Optimal spatial data matching for conflation: A network flow‐based approach

Lei, Ting; Lei, Zhen

doi:10.1111/tgis.12561

Cited by 15 publications

(13 citation statements)

References 14 publications

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“…The various limitations of existing conflation methods have been widely discussed (see [68][69][70][71][72] for further details). As a result, manual approaches to conflation still continue to be widely used today for conflating roads and other map data [73]; however, such methods are not scalable for our study. Given these considerations, in this paper we focus on spatial coverage, which could be more reliably computed, and not spatial completeness.…”

Section: Methodsmentioning

confidence: 99%

Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

Mahabir

Schuchard

Crooks

et al. 2020

IJGI

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Over the last decade, Volunteered Geographic Information (VGI) has emerged as a viable source of information on cities. During this time, the nature of VGI has been evolving, with new types and sources of data continually being added. In light of this trend, this paper explores one such type of VGI data: Volunteered Street View Imagery (VSVI). Two VSVI sources, Mapillary and OpenStreetCam, were extracted and analyzed to study road coverage and contribution patterns for four US metropolitan areas. Results show that coverage patterns vary across sites, with most contributions occurring along local roads and in populated areas. We also found that a few users contributed most of the data. Moreover, the results suggest that most data are being collected during three distinct times of day (i.e., morning, lunch and late afternoon). The paper concludes with a discussion that while VSVI data is still relatively new, it has the potential to be a rich source of spatial and temporal information for monitoring cities.

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Section: Methodsmentioning

confidence: 99%

Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

Mahabir

Schuchard

Crooks

et al. 2020

IJGI

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“…One limitation of the assignment problem is its stringent assumption that every feature in (at least one of) the two datasets must be assigned. To address such issues, [18] introduced two network-flow-based models called the p-matching and p-double-matching models based on the classic minimum cost network problem (called network flow problems for short). The network problem is another cluster of classic models in operations research.…”

Section: Optimized Conflation Modelsmentioning

confidence: 99%

“…It is more expressive than the assignment problem and subsumes several other optimization problems including the assignment problem and the shortest path problem as its special cases. The authors of [18] recognized the need to balance between two objectives: making a large number of matches and keeping false positives low. They developed an adaptive procedure to test an increasing match number until the maximum discrepancy/distance between feature pairs reaches a predefined critical value.…”

Section: Optimized Conflation Modelsmentioning

confidence: 99%

Harmonizing Full and Partial Matching in Geospatial Conflation: A Unified Optimization Model

Lei

2022

IJGI

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Spatial data conflation is aimed at matching and merging objects in two datasets into a more comprehensive one. Starting from the “map assignment problem” in the 1980s, optimized conflation models treat feature matching as a natural optimization problem of minimizing certain metrics, such as the total discrepancy. One complication in optimized conflation is that heterogeneous datasets can represent geographic features differently. Features can correspond to target features in the other dataset either on a one-to-one basis (forming full matches) or on a many-to-one basis (forming partial matches). Traditional models consider either full matching or partial matches exclusively. This dichotomy has several issues. Firstly, full matching models are limited and cannot capture any partial match. Secondly, partial matching models treat full matches just as partial matches, and they are more prone to admit false matches. Thirdly, existing conflation models may introduce conflicting directional matches. This paper presents a new model that captures both full and partial matches simultaneously. This allows us to impose structural constraints differently on full/partial matches and enforce the consistency between directional matches. Experimental results show that the new model outperforms conventional optimized conflation models in terms of precision (89.2%), while achieving a similar recall (93.2%).

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“…The outcome of spatial data integration is not just data overlayed and displayed together. It must have connections among features in different datasets and merge them into a single representation hoping to find new knowledge that cannot be derived from the individual datasets alone [19][20][21][22]. Data sources used in the referenced works concerning spatial data integration can be classified into two categories: official (or authoritative) and volunteered geographic information (VGI).…”

Section: Spatial Data Integrationmentioning

confidence: 99%

Creation of a Multimodal Urban Transportation Network through Spatial Data Integration from Authoritative and Crowdsourced Data

Smarzaro

Davis

Quintanilha

2021

IJGI

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One of the most significant challenges in cities concerns urban mobility. Urban mobility involves the use of different modes of transport, which can be individual or collective, and different organizations can produce their respective datasets that, usually, are used isolated from each other. The lack of an integrated view of the entire multimodal urban transportation network (MUTN) brings difficulties to citizens and urban planning. However, obtaining reliable and up-to-date spatial data is not an easy task. To address this problem, we propose a framework for creating a multimodal urban transportation network by integrating spatial data from heterogeneous sources. The framework standardizes the representation of different datasets through a common conceptual model for spatial data (schema matching), uses topological, geometric, and semantic information to find matches among objects from different datasets (data matching), and consolidated them into a single representation using data fusion techniques in a complementary, redundant and cooperative way. Spatial data integration makes it possible to use reliable data from official sources (possibly outdated and expensive to produce) and crowdsourced data (continuously updated and low cost to use). To evaluate the framework, a MUTN for the Brazilian city of Belo Horizonte was built integrating authoritative and crowdsourced data (OpenStreetMap, Foursquare, Facebook Places, Google Places, and Yelp), and then it was used to compute routes among eighty locations using four transportation possibilities: walk, drive, transit, and drive–walk. The time and distance of each route were compared against their equivalent from Google Maps, and the results point to a great potential for using the framework in urban computing applications that require an integrated view of the entire multimodal urban transportation network.

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Optimal spatial data matching for conflation: A network flow‐based approach

Cited by 15 publications

References 14 publications

Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

Crowdsourcing Street View Imagery: A Comparison of Mapillary and OpenStreetCam

Harmonizing Full and Partial Matching in Geospatial Conflation: A Unified Optimization Model

Creation of a Multimodal Urban Transportation Network through Spatial Data Integration from Authoritative and Crowdsourced Data

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