Enhanced semantic representation model for multisource point of interest attribute alignment

Point of Interest (POI) data is widely used in many modern applications and services related to navigation, tourism, social networking, logistics, and many more. In this paper, we propose a comprehensive and vendor-agnostic data model to represent multi-faceted and enriched POI profiles. Harnessing the versatility of Linked Data technologies, this semantically rich ontology accommodates and extends existing POI formats for assembling and managing POI data from heterogeneous sources. Furthermore, we have developed the open-source software TripleGeo, which can effectively transform POI data from diverse sources and formats (geographical files, databases, and semi-structured data) to their RDF representations and vice versa. Thus, it is possible to import POI data from various existing systems and products, transfer and address the data integration challenges in the Linked Data domain, and export back the results. Our empirical study confirms the validity and efficiency of this framework for a variety of real-world POI assets and formats, underscoring its robustness to cope with scalable data volumes.

The identification of urban functional zones (UFZs) is crucial for urban planning and optimizing industrial layout. Fusing remote sensing images and social perception data is an effective way to identify UFZs. Previous studies on UFZs recognition often ignored band information outside the red–green–blue (RGB), especially three-dimensional (3D) urban morphology information. In addition, the probabilistic methods ignore the potential semantic information of Point of Interest (POI) data. Therefore, we propose an “Image + Text” multimodal data fusion framework for UFZs recognition. To effectively utilize the information of Gaofen-7(GF-7) stereo images, we designed a semi-transfer UFZs recognition model. The transferred model uses the pre-trained model to extract the deep features from RGB images, and a small self-built convolutional network is designed to extract the features from RGB bands, near-infrared (NIR) band, and normalized digital surface model (nDSM) generated by GF-7. Latent Dirichlet allocation (LDA) is employed to extract POI semantic features. The fusion features of the deep features of the GF-7 image and the semantic features of POI are fed into a classifier to identify UFZs. The experimental results show that: (1) The highest overall accuracy of 88.17% and the highest kappa coefficient of 83.91% are obtained in the Beijing Fourth Ring District. (2) nDSM and NIR data improve the overall accuracy of UFZs identification. (3) POI data significantly enhance the recognition accuracy of UFZs, except for shantytowns. This UFZs identification is simple and easy to implement, which can provide a reference for related research. However, considering the availability of POI data distribution, other data with socioeconomic attributes should be considered, and other multimodal fusion strategies are worth exploring in the future.

BackgroundInterest in the role of food environments in shaping food consumption behaviours has grown in recent years. However, commonly used secondary food environment data sources have not yet been fully evaluated for completeness and systematic biases. This paper assessed the accuracy of UK Points of Interest (POI) data, compared to local council food outlet data for the county of Cambridgeshire.MethodsPercentage agreement, positive predictive values (PPVs) and sensitivities were calculated for all food outlets across the study area, by outlet type, and across urban/rural/SES divisions.ResultsPercentage agreement by outlet type (29.7-63.5%) differed significantly to overall percentage agreement (49%), differed significantly in rural areas (43%) compared to urban (52.8%), and by SES quintiles. POI data had an overall PPV of 74.9%, differing significantly for Convenience Stores (57.9%), Specialist Stores (68.3%), and Restaurants (82.6%). POI showed an overall ‘moderate’ sensitivity, although this varied significantly by outlet type. Whilst sensitivies by urban/rural/SES divides varied significantly from urban and least deprived reference categories, values remained ‘moderate’.ConclusionsResults suggest POI is a viable alternative to council data, particularly in terms of PPVs, which remain robust across urban/rural and SES divides. Most variation in completeness was by outlet type; lowest levels were for Convenience Stores, which are commonly cited as ‘obesogenic’.

Effective urban planning, city digital twins, and informed policy formulation rely heavily on precise building use information. While existing research often focuses on broad categories of building use, there is a noticeable gap in the classification of buildings’ detailed use. This study addresses this gap by concurrently extracting both broad and detailed hierarchical information regarding building use. Our approach involves leveraging multiple data sources, including high spatial resolution remote sensing images (RS), digital surface models (DSM), street view images (SVI), and textual information from point of interest (POI) data. Given the complexity of mixed-use buildings, where different functions coexist, we treat building hierarchical use classification as a multi-label task, determining the presence of specific categories within a building. To maximize the utility of features across diverse modalities and their interrelationships, we introduce a novel multi-label multimodal Transformer-based feature fusion network. This network can simultaneously predict four broad categories and thirteen detailed categories, representing the first instance of utilizing these four modalities for building use classification. Experimental results demonstrate the effectiveness of our model, achieving a weighted average F1 score (WAF) of 91% for broad categories, 77% for detailed categories, and 84% for hierarchical categories. The macro average F1 scores (MAF) are 81%, 48%, and 56%, respectively. Ablation experiments highlight RS data as the cornerstone for hierarchical building use classification. DSM and POI provide slight supplementary information, while SVI data may introduce more noise than effective information. Our analysis of hierarchy consistency, supplementary, and exclusiveness between broad and detailed categories shows our model can effectively learn these relations. We compared two ways to obtain broad categories: classifying them directly and scaling up detailed categories, associating them with their broad counterparts. Experiments show that the WAF and MAF of the former are 3.8% and 6% higher than the latter. Notably, our research visualizes attention models for different modalities, revealing the synergy among them. Despite the model’s emphasis on SVI and POI data, the critical role of RS and DSM in building hierarchical use classification is underscored. By considering hierarchical use categories and accommodating mixed-use scenarios, our method provides more accurate and comprehensive insights into land use patterns.

논문투고일：2011년 10월 21일 논문수정완료일：2011년 12월 01일 논문게재확정일：2011년 12월 06일*이 논문은 2011학년도 경성대학교 학술연구비지원에 의하여 연구되었음.** 경성대학교 컴퓨터학부 조교수Mobile Augmented Reality (mobile AR), as one of the most prospe ctive mobile applications, intends to provide richer experiences by annotating tags or virtual objects over the scen e observed through camera embedded in a handheld device like smartphone or pad. In this paper, we analyzed the c urrent status of the art of mobile AR and proposed a novel Points of Interest (POIs) data model based on ontology to provide context-aware information retrievals on lots of POIs data. Proposed ontology was expanded from the stan dard POIs data model of W3C POIs Working Group and established using OWL (Web Ontology Language) and Protege. We also proposed a context-aware mobile AR platform which can resolve three distinguished issues in curren t platforms：interoperability problem of POI tags, POIs data retrieval issue, and context-aware service issue. Keyword：Mobile Augmented Reality, Points of Interest(POI), Context Awar eness, Ontology

Roles of locational factors in the rise and fall of restaurants: A case study of Beijing with POI data

Point-of-Interest (POI) demand modeling in urban regions is critical for building smart cities with various applications, e.g., business location selection and urban planning. However, existing work does not fully utilize human mobility data and ignores the interactive-aware information. In this work, we design a refined POI demand modeling framework, named RePiDeM, to identify region POI demands based on multi-source data, including cellular data, POI data, satellite image, geographic data, etc. Specifically, we introduce a Cellular Data (CD) based visit inference algorithm to estimate the POI visit probability based on human mobility and POI data. Further, to address the data sparsity issue, we design a multi-source attention neural collaborative filtering (MANCF) model to output region POI demands considering various aspect attention. We conduct extensive experiments on real-world data collected in the Chinese city Shenyang, which show that RePiDeM is effective for modeling region POI demands.

Exploring intra-urban human mobility and daily activity patterns from the lens of dockless bike-sharing: A case study of Beijing, China

Population spatialization data is crucial to conducting scientific studies of coupled human–environment systems. Although significant progress has been made in population spatialization, the spatialization of different age populations is still weak. POI data with rich information have great potential to simulate the spatial distribution of different age populations, but the relationship between spatial distributions of POI and different age populations is still unclear, and whether it can be used as an auxiliary variable for the different age population spatialization remains to be explored. Therefore, this study collected and sorted out the number of different age populations and POIs in 2846 county-level administrative units of the Chinese mainland in 2010, divided the research data by region and city size, and explored the relationship between the different age populations and POIs. We found that there is a complex relationship between POI and different age populations. Firstly, there are positive, moderate-to-strong linear correlations between POI and population indicators. Secondly, POI has a different explanatory power for different age populations, and it has a higher explanatory power for the young and middle-aged population than the child and old population. Thirdly, the explanatory power of POI to different age populations is positively correlated with the urban economic development level. Finally, a small number of a certain kinds of POIs can be used to effectively simulate the spatial distributions of different age populations, which can improve the efficiency of obtaining spatialization data of different age populations and greatly save on costs. The study can provide data support for the precise spatialization of different age populations and inspire the spatialization of the other population attributes by POI in the future.

Competition between Points of Interest (POIs) refers to the situation in which two POIs directly or indirectly provide similar services to secure businesses. A large portion of prior studies on competition analysis focuses on mining textual data, e.g., news articles and social comments. However, the increasing availability of human mobility and mobile query data enables a new paradigm for analyzing the competitive relationships among POIs, which remains largely unexplored. To this end, in this paper, we attempt to mine large-scale online map search query data for better understanding POI competitive relationships. Based on a co-query POI graph built from the map search query data, we develop a novel neural graphlet-based prediction framework to predict the competitive relationships among POIs. A unique perspective of our model is to infer latent POI competitive relationships by integrating multiple distinct factors, e.g., graphlet structure, geographical distance, and regional features, reflected in map search query data and POI data. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness of the proposed framework, and show that our framework outperforms all baselines with a significant margin in all evaluation metrics.

Functional urban land use recognition integrating multi-source geospatial data and cross-correlations

With the increased popularity of mobile devices and smart phones, location-based services (LBS) have become a common need in our daily life. Therefore, maintaining the correctness of POI (Points of Interest) data has become an important issue for many location-based services such as Google Maps and Garmin navigation systems. The simplest form of POI contains a location (e.g., represented by an address) and an identifier (e.g., an organization name) that describes the location. As time goes by, the POI relationship of a location and organization pair may change due to the opening, moving, or closing of a business. Thus, effectively identifying outdated or emerging POI relations is an important issue for improving the quality of POI data. In this paper, we examine the possibility of using location-related pages on the Web to verify existing POI relations via weakly labeled data, e.g., the co-occurrence of an organization and an address in Web pages, the published date of such pages, and the pairing diversity of an address or an organization, etc. The preliminary result shows a promising direction for discovering emerging POI and mandates more research for outdated POI.

According to the Japan Tourism Agency, in 2024, visitor numbers are estimated to exceed previous records, reaching 35 million people. The issues of overtourism and overcrowding in popular tourist areas pose challenges to the sustainable development of the industry. This paper takes Kyoto City in Japan as a case study, using POI (Points of Interest) data and GPS data as the data sources. It combines the G1 sequential relationship analysis method with the entropy weight method to determine the influence weights of various types of POIs on tourism activities. By employing kernel density estimation and weighted overlay analysis methods, the study integrates tourism-related resources and existing tourism demand to identify tourism hotspots and potential areas. The research findings indicate that traditional popular areas in Kyoto, such as the Gion and Kiyomizu-dera Temple areas, have high tourist density. Additionally, new areas with development potential, such as the Momoyama and Daigo-ji Temple areas, have been identified. These research results can provide data support and a basis for future tourism promotion and planning.

As a result of the increasing popularity of indoor activities, many facilities and services are provided inside buildings; hence, there is a need to visualize points-of-interest (POIs) that can describe these indoor service facilities on indoor maps. Over the last few years, indoor mapping has been a rapidly developing area, with the emergence of many forms of indoor representation. In the design of indoor map applications, cartographical methodologies such as generalization and symbolization can make important contributions. In this study, a self-adaptive method is applied for the design of a multi-scale and personalized indoor map. Based on methods of map generalization and multi-scale representation, we adopt a scale-adaptive strategy to visualize the building structure and POI data on indoor maps. At smaller map scales, the general floor distribution and functional partitioning of each floor are represented, while the POI data are visualized by simple symbols. At larger map scales, the detailed room distribution is displayed, and the service information of the POIs is described by detailed symbols. Different strategies are used for the generalization of the background building structure and the foreground POI data to ensure that both can satisfy real-time performance requirements. In addition, for better personalization, different POI data, symbols or color schemes are shown to users in different age groups, with different genders or with different purposes for using the map. Because this indoor map is adaptive to both the scale and the user, each map scale can provide different map users with decision support from different perspectives.

In light of growing urban traffic, car parking becomes increasingly critical for cities to manage. As a result, the prediction of parking occupancy has sparked significant research interest in recent years. While many external data sources have been considered in the prediction models, the underlying geographic context has mostly been ignored. Thus, in order to study the contribution of geospatial information to parking occupancy prediction models, road network centrality, land use, and Point of Interest (POI) data were incorporated in Random Forest (RF) and Artificial Neural Network (ANN, specifically Feedforward Neural Network FFNN) prediction models in this work. Model performances were compared to a baseline, which only considers historical and temporal input data. Moreover, the influence of the amount of training data, the prediction horizon, and the spatial variation of the prediction were explored. The results show that the inclusion of geospatial information led to a performance improvement of up to 25% compared to the baseline. Besides, as the prediction horizon expanded, predictions became less reliable, while the relevance of geospatial data increased. In general, land use and POI data proved to be more beneficial than road network centrality. The amount of training data did not have a significant influence on the performance of the RF model. The ANN model, conversely, achieved optimal results on a training input of 5 days. Likely attributable to varying occupancy patterns, prediction performance disparities could be identified for different parking districts and street segments. Generally, the RF model outperformed the ANN model on all predictions.