With the expansion of urban areas, a mix of transportation modes has become prevalent during the daily commutes of city dwellers. That is, commuters often need to transfer between various modes to reach their destinations. Accurate identification and analysis of these transfer behaviors are crucial for advancing urban transportation research. Current research tends to focus on distance or time thresholds, typically derived from walking speeds or anecdotal experience. However, these approaches often overlook the distinct station densities within cities. Other studies, while utilizing GPS, GTFS, and similar datasets, construct intricate transfer identification methods that lack generalizability. Against this backdrop, we introduce a time-distance dual-constraint transfer recognition algorithm. Firstly, leveraging extensive traffic IC card data, based on the statistical characteristics of the proximity distance sequences between bus or subway stations and their M neighboring stations, distance thresholds for bus-bus, bus-subway, and subway-bus transfer are detected individually. Subsequently, a filtering algorithm based on these distance thresholds is applied to daily data to produce a candidate transfer data set. Based on this, four time thresholds for each day are determined by analyzing the statistical characteristics of the transit time differences within the datasets. Finally, these dual thresholds facilitate the precise extraction of transfer behaviors. Furthermore, we establish a classification framework for these behaviors, classifying them into nine distinct transfer modes. These modes are defined based on the duration of travel time in the first and second journeys, encompassing variations including long-long, long-medium, long-short, middle-long, middle-middle, middle-short, short-long, short-middle, and short-short. We analyze these models individually for their travel characteristics. Results reveal that the morning peak for all transfer trips precedes that of buses and subways, with short-long transfers leading by up to 30 minutes. This underscores the added effort required by commuters who rely on transfers. In contrast, evening peak times vary, with certain transfer modes like long-long and long-short lagging notably behind the general evening peak. This further emphasizes the increased commuting burden associated with transfers. In terms of travel distances, the peak of regular subway travel distances is around 10 km, while that of the bus travel distances is around 1 km. The peak commuting distances for all nine transfer behaviors are greater than those of typical trips and are distributed within a range of 20~40 km. In summary, our method for extracting and analyzing transfer behaviors offers a robust and effective tool for urban transportation research, urban vitality assessment, public transportation planning, and urban planning.

Accurate and explainable prediction of PM_2.5 concentration can help humans avoid exposure risks to air pollution, which is of great significance for human health risk assessment and policy implementation. Currently, the existing PM_2.5 concentration prediction models focus on improving the model prediction accuracy without considering model interpretability, resulting in poor model reusability and trustworthiness. Therefore, this paper proposes an Attentional Spatiotemporal Ordinary Differential Equation (ASTODE) model for PM_2.5 concentration prediction tasks considering both prediction accuracy and model interpretability. Specifically, this paper integrates the Neural Ordinary Differential Equation (NODE) into the PM_2.5 concentration prediction task to improve the interpretability of the prediction model. In addition, to address the challenge of traditional NODE in mining spatial dependencies in PM_2.5 concentration data, this paper proposes a novel spatiotemporal derivative network that extends the traditional NODE to spatiotemporal ordinary differential equations. To address the challenges of traditional NODE in mining long-term dependencies in PM_2.5 concentration data, this paper proposes a spatiotemporal attention mechanism to fuse hidden states of multiple time nodes. In the experimental section, the proposed ASTODE model is validated using a real PM_2.5 concentration dataset. This paper quantifies the prediction errors of the ASTODE model in both temporal and spatial dimensions. By comparing with six existing baseline methods, our proposed ASTODE model obtains a similar or higher prediction accuracy. This paper also analyzes the interpretability of our proposed ASTODE model from a visualization perspective, demonstrating that the proposed ASTODE model balances the prediction accuracy and interpretability to some extent.

With the continuous advancement of the globalization process, communication and cooperation among countries and regions around the world are becoming increasingly closer, and the scale of international migration flows is also expanding. Asia stands out as an active region for international migration, with a large portion of migratory movements occurring within its borders. In addition to the social and economic factors of the origin and destination regions, spatial and temporal dependence among migration flows is crucial in understanding international migration dynamics, indicating that migration is influenced by neighboring and past migration flows. Different from other kinds of data (e.g., regional GDP), migration flows between different regions often contain many zero values, necessitating specific methods for handling them. Additionally, spatial and temporal dependence among migration flows can be categorized into space-time contemporaneous and lagged structures, with the former reflecting the links to the preceding location and the instantaneous neighboring locations, and the latter pertaining to the preceding location and the preceding neighboring locations. Based on the bilateral migration data of Asian countries in six periods from 1990 to 2020, this study utilizes eigenvector space-time filtering models, along with contemporaneous and lagged dependent structures, as well as eigenvector spatial filtering models and zero-inflated negative binomial regression models, to explore the influential factors of the international migration flows within Asia and their changes during 1990-2020. Finally, this study aims to forecast international flows within Asia between 2020 and 2025 based on two types of space-time filtering models. Preliminary results indicate significant space-time autocorrelation of international migration flows within Asia, with neighboring migration flows exerting a greater influence over the same time period compared to the past. Incorporating eigenvectors to represent spatial and temporal dependence effectively improves the goodness-of-fit of the models. Main factors affecting international migration flows within Asia include population size, economic level, war situation, and proximity. During the 30 years (1990-2020), the influence of population size fluctuated, economic disparities initially increased before weakening, wars continued to drive emigration, geographical barriers decreased, and factors like language proximity and economic cooperation significantly influenced migration. Looking ahead from 2020 to 2025, migration trends are evident between Pakistan and India, as well as from India to Saudi Arabia, from Pakistan to Afghanistan and from Syria to Jordan. Combining the forecasting results of the two eigenvector space-time filtering models, the mean value of the total volume of international migration flows within Asia from 2020 to 2025 is projected to be approximately 1.8×10⁷. India emerges as a major country for international migration. Understanding the spatial and temporal dependence and other characteristics of international migration within Asia is crucial for accurately forecasting future migration flows and providing scientific reference for policy making.

The movement of people within urban areas serves as a driving force for the development of social phenomena. Accurate Origin-Destination (OD) flow data record spatial interaction patterns of individuals, goods, or information from their starting points (Origin [O]) to their destinations (Destination [D]). Precise prediction of internal city OD flows is crucial for optimizing urban traffic operational efficiency, enhancing resource utilization, and fostering sustainable urban development. However, obtaining high-quality OD flow data is challenging due to issues such as privacy protection. There are significant hurdles, including high acquisition costs, limited coverage within large areas, and sparse spatial distribution, which hinder extensive research in urban computation. Current research often relies on a single scale, utilizing extensive historical traffic data between geographic locations to predict future flows. Yet, there has been limited exploration into crucial features and model accuracy for different spatial scales. This study addresses this gap by employing taxi trajectories in Beijing and leveraging the Deep Gravity model to predict OD flow at different spatial scales. Additionally, the integration of SHapley Additive exPlanations (SHAP) values sheds light on the pivotal features influencing OD flow predictions across diverse scales. Results show that: 1) Compared to Gravity model and Radiation model, the Deep Gravity model at the street scale exhibits the highest accuracy in predicting OD flows, achieving an impressive Common Part of Commuters (CPC) value of 0.83. The Deep Gravity model effectively captures the overall structure of the OD flow network during peak morning and evening hours in Beijing, revealing a distinctive "circular dispersal" pattern; 2) For the selected spatial scales, the four features with the most significant impact on OD flow prediction accuracy are the travel distance between O and D points, the number of businesses around O and D points, the quantity of dining establishments, and the number of shopping services; 3) The local impact of the same feature on OD flow prediction models differs from its global impact. For instance, features related to education, science, and culture, as well as sports and leisure Points of Interest (POI), exhibit relatively minor effects on the model at a global scale. However, on a local scale, these features demonstrate a significant influence. This study has achieved high-precision prediction of OD flows at various spatial scales. Additionally, it quantitatively reveals the crucial factors influencing OD flow modeling at different spatial scales, thereby providing valuable insights into understanding population movements within urban areas. The findings of this research hold significant practical implications for urban planning, traffic management, and the development of smart cities.

Traditional settlements have gathered a wealth of traditional cultural resources such as ancient architecture and folklore, which have attracted significant attention for their outstanding historical, cultural and artistic values, and it is of positive significance to extract their abundant historical and cultural information and serve them for modern industrial development. Currently, there is a lack of knowledge extraction, organization and expression of the rich historical and cultural information of traditional settlements based on geographic knowledge extraction and expression perspectives to achieve the transformation of "data-information-knowledge-wisdom", this paper proposes the geographic ontology of cultural landscape genes of traditional settlements (GeoOnto-CLGTS) and explores the intrinsic correlation characteristics of the traditional landscape genes of traditional settlements. Firstly, combining the geographic information ontology and characteristics of traditional settlement landscape genes, the concept and expression method of GeoOnto-CLGTS are analyzed, and this paper proposes the construction method of GeoOnto-CLGTS model. Secondly, combing the landscape gene concepts, association relationships and data attribute characteristics, the seven-step geographic information ontology modeling method is applied to construct the conceptual layer of GeoOnto-CLGTS from top-down. By utilizing Protege tool to supplement examples using 123 traditional Chinese settlements as cases, the instance layer construction of the GeoOnto-CLGTS model is achieved. Finally, the GeoOnto-CLGTS data is stored through the Neo4j graph database to complete the construction of the knowledge graph of traditional settlement landscape genes, enabling the retrieval of landscape gene information. The results show that the GeoOnto-CLGTS constructed in this paper can provide a valuable reference for carrying out knowledge discovery of traditional settlement cultural resources and promoting digital preservation of traditional settlements in the future.