Rising customer demands and the complexities of dynamic urban systems pose significant challenges for logistics distribution, especially since large-scale real-time dynamic traffic information is not always accessible. However, few studies have focused on optimizing logistics in the ever-changing traffic environments of megacities with multiple distribution centers. This study proposes two deep reinforcement learning models with Transformer architectures to optimize logistics distribution time costs across multiple depots in static and dynamic traffic scenarios, respectively. The first model (DTM-MDVRP) incorporates travel times between customers as edge information in the encoder to pre-plan delivery routes. The second model (DTM-DMDVRP) introduces a feature embedding module to extract real-time traffic information for dynamic route optimization. Wuhan city was selected for logistics optimization experiments. Results indicate that DTM-MDVRP surpasses heuristic methods and other deep reinforcement learning methods in optimization effectiveness and computation time. In dynamic urban traffic environments, DTM-DMDVRP further improves distribution efficiency. Compared to the traditional attention model, DTM-DMDVRP reduces time costs by 7.77, 3.51, and 3.58% across three problem scales and can optimize delivery routes for 100 customer points within 0.30 seconds. The proposed DTM-DMDVRP enables the real-time dynamic scheduling of logistics vehicles for logistics enterprises.

Understanding and explaining complex geographic phenomena—ranging from climate change to socioeconomic disparities—is a central focus in both geography and the broader scientific community. Various methods have been developed to elucidate relationships between variables, from coefficient estimates in linear regression models to the increasingly dominant use of feature attribution scores in Explainable AI (XAI) techniques. However, explanations generated by XAI methods often carry uncertainty, stemming from the model itself and the data used to train the model. Despite the critical importance of accounting for such uncertainty, this issue remains largely overlooked in the geospatial domain. In this study, we developed an uncertainty quantification framework for XAI explanations based on conformal prediction, termed Geospatial eXplanation Conformal Prediction (GeoXCP). By incorporating spatial dependence into the modeling process, GeoXCP produced spatially adaptive explanations with calibrated uncertainty estimates. We validated the effectiveness of GeoXCP through extensive simulation experiments and real-world datasets. The results demonstrated that GeoXCP provided reliable explanations while effectively quantifying uncertainty across diverse geospatial scenarios. Our approach represented a significant advancement in explainable geospatial machine learning, enabling decision-makers to better assess the trustworthiness of model-driven insights. The proposed framework was implemented in a python package, named GeoXCP.

Understanding the relationships between geographical variables is a fundamental task in spatial analysis. However, existing spatial methods often underperform in scenarios involving nonlinear relationships and complex interactions among geographical variables. Identifying the relationships between individual variables (i.e. univariate effect) within multiple interacting variables remains challenging and long-lasting. In this study, we propose a novel model—Geographical Pattern Interaction (GPI)—based on the premise that the spatial pattern of a response variable emerges from the interaction of spatial patterns in explanatory variables. GPI leverages decision trees and Shapley value explanations to quantify both global and local univariate effects by measuring the alignment between the spatial distribution of the target variable and those of the predictors. Through simulation experiments, we demonstrate GPI’s superior performance compared to traditional regression-based spatial explanation methods. Notably, the GPI framework is stable across varying spatial scales and sample sizes, making it particularly suitable for spatial explanation tasks under small data and multi-scale conditions. A case study on homelessness risk in Australia demonstrates GPI’s ability to reveal nonlinear spatial associations and interaction effects. By capturing overlooked pattern similarities and interactions, GPI offers an interpretable and transferable tool for analyzing complex spatial relationships.

Understanding the intricacies of fine-grained population distribution, including both predictability and uncertainty, is crucial for urban planning, social equity, and environmental sustainability. The spatial processes associated with the distribution of populations are complex, and enhancing their predictability involves revealing nonlinear interactions among various explanatory variables. Additionally, population distribution is influenced by various factors that are often challenging to quantify, thereby introducing uncertainty into predictive models. Although the development of explainable artificial intelligence (XAI) helps identify underlying factors, the complex geographical processes and the special nature of spatial data present challenges for purely statistical-based explanation methods, leading to incomplete or incorrect explanations. To address these challenges, we introduce GeoVisX, a geospatial visual analytics framework integrated with XAI. GeoVisX integrates XAI with visual analytics to dissect the spatial processes. Through a case study of Munich, GeoVisX demonstrates its utility in analyzing spatial distribution and identifying key factors impacting population distribution at the 100 m grid level. Our findings highlight the GeoVisX’s capability to enhance understanding of geographical phenomena, contributing to more informed urban policy and planning strategies. This study not only validates the effectiveness of GeoVisX but also emphasizes the importance of incorporating visual analytics and explainable methodologies for addressing complex geographical issues.

A high-quality land-use dataset is crucial for constructing a high-performance land-use classification model. Due to the complexity and spatial heterogeneity of land-use, the dataset construction process is inefficient and costly. This challenge affects the quality of datasets, consequently impacting the model’s performance. The emerging field of Data-Centric Artificial Intelligence (DCAI) is expected to deliver techniques for dataset optimization, offering a promising solution to the problem. Therefore, this study proposes a data-centric framework named DCAI-CLUD for the construction of land-use datasets. Based on this framework, the accuracy and rate of data labeling are improved by 5.93 and 28.97%. The Gini index of the dataset and the proportion of samples with non-mixed land-use categories are enhanced by 3.27 and 8.52%. The overall accuracy (OA) and Kappa of the land-use classification model improved significantly by 27.87 and 58.08%. This study is the first to introduce DCAI into the field of geographic information and remote sensing and verify its effectiveness. The proposed framework can effectively improve the construction efficiency and quality of the dataset and synchronously optimize the model performance. Based on the proposed framework, we constructed a multi-source land-use dataset of major cities in China named CN-MSLU-100K.

Urban logistics is vital to the development and operation of cities, and its optimization is highly beneficial to economic growth. The increasing customer needs and the complexity of urban systems are two challenges for current logistics optimization. However, little research considers both, failing to balance efficiency and cost. In this study, we propose a hybrid sparrow search algorithm (SA-SSA) by combining the sparrow search algorithm with fast computational speed and the simulated annealing algorithm with the ability to get the global optimum solution. Wuhan city was selected for logistics optimization experiments. The results show that the SA-SSA can optimize large-scale urban logistics with guaranteed efficiency and solution quality. Compared with simulated annealing, sparrow search, and genetic algorithm, the cost of SA-SSA was reduced by 17.12, 18.62, and 14.72%, respectively. Although the cost of SS-SSA was 11.50% higher than the ant colony algorithm, its computation time was reduced by 99.06%. In addition, the simulation experiments were conducted to explore the impact of spatial elements on the algorithm performance. The SA-SSA can provide high-quality solutions with high efficiency, considering the constraints of many customers and complex road networks. It can support realizing the scientific scheduling of distribution vehicles by logistics enterprises.

Spatial heterogeneity refers to uneven distributions of geographical variables. Spatial interpolation methods that utilize spatial heterogeneity are sensitive to the way in which spatial heterogeneity is characterized. This study developed a Generalized Heterogeneity Model (GHM) for characterizing local and stratified heterogeneity within variables and to improve interpolation accuracy. GHM first divides a study area into multiple spatial strata according to the sample values and locations of a variable. Then, GHM estimates simultaneously the spatial variations of the variable within and between the spatial strata. Finally, GHM interpolates unbiased estimates and uncertainty at unsampled locations. We demonstrated the GHM by predicting the spatial distributions of marine chlorophyll in Townsville, Queensland, Australia. Results show that GHM improved both the overall interpolation accuracy across the study area and along strata boundaries compared with previous interpolation models. GHM also avoided bull’s eye patterns and abrupt changes along strata boundaries. In future studies, GHM has the potential to be integrated with machine learning and advanced algorithms to improve spatial prediction accuracy for studies in broader fields.

The explanation of spatial errors in geospatial modelling has long been a challenge. This study introduces an index that captures the complexity of local spatial distribution, which can partially provide insight into spatial errors. While previous studies have explored the complexity of geographical data from various perspectives, there is limited knowledge on assessing the complexity while taking spatial dependence into account. This study proposes a measure of geocomplexity, i.e. the spatial local complexity indicator, which characterizes the complexity of local spatial patterns while considering spatial neighbor dependence. We used both aspatial and spatial models to estimate the economic inequality in Australia, and applied the spatial local complexity indicator to explain spatial errors in these models. Results show that the developed geocomplexity indicator, using a binary spatial matrix, can effectively explain spatial errors arising from models, including 17%-47% of errors in aspatial models and 14% in a spatial model. The experiments in this study support our hypothesis that geocomplexity is an essential component in explaining spatial errors. The proposed geocomplexity indicator, along with our hypothesis, has the potential for advancing the understanding complex geospatial systems and enabling applications in various fields related to spatial data analysis.

Semantic place annotation can provide individual semantics, greatly helping the field of trajectory data mining. Most existing methods rely on annotated or external data and require retraining models following a region change, thus preventing their large-scale applications. Herein, we propose an unsupervised method denoted as UPAPP for the semantic place annotation of individual trajectories using spatiotemporal information. The Bayesian Criterion is specifically employed to decompose the spatiotemporal probability of visiting the candidate place into spatial probability, duration probability, and visiting time probability. Spatial information in two geospatial data sources is comprehensively integrated to calculate the spatial probability. In terms of the temporal probabilities, the Term Frequency–Inverse Document Frequency weighting algorithm is used to count the potential visits to different place types in the trajectories and to generate the prior probabilities of the visiting time and duration. Finally, the spatiotemporal probability of the candidate place is then combined with the importance of the place category to annotate the visited places. Experimental results in a trajectory dataset collected by 709 volunteers in Beijing showed that our method achieved an overall and average accuracy of 0.712 and 0.720, respectively, indicating that the visited places can be annotated accurately without any annotated data.