Points Of Interest Dataset Research Articles

Multi-Source Data-Driven Extraction of Urban Residential Space: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area Urban Agglomeration

The accurate extraction of urban residential space (URS) is of great significance for recognizing the spatial structure of urban function, understanding the complex urban operating system, and scientific allocation and management of urban resources. The traditional URS identification process is generally conducted through statistical analysis or a manual field survey. Currently, there are also superpixel segmentation and wavelet transform (WT) processes to extract urban spatial information, but these methods have shortcomings in extraction efficiency and accuracy. The superpixel wavelet fusion (SWF) method proposed in this paper is a convenient method to extract URS by integrating multi-source data such as Point of Interest (POI) data, Nighttime Light (NTL) data, LandScan (LDS) data, and High-resolution Image (HRI) data. This method fully considers the distribution law of image information in HRI and imparts the spatial information of URS into the WT so as to obtain the recognition results of URS based on multi-source data fusion under the perception of spatial structure. The steps of this study are as follows: Firstly, the SLIC algorithm is used to segment HRI in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration. Then, the discrete cosine wavelet transform (DCWT) is applied to POI–NTL, POI–LDS, and POI–NTL–LDS data sets, and the SWF is carried out based on different superpixel scale perspectives. Finally, the OSTU adaptive threshold algorithm is used to extract URS. The results show that the extraction accuracy of the NLT–POI data set is 81.52%, that of the LDS–POI data set is 77.70%, and that of the NLT–LDS–POI data set is 90.40%. The method proposed in this paper not only improves the accuracy of the extraction of URS, but also has good practical value for the optimal layout of residential space and regional planning of urban agglomerations.

Adaptive weighted multi-view subspace clustering method for recognizing urban functions from multi-source social sensing data

ABSTRACT Multi-source social sensing data provide new opportunities to identify urban functions from the perspective of human activity. The information embedded in multi-source data typically needs to be fused to obtain a comprehensive view of urban functions. Although multi-view clustering has been successfully used to fuse multi-source social sensing data, the adaptive determination of fusion weights for high-dimensional and noisy multi-source social sensing data remains challenging. Therefore, this study proposes an adaptive weighted multi-view subspace clustering (AWMSC) method. First, we use two neural networks to map multi-source data into a common latent representation and multiple specific latent representations, which serve as the query vector and input vectors of the attention mechanism, respectively. Then, the weight of each type of data is calculated based on the attention mechanism. Finally, the specific latent representations of the multi-source data are weighted and fused into a shared subspace representation, which is used as the input of the spectral clustering algorithm to obtain clustering results. AWMSC is applied to identify urban functional zones in Beijing using bus transactions, taxi trajectories, and points of interest datasets. The results show that AWMSC outperforms the typical single-view, weighted-average, and representative multi-view methods. AWMSC can obtain a comprehensive understanding of urban functional zones which may help government departments make more accurate strategic decisions.

The proposed model for analyzing off-street parking Dynamics: A case study of Taipei City

To reduce traffic congestion, air pollution, and lost revenue due to wasted driving around looking for parking spaces, effective parking management has become a crucial issue in urban areas. Previous studies have shown that parking demand is related to the location of parking spaces/lots, parking fees, time spent looking for spaces, and various driver and environmental characteristics. However, there have been more studies of on-street rather than off-street parking. Also, the spatio-temporal variance between variables and turnover rate (such as the hours of operation for various types of Points of Interest (POIs) as well as the causes of bias estimators has been largely overlooked.To solve the above problems, the Geographical and Temporal Weighted Regression (GTWR) model was utilized in this study to predict the hourly parking turnover rate in off-street parking lots. The flexible coefficient setting of this model can be used to capture the spatial and temporal variance. In addition to this model, the Geographically Weighted Regression (GWR) and the Ordinary Least Squares (OLS) models were used as a basis for comparison. The study included 346 public off-street parking lots in Taipei and 10 types of nearby POIs. The study period was for 24 h on August 31, 2021. Due to the dissimilar trend of the turnover rate, the researchers separated all parking lots into three groups for regression analysis: those that experience a daytime decrease, remain stable, and those with an increase during the day.Our results show the following: (1) For the types of parking lots that experienced increases and decreases, the GTWR model performed best for the prediction of hourly turnover rate, while all models performed similarly for the prediction of the stable type parking lot. (2) With regard to related factors (the type 1 parking lot for example), parking lots in areas with more traditional markets, MRT stations, and with fewer schools, supermarkets, hotel bus stops, and lower parking fees tended to have higher turnover rates. (3) Incorporating a more precise POI dataset (the number and types of POIs as well as their business hours) and applying a suitable spatial temporal model improved the parking demand model’s performance. However, the GTWR model’s limitations may hamper its performance, especially with regard to analysis of the time-stable parking lots or variables. Future studies might include other datasets, such as nearby private parking lots or holiday data to improve the model fit.

An OpenStreetMap-based approach for generating capacity-restricted POIs for activity-based travel demand modeling

The generation of Points of Interest including capacities for travel demand models is usually costly and laborious. Recently, several methods have been developed to automate this process based on Volunteered Geographic Information data as well as to validate the corresponding results. The methodology presented in this paper takes on the task of setting up such a workflow on a large scale, including capacities and all common activity types, by making use of OpenStreetMap data. As a result, a Points of Interest dataset could be created for the whole of Germany, which maps around 24 million destinations including capacities divided into seven activity types. An exemplary validation based on the generated workplace and shopping locations as well as capacities indicates a varying quality of results depending on the respective regional type. The discussion additionally concludes that assignment of several activities to one location presents challenges that should be addressed in future approaches.

Quantitative and qualitative interpretation of community partitions by map overlaying and calculating the distribution of related geographical features

Abstract Applying community detection algorithms in spatial interaction networks constructed from modern human communication records is an essential means of evaluating urban territorial subdivisions. Previous studies have usually involved qualitative rather than quantitative interpretations of community detection results. This article proposes a method of quantitatively and qualitatively interpreting community partition results by map overlaying the spatial regions corresponding to the detected communities with the related geographical features and by calculating the distribution of the geographical features contained in the regions and the entropy value of each distribution. The interpretation of the communities detected from the spatial interaction networks is carried out from the perspective of multi-temporal and multi-spatial scales and multi-geographical features. Extensive experiments were conducted with Milan, Italy, as the study area. The spatial interaction records reflected by telephone calls, land use, and point of interest (POI) data were used as the experimental data. Experimental results demonstrated the effectiveness of our method, and the specific results include: (1) Qualitative interpretation of multi-spatial resolution scale communities detected from the long-term aggregated spatial interaction network. The cohesiveness, homogeneity, and heterogeneity of the detected communities were qualitatively interpreted by the spatial distribution patterns of the land use dataset and the POI dataset. (2) Quantitative interpretation of multi-spatial resolution scale communities detected from the long-term aggregated spatial interaction network. The low spatial resolution scale community partitions and the high spatial resolution scale community partitions were interpreted through the statistical distribution of the land use dataset and the POI dataset, respectively. (3) Qualitative interpretation of the stable and active regions discovered from the community time series. Regardless of the community partitions’ spatial resolution scales, the stable and active regions were distinguished with the statistical distributions of the land use dataset and the POI dataset.

Delineating Peri-Urban Areas Using Multi-Source Geo-Data: A Neural Network Approach and SHAP Explanation

Delineating urban and peri-urban areas has often used information from multiple sources including remote sensing images, nighttime light images, and points-of-interest (POIs). Human mobility from big geo-spatial data could also be relevant for delineating peri-urban areas but its use is not fully explored. Moreover, it is necessary to assess how individual data sources are associated with identification results. Aiming at these gaps, we apply a neural network model to integrate indicators from multi-sources including land cover maps, nighttime light imagery as well as incorporating information about human movement from taxi trips to identify peri-urban areas. SHapley Additive exPlanations (SHAP) values are used as an explanation tool to assess how different data sources and indicators may be associated with delineation results. Wuhan, China is selected as a case study. Our findings highlight that socio-economic indicators, such as nighttime light intensity, have significant impacts on the identification of peri-urban areas. Spatial/physical attributes derived from land cover images and road density have relative low associations. Moreover, taxi intensity as a typical human movement dataset may complement nighttime light and POIs datasets, especially in refining boundaries between peri-urban and urban areas. Our study could inform the selection of data sources for identifying peri-urban areas, especially when facing data availability issues.

In-Path Oracles for Road Networks

Many spatial applications benefit from the fast answering to a seemingly simple spatial query: “Is a point of interest (POI) ‘in-path’ to the shortest path between a source and a destination?” In this context, an in-path POI is one that is either on the shortest path or can be reached within a bounded yet small detour from the shortest path. The fast answering of the in-path queries is contingent on being able to determine without having to actually compute the shortest paths during runtime. Thus, this requires a precomputation solution. The key contribution of the paper is the development of an in-path oracle that is based on precomputation of which pairs of sources and destinations are in-path with respect to the given POI. For a given road network with n nodes and m POIs, an O(m×n)-sized oracle is envisioned based on the reduction of the well-separated pairs (WSP) decomposition of the road network. Furthermore, an oracle can be indexed in a database using a B-tree that can answer queries at very high throughput. Experimental results on the real road network POI dataset illustrate the superiority of this technique compared to a baseline algorithm. The proposed approach can answer ≈ 1.5 million in-path queries per second compared to a few hundred per second using a suitable baseline approach.

EPGQ: Efficient and Private Feature-Based Group Nearest Neighbor Query Over Road Networks

The rapidly growing location-based services enable service providers to accumulate plentiful descriptions on points of interest (POIs), which can be used to support expressive POI queries. In this article, we study a type of POI query, named feature-based group <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighbor query over road networks, in which a user has a feature set and several locations and wishes to find <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> closest POIs that have similar sets of features to the query. As the POI data sets grow, service providers tend to outsource their data sets to a powerful yet not-fully trusted cloud, which calls for privacy preservation on data sets and user queries. Although many schemes have been proposed for privacy-preserving POI queries, none of them can simultaneously support privacy-preserving set similarity and road network distance comparison. To address this challenge, we propose an efficient and private feature-based group nearest neighbor query scheme. In our scheme, we achieve privacy-preserving distance comparison by employing the road network hypercube embedding technique, and design an encrypted index based on B+-tree for privacy-preserving set similarity range queries. Security analysis shows our proposed scheme can preserve the privacy of the data set and queries, and performance evaluation also demonstrates it is computationally efficient.

A Method with Adaptive Graphs to Constrain Multi-View Subspace Clustering of Geospatial Big Data from Multiple Sources

Clustering of multi-source geospatial big data provides opportunities to comprehensively describe urban structures. Most existing studies focus only on the clustering of a single type of geospatial big data, which leads to biased results. Although multi-view subspace clustering methods are advantageous for fusing multi-source geospatial big data, exploiting a robust shared subspace in high-dimensional, non-uniform, and noisy geospatial big data remains a challenge. Therefore, we developed a method with adaptive graphs to constrain multi-view subspace clustering of multi-source geospatial big data (agc2msc). First, for each type of data, high-dimensional and noisy original features were projected into a low-dimensional latent representation using autoencoder networks. Then, adaptive graph constraints were used to fuse the latent representations of multi-source data into a shared subspace representation, which preserved the neighboring relationships of data points. Finally, the shared subspace representation was used to obtain the clustering results by employing a spectral clustering algorithm. Experiments on four benchmark datasets showed that agc2msc outperformed nine state-of-the-art methods. agc2msc was applied to infer urban land use types in Beijing using the taxi GPS trajectory, bus smart card transaction, and points of interest datasets. The clustering results may provide useful calibration and reference for urban planning.

Deep learning method for Chinese multisource point of interest matching

Multisource point of interest (POI) matching refers to the pairing of POIs that refer to the same geographic entity in different data sources. This also constitutes the core issue in geospatial data fusion and update. The existing methods cannot effectively capture the complex semantic information from a text, and the manually defined rules largely affect matching results. This study developed a multisource POI matching method based on deep learning that transforms the POI pair matching problem into a binary classification problem. First, we used three different Chinese word segmentation methods to segment the POI text attributes and used the segmentation results to train the Word2Vec model to generate the corresponding word vector representation. Then, we used the text convolutional neural network (Text-CNN) and multilayer perceptron (MLP) to extract the POI attributes' features and generate the corresponding feature vector representation. Finally, we used the enhanced sequential inference model (ESIM) to perform local inference and inference combination on each attribute to realize the classification of POI pairs. We used the POI dataset containing Baidu Map, Tencent Map, and Gaode Map from Chengdu to train, verify, and test the model. The experimental results show that the matching precision, recall rate, and F1 score of the proposed method exceed 98% on the test set, and it is significantly better than the existing matching methods.

Deep-learning generation of POI data with scene images

Point of interest (POI) is essential to urban scene understanding and location-based services. However, most of the POI data sets are collected manually on the spot, which is time-consuming and laborious. In this study, we propose a deep learning-based three-stage framework to automatically generate POI data sets from scene images by integrating instance segmentation, scene text recognition (STR), and multimodal technology. Firstly, we utilize an instance segmentation model to extract the region of interest (ROI) that contains POI text information from the scene images. Secondly, a STR method is used to locate and identify the text lines from the ROI. Thirdly, we develop a novel visual-linguistic multi-task classification model (VLMC) to classify ROIs and text lines through fusing text and image information. It is the first deep learning-based framework that allows generating POI information with different attributes (such as title, address, and tag) from the text lines of scene images and updating with high-performance models in the three-stage technique. In the experiments, we employ multiple STR data sets and annotated street view images for model training. The result shows that the deep learning-based framework can generate POI records from scene images with high accuracy (F1-score = 52.62%). Moreover, we find that the multi-modal VLMC model integrating the linguistic and visual embeddings has a higher accuracy in POI-generation than single-modal methods. We further use a trained framework to generate POI from Baidu Street View (BSV) images and Tencent Street View (TSV) images in Shenzhen, China, and ultimately obtain a long-term POI data set during 2013 – 2020 with 2,699,895 street view images. Of 815,616 records in the generated POI data set in 2020, 70.94% are covered by the existing Baidu POI data set of Shenzhen in 2013. This confirms the validity of the newly generated POI data set. These results demonstrate that the proposed deep-learning POI-generation framework and dataset can provide new insights for geographic data updating and urban scene understanding for fast growing cities. To facilitate future research, an implementation is made available at https://github.com/KampauCheung/scene-image-poi-generation.

An End-to-End Point of Interest (POI) Conflation Framework

Point of interest (POI) data serves as a valuable source of semantic information for places of interest and has many geospatial applications in real estate, transportation, and urban planning. With the availability of different data sources, POI conflation serves as a valuable technique for enriching data quality and coverage by merging the POI data from multiple sources. This study proposes a novel end-to-end POI conflation framework consisting of six steps, starting with data procurement, schema standardisation, taxonomy mapping, POI matching, POI unification, and data verification. The feasibility of the proposed framework was demonstrated in a case study conducted in the eastern region of Singapore, where the POI data from five data sources was conflated to form a unified POI dataset. Based on the evaluation conducted, the resulting unified dataset was found to be more comprehensive and complete than any of the five POI data sources alone. Furthermore, the proposed approach for identifying POI matches between different data sources outperformed all baseline approaches with a matching accuracy of 97.6% with an average run time below 3 min when matching over 12,000 POIs to result in 8699 unique POIs, thereby demonstrating the framework’s scalability for large scale implementation in dense urban contexts.

Quickly locating POIs in large datasets from descriptions based on improved address matching and compact qualitative representations

AbstractLocating points of interest (POIs) from descriptions can support intelligent location‐based services. Available research achieves it through address matching and spatial reasoning. However, semantic characteristics and spatial proximities of address fields are usually neglected in address matching; current applications of spatial reasoning represent qualitative spatial relations in semantic networks for efficient queries, but they do not yet scale to large datasets for qualitative direction reasoning due to massive qualitative direction relations between objects; moreover, spatial reasoning on various quantitative distances should be optimized. This study proposes a method that improves the accuracy of address matching by combining multiple similarities and enables quick spatial reasoning through the faster relation retrieval of compact qualitative direction representations implemented on global equal latitude and longitude grids (ELLGs) and the ELLG‐based quantitative calculations. The proposed method has been verified by two real‐world datasets and proven to be efficient and accurate when locating POIs in large POI datasets from descriptions.

Delineating urban functional use from points of interest data with neural network embedding: A case study in Greater London

Delineating urban functional use plays a key role in understanding urban dynamics, evaluating planning strategies and supporting policymaking. In recent years, Points of Interest (POI) data, with precise geolocation and detailed attributes, have become the primary data source for exploring urban functional use from a bottom-up perspective, using local, highly disaggregated, big datasets. Previous studies using POI data have given insufficient consideration to the relationship among POI classes in the spatial context, and have failed to provide a straightforward means by which to classify urban functional areas. This study proposes an approach for delineating urban functional use at the scale of the Lower Layer Super Output Area (LSOA) in Greater London by integrating the Doc2Vec model, a neural network embedding method commonly used in natural language processing for vectoring words and documents from their context. In this study, the neural network vectorises both POI classes (‘Word’) and urban areas (‘Document’) based on their functional context by learning features from the spatial distribution of POIs in the city. Specifically, we first construct POI sequences based on the distribution of POI classes, and add their LSOA IDs as ‘document’ tags. By utilising these constructed POI–LSOA sequences, the Doc2Vec model trains the vectors of 574 POI classes (word vectors) and 4836 LSOAs (document vectors). The vectors of POI classes are then used in calculating the functional similarity scores based on their cosine distance, with the vectors of LSOAs grouped into clusters (i.e., functional areas) via the k-means clustering algorithm. We also identify latent functions in each cluster of LSOAs by performing topic modelling and enrichment factor. Compared with TF–IDF, LDA and Word2Vec models, the Doc2Vec model obtains the highest accuracy when classifying functional areas. This study proposes a straightforward approach in which the model directly trains vectors for urban areas, subsequently using them to classify urban functional areas. By employing the enhanced neural network model with low-cost and ubiquitous POI datasets, this study provides a potential tool with which to monitor urban dynamics in a timely and adaptive manner, thereby providing enhanced, data-driven support to urban planning, development and management.

Study on the Spatial Classification of Construction Land Types in Chinese Cities: A Case Study in Zhejiang Province

Identifying the land-use type and spatial distribution of urban construction land is the basis of studying the degree of exposure and the economic value of disaster-affected bodies, which are of great significance for disaster risk predictions, emergency disaster reductions, and asset allocations. Based on point of interest (POI) data, this study adopts POI spatialization and the density-based spatial clustering of applications with noise (DBSCAN) algorithm to accomplish the spatial classification of construction land. Zhejiang province is selected as a study area, and its construction land is divided into 11 land types using an accurate spatial classification method based on measuring the area of ground items. In the research, the POI dataset, which includes information, such as spatial locations and usage types, was constructed by big data cleaning and visual interpretation and approximately 620,000 pieces in total. The overall accuracy of the confusion matrix is 76.86%, which is greatly improved compared with that constructed with EULUC data (61.2%). In addition, compared with the official statistical data of 11 cities in Zhejiang Province, the differences between the calculated spatial proportions and statistics are not substantial. Meanwhile, the spatial characteristics of the studied land-use types are consistent with the urban planning data but with higher accuracy. The research shows that the construction land in Zhejiang Province has a high degree of land intensity, concentrated assets, and high economic exposure. The approach proposed in this study can provide a reference for city management including urbanization process, risk assessment, emergency management and asset allocation.

Optimizing and accelerating space–time Ripley ’s K function based on Apache Spark for distributed spatiotemporal point pattern analysis

With increasing point of interest (POI) datasets available with fine-grained spatial and temporal attributes, space–time Ripley’s K function has been regarded as a powerful approach to analyze spatiotemporal point process. However, space–time Ripley’s K function is computationally intensive for point-wise distance comparisons, edge correction and simulations for significance testing. Parallel computing technologies like OpenMP, MPI and CUDA have been leveraged to accelerate the K function, and related experiments have demonstrated the substantial acceleration. Nevertheless, previous works have not extended optimization of Ripley’s K function from space dimension to space–time dimension. Without sophisticated spatiotemporal query and partitioning mechanisms, extra computational overhead can be problematic. Meanwhile, these researches were limited by the restricted scalability and relative expensive programming cost of parallel frameworks and impeded their applications for large POI dataset and Ripley’s K function variations. This paper presents a distributed computing method to accelerate space–time Ripley’s K function upon state-of-the-art distributed computing framework Apache Spark, and four strategies are adopted to simplify calculation procedures and accelerate distributed computing respectively: (1) spatiotemporal index based on R-tree is utilized to retrieve potential spatiotemporally neighboring points with less distance comparison; (2) spatiotemporal edge correction weights are reused by 2-tier cache to reduce repetitive computation in L value estimation and simulations; (3) spatiotemporal partitioning using KDB-tree is adopted to decrease ghost buffer redundancy in partitions and support near-balanced distributed processing; (4) customized serialization with compact representations of spatiotemporal objects and indexes is developed to lower the cost of data transmission. Based on the optimized method, a web-based visual analytics framework prototype has been developed. Experiments prove the feasibility and time efficiency of the proposed method, and also demonstrate its value on promoting applications of space–time Ripley’s K function in ecology, geography, sociology, economics, urban transportation and other fields.

Efficient Scheduling of Generalized Group Trips in Road Networks

In this article, we introduce generalized group trip scheduling (GGTS) queries that enable friends and families to perform activities at different points of interest (POIs), such as a shopping center, a restaurant and a pharmacy with the minimum total travel distance. Trip planning and scheduling for groups, an important class of location-based services (LBSs), have recently received attention from researchers. However, both group trip planning (GTP) and group trip scheduling (GTS) queries have restrictions: a GTP query assumes that all group members visit all required POIs together, whereas a GTS query requires that each POI is visited by a single group member. A GGTS query is more general and allows any number of group members to visit a POI together. We propose an efficient algorithm to evaluate the exact answers for GGTS queries in road networks. Since finding the answer for a GGTS query is an NP-hard problem, to reduce the processing overhead for a large group size or a large number of required POI types or a large POI dataset, we propose two heuristic solutions—trip-scheduling heuristic (TSH) and search region refinement heuristic (SRH)—for processing GGTS queries. Extensive experiments with real datasets show that our optimal algorithm is preferable for small parameter settings, and the heuristic solutions reduce the processing overhead significantly in return for sacrificing the accuracy slightly.

Hierarchical Multi-Clue Modelling for POI Popularity Prediction with Heterogeneous Tourist Information

Predicting the popularity of Point of Interest (POI) has become increasingly crucial for location-based services, such as POI recommendation. Most of the existing methods can seldom achieve satisfactory performance due to the scarcity of POI's information, which tendentiously confines the recommendation to popular scene spots, and ignores the unpopular attractions with potentially precious values. In this paper, we propose a novel approach, termed Hierarchical Multi-Clue Fusion ( HMCF ), for predicting the popularity of POIs. Specifically, in order to cope with the problem of data sparsity, we propose to comprehensively describe POI using various types of user generated content (UGC) (e.g., text and image) from multiple sources. Then, we devise an effective POI modelling method in a hierarchical manner, which simultaneously injects semantic knowledge as well as multi-clue representative power into POIs. For evaluation, we construct a multi-source POI dataset by collecting all the textual and visual content of several specific provinces in China from four main-stream tourism platforms during 2006 to 2017. Extensive experimental results show that the proposed method can significantly improve the performance of predicting the attractions’ popularity as compared to several baseline methods.

DeepPIM: A deep neural point-of-interest imputation model

A point-of-interest (POI) is a specific location in which someone is interested. In social network services such as Instagram, users share their experiences with text and photos, and link POIs to their posts. POIs can be utilized to understand user preferences and behavior. However, not all posts have POI information. In our study, we found more than half of the posts do not have POI information. The current state-of-the-art POI imputation model adds missing POI information. However, it relies on a conventional machine learning method that requires a substantial amount of laborious feature engineering. To address this problem, we propose DeepPIM, a deep neural POI imputation model that does not require feature engineering. DeepPIM automatically generates textual, visual, user, and temporal features from text, photo, user, and posting time information, respectively. For evaluating DeepPIM, we construct a new large-scale POI dataset. We show that DeepPIM significantly outperforms the current state-of-the-art model on the dataset. Our newly created large-scale POI dataset and the source code of DeepPIM are available at http://github.com/qnfnwkd/DeepPIM.

Defining Fitness-for-Use for Crowdsourced Points of Interest (POI)

(1) Background: Due to the advent of Volunteered Geographic Information (VGI), large datasets of user-generated Points of Interest (POI) are now available. As with all VGI, however, there is uncertainty concerning data quality and fitness-for-use. Currently, the task of evaluating fitness-for-use of POI is left to the data user, with no guidance framework being available which is why this research proposes a generic approach to choose appropriate measures for assessing fitness-for-use of crowdsourced POI for different tasks. (2) Methods: POI are related to the higher-level concept of geo-atoms in order to identify and distinguish their two basic functions, geo-referencing and object-referencing. Then, for each of these functions, suitable measures of positional and thematic quality are developed based on existing quality indicators. (3) Results: Typical use cases of POI are evaluated with regards to their use of the two basic functions of POI, and allocated appropriate measures for fitness-for-use. The general procedure is illustrated on a brief practical example. (4) Conclusion: This research addresses the issue of fitness-for-use of POI on a higher conceptual level by relating it to more fundamental notions of geographical information representation. The results are expected to assist users of crowdsourced POI datasets in determining an appropriate method to evaluate fitness-for-use.