OpenStreetMap history for intrinsic quality assessment: Is OSM up-to-date?

Minghini, Marco; Francesco, Frassinelli

doi:10.1186/s40965-019-0067-x

Cited by 58 publications

(45 citation statements)

References 37 publications

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“…It is interesting to note that the countries for which intrinsic quality assessments of OSM data and contributors have been done do not only include developed countries, such as Canada [4,33]; Ireland and Lithuania [6]; Austria [34]; UK [35]; Germany [32]; Ireland, UK, Germany, and Austria [7]; Germany, France and UK [16], but also Haiti, Philippines, Liberia and Nepal [14]; Tanzania [11]; Jakarta [36]; India [37]. Tools have also been developed to support intrinsic quality assessment [38].…”

Section: Data Quality Assessmentmentioning

confidence: 99%

A Contributor-Focused Intrinsic Quality Assessment of OpenStreetMap in Mozambique Using Unsupervised Machine Learning

Madubedube

Coetzee

Rautenbach

2021

IJGI

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Anyone can contribute geographic information to OpenStreetMap (OSM), regardless of their level of experience or skills, which has raised concerns about quality. When reference data is not available to assess the quality of OSM data, intrinsic methods that assess the data and its metadata can be used. In this study, we applied unsupervised machine learning for analysing OSM history data to get a better understanding of who contributed when and how in Mozambique. Even though no absolute statements can be made about the quality of the data, the results provide valuable insight into the quality. Most of the data in Mozambique (93%) was contributed by a small group of active contributors (25%). However, these were less active than the OSM Foundation’s definition of active contributorship and the Humanitarian OpenStreetMap Team (HOT) definition for intermediate mappers. Compared to other contributor classifications, our results revealed a new class: contributors who were new in the area and most likely attracted by HOT mapping events during disaster relief operations in Mozambique in 2019. More studies in different parts of the world would establish whether the patterns observed here are typical for developing countries. Intrinsic methods cannot replace ground truthing or extrinsic methods, but provide alternative ways for gaining insight about quality, and they can also be used to inform efforts to further improve the quality. We provide suggestions for how contributor-focused intrinsic quality assessments could be further refined.

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Section: Data Quality Assessmentmentioning

confidence: 99%

A Contributor-Focused Intrinsic Quality Assessment of OpenStreetMap in Mozambique Using Unsupervised Machine Learning

Madubedube

Coetzee

Rautenbach

2021

IJGI

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“…Four of them describe software applications aimed to extract useful information from the OSM database through analysis, processing, visualization or a combination of them. The papers by Minghini and Frassinelli [25] and Raifer et al [26] specifically address the analysis of OSM history, which represents a peculiar component of the OSM database itself.…”

Section: Open Geospatial Data and Openstreetmapmentioning

confidence: 99%

“…Minghini and Frassinelli [25] offer two significant contributions. First, the authors provide an up-to-date, comprehensive review of available software applications focused on OSM history.…”

Section: Open Geospatial Data and Openstreetmapmentioning

confidence: 99%

Geospatial openness: from software to standards & data

Minghini

Mobasheri

Rautenbach

et al. 2020

Open geospatial data, softw. stand.

Self Cite

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This paper is the editorial of the Special Issue "Open Source Geospatial Software", which features 10 published papers. The editorial introduces the concept of openness and, within the geospatial context, declines it into the three main components of software, data and standards. According to this classification, the papers published in the Special Issue are briefly summarized and a future research agenda in the open geospatial domain is finally outlined.

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“…The topic of data heterogeneity has been covered by Jasim and Al-Hamadani (2020), Minghini and Frassinelli (2019), and Zhou (2018). Because their analyses were carried out in different study areas, it is still unclear whether heterogeneity serves as a quality indicator per ISO 19157 (ISO 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Regardless, heterogeneity should be evaluated and presented for the user. So far, Minghini and Frassinelli (2019) suggested the most suitable approach, where they were able to detect the issues associated with VGI data updating. Jasim and Al-Hamadani (2020), Küçük and Anbaroğlu (2019) analyzed geospatial data in the commercial software ArcGIS, which limits the user access by its high license cost.…”

Section: Introductionmentioning

confidence: 99%

The quality of OpenStreetMap in a large metropolis in northeast Brazil: Preliminary assessment of geospatial data for road axes

et al. 2020

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This paper evaluates the data quality of road axes using the OpenStreetMap (OSM) collaborative mapping platform. OSM was chosen owing to the abundance of data and registered contributors (~ 6 million). We assumed the OSM collaborative data could complement the reference mappings by its quality parameters. We used the cartographic quality indicators of positional accuracy, thematic accuracy, and completeness to validate vector files from OSM. We analyzed the positional accuracy of linear features and we developed the automation of the positional accuracy process. The tool verified the completeness of road axes and thematic accuracy. The positional accuracy of linear features was also used, performed to obtain a range of scales, which reflected the characteristics of mapped areas and varied from 1:22,500 to 1:25,000. The completeness of road axes was 82% of the checked areas. By evaluating the thematic accuracy, we found that the absence of road axes toponymy in editions caused errors in the OSM features (i.e., 58% of road axes without information). As such, we concluded that collaborative data complements the reference cartography by measuring the heterogeneity of information in various regions and filtering the OSM data, despite its being useful for certain analyses.

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OpenStreetMap history for intrinsic quality assessment: Is OSM up-to-date?

Cited by 58 publications

References 37 publications

A Contributor-Focused Intrinsic Quality Assessment of OpenStreetMap in Mozambique Using Unsupervised Machine Learning

A Contributor-Focused Intrinsic Quality Assessment of OpenStreetMap in Mozambique Using Unsupervised Machine Learning

Geospatial openness: from software to standards & data

The quality of OpenStreetMap in a large metropolis in northeast Brazil: Preliminary assessment of geospatial data for road axes

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