Search Results (613)

Next Point-of-Interest (POI) recommendation is aimed at predicting users’ future visits based on their current status and historical check-in records, providing convenience to users and potential profits to businesses. The Graph Neural Network (GNN) has become a common approach for this task due to the capabilities of modeling relations between nodes in a global perspective. However, most existing studies overlook the more prevalent heterogeneous relations in real-world scenarios, and manually constructed graphs may suffer from inaccuracies. To address these limitations, we propose a model called Heterogeneous Graph Structure Learning for Next POI Recommendation (HGSL-POI), which integrates three key components: heterogeneous graph contrastive learning, graph structure learning, and sequence modeling. The model first employs meta-path-based subgraphs and the user–POI interaction graph to obtain initial representations of users and POIs. Based on these representations, it reconstructs the subgraphs through graph structure learning. Finally, based on the embeddings from the reconstructed graphs, sequence modeling incorporating graph neural networks captures users’ sequential preferences to make recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model. Additional studies confirm its robustness and superior performance across diverse recommendation tasks. Full article

Spatial association analysis is essential for understanding interdependencies, spatial proximity, and distribution patterns within spatial data. The spatial scale is a key factor that significantly affects the result of spatial association mining. Traditional methods often rely on a fixed distance threshold (bandwidth) to define the scale effect, which can lead to scale sensitivity and discontinuity results. To address these limitations, this study introduces the Fuzzy Geographically Weighted Colocation Quotient (FGWCLQ) method. By integrating fuzzy theory, FGWCLQ replaces binary distance cutoffs with continuous membership functions, providing a more flexible and stable approach to spatial association mining. Using Point of Interest (POI) data from the Beijing urban area, FGWCLQ was applied to explore both intra- and inter-category spatial association patterns among star hotels, transportation facilities, and tourist attractions at different fuzzy neighborhoods. The results indicate that FGWCLQ can reliably discover global prevalent spatial associations among diverse facility types and visualize the spatial heterogeneity at various spatial scales. Compared to the deterministic GWCLQ method, FGWCLQ delivers more stable and robust results across varying spatial scales and generates more continuous association surfaces, which enable clear visualization of hierarchical clustering. Empirical findings provide valuable insights for optimizing the location of star hotels and supporting decision-making in urban planning. The method is available as an open-source Matlab package, providing a practical tool for diverse spatial association investigations. Full article

Sports stadiums significantly influence urban morphology; however, empirical quantification of these effects remains limited. This study quantitatively examines the spatiotemporal relationship between sports architecture and urban functional evolution using Jiangwan Stadium in Shanghai—China’s first Western-style sports facility—as a case study. Employing Point of Interest (POI) data, ArcGIS spatial analyses, chi-square tests, and linear regression-based predictive modeling, we illustrate how the stadium has catalyzed urban regeneration and functional diversification over nearly a century. Our findings demonstrate a transition from sparse distributions to concentrated commercial and service clusters within a 1000 m radius around the stadium, notably in food and beverage, shopping, finance, insurance, and transportation sectors, significantly boosting local economic vitality. The area achieved peak functional diversity in 2016, showcasing a balanced integration of residential, commercial, and service activities. This research provides actionable insights for urban planners and policymakers on leveraging sports facilities to foster sustainable urban regeneration. Full article

Urban commercial districts, with their unique characteristics, serve as a reflection of broader urban development patterns. However, only a handful of studies have harnessed point-of-interest (POI) data to model the intricate relationship between retail commercial space types and other factors. This paper endeavors to bridge this gap, focusing on the influence of urban development factors on retail commerce districts through the lens of POI data. Our exploration underscores how commercial zones impact the density of residential neighborhoods and the coherence of pedestrian pathways. To facilitate our investigation, we propose an ensemble clustering technique for identifying and outlining urban commercial areas, including Kernel Density Analysis (KDE), Density-based Spatial Clustering of Applications with Noise (DBSCAN), Geographically Weighted Regression (GWR). Our research uses the city of Manchester as a case study, unearthing the relationship between commercial retail catchment areas and a range of factors (retail commercial space types, land use function, walking coverage). These include land use function, walking coverage, and green park within the specified areas. As we explore the multiple impacts of different urban development factors on retail commerce models, we hope this study acts as a springboard for further exploration of the untapped potential of POI data in urban business development and planning. Full article

Indoor positioning remains a significant challenge, particularly in environments such as museums, where the installation of specialized positioning infrastructure may be impractical. Recent advances in image processing offer effective and precise methods for object recognition, presenting a viable alternative. This study explores the feasibility of employing real-time image processing techniques for identifying points of interest (POIs) within museum settings. It outlines the ideation, design, development, and evaluation of an image-based POI identification system implemented in a real-world environment. To evaluate the system’s effectiveness, a user study was conducted with regular visitors at the Hecht Museum. The results showed that the algorithm successfully and quickly identified POIs in 97.6% of cases. Additionally, participants completed the System Usability Scale (SUS) and provided open-ended feedback, indicating high satisfaction with the system’s accuracy and speed while also offering suggestions for future improvements. Full article

The construction of transition rules is the core and difficulty faced by the cellular automata (CA) model. Dynamic mining of transition rules can more accurately simulate urban land use change. By introducing a graph attention network (GAT) to mine CA model transition rules, the temporal and spatial dynamics of the model are increased based on the construction of a real-time dynamic graph structure. At the same time, by adding an agent-based model (ABM) to the CA model, the simulation evolution of different human decision-making behaviors can be achieved. Based on this, an urban expansion scenario prediction (UESP) model has been proposed: (1) the UESP model employs a multi-head attention mechanism to dynamically capture high-order spatial dependencies, supporting the efficient processing of large-scale datasets with over 50,000 points of interest (POIs); (2) it incorporates the behaviors of agents such as residents, governments, and transportation systems to more realistically reflect human micro-level decision-making; and (3) by integrating macro-structural learning with micro-behavioral modeling, it effectively addresses the existing limitations in representing high-order spatial relationships and human decision-making processes in urban expansion simulations. Based on the policy context of the Outline of the Beijing–Tianjin–Hebei (BTH) Coordinated Development Plan, four development scenarios were designed to simulate construction land change by 2030. The results show that (1) the UESP model achieved an overall accuracy of 0.925, a Kappa coefficient of 0.878, and a FoM index of 0.048, outperforming traditional models, with the FoM being 3.5% higher; (2) through multi-scenario simulation prediction, it is found that under the scenario of ecological conservation and farmland protection, forest and grassland increase by 3142 km², and cultivated land increases by 896 km², with construction land showing a concentrated growth trend; and (3) the expansion of construction land will mainly occur at the expense of farmland, concentrated around Beijing, Tianjin, Tangshan, Shijiazhuang, and southern core cities in Hebei, forming a “core-driven, axis-extended, and cluster-expanded” spatial pattern. Full article

The experimentation of agricultural robots has been increasing in recent years, both in greenhouses and open fields. While agricultural robots are inherently useful for automating various farming tasks, their presence can also be leveraged to collect measurements along their paths. This approach enables the creation of a complete and detailed picture of the climate conditions inside a greenhouse, reducing the need to distribute a large number of physical devices among the crops. In this regard, choosing the best visiting sequence of the Points of Interest (PoIs) regarding where to perform the measurements deserves particular attention. This trajectory planning has to carefully combine the amount and significance of the collected data with the energy requirements of the robot. In this paper, we propose a method based on Deep Reinforcement Learning enriched with a Proximal Policy Optimization (PPO) algorithm for determining the best trajectory an agricultural robot must follow to balance the number of measurements and autonomy adequately. The proposed approach has been applied to a real-world case study regarding a greenhouse in Verona (Italy) and compared with other existing state-of-the-art approaches. Full article

Urban mobility structure detection and functional distribution learning are significant for urban planning and management. However, existing methods have limitations in handling complex urban data and capturing global spatial structure features. To deal with these challenges, we proposed a multi-scale feature-aware urban mobility structure embedding method based on contrastive learning. First, we designed a multi-scale contrastive learning strategy to effectively learn local human activity features and global spatial structure features, determine the community affiliation of regions, and generate regional embedding vectors. Next, we introduced a correlation matrix to encode the functional synergy and competition of Point of Interests (POIs) and construct the complex correlation between urban mobility structure and urban functional distribution to evaluate the quality of regional embedding vectors. Experiments in Haikou City show that the proposed method can accurately detect the urban mobility structure and functional distribution. The analysis reveals that the central urban area of Haikou exhibits concentrated functions and significant traffic tidal effects, while the suburban areas have relatively weaker functions, with residents displaying a high level of dependence on the central area. Therefore, urban planning needs to optimize the functional layout, improve the functions of the suburbs, and promote the balance of urban space. Full article

Territorial tourism has brought new development opportunities for historic and cultural neighborhoods. However, an insufficient understanding of the spatial distribution and influencing mechanisms of neighborhood vitality continues to constrain effective revitalization strategies. This study takes the Shuitingmen Historical and Cultural Neighborhood in Quzhou, China, as a case study and develops a multi-dimensional vitality evaluation framework incorporating point-of-interest (POI) data, location-based service (LBS) heatmaps, street network data, historical resources, and environmental perception indicators. The Analytic Hierarchy Process (AHP) is applied to assign indicator weights and calculate composite vitality scores across 19 streets. The results reveal that (1) comprehensive evaluation corrects the bias of single indicators and highlights the value of integrated assessment; (2) vitality is higher on rest days than on weekdays, with clear temporal patterns and two types of daily fluctuation trends—similar and differential; and (3) vitality levels are spatially uneven, with higher vitality in central and western areas and lower performance in the southeast, often related to low accessibility and functional monotony. This study confirms a strong positive correlation between street vitality and objective spatial factors, offering strategic insights for the micro-scale renewal and sustainable revitalization of historic neighborhoods. Full article

Point-of-interest (POI) recommendation is a crucial task in location-based social networks, especially for enhancing personalized travel experiences in smart tourism. Recently, large language models (LLMs) have demonstrated significant potential in this domain. Unlike classical deep learning-based methods, which focus on capturing various user preferences, LLM-based approaches can further analyze candidate POIs using common sense and provide corresponding reasons. However, existing methods often fail to fully capture user preferences due to limited contextual inputs and insufficient incorporation of cooperative signals. Additionally, most methods inadequately address target temporal information, which is essential for planning travel itineraries. To address these limitations, we propose PSLM4ST, a novel framework that enables synergistic interaction between LLMs and a lightweight temporal knowledge graph reasoning model. This plugin model enhances the input to LLMs by making adjustments and additions, guiding them to focus on reasoning processes related to fine-grained preferences and temporal information. Extensive experiments on three real-world datasets demonstrate the efficacy of PSLM4ST. Full article

Urban vitality is a critical indicator of urban development quality and livability. However, existing studies often rely on single-source data or subjective evaluation methods, making it challenging to comprehensively and objectively capture the spatial-temporal characteristics of urban vitality. This study takes Baiyun District in Guangzhou as a case study, integrating multiple data sources—including Points of Interest (POI) data, streetscape elements, transportation networks, land use data, and Baidu heat maps—to construct an urban vitality index and explore its key influencing factors. The results reveal the spatial distribution patterns of urban vitality and the varying significance of different determinants, providing data-driven insights and policy implications for urban planning and development. Full article

Accurate address matching is crucial for the analysis, integration, and intelligent management of urban geospatial data and is also a key step in achieving geocoding. However, due to the complexity, diversity, and irregularity of address expression, address matching becomes a challenging task. This paper proposes a multi-semantic feature fusion method for complex address matching of Chinese addresses that formulates address matching as a classification task that directly predicts whether two addresses refer to the same location, without relying on predefined similarity thresholds. First, the address is resolved into address elements, and the Word2vec model is trained to generate word vector representations using these address elements. Then, multi-semantic features of the addresses are extracted using a Text Recurrent Convolutional Neural Network (Text-RCNN) and a Graph Attention Network (GAT). Finally, the Enhanced Sequential Inference Model (ESIM) is used to perform both local inference and inference composition on the multi-semantic features of the addresses to achieve accurate matching of addresses. Experiments were conducted using Points of Interest (POI) address data from Baidu Maps, Tencent Maps, and Amap within the Chengdu area. The results demonstrate that the proposed method outperforms existing address matching methods, with precision, recall, and F1 values all exceeding 95%. In addition, transfer experiments using datasets from five other cities including Beijing, Shanghai, Xi’an, Guangzhou, and Wuhan show that the model maintains strong generalization ability, achieving F1 values above 84% in cities such as Xi’an and Wuhan. Full article

Discovering spatially associated objects involves measuring objects’ similarities and retrieving associated objects. The integration of spatial topology and network models for discovering associated objects remains largely unexplored. Here, the concept of a maximum topological accessibility path was developed to quantify objects’ similarity attenuation. Considering the topological accessibility and spatial feature similarity of network nodes, an approach named the Weighted Similarity measure method considering Topological Accessibility (WSTA) is proposed to measure object association. The WSTA can capture both spatial interaction patterns and topological relationships in complex urban environments, thereby improving the accuracy of spatially associated object discovery. The proposed approach is validated using real-world point-of-interest (POI) datasets from Beijing city. The results suggest that integrating topological relationship approaches yields significant accuracy improvements in existing baseline methods, thereby enriching geospatial data retrieval in the era of big geospatial data. Full article

Against the backdrop of rapid rail transit development, this study takes Urumqi Metro Line 1 as a case, using geographic information system (GIS) spatial analysis and space syntax Pearson correlation coefficient methods. Focusing on an 800 m radius around station areas, the research investigates the distribution characteristics of commercial facilities and the impact of metro development on commercial patterns through the quantitative analysis and distribution trends of points of interest (POI) data across different historical periods. The study reveals that following the opening of Urumqi Metro Line 1, commercial facilities have predominantly clustered around stations including Erdaoqiao, Nanmen, Beimen, Nanhu Square, Nanhu Beilu, Daxigou, and Sports Center, with kernel density values surging by 28–39%, indicating significantly enhanced commercial agglomeration. Metro construction has promoted commercial POI quantity growth and commercial sector enrichment. Surrounding commercial areas have developed rapidly after metro construction, with the most significant impacts observed in the catering, shopping, and residential-oriented living commercial sectors. After the construction of the subway, the distribution pattern of commercial facilities presents two kinds of aggregation patterns: one is the original centripetal aggregation layout before construction and further strengthened after construction; the other is the centripetal aggregation layout before construction and further weakened after construction, tending to the site level of face-like aggregation. The clustering characteristics of different business types vary. Factors such as subway accessibility, population density, and living infrastructure all impact the distribution of businesses around the subway. The impact of subway accessibility on commercial facilities varies by station infrastructure and urban area. The findings demonstrate how transit infrastructure development can catalyze sustainable urban form evolution by optimizing spatial resource allocation and fostering transportation–commerce synergy. It provides empirical support for applying the theory of transit-oriented development (TOD) in the urban planning of western developing regions. The research not only fills a research gap concerning the commercial space differentiation law of metro systems in megacities in arid areas but also provides a scientific decision-making basis for optimizing the spatial resource allocation of stations and realizing the synergistic development of transportation and commerce in the node cities along the “Belt and Road”. Full article

Personalized travel experiences have become a growing priority for tourists, while the built environment increasingly shapes tourists’ behavior. However, limited research has integrated behavioral drivers with algorithmic travel route optimization, particularly in the context of Generation Z tourists. To address this gap, this study proposes a hybrid framework that combines behavioral modeling with enhanced algorithmic techniques to generate customized travel itineraries for Generation Z. A behavioral influencing factors model is first constructed based on the Theory of Planned Behavior (TPB) and Social Influence Theory (SIT), identifying media influence (MI), subjective norms (SNs), and perceived built environment (PBE) as potential determinants of travel behavioral intention (BI). A Structural Equation Model (SEM) is then applied to empirically validate the hypothesized relationships. Results reveal that all three factors have a significant and positive impact on BI (p < 0.05). Building on this behavioral mechanism, an interest-based Ant Colony Optimization (ACO) algorithm is implemented by incorporating point-of-interest (POI) preferences and distance matrices to improve personalized route generation. Comparative analysis using social media keyword data demonstrates that the proposed method outperforms conventional travel route planning approaches in terms of route relevance and overall path satisfaction. This study offers a novel integration of psychological theory and computational optimization, providing both theoretical insights and practical implications for urban tourism planning and the development of smart tourism services. Full article