Systematic Literature Review of Data Quality Within OpenStreetMap

Kaur, Jasmeet; Singh, Jaiteg; Sehra, Sukhjit Singh; Singh, Hardeep

doi:10.1109/icngcis.2017.35

Cited by 14 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since individual analyses alone are of limited value, the use of frameworks is desirable (17). Most studies on the quality of OSM data are focused on extrinsic studies of the transport network, which is why there is still a need for research on the quality of POI data (18).…”

Section: Open Street Mapmentioning

confidence: 99%

Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models

Klinkhardt

Woerle

Briem

et al. 2021

Transportation Research Record

View full text Add to dashboard Cite

We present a methodology to extract points of interest (POIs) data from OpenStreetMap (OSM) for application in travel demand models. We use custom taglists to identify and assign POI elements to typical activities used in travel demand models. We then compare the extracted OSM data with official sources and point out that the OSM data quality depends on the type of POI and that it generally matches the quality of official sources. It can therefore be used in travel demand models. However, we recommend that plausibility checks should be done to ensure a certain quality. Further, we present a methodology for calculating attractiveness measures for typical activities from single POIs and national trip generation guidelines. We show that the quality of these calculated measures is good enough for them to be used in travel demand models. Using our approach, therefore, allows the quick, automated, and flexible generation of attractiveness measures for travel demand models.

show abstract

Section: Open Street Mapmentioning

confidence: 99%

Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models

Klinkhardt

Woerle

Briem

et al. 2021

Transportation Research Record

View full text Add to dashboard Cite

show abstract

“…The quality of data in UGC is often questioned as users are not experts. As a result, UGC is more vulnerable to low-quality data compared to other domains [48]. For this reason, some projects use specific tools, like sensors, for data collection to make data more reliable compared to just human-computer interaction [49].…”

Section: User-generated Contentmentioning

confidence: 99%

Quality Characteristics for User-Generated Content

Musto

Dahanayake

2022

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

Today, vast amounts of data are collected from the internet, and the general public generates most data using social networks. There is a need to have a comprehensive approach to characterize the quality of such user-generated data collection from the internet. The data quality characteristics accepted among database and computer science communities have definitions that are not domain-specific. Therefore, there is no clear understanding of the data quality characteristics specific to user-generated content. This research examines different user-generated content platforms against the general data quality characteristics to determine which quality characteristics are essential for user-generated content. The research contributes to a list of definitions of those data quality characteristics specific to user-generated content. These definitions help identify quality characteristics useful for user-generated content platforms and their implementations. The quality of the content of Atlas of Living Australia, Twitter, YouTube, Wikipedia, and WalkingPaths is evaluated to assess the essence of the quality characteristics defined in this research.

show abstract

“…As the maps and data editors of OSM could have different levels of experience and skills, OSM data may be vulnerable to errors and gaps. Quality issues of the OSM database are wellknown [71]; therefore, using data sets extracted from OSM could be somewhat questionable. The main quality issues include: accuracy of the geometric position, completeness of the database, qualitative and quantitative information accuracy, topological consistency, and semantic accuracy.…”

Section: Limitations and Advancement In The Research Fieldmentioning

confidence: 99%

Built Environment Typologies Prone to Risk: A Cluster Analysis of Open Spaces in Italian Cities

et al. 2021

View full text Add to dashboard Cite

Planning for preparedness, in terms of multi-hazard disasters, involves testing the relevant abilities to mitigate damage and build resilience, through the assessment of deterministic disaster scenarios. Among risk-prone assets, open spaces (OSs) play a significant role in the characterization of the built environment (BE) and represent the relevant urban portion on which to develop multi-risk scenarios. The aim of this paper is to elaborate ideal scenarios—namely, Built Environment Typologies (BETs)—for simulation-based risk assessment actions, considering the safety and resilience of BEs in emergency conditions. The investigation is conducted through the GIS data collection of the common characteristics of OSs (i.e., squares), identified through five parameters considered significant in the scientific literature. These data were processed through a non-hierarchical cluster analysis. The results of the cluster analysis identified five groups of OSs, characterized by specific morphological, functional, and physical characteristics. Combining the outcomes of the cluster analysis with a critical analysis, nine final BETs were identified. The resulting BETs were linked to characteristic risk combinations, according to the analysed parameters. Thus, the multi-risk scenarios identified through the statistical analysis lay the basis for future risk assessments of BEs, based on the peculiar characteristics of Italian towns.

show abstract

Systematic Literature Review of Data Quality Within OpenStreetMap

Cited by 14 publications

References 23 publications

Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models

Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models

Quality Characteristics for User-Generated Content

Built Environment Typologies Prone to Risk: A Cluster Analysis of Open Spaces in Italian Cities

Contact Info

Product

Resources

About