Automatic analysis of positional plausibility for points of interest in OpenStreetMap using coexistence patterns

Kashian, Alireza; Rajabifard, Abbas; Richter, Kai-Florian; Chen, Yiqun

doi:10.1080/13658816.2019.1584803

Cited by 14 publications

(10 citation statements)

References 24 publications

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“…Articles from the peak in 2014 generally focus on laying the frameworks for assessing volunteered geographic information (VGI) quality [18,19], along with the development of methods for assessment [20][21][22][23]. In recent years, despite the lower number of articles, the field developed in several new directions, such as in more sophisticated statistical machine learning methods for quality assessment [24,25] and POI matching [26,27]. Recent studies also explore novel data sources beyond OSM, such as LBSNs and review websites [27,28], and broader applications of POI data [10,29,30].…”

Section: Review Of Approaches For Validating Poi Data Qualitymentioning

confidence: 99%

Point-of-Interest (POI) Data Validation Methods: An Urban Case Study

Yeow

Low

Tan

et al. 2021

IJGI

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Point-of-interest (POI) data from map sources are increasingly used in a wide range of applications, including real estate, land use, and transport planning. However, uncertainties in data quality arise from the fact that some of this data are crowdsourced and proprietary validation workflows lack transparency. Comparing data quality between POI sources without standardized validation metrics is a challenge. This study reviews and implements the available POI validation methods, working towards identifying a set of metrics that is applicable across datasets. Twenty-three validation methods were found and categorized. Most methods evaluated positional accuracy, while logical consistency and usability were the least represented. A subset of nine methods was implemented to assess four real-world POI datasets extracted for a highly urbanized neighborhood in Singapore. The datasets were found to have poor completeness with errors of commission and omission, although spatial errors were reasonably low (<60 m). Thematic accuracy in names and place types varied. The move towards standardized validation metrics depends on factors such as data availability for intrinsic or extrinsic methods, varying levels of detail across POI datasets, the influence of matching procedures, and the intended application of POI data.

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Section: Review Of Approaches For Validating Poi Data Qualitymentioning

confidence: 99%

Point-of-Interest (POI) Data Validation Methods: An Urban Case Study

Yeow

Low

Tan

et al. 2021

IJGI

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“…Mülligann, Janowicz, Ye, and Lee (2011) developed a spatial-semantic interaction model to analyze the semantic and spatial co-occurrences of different feature types in OSM. Kashian et al (2019) used an adapted form of spatial association rule mining to extract spatial coexistence patterns between different POI types in OSM. The results were integrated within a tag recommender system to quantify the plausibility of newly created POIs.…”

Section: Rel Ated Workmentioning

confidence: 99%

“…Several studies have applied methods from KDD such as association rule mining to discover spatial relationships within large databases (Bahrdt, Funke, Gelhausen, & Storandt, 2017). In the context of OSM, such co-occurrence rules may be used within data quality assessment by identifying logical inconsistencies (Mocnik et al, 2018) and have already been applied within tag recommendation systems (Kashian, Rajabifard, Richter, & Chen, 2019;Vandecasteele & Devillers, 2015). However, Kashian et al (2019) have mentioned issues when mining association rules from OSM due to its spatial heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the cultural background of the mappers will influence what is represented in the data. When using co-occurrence patterns for tag recommendation systems or data quality assessment, both cultural and data quality aspects are important to consider (Ali, Sirilertworakul, Zipf, & Mobasheri, 2016;Kashian et al, 2019). Yet, to date no study has investigated the variability of co-occurrence patterns in OSM data depending on cultural and map production contexts.…”

Section: Introductionmentioning

confidence: 99%

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Regional variations of context‐based association rules in OpenStreetMap

Ludwig

Fendrich²,

Zipf

2020

Transactions in GIS

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As a user-generated map of the whole world, OpenStreetMap (OSM) provides valuable information about the natural and built environment. However, the spatial heterogeneity of the data due to cultural differences and the spatially varying mapping process makes the extraction of reliable information difficult. This study investigates the variability of association rules extracted from OSM across different geographic regions and depending on different context variables, such as the number of OSM mappers. The focus of this study is the spatial co-occurrence of OSM tags mapped inside of parks within eight different cities. Without considering any context variable, most association rules were very region-specific without any rule being valid across all cities. Limiting the association rule analysis to parks based on specific context variables increased the number of rules which are applicable across multiple cities. Furthermore, additional region-specific association rules emerged. The most important context variables were found to be the number of features mapped inside the park, the number of tags and the park size. These results suggest that the mapping process has a significant influence on the emergence of association rules within user-generated data.Therefore, this subject needs further investigation to enable effective usage of OSM data across different cultural realms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…It is natural that most of these functions do not appear individually as a single function in a particular area. Many previous studies [1][2][3][4][5] showed that these functions are coexistent, and several such relationships are often occurring within every area in cities. Each function has its own characteristics that we can easily discover through various data.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Identification of Urban Functions with Fishers’ Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing

Yang

Liu

et al. 2019

IJGI

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Urban areas involve different functions that attract individuals and fit personal needs. Understanding the distribution and combination of these functions in a specific district is significant for urban development in cities. Many researchers have already studied the methods of identifying the dominant functions in a district. However, the degree of collection and the representativeness of a function in a district are controlled not only by its number in the district but also by the number outside this district and a number of other functions. Thus, this study proposed a quantitative method to identify urban functions, using Fisher’s exact test and point of interest (POI) data, applied in determining the urban districts within the Sixth Ring Road in Beijing. To begin with, we defined a functional score based on three statistical features: the p-value, odds-ratio, and the frequency of each POI tag. The p-value and odds-ratio resulted from a statistical significance test, the Fisher’s exact test. Next, we ran a k-modes clustering algorithm to classify all urban districts in accordance with the score of each function and their combination in one district, and then we detected four different groups, namely, Work and Tourism Mixed-developed district, Mixed-developed Residential district, Developing Greenland district, and Mixed Recreation district. Compared with the other identifying methods, our method had good performance in identifying functions, except for transportation. In addition, the Coincidence Degree was used to evaluate the accuracy of classification. In our study, the total accuracy of identifying urban districts was 83.7%. Overall, the proposed identifying method provides an additional method to the various methods used to identify functions. Additionally, analyzing urban spatial structure can be simpler, which has certain theoretical and practical value for urban geospatial planning.

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Automatic analysis of positional plausibility for points of interest in OpenStreetMap using coexistence patterns

Cited by 14 publications

References 24 publications

Point-of-Interest (POI) Data Validation Methods: An Urban Case Study

Point-of-Interest (POI) Data Validation Methods: An Urban Case Study

Regional variations of context‐based association rules in OpenStreetMap

Quantitative Identification of Urban Functions with Fishers’ Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing

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