Cervical cancer and breast cancer (CBC) are malignant tumours that threaten women's health. Previous studies have primarily explored spatiotemporal variations in CBC incidence at large and medium scales and the direct effects of influencing factors on CBC incidence. However, small-scale studies and investigations of the spatial spillover effects of influencing factors remain limited. In this paper, statistical and spatial pattern analysis methods reveal the spatiotemporal variations in CBC incidence that occurred in Anhui Province at the county level from 2020 to 2023. Moreover, the direct, spatial spillover and total effects of the influencing factors on CBC incidence are determined using the spatial Durbin model (SDM). The results reveal following: (1) The average annual growth rates in the incidence of cervical cancer and breast cancer are 15.7% and 9.7%, respectively. (2) The incidence of cervical cancer tends to decrease from southeast to northwest. The incidence of breast cancer decreases from the southern and northern regions to the central region. (3) Reproductive tract infections (0.631) and benign breast diseases (0.607) have the strongest total effects on CBC incidence. PM2.5 has a strong positive spatial spillover effect on CBC incidence, whereas GDP has a relatively negative spatial spillover effect on CBC incidence in adjacent regions. These spatial spillover effects call for cross-county air pollution governance and regionally equitable healthcare allocation.

Exploring the spatial variation of emotion has attracted increasing attention in recent years. However, few scholarly attempts have focused on the spatiotemporal process of emotional dynamics. This study aimed to depict continuous and fine-grained human emotions and investigate the patterns of emotional changes from a spatiotemporal perspective. Electrodermal activity (EDA), audio and GPS data were collected from 256 volunteers in the Fanta Oriental Heritage theme park. We developed an integrated framework that combines multimodal emotion recognition, emotional trajectory construction, and pattern mining to examine affective dynamics in a theme park environment. The emotional trajectories were constructed by matching emotional status and GPS. With the support of the hierarchical clustering, the similarities among the emotional trajectories were investigated. Compared with relying solely on single-modal data, our recognition method exhibited significant improvement by fusing EDA and audio, achieving a four-class classification accuracy of more than 0.68. Our results demonstrated that the trajectory model can represent the continuous spatiotemporal changes of minute-by-minute emotional statuses effectively. Further, we have discovered three patterns from emotional trajectories within the theme park. The “Peak-decrease” pattern is sensitive to the exciting experiences and includes most young individuals and females. All tourism projects can stimulate the emotion of individuals belong to the “High-low” pattern significantly. The “Stable-fluctuation” pattern showed the stable emotional dynamic and contained half of the individuals older than 40. This work provides new insights into the fine-grained human emotional changes to enrich the spatiotemporal theories of human emotions, and highlights the potential applications of our findings in urban planning and experience monitoring.

ABSTRACT Physiological arousal, as a typical objective perception, can avoid the influence of human subjective biases and directly express emotional intensity. Existing studies on physiological arousal were limited to small‐scale urban areas, lacking the exploration of large‐scale estimation methods and the spatial heterogeneity of physiological arousal. This paper proposes a framework for analyzing physiological arousal by combining street view image (SVI) and electrodermal activity (EDA). An interpretable machine learning method was applied to reveal the impact of visual factors on physiological arousal. By capturing the neighboring visual environment, a hybrid deep learning model was constructed to estimate physiological arousal. Our results revealed that horizontal vegetation and soil exerted a stronger modulatory effect on physiological arousal, while traffic signs reduced arousal, contrasting with arousal enhancement from traffic lights. The model accounting for the neighboring visual environment outperforms the single‐SVI model. These findings provide an “objective” perspective for urban planning and mental health interventions.

Urban residents' transportation modes play a pivotal role in shaping transportation planning and policies for sustainable cities. Mining refined transportation modes from mobile phone location (MPL) data is a key spatiotemporal big data application for sustainable city planning and traffic management. However, key challenges persist: low recognition accuracy due to insufficient consideration of travel features of transportation modes, the positioning uncertainty of MPL data, and ineffective evaluation due to lacking validation datasets. To address these limitations, we propose an analytical framework for transportation mode recognition. First, precise moving segments are constructed through road network matching and linear interpolation, resolving the positioning uncertainty issues of MPL data. Then, we propose a comprehensive feature parameter system for transportation mode recognition and construct a transportation mode recognition model based on eXtreme Gradient Boosting (XGBoost). Finally, using synchronously collected GPS data and travel logs, we validated the framework's recognition results, demonstrating its ability to improve the accuracy of transportation mode recognition.

As urbanization intensifies, congestion and safety concerns in various public venues have garnered significant societal attention, presenting substantial challenges for public safety management and emergency response. To address this issue, developing a technological framework capable of automatic perception, analysis, and early warning of crowd dynamics is urgently needed. Current technologies face hindrances such as low data precision, limited generalization across diverse scenarios, incomplete crowd coverage, and sluggish responsiveness. The popularity of sensor networks and the rapid development of computer vision technology have made it possible to collect and perceive spatial and temporal information ubiquitously, creating valuable opportunities for the rapid analysis of geographic phenomena and timely detection of potential problems. This study drew on the concepts of social comfort distance and spatial carrying capacity by employing high-coverage surveillance video streams as the data source. We introduced a framework for crowd activity perception and scene-adaptive congestion risk assessment based on a multi-object tracking model. This framework established a mutual mapping between the image and geographic spaces, facilitating precise spatiotemporal identification and grading of congestion. The validity of the method was demonstrated across various scenes in a tourist area through a comprehensive analysis of its perception accuracy and efficiency. The flexible and modular architecture of the proposed technology paves the way for its broader application and offers an effective solution for refined crowd management in public spaces.

Understanding and modelling the fine-grained spatiotemporal behaviours of tourists is crucial for intelligent tourism management and for improving visitor services. However, existing methods often lack cost-effective and sustainable approaches for analyzing micro-level tourist activities and human-environment interactions, limiting insights into movement patterns within specific geographic contexts. To address this gap, we introduce video data and propose a scene adaptive slicing inference-multi-object tracking model alongside a spatiotemporal trajectory clustering method for the detailed exploration of tourist movement behaviours. Our approach enhances the accuracy of tourist trajectory modelling and provides valuable insights into the relationship between tourist behaviour and resource services. Through a case study of a popular tourist destination in Nanjing, we identified patterns in tourist behaviour influenced by terrain slope, proximity to core attractions, resting facilities, and road vegetation. These findings offer practical guidance for optimising resource allocation and management services in tourist areas. The proposed framework is a robust tool for understanding micro-level tourist behaviour and improving management quality. Future research should explore seasonal patterns and group differences in tourist behaviour by expanding the dataset and incorporating demographic features.

Urban greenery and shaded sidewalks can mitigate heat radiation hazards, enhance physiological comfort, and, therefore, contribute to public health and sustainable urban development. Advances in big data and computer vision technologies are driving human-centered measurements for street environments in urban spatial modeling and analysis. Currently, remote sensing image classification and green-view index calculations provide the basis for assessing street canopies but are ineffective in extracting precise segments of shaded sidewalk. This study introduces a novel method to construct three-dimensional (3D) semantic point clouds via integrating geospatial semantics from panoramic images with depth information. The method extracts sidewalk lines and canopy shadows along sidewalks to increase mapping accuracy. Manually annotated ground-truth data and experimental results from Hong Kong were used to validate the reliability of the model, which identified the canopy with 94.8% accuracy. The results of canopy coverage analysis revealed large spatial inequity in pedestrian access to shaded streets in the study area, which can be used to inform greenspace optimization and pedestrian route planning. This method is generalizable to other cities, supporting the development of healthy cities and user-centric information services.

Emotion has significant spatio-temporal characteristics, and predicting the spatio-temporal changes in emotion is an important premise for monitoring the emotional state of urban residents. Most prediction methods focus on the prediction of emotion in time series without considering the spatial properties of emotion. Based on geotagged image data on the Weibo platform from Shanghai, a user location emotion prediction method that considers multidimensional spatio-temporal dependencies between different emotional states is proposed in this paper. The method introduces the HiSpatialCluster algorithm to identify the users’ stay area. Then, the FaceReader algorithm is applied to determine the emotional quadrant of users from image data, and a graph embedding algorithm is employed to obtain the feature vector representing each stay area. Finally, an attention-based BiLSTM method is applied to construct the multidimensional spatio-temporal dependencies of emotion for prediction. Experiments on the Weibo dataset show that the prediction accuracy of location emotion reaches 75%, which is better than that of the single LSTM and CNN method. The results of this paper can not only deepen the understanding of the spatio-temporal variation patterns of emotion but also optimize location-based recommendation services.

Urban functions often diverge from initial planning due to changes driven by residents’ behaviors. Effective urban planning and renewal require accurately identifying urban functional regions based on residents’ behavior data (including activity and travel data). However, previous methods have primarily relied on either point of interest (POI) data or a single source of traffic data, and often ignore the combined influence of residents’ activities and travel behaviors. In this study, we introduce a novel framework that integrates multiple sources of traffic data (such as metro smart card data and car-hailing data) with POI data to identify urban functional regions. This approach is unique because it simultaneously considers two critical dimensions of residents’ behavior: travel and activity behaviors. By combining these dimensions, we extract a comprehensive set of characteristics, including travel time, travel flow, origin-destination patterns, activity types, and activity time, which are then aggregated at the regional level (i.e., traffic analysis zone). To process these characteristics, we use latent Dirichlet allocation (LDA) to extract high-level semantic features from each data type. Additionally, to handle the sparse data from metro smart cards, we employ a specialized clustering technique. The integration of diverse and complementary information from multiple data sources enables more accurate and nuanced identification of urban functional regions than single data source and k-means clustering algorithm, providing valuable insights for urban planners.

The positioning uncertainty of mobile phone location (MPL) data greatly influences location services and crowd behavior analysis. Although many achievements have been made in controlling its main sources (signal drift and ping-pong effects), several problems, such as single-oscillation patterns, insufficient position optimization, and a lack of effective evaluation, remain. In this study, a set of MPL data quality optimization methods are proposed. First, the characteristics of drift records and the oscillation patterns of ping-pong records are discussed. The quality of the MPL data is subsequently controlled with the proposed feature-based drift-record detection method, complex oscillation pattern-based ping-pong-record detection method, and cumulative duration weighting-based ping-pong-record optimization method. These methods are applied to the MPL dataset of a major operator in Nanjing city, and the optimization effect is evaluated with GPS data collected synchronously. The results show that the proposed detection and optimization methods can effectively improve the accuracy of MPL data.

Tourism is an emotional sphere, and researchers focus on emotions to optimize tourism experiences. Tourism studies on emotions mostly ignore differences in emotions across demographic tourist groups by gender and age, thus limiting the understanding of emotions to the explicit characteristics of tourists’ emotions. On the basis of geotagged facial expressions on social media platforms, this study aims to visualize the emotions of groups in scenic spots and then reveal the variations between groups’ emotions within theme parks. By employing a facial recognition algorithm, an emotion distribution graph was proposed to represent groups’ emotions in detail. Some analytical methods were combined to characterize of the emotion distribution of each group. Through a comprehensive comparison, the results suggest that there are unique characteristics of emotion distribution for each group and considerable variations between them. This study helps researchers achieve a deeper understanding of tourists’ emotional differences and enhances the theorization of emotions. This research also highlights the advantages and significant practical implications of our method framework.

Microscale research on tourism flows is crucial for controlling such flows, analyzing resource-carrying capacity, and promoting sustainable development in the tourism industry. Current fine-grained monitoring of tourism flows using location-based big data faces challenges, such as inadequate user representation, data acquisition difficulties and spatiotemporal uncertainty. This study presents a method for the spatiotemporal modeling and estimation of regional tourism flows based on the collaborative perception of discrete surveillance videos. The method employed bridges the gap between physical and video image scenes by establishing collaborative perception relationships among multiple devices, thereby enabling the precise modeling and estimation of the dynamic spatiotemporal processes of population movement in the region. Empirical studies in real scenic areas confirm the adaptability of this technology to diverse geographical scenes and ensure the accuracy of the spatiotemporal flow estimation. This study addresses the challenges of high sampling costs and low spatiotemporal accuracy in regional fine-grained crowd estimation and offers technical support for near real-time dynamic crowd modeling and monitoring. The experimental results have the potential to assist in applications, such as tourism flow management, dynamic regulation and the risk analysis of group activities in scenic areas.

Urban space vitality is a critical indicator for supporting rational urban spatial planning and updating and formulating sustainable development strategies. However, in many areas (e.g., aging urban areas), there is often a mismatch between the conditions of the physical built environment and its spatial attractiveness. Traditional methods based on physical space design theory often fail to accurately measure the spatial vitality of these areas. Street view images directly reflect the actual construction situation and effectively compensate for the lack of visual, subjective, perception dimension information. This study proposes a novel method that integrates objective and subjective dimensions to measure urban vitality, which is captured by incorporating spatial data of points of interest, building outlines, road networks, and street view images. Then, taking mobile phone signaling data as a source of ground truth validation, we choose Nanjing as a case study to demonstrate that our multidimensional fusion method exhibits higher explanatory power and better alignment with actual conditions by comparing it against single-dimensional methods. The results underscore the importance of integrating subjective and perceptual dimensions in measurements of urban vitality. We believe that the localized samples of the subjective perception survey will further enhance the accuracy and generalizability of this method in the future.

故事地图与山水游记结合,能生动刻画游记中山水现象和游历过程的时空变化,诠释其文化地理意义,对传递史地知识、建构山水审美意识和解读自然人文现象有着重要的意义.从山水游记的内涵与山水游记文本的特点出发,分析了山水游记故事地图的制图六要素,提出了山水游记故事地图的混合信息架构方式(时间、空间、主题),并阐述了山水游记故事地图的界面版式设计、多媒体与符号设计表达方法.以徐霞客庐山游记为例,实现了山水游记故事地图.提出了基于山水游记文本的故事地图设计理论与方法,丰富了现有故事地图的理论体系,可为相关的故事地图可视化设计与实现提供参考.

Quantitative evaluation of the environmental amenities (EAs) in urban recreation and leisure regions (URLRs) can provide stronger support for the government to enhance the quality of urban leisure space and improve the well-being of urban residents. Considering the diversity of leisure spaces and the complexity of environmental perception perspectives, this study proposes a comprehensive environmental measurement framework based on image and text fusion perception, which utilizes big data to perceive and quantify the EA features of URLRs comprehensively and efficiently. The study of the URLRs in Nanjing, China, was conducted as an empirical study. The results indicate the following: (1) When it comes to leisure environments, the top concerns for most people are service, hygiene, reputation, and walkability. (2) The EA level of URLRs in Nanjing generally decreases from the center to the outside and shows regional differentiation. (3) EA features in Nanjing’s URLRs exhibit a spatial pattern of similarity in the center and at each district’s edges. This study enhances our understanding of leisure regions’ environmental features that contribute to quality. The measurement results support understanding the spatial heterogeneity patterns of urban leisure activities and vibrancy. Furthermore, valuable urban planning and policy suggestions are made to promote sustainable urban development.

With the rising popularity of portable mobile positioning equipment, the volume of mobile trajectory data is increasing. Therefore, trajectory data compression has become an important basis for trajectory data processing, analysis, and mining. According to the literature, it is difficult with trajectory compression methods to balance compression accuracy and efficiency. Among these methods, the one based on spatiotemporal characteristics has low compression accuracy due to its failure to consider the relationship with the road network, while the one based on map matching has low compression efficiency because of the low efficiency of the original method. Therefore, this paper proposes a trajectory segmentation and ranking compression (TSRC) method based on the road network to improve trajectory compression precision and efficiency. The TSRC method first extracts feature points of a trajectory based on road network structural characteristics, splits the trajectory at the feature points, ranks the trajectory points of segmented sub-trajectories based on a binary line generalization (BLG) tree, and finally merges queuing feature points and sub-trajectory points and compresses trajectories. The TSRC method is verified on two taxi trajectory datasets with different levels of sampling frequency. Compared with the classic spatiotemporal compression method, the TSRC method has higher accuracy under different compression degrees and higher overall efficiency. Moreover, when the two methods are combined with the map-matching method, the TSRC method not only has higher accuracy but also can improve the efficiency of map matching.

Online texts and images have become an important data source for investigating tourism emotion. However, tourism emotion extracted from online data cannot reflect the emotions in the real world. Few scholarly attempts have focused on the complex biases in tourism emotion based on online data. By comparing online and offline emotion, this study quantified biases in tourism emotion and explored patterns among biases. Facial expressions and texts within Disney Resort were collected. Based on facial expression recognition and text mining, several indices were proposed to measure tourism emotion and biases. Our results reveal that tourists tend to share stronger happiness, increased anger in commerce areas and exaggerated disgust in waiting areas. In addition, males show "surprise-suppress"; while females show "surprise-exaggeration" in recreation areas. Our research can help scholars reexamine previous conclusions based on online tourism emotion and provides a theoretical basis for improving the quality of online tourism emotion.

Efficient bipartite graph matching of orders and drivers is a central operational problem in industrial mobility-on-demand (MOD) systems. Traditional studies adopt pure combinatorial optimization models for order-and-driver matching, which do not consider the long-term rewards of the dynamic MOD decision making process. Toward long-term optimization, this article presents a systemic paradigm of online matching with federated neural temporal difference learning (FedTDLearning), which encompasses learning and matching phases. During the learning phase, the long-term matching process is modeled as a Markov decision process, which is typically solved by employing data-driven reinforcement learning in an offline central training scheme. Massive amounts of data would be generated on the network by industrial MOD systems. It is impossible to send all the large-scale industrial data to the cloud server for centralized model training due to network bandwidth limitations and safety concerns. Therefore, a generic and innovative FedTDLearning is proposed to achieve long-term matching in a distributed manner. During the matching phase, a real-time bipartite matching optimization problem is formulated to maximize the learned spatiotemporal value and minimize the pickup distance, which is reducible to the minimum-cost maximum weight bipartite graph matching problem. A distance-learned-value ratio algorithm is proposed to find an optimal matching in the bipartite graph based on the joint optimization of FedTDLearning and the combinatorial fractional programming approach. Furthermore, to pursue optimal computation efficiency, we solve the real-time matching problem by constructing a k -nearest neighbor (kNN) bipartite graph where each order is connected with k NN drivers. Using openly available real-world data, the prototype system and experimental evaluations show that our proposed algorithms have effective problem-solving capability in practice.

Geographic information system navigation services are now incorporating psychological well‐being as a factor when devising navigation routes. However, challenges such as limited data, method generalizability, and subjective human perception remain unresolved. Therefore, a general humanized path‐navigation method that effectively quantifies human emotional perception demands is required. In this study, we designed a deep learning model using a large, crowdsourced dataset to predict emotional responses to street‐view images. Our method enhances urban path planning, thus providing comprehensive emotional benefits. Our approach and several goal‐oriented methods were applied in Nanjing, and the findings were compared via comparative analyses and questionnaire surveys. The results confirmed that our proposed method outperforms utilitarian goal‐driven path planning methods in terms of subjective perception. This study provides a widely available technique for high‐quality navigation planning that meets psychological perception needs and offers a valuable guidance for the research on humanized geographic information services.