Search Results (52)

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

The risks associated with hazardous materials (HazMat) transportation exhibit typical characteristics of chain-like distribution, spatiotemporal regularity, and individual heterogeneity. A personalized trip-chain feature spectra recognition framework for HazMat vehicles is proposed to enhance the capability to assess and analyze individual risks using vehicle positioning data. The proposed framework addresses the challenges of deriving personalized risk feature maps arising from missing real-time trajectory data, complex sub-trip-chain segmentation, and the extraction of personalized risk feature representations. An improved conditional Wasserstein Generative Adversarial Network (WGAN) model is initially developed to impute trajectories with missing positional data, and it can robustly reconstruct trajectories with large-scale missing segments by integrating a multi-head self-attention mechanism and a gradient penalty. A two-layer clustering algorithm, K-Means-multiplE-THreshOlds-adaptive-DBSCAN (KMETHOD), which combines an adaptive mechanism with threshold rules, is subsequently designed to identify the dwell time and related spatial attributes of dwell points along vehicle trips. A BERT-based model is incorporated to filter Points of Interest (POIs) around dwell points, which enables the extraction of their detailed location semantics and trip characteristics and thus supports trip chain identification and segmentation. A threshold-activated multilayer trajectory feature-map method (TAFEM) is constructed to generate feature maps for each trip chain. The Liquefied Natural Gas (LNG) transportation trajectory data from Guangdong Province is selected to evaluate the effectiveness of the proposed methods. The experimental results demonstrate that the proposed framework can effectively identify trip chains and generate their corresponding feature maps. The trajectory imputation model achieved the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Dynamic Time Warping (DTW) of 2.34–3.33, 6.05–7.74, and 0.74–1.21, respectively, across different missing-rate scenarios, outperforming other benchmark models. The identification accuracy of dwell-point duration and location reaches 98.35%. The BERT-based method achieves a maximum accuracy of 92.83% in origin–destination (OD) point recognition, effectively capturing comprehensive trip-chain information. TAFEM accurately characterizes the spatiotemporal distribution and potential causal factors of personalized HazMat transportation safety risks, providing a reliable foundation for risk identification and safety management strategies. Full article

Spatial function is critical to sustainable development in modern metropolises. Traditional spatial function patterns are mainly shaped by market forces, policies and zoning regulations. The role of human perceptual experience remains understudied. Taking Beijing as the study area, this study examines the relationship between public perception and spatial function. It focuses on how consistency or inconsistency between the two environments influences spatial function service value. A two-step method is adopted in this research. First, the Weighted Average Cluster Index (WACI) works to analyze the spatial clustering of points of interest (POIs). This provides a quantitative basis for identifying spatial functions from integrated multi-source data. Second, a CatBoost binary classification model is applied to evaluate consistency and interpret the driving mechanisms. Key findings are obtained: (1) Perceptual underestimation of agricultural and cultural POI is significant in urban–rural transition zones. (2) Global analysis identifies education, commercial and sports POIs as the strongest contributors to function recognition. Local analysis reveals heterogeneous effects of POI categories across spatial scales. (3) Positive synergies occur between education–commercial and leisure–scenic areas. Industrial zones show functional competition with leisure and scenic areas. Shapley Additive Explanations (SHAP) clarifies the causes of perceptual discrepancies. It emphasizes the impacts of diverse urban morphological features and their interactive effects on public perception. Accordingly, strategies are provided for urban planners and policymakers such as promoting functionally mixed layouts with high consistency. This study offers an alternative approach to improving spatial function efficiency towards a sustainable development of modern metropolises. Full article

Unstructured POI name texts are widely used in fine-grained urban analysis, yet missing labels and semantic ambiguity often limit their value for spatial inference. This study proposes a large language model-based semantic–spatial inference framework (LLM-SSIF), a lightweight semantic–spatial pipeline that translates POI texts into interpretable, fine-grained spatial evidence through an end-to-end workflow that couples scalable label expansion with scale-controlled spatial diagnostics at a 500 m resolution. A key advantage of LLM-SSIF is its deployability: LoRA-based parameter-efficient fine-tuning of an open LLM enables lightweight adaptation under limited compute while scaling fine-label coverage. Trained on a nationwide cuisine-labeled dataset (~220,000 records), the model achieves strong multi-class short-text recognition (macro-F1 = 0.843) and, in the Guangzhou–Shenzhen demonstration, expands usable fine-category labels by ~14–15× to support grid-level inference under long-tail sparsity. The spatial module then isolates cuisine-specific over/under-representation beyond overall restaurant intensity, revealing contrasting cultural configurations between Guangzhou and Shenzhen. Overall, LLM-SSIF provides a reproducible and transferable way to translate unstructured POI texts into spatial–statistical evidence for comparative urban analysis. Full article

To achieve effective coordination among planning, operation, and service in urban management, and based on the fundamental characteristic of urban spatial development expanding from points to areas, this paper proposes an approach for identifying high-intensity urban development zones based on seed grid growth. First, seed grids are selected using the Getis–Ord $G_i$* of grid floor area ratios as the criterion. Second, drawing on relevant image recognition methods, high-intensity development zones are derived through seed-grid-based zone growth, as well as zone merging and segmentation. Furthermore, the rationality of the geometric morphology and the independence of the spatial relationships of the identified zones are evaluated. Meanwhile, the utilization efficiency of these zones is assessed from the perspectives of population carrying capacity and industrial agglomeration, using data on population, digital brightness of nighttime lights, and points of interest (POI). Finally, the proposed identification and utilization efficiency assessment method is verified through a case study of Shenzhen City. Full article

Background: Understanding visual context in images is essential for enhanced Point-of-Interest (POI) recommender systems. Traditional models often rely on global features, overlooking object-level information, which can limit contextual accuracy. Methods: This study introduces micro-level contextual tagging, a method for extracting metadata from individual objects in images, including object type, frequency, and color. This enriched information is used to train WORLDO, a Vision Transformer model designed for multi-task learning. The model performs scene classification, contextual tag prediction, and object presence detection. It is then integrated into a content-based recommender system that supports feature configurations. Results: The model was evaluated on its ability to classify scenes, predict tags, and detect objects within images. Ablation analysis confirmed the complementary role of tag, object, and scene features in representation learning, while benchmarking against CNN architectures showed the superior performance of the transformer-based model. Additionally, its integration with a POI recommender system demonstrated consistent performance across different feature settings. The recommender system produced relevant suggestions and maintained robustness even when specific components were disabled. Conclusions: Micro-level contextual tagging enhances the representation of scene context and supports more informative recommendations. WORLDO provides a practical framework for incorporating object-level semantics into POI applications through efficient visual modeling. Full article

Premature ovarian insufficiency (POI) and diminished ovarian reserve (DOR) are two related conditions characterized by a reduced ovarian reserve. Their etiologies are multifactorial, encompassing iatrogenic causes such as chemotherapy, pelvic surgery, or radiotherapy, as well as non-iatrogenic factors including genetic and chromosomal abnormalities, environmental exposures, autoimmune mechanisms and idiopathic sources. Early recognition of these conditions is essential, as timely and appropriate management can significantly impact both reproductive potential and long-term health. In women with POI, hormone replacement therapy is required to prevent the detrimental effects of estrogen deficiency on wellbeing and overall health, while in women with DOR, management focuses on counseling, fertility preservation when pregnancy is not an immediate goal, and strategies to optimize assisted reproductive outcomes when conception is desired. In addition, emerging research into ovarian rejuvenation offers promising new avenues for future therapeutic approaches. This review summarizes current knowledge on the pathophysiology, diagnosis, and management of POI and DOR, while highlighting innovative developments in reproductive medicine. 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

Background/Objectives: Premature ovarian insufficiency (POI) is a leading cause of hypoestrogenism in women under the age of 40 years and is associated with an increased risk of impaired bone health. Early diagnosis and timely hormonal intervention are essential to prevent irreversible bone loss. However, diagnostic delay is not uncommon in clinical practice. Methods: We conducted a retrospective analysis of 168 women diagnosed with POI or early menopause (EM) between 2017 and 2024 at a tertiary gynecological endocrinology unit. Bone mineral density (BMD) and T-score were assessed by dual-energy X-ray absorptiometry (DXA) at the time of diagnosis in 125 patients, of whom 116 had secondary amenorrhea. The interval between the last menstrual period (LMP) and diagnosis was used to assess the impact of diagnostic delay. The patients were further stratified by serum estradiol (E2) levels and body mass index (BMI). Results: At the time of diagnosis, 43.1% of patients had osteopenia, and 10.3% had osteoporosis. A statistically significant negative correlation was observed between time to diagnosis and BMD (r = −0.225, p = 0.022), with a similar trend seen for T-score (r = −0.211, p = 0.031). In patients with E2 ≤ 5 ng/L, the association was stronger (BMD: r = −0.401, p = 0.026). Lower E2 levels tended to be associated with poorer bone health in women with a BMI < 25 kg/m², whereas no such trend was observed in those with a higher BMI. Conclusions: Our findings indicate that diagnostic delay in POI is associated with deterioration in bone health, particularly in lean patients and those with severe hypoestrogenism. These results underscore the importance of early recognition and timely initiation of hormone therapy to preserve bone mass and reduce long-term skeletal complications. Full article

Recognition and detection of building groups are core tasks in cartographic research. Current recognition methods that rely on spatial and geometric features often neglect semantic aspects, failing to account for the complex relationships between buildings and their real-world semantic associations. This limitation hampers the ability to fully capture human understanding of the real world. Based on this, this paper proposes a novel method for building group recognition that integrates both spatial geometric and semantic features. The method effectively identifies building group structures by considering spatial proximity, geometry, and semantic similarity. First, spatial proximity between buildings is defined by constructing a neighborhood graph based on Delaunay triangulation, and the spatial geometric features of each building are extracted. The spatial distance and semantic intensity relationships between Point of Interest (POI) data and buildings are used for semantic feature extraction. Subsequently, a spatial–semantic dual clustering strategy is applied in two stages to aggregate the buildings and generate preliminary grouping results. Finally, the grouping results are refined through an optimal graph segmentation strategy, which ensures both global and local optimization. The proposed method is applied to two areas in Shenzhen City, China, and the experimental results demonstrate that, compared with other methods, it more effectively identifies building groups with coherent spatial, geometric, and semantic features, improving the Adjusted Rand Index (ARI) from 0.589 to 0.701. This approach provides significant support for intelligent map generalization and personalized knowledge services in the era of big data. Full article

Urban Functional Zones (UFZs) are spatial units of the city divided according to specific functional activities. Detailed identification of UFZs is vital for optimizing urban management, guiding planning and design, and promoting sustainable development. However, existing UFZ recognition methods face significant challenges, such as difficulties in effectively integrating multi-source heterogeneous data, capturing dynamic spatiotemporal patterns, and addressing the complex interrelationships among various data types. These issues significantly limit the applicability of UFZ mapping in complex urban scenarios. To address these challenges, this paper proposes a tripartite neural network (TriNet) for multimodal data processing, including Remote Sensing (RS) images, Point of Interest (POI) data, and Origin–Destination (OD) data, fully utilizing the complementarity of different data types. TriNet comprises three specialized branches: ImgNet for spatial features extraction from images, POINet for functional density distribution features extraction from POI data, and TrajNet for spatiotemporal pattern features extraction from OD data. Finally, the method deeply fuses these features through a feature fusion module, which utilizes a two-layer fully connected network for deep fusion, allowing the model to fully utilize the interdependencies among the data types, significantly improving the UFZ classification accuracy. The experimental data are generated by mapping OpenStreetMap (OSM) vector into conceptual representations, integrating images with social sensing data to create a comprehensive UFZ classification benchmark. The method achieved an overall accuracy of 84.13% on the test set of Chongqing’s main urban area, demonstrating high accuracy and robustness in UFZ classification tasks. The experimental results show that the TriNet model performs effectively in UFZ classification. Full article

Urban functional zones (UFZs) are related to people’s daily activities. Accurate recognition of UFZs is of great significance for an in-depth understanding of the complex urban system and optimizing the urban spatial structure. Emerging geospatial big data provide new ideas for humans to recognize urban functional zones. Point-of-interest (POI) data have achieved good results in the recognition of UFZs. However, since humans are the actual users of urban functions, and POI data only reflect static socioeconomic characteristics without considering the semantic and temporal features of dynamic human activities, it leads to an incomplete and insufficient representation of complex UFZs. To solve these problems, we proposed a data-synthesis-driven approach to quantify and analyze the distribution and mixing of urban functional zones. Firstly, representation learning is used to mine the spatial semantic features, activity temporal features, and activity semantic features that are embedded in POI data and social media check-in data from spatial, temporal, and semantic aspects. Secondly, a weighted Stacking ensemble model is used to fully integrate the advantages between different features and classifiers to infer the proportions of urban functions and dominant functions of each urban functional zone. A case study within the 5th Ring Road of Beijing, China, is used to evaluate the proposed method. The results show that the approach combining dynamic and static features of POI data and social media data effectively represents the semantic information of UFZs, thereby further improving the accuracy of UFZ recognition. This work can provide a reference for uncovering the hidden linkages between human activity characteristics and urban functions. Full article

Robust autonomous navigation systems rely on mapping, locomotion, path planning, and localization factors. Localization, one of the most essential factors of navigation, is a crucial requirement for a mobile robot because it needs the capability to localize itself in the environment. Global Positioning Systems (GPSs) are commonly used for outdoor mobile robot localization tasks. However, various environmental circumstances, such as high-rise buildings and trees, affect GPS signal quality, which leads to reduced precision or complete signal blockage. This study proposes a visual-based localization system for outdoor mobile robots in crowded urban environments. The proposed system comprises three steps. The first step is to detect the text in urban areas using the “Efficient and Accurate Scene Text Detector (EAST)” algorithm. Then, EasyOCR was applied to the detected text for the recognition phase to extract text from images that were obtained from EAST. The results from text detection and recognition algorithms were enhanced by applying post-processing and word similarity algorithms. In the second step, once the text detection and recognition process is completed, the recognized word (label/tag) is sent to the Places API in order to return the recognized word’s coordinates that are passed within the specified radius. Parallely, points of interest (POI) data are collected for a defined area by a certain radius while the robot has an accurate internet connection. The proposed system was tested in three distinct urban areas by creating five scenarios under different lighting conditions, such as morning and evening, using the outdoor delivery robot utilized in this study. In the case studies, it has been shown that the proposed system provides a low error of around 4 m for localization tasks. Compared to existing works, the proposed system consistently outperforms all other approaches using just one sensor. The results indicate the efficacy of the proposed system for localization tasks in environments where GPS signals are limited or completely blocked. Full article

Effective production, living, and ecological space allocation is essential for improving and optimizing urban space development. In this study, we proposed a production–living–ecological space (PLES) identification method based on Point of Interest (POI) data and China Land Cover Dataset (CLCD) to identify PLESs in Xuzhou City for the years 2012, 2018, and 2022, with an average recognition accuracy of 89.81%. Moreover, the land-use transfer matrix, center of gravity migration, and Geo-detector were used to reveal the spatiotemporal pattern evolution of PLESs. The results showed that: (1) The distribution of PLESs presented significant differentiation between Urban Built-Up Area (UBUA) and Non-Urban Built-Up Area (NUBUA). UBUA was mainly composed of living spaces, while NUBUA was primarily characterized by production–ecological spaces. (2) The intensive utilization of urban land led to an increase in the area of multifunctional spaces, while the complexity of urban space increased. (3) During 2012 to 2022, the center of gravity of PLESs remained relatively stable. The moving distances were all less than 1 km (except for ecological space from 2012 to 2018). (4) The evolution of PLESs was closely linked with socio-economic factors, and the interactions between the factors also had a significant driving effect on PLESs. Full article

Prior studies have failed to adequately address intangible characteristics and lacked a comprehensive quantification of cultural dimensions. Additionally, such works have not merged supervised and unsupervised classification methodologies. To address these gaps, this study employed multidimensional numerical techniques for precise spatial pattern recognition and urban morphology classification at the block scale. By examining building density, mean floor numbers, functional compositions, and street block mixed-use intensities, alongside historical and contemporary cultural assets within blocks—with assigned weights and entropy calculations from road networks, building vectors, and POI data—a hierarchical categorization of high, medium, and low groups was established. As a consequence, cluster analysis revealed seven distinctive morphology classifications within the studied area, each with unique spatial configurations and evolutionary tendencies. Key findings include the dominance of high-density, mixed-use blocks in the urban core, the persistence of historical morphologies in certain areas, and the emergence of new, high-rise clusters in recently developed zones. The investigation further elucidated the spatial configurations and evolutionary tendencies of each morphology category. These insights lay the groundwork for forthcoming studies to devise morphology-specific management strategies, thereby advancing towards a more scientifically grounded, rational, and precision-focused approach to urban morphology governance. Full article