assistance on computationally intensive spatial control problems.