A Method for Matching Crowd‐sourced and Authoritative Geospatial Data

Du, Heshan; Alechina, Natasha; Jackson, M. J.; Hart, Glen

doi:10.1111/tgis.12210

Cited by 23 publications

(17 citation statements)

References 22 publications

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“…Aiming to develop an assistive system for data editing, [34] compute the similarity of POI in OSM based on the change history of their respective tags. Apart from POI, there has been work focusing on matching co-referent geo-objects of linear (e.g., [35][36][37]) or polygonal (e.g., [15,38]) geometry types. In a combined evaluation of quality control measures and data conflation from different VGI sources, [7] state that in practice, the two steps are often entangled, which, according to the authors, limits the possibilities to evaluate the fitness-for-use of such data.…”

Section: Methods For Poi Quality Assessmentmentioning

confidence: 99%

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

Jonietz

Zipf

2016

IJGI

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(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.

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Section: Methods For Poi Quality Assessmentmentioning

confidence: 99%

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

Jonietz

Zipf

2016

IJGI

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“…Object matching has been studied for many years and numerous methods have been proposed (Du, Alechina, Jackson, & Hart, ; Gabay & Doytsher, ; Kim, Yu, Heo, & Lee, ; Masuyama, ; Mustière & Devogele, ; Saalfeld, ; Samal, Seth, & Cueto, ; Xu, Xie, Chen, & Wu, ). The predominant existing matching methods focus on the following two issues.…”

Section: Related Workmentioning

confidence: 99%

M:N Object matching on multiscale datasets based on MBR combinatorial optimization algorithm and spatial district

Liu

Zhu

et al. 2018

Transactions in GIS

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Object matching is critical for updating, maintaining, integrating, and quality assessing spatial data. However, matching data are often obtained from different sources and have problems of positional discrepancy and different levels of detail. To resolve these problems, this article presents a multiscale polygonal object-matching approach, called the minimum bounding rectangle combinatorial optimization (MBRCO) with spatial district (SD). This method starts with the MBRCO algorithm and its enhancement using the SD to find corresponding MBRs of one-to-one, one-to-many, and many-to-many matching pairs. Then, it aligns the MBRs of the matching pairs to identify objectmatching pairs, which are evaluated using a matching criterion to find geometrically corresponding objects. Our approach was experimentally validated using two topographical datasets at 1:2k and 1:10k. The proposed approach outperforms the common two-way area overlap method and another method based on the contextual information and relaxation labeling algorithm. The proposed method achieved accurate aggregation of the many-tomany matching pairs under the positional discrepancy scenario.

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“…The numbers of spatial objects in the case studies are shown in Table 1 The initial matches are generated by the matching method implemented in MatchMaps. The detailed matching method is provided by Du et al (2016). The method consists of two main steps: matching geometries and matching spatial objects.…”

Section: Validating Matches Using Spatial Logicmentioning

confidence: 99%

“…The OSM positional accuracy was estimated to be about 20 meters in UK (Haklay, 2010). In our more recent work (Du et al, 2016), we analysed how the level of tolerance affects the precision and recall of matching results for the same geographic area in Nottingham (the same data as shown in the first row of Table 1) using 12 different levels of tolerance within a range of 1 to 80 meters. It shows that, for the Nottingham case, 20 meters is a good estimate, though it is not the optimal value.…”

Section: Validating Matches Using Spatial Logicmentioning

confidence: 99%

“…One way to produce such matches is to use locations and geometries of objects, although of course we also use any lexical labels associated with the objects, such as names of restaurants etc. In our previous work (Du, Alechina, Jackson, & Hart, 2016), we present a method which generates matches using both location and lexical information about spatial objects. As generated matches may contain errors, they are seen as retractable assumptions and require further validation and checking.…”

Section: Introductionmentioning

confidence: 99%

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Qualitative Spatial Logics for Buffered Geometries

Du¹,

Alechina²

2016

jair

Self Cite

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This paper describes a series of new qualitative spatial logics for checking consistency of sameAs and partOf matches between spatial objects from different geospatial datasets, especially from crowd-sourced datasets. Since geometries in crowd-sourced data are usually not very accurate or precise, we buffer geometries by a margin of error or a level of tolerance σ ∈ R ≥0 , and define spatial relations for buffered geometries. The spatial logics formalize the notions of 'buffered equal' (intuitively corresponding to 'possibly sameAs'), 'buffered part of' ('possibly partOf'), 'near' ('possibly connected') and 'far' ('definitely disconnected'). A sound and complete axiomatisation of each logic is provided with respect to models based on metric spaces. For each of the logics, the satisfiability problem is shown to be NP-complete. Finally, we briefly describe how the logics are used in a system for generating and debugging matches between spatial objects, and report positive experimental evaluation results for the system.

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A Method for Matching Crowd‐sourced and Authoritative Geospatial Data

Cited by 23 publications

References 22 publications

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

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

M:N Object matching on multiscale datasets based on MBR combinatorial optimization algorithm and spatial district

Qualitative Spatial Logics for Buffered Geometries

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