Search Results (708)

Point-of-interest (POI) data are widely used to spatialize and predict socioeconomic variables, yet their consistency across regions and over time, as well as their cross-regional generalizability, remain insufficiently understood. This study examines these issues using county-level units in China for 2010 and 2020 from three perspectives: relationship structure, cross-regional generalization, and model improvement. First, a power-law model is applied to characterize the nonlinear relationship between POI density and population density and to assess its spatiotemporal heterogeneity. Second, generalizability is evaluated by comparing model parameters and predictive performance under random and spatially stratified sampling. Third, multi-source geospatial data, including nighttime lights, road networks, and land use, are integrated to compare linear, spatial, machine learning, and ensemble models. Results reveal a consistent sublinear relationship with strong spatial heterogeneity. Under spatially independent validation, predictive accuracy declines and becomes more variable, indicating limited cross-regional generalization. Integrating multi-source data with ensemble learning improves stability and reduces uncertainty. POI remains the dominant predictor, though its relative importance becomes more concentrated in 2020. Overall, the study highlights the limitations of POI-based population estimation and proposes strategies to enhance robustness and generalizability. Full article

The rapid expansion of ski tourism in Northeast China has triggered extensive land use and land cover change (LULCC), yet the micro-scale spatial mechanisms linking historical land conversion to the accessibility of tourist services remain largely unquantified. This study addresses this gap by integrating annual 30 m CLCD land cover data with GIS network analysis of Points of Interest (POIs) around 30 major ski resorts (2018–2023). Specifically, it makes a novel distinction between the accessibility outcomes of construction-oriented and agriculture-oriented land transitions. Results indicate that while forest-to-construction conversion significantly predicts reduced travel distances to services (e.g., hotels: r = −0.532, p < 0.01), a distinct and previously unreported agri-tourism synergy emerges: forest-to-cropland conversion is positively associated with higher per capita tourist spending (r = 0.366, p < 0.05). This finding challenges the conventional zero-sum view of land use competition and suggests that cultivated landscapes can function as complementary tourism assets. These empirical patterns provide an evidence-based framework for integrated land-transport planning in emerging winter sports destinations. Full article

Urban fire risk in high-density cities is characterized by complex spatial heterogeneity and nonlinear relationships with the built environment, population distribution, and climatic conditions. However, most existing studies rely on linear assumptions and offer limited interpretability. To address this gap, we developed an interpretable analytical framework that integrates the CatBoost model with SHAP (SHapley Additive exPlanations), using Futian District in Shenzhen as a case study. We constructed a fire risk surface from historical fire incident data using kernel density estimation (KDE) and incorporated multiple urban environmental factors—including points of interest (POIs), road networks, and meteorological variables—as explanatory variables. The CatBoost model captured nonlinear relationships, while SHAP quantified feature importance and revealed interaction effects. The results show that urban fire risk is strongly associated with the spatial agglomeration of population-related facilities, especially high-density commercial and residential areas, as well as thermal conditions. Several variables exhibit clear nonlinear threshold effects, with their influence on fire risk varying markedly across different intensity ranges. Interaction analysis further indicates that combinations of built-environment characteristics and climatic factors jointly shape the spatial pattern of fire risk. These findings provide empirical insights into the spatial mechanisms underlying urban fire risk and highlight the value of interpretable machine learning in urban safety research. The proposed framework offers a practical tool for developing more targeted, evidence-based fire risk management strategies in high-density urban areas. Full article

Rapid metropolitan expansion reshapes not only land-use patterns and infrastructure networks but also the spatial organization of micro-commercial systems embedded in everyday urban life. While large-scale retail restructuring has been extensively examined, the mechanisms underlying micro-commercial spatial transformation remain insufficiently theorized, particularly in rapidly urbanizing contexts. This study investigates the spatio-temporal restructuring of a representative low-threshold urban food economy in Xi’an between 2014 and 2024. Using multi-temporal point-of-interest (POI) data, kernel density estimation, and spatial Shannon entropy, we model changes in intensity gradients, distributional complexity, and zonal differentiation across morphologically distinct urban belts. The results reveal a systematic transition from centralized concentration toward polycentric embedding, characterized by the relocation of clustered micro-commercial activities along metro corridors and within emerging residential zones. Unlike classical decentralization, which implies outward diffusion, polycentric embedding reflects the infrastructural and demographic re-anchoring of clustered economic activities within newly stabilized urban territories. Entropy analysis further indicates increasing structural heterogeneity in metropolitan expansion zones, while historic cores retain symbolic concentration but exhibit declining structural dominance. These findings demonstrate that micro-commercial systems reorganize not through random dispersion, but through infrastructure-mediated embedding processes driven by metro expansion, residential aggregation, and institutional anchoring. By integrating longitudinal POI data with spatial complexity metrics, this study advances a replicable analytical framework for linking micro-scale commercial dynamics with metropolitan structural transformation. The study contributes to urban theory by reframing low-threshold economic systems as embedded infrastructures of everyday urban reproduction and provides planning insights for fostering resilient and spatially balanced commercial ecosystems under rapid metropolitan growth. Full article

Safety-oriented UAV motion planning relies on distance-to-obstacle fields and their gradients, yet onboard mapping is typically limited to bounded local distance updates. Consequently, optimization may stall outside the updated band due to missing gradients, while enlarging the update range substantially increases computational cost. Our key insight is that motion-planning locality implies only a small subset of obstacles governs local trajectory refinement. We term this subset points of interest (POIs). Motivated by this observation, we develop a locality-aware sequential motion planning framework with a POI-driven feedback mechanism that continuously identifies and augments these trajectory-relevant obstacles during search and optimization. The mechanism tightly couples mapping, search, and optimization and enables safe trajectory refinement without requiring global distance updates. The framework adopts a heuristic mapping strategy that combines a long-term occupancy grid with bounded incremental distance updates and a POI-based short-term k-d tree, enabling efficient nearest-neighbor queries and gradient proxies beyond the update band. The search process generates a dynamically feasible initial trajectory in the long-term map while collecting POIs, which are then used to construct the short-term component. The trajectory is subsequently refined through iterative optimization loops, where newly exposed closest obstacles are incorporated into the POI set and the short-term map is updated until convergence. Safety is enforced through conservative collision checking against the inflated long-term occupancy map. Simulations in building and forest environments show that 99.7% of trials converge within two refinements in sparse scenes and none exceed four overall. Compared with FastPlanner and EgoPlanner, the proposed method achieves consistently larger obstacle clearances. Onboard experiments further validate its practicality under real sensing and computational constraints. Full article

Social Virtual Reality (VR) is emerging as a powerful medium for remote social interaction and collaboration, enabling multiple users to share experiences together while apart. Likewise, recent advances in multimedia technologies have proposed strategically combining diverse content formats and introducing interaction techniques for recreating virtual environments and engaging with them, respectively. This study pioneers the joint exploration of Social VR enhanced with holographic communication, multimodal content integration, and advanced interaction methods to deliver realistic and interactive group visits to reconstructed cultural heritage sites, specifically an existing restaurant–museum. The reconstructed space is further augmented with Points of Interest (PoIs), which can be freely visited and dynamically activated to provide rich contextual and historical information about the venue. The proposed technology and scenario have been evaluated objectively and subjectively. Results from objective tests offer relevant insights into the technical requirements, performance metrics (including bandwidth usage and latency), and overall system stability. Results from subjective tests with 22 participant pairs reveal high levels of user satisfaction, particularly in terms of immersion, presence, togetherness, and interaction quality regardless of whether participants acted as Guides (interacting with the VR environment) or Followers (observing and following the Guide’s actions). Beyond demonstrating feasibility, the findings from this study prove, for the first time, how strategically combining multi-user holoportation with multimodal content and role-based interactions can enable guided, collaborative cultural or touristic visits that preserve social presence while supporting rich exploration and contextual learning. Full article

Accurately identifying urban functional zones and revealing their spatial interaction characteristics is crucial for understanding urban operational mechanisms and optimizing spatial layouts. Addressing the limitations of traditional research in simultaneously capturing static functional attributes and dynamic resident travel behaviors, this study takes the central urban area of Lhasa as the research object, integrating ride-hailing trajectory data with Point of Interest (POI) data to conduct research on urban functional zone identification and spatial interaction characteristics. First, Thiessen polygons were used to quantify the spatial influence range of POIs, and an address matching algorithm was employed to associate ride-hailing origins and destinations (ODs) with POIs. A weighted land use intensity index was constructed, and functional zones were precisely identified using information entropy and K-Means clustering. Secondly, with basic research units as nodes and OD flows as edges, a directed weighted spatial interaction network was constructed. Complex-network indicators and the Infomap community detection algorithm were utilized to analyze network characteristics, node importance, and community interaction patterns. The results show that: (1) The functional mixing degree in the study area exhibits a pattern of “highly composite core, relatively differentiated periphery.” Eight functional zone types, including commercial–residential mixed, science–education–culture, and transportation service zones, were ultimately identified. Residential areas form the base, while the core area features multi-functional agglomeration. (2) The spatial interaction network exhibits typical small-world effects, while its degree distribution is better characterized by a lognormal distribution rather than a power law. Node importance is dominated by betweenness centrality, with Lhasa Station, the Potala Palace, and core commercial areas constituting key hubs. (3) The network can be divided into four functionally coupled communities: the core multi-functional area, the western industry–residence integrated area, the eastern science–education-dominated area, and the southern transportation hub area, forming a “core leading, two wings supporting” center–subcenter spatial organization pattern. This study verifies the effectiveness of integrating trajectory and POI data for identifying urban functional zones and provides a new perspective for understanding the spatial structure and planning of plateau cities. Full article

With urbanization transforming from scale expansion to high-quality development and the increasing prominence of the ecological environment constraints of drainage basins, systematically identifying the mechanism of action of non-point source pollution from a high-quality development perspective is significant for coordinating urban development and environmental protection. Based on remote sensing data on atmospheric pollution and multi-source spatial big data such as nighttime light (NTL), LandScan population, point of interest (POI), and land use data from 2013 to 2025, this study applies methods including deposition flux analysis, deep learning fusion, bivariate spatial autocorrelation, and geographically weighted regression (GWR) to empirically analyze the spatiotemporal evolution characteristics, spatial correlation, and local impacts of high-quality urban development on non-point source pollution in the Chenghai drainage basin. We find that, firstly, non-point source pollution and high-quality urban development in the Chenghai drainage basin both present significant stage-specific and spatial heterogeneity. In other words, the two are not mutually independent spatial elements in space; instead, they are closely and significantly correlated, with their correlation types showing obvious spatial agglomeration characteristics. Secondly, the impact of high-quality urban development on non-point source pollution evolves in stages. It gradually shifts from a whole-region, homogeneous, strongly positive driving force to spatial differentiation. Specifically, from 2013 to 2017, the whole-region regression coefficients are generally greater than 0.5, meaning that urban development represents a strong, whole-region driving force promoting pollution. However, after 2017, this impact evolves into a stable spatial differentiation pattern. It mainly shows that the northern urban core area, where coefficients are greater than 0.5, maintains a continuous strong positive driving force. Meanwhile, the peripheral area, where coefficients are generally lower than 0, creates a negative inhibition effect. Based on the above rules, further analysis shows that the impact of high-quality urban development on non-point source pollution is absolutely not a simple linear relationship. Instead, it is a result of the coupling effect of multiple factors, including development stage, spatial location, and governance level. Therefore, to positively affect the ecological environment through high-quality development, model transformation and precise governance are essential. The findings of this study deepen our understanding of the transformation of urban development models and the response mechanism of non-point source pollution. They also provide a scientific basis and decision support for promoting the coordinated governance of high-quality urban development and non-point source pollution by region and stage in plateau lake drainage basins, as well as for improving the sustainable development of drainage basins. Full article

Precise, spatially explicit sub-provincial GDP estimates are essential for regional planning, especially in mountainous areas where official economic data remain spatially coarse and unevenly distributed. This study develops a multisource county-level GDP spatialization framework for Sichuan Province, China, integrating corrected NPP/VIIRS nighttime-light (NTL) data with Points of Interest (POIs), land-use structure indicators (proportion of farmland (PFL); proportion of construction land (PCL)), elevation, precipitation, accessibility and population density within a unified indicator system. Two regression approaches—Ordinary Least Squares (OLS) as a global benchmark and Geographically Weighted Regression (GWR) as the spatially adaptive primary model—are calibrated on county-level cross-sectional data for 2020 (n = 183) and evaluated using R², adjusted R², AICc and residual spatial diagnostics. The multisource GWR model achieves R² = 0.882 (adjusted R² = 0.872, AICc = 5712.26), substantially outperforming both the global OLS benchmark (R² = 0.801) and NTL-only GWR baseline (R² = 0.662), confirming that spatial nonstationarity is an intrinsic feature of the GDP–proxy relationship and that integrating complementary geospatial proxies is the primary pathway to improved estimation accuracy in topographically heterogeneous regions. The GWR-based GDP surface exhibits a pronounced basin–plateau contrast: high-value clusters concentrate along the Chengdu Plain and adjacent city corridors, while extensive low-value zones prevail across the western highlands (global Moran’s I = 0.33, Z = 14.26, p < 0.001). Spatially varying GWR coefficients reveal that elevation and precipitation constrain GDP most strongly in high-altitude counties, construction land exerts a consistently positive but spatially graded effect, and the influences of accessibility and population density are context-dependent and locally differentiated. These findings support differentiated territorial development policies: plateau counties require accessibility-first strategies; hill counties benefit from targeted small-city industrialization; and basin cores need managed growth to balance agglomeration advantages against congestion pressures. The framework relies exclusively on globally or nationally available data and is portable to other mountainous regions, though cross-regional validation and extension to multi-year panels using geographically weighted panel regression remain important directions for future work. Full article

City-scale augmented reality (AR) learning paths are outdoor, multi-stop educational routes delivered through mobile devices in public space. This paper examines the Art Nouveau Path, a mobile AR game (MARG) route in Aveiro, Portugal, as a deployable learning service. The focus is on implementation requirements and traceability rather than learning outcomes. The analysis combined profiling of eight points of interest (POIs) and 36 tasks, group-session logs from 118 sessions, and teacher-facing evidence from a validation workshop (T1-VAL, N = 30) and on-site observation (T2-OBS, N = 24). Open-text responses were segmented into meaning units and coded with an eight-determinant taxonomy, with good intercoder reliability (Krippendorff’s alpha = 0.83). Logs and the post-path questionnaire (S2-POST, N = 439) were used only to describe enactment feasibility and data integrity. The strongest determinants concerned onboarding and legibility, marker robustness and recovery, and curriculum alignment, together with safety and fallback needs. These signals were translated into 18 testable requirements linked to six transfer artefacts for enactment, maintenance, incident handling, and fallback. Overall, the study provides an implementation-oriented specification to support auditability, replication, and transfer in city-scale AR learning services. Full article

This study examines how high-speed rail (HSR) hubs shape public space vitality in central-city station areas, using Shapingba Station (Chongqing, China) as a representative case of station–city integration. We delineated pedestrian catchments using Baidu Map walking isochrones (300–1200 s) and integrated multi-source data, including Public Space Public Life (PSPL) field observations (eight monitoring points, 07:00–24:00), Baidu heat maps, point-of-interest (POI) records, streetscape semantic segmentation, and a perception questionnaire. Indicators were synthesized via entropy weighting, and multivariate associations between perceived vitality and environmental variables were examined using Mantel tests. Pedestrian flow exhibits a clear double-peak pattern (09:00–11:00 and 15:00–16:00), averaging 42,248 pedestrians per day (2347 per hour) and showing strong spatial heterogeneity across monitoring points. POIs show a pronounced core–periphery structure: totals increase from 803 (300 s) to 4365 (600 s) and 7539 (1200 s), while overall density declines from 7477 to 2492 POIs/km², highlighting a 600 s core where accessibility and functional agglomeration are most strongly coupled. Overall, this study contributes a replicable multi-source evaluation framework and quantitative evidence on accessibility–function coupling and micro-scale design effects in HSR station areas, enabling theory-informed comparisons across station typologies and urban contexts. Full article

Accurately identifying an urban functional zone (UFZ) is crucial for rationally allocating urban land resources and optimizing urban spatial structure. Existing research based on Points of Interest (POIs) mostly uses the relationship between the number of various types of POIs as the basis for identification. However, this approach neglects the difference of physical surface property of urban functional zones—imperviousness. Based on the FD-CR method, this study proposes the RFD-ECR identification method by combining TF-IDF and ISI. This study divides research units according to OpenStreetMap (OSM), and reclassifies POI data. It then uses the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to highlight the dominant function of study units and incorporates the impervious surface index (ISI) as a correction to recognize urban functional zones. Experiments conducted in the central urban area of Chengdu demonstrate that this method is effective in identifying urban functional zones, achieving an accuracy rate of 80.21%. Comparison with the Frequency Density-Category Ratio (FD-CR) method reveals that this method, through the TF-IDF algorithm and the impervious surface index constraint, effectively improves the classification accuracy of mixed commercial UFZs. This method broadens the scope of research on urban functional zone identification based on POI data, and also provides a valuable reference for other cities undertaking functional zone identification. Full article

With the rapid advancement of location-based services, next Point-of-Interest (POI) recommendation has emerged as a critical task in personalized mobility modeling and recommendation systems. It aims to predict users’ future locations based on their historical trajectories, thereby enhancing the personalization and intelligence of recommendation systems. Despite the promising progress, two key challenges remain insufficiently addressed. First, many existing methods overlook the dynamic evolution of user trajectories across multiple perspectives, resulting in entangled representations that fail to capture user intent accurately. Second, they often ignore the latent synergy across diverse perspectives, which limits the effective utilization of complementary information for recommendation. To address these issues, we propose a novel framework called MRHL. MRHL constructs multiple hypergraphs to represent distinct views of user behavior, including interaction frequency, time decay, and geographical proximity. An enhanced hypergraph convolutional network is employed to effectively model the high-order relationships within them. We propose a cascaded enhancement fusion mechanism that progressively integrates multi-view hypergraph representations to enrich the semantic information of user representations. In addition, a multi-relational contrastive learning strategy is developed to capture the consistent signals across different views, thereby enhancing the robustness and discriminative capability of user and POI representations. Extensive experiments on three public datasets consistently demonstrate that MRHL outperforms a range of strong baselines. Full article

Measuring the service levels and spatial equity of urban parks constitutes a core research topic within the field of environmental justice. Against the backdrop of low-carbon urban transformation and sustainable development, this study constructs an ecological supply indicator calculation model for parks based on landscape ecology theory. Leveraging spatio-temporal big data such as Points of Interest (POI) and second-hand property transactions, it establishes a demand evaluation indicator system centered on human activity intensity. The study employs the Gini coefficient and location entropy to gauge the spatial equity of park supply–demand balance, utilizing the Z-score method to classify supply–demand matching types. An empirical case study is conducted in Shenzhen. Findings indicate that despite Shenzhen possessing abundant global-scale park resources, a Gini coefficient of 0.489 reveals significant deficiencies in the equitable provision of park services, with spatial distribution exhibiting pronounced social stratification. Specifically: (1) location entropy values exhibit an east-high, west-low spatial pattern; (2) areas with high location entropy are predominantly concentrated in Dapeng New District, rich in green space resources, where supply exceeds demand, creating an imbalance; and (3) areas with low locational entropy values are predominantly distributed in industrial clusters such as western Bao’an and western Longgang, exhibiting contradictory characteristics of low supply and high demand. Overall, the distribution of park and green space resources exhibits a polarized pattern. Full article

Building height is a key indicator of vertical urbanization and urban morphological complexity, yet accurately mapping building height at fine spatial resolution and large spatial scales remains challenging. This study proposes an attention-based deep learning framework (ABHNet) for building height estimation at a 10 m spatial resolution by integrating multi-source remote sensing data and socioeconomic information. The model jointly exploits Sentinel-1 synthetic aperture radar data, Sentinel-2 multispectral imagery, and point of interest (POI) data. The proposed framework is evaluated in Shanghai, a megacity with dense and vertically complex urban structures, using Baidu Maps-derived building height data as reference information. The results demonstrate that the proposed method achieves accurate building height estimation, with a root mean squared error (RMSE) of 3.81 m and a mean absolute error (MAE) of 0.96 m for 2023, and an RMSE of 3.30 m and an MAE of 0.78 m for 2019, indicating robust performance across different time periods. Also, this model is applied in two other cities (Changzhou and Guiyang) and the results indicate good performance. In addition, the expandability of the framework is examined by incorporating higher-resolution ZY-3 imagery, for which the spatial resolution was increased to 2.5 m, highlighting the potential extension of the model to heterogeneous data sources. Overall, this study demonstrates the effectiveness of attention-based deep learning and multimodal data fusion for large-scale and fine-resolution building height estimation using open-source data. Full article