Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap

Sehra, Sukhjit Singh; Singh, Jaiteg; Singh, Hardeep

doi:10.3390/ijgi6070195

Cited by 26 publications

(24 citation statements)

References 218 publications

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“…During the last decade, OSM has gained maturity and numerous articles have been published. Furthermore, a research drift toward analysis and fitness for use of OSM in various application domains has been witnessed (Sehra, Singh & Rai, , , ). The OSM project produces a huge amount of labeled spatial data contributed by users.…”

Section: Introductionmentioning

confidence: 99%

Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data

Sehra

Singh

et al. 2019

Transactions in GIS

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OpenStreetMap (OSM) produces a huge amount of labeled spatial data, but its quality has always been a deep concern.Numerous quality issues have been discussed in the vast literature, while the fitness of OSM for road navigability is only partly explored. Navigability depends on logical consistency, which focuses on the existence of logical contradictions within a data set. Researchers have discussed the insufficiency of established methods and the lack of a computational paradigm to assess the quality of the OSM data.To address the research gaps, the current work extended the capabilities of the Quantum GIS Processing Toolbox for assessment of spatial data. The models and scripts developed are able to assess logical consistency based on geographical topological consistency, semantic information, and morphological consistency. The established and proxy indicators are selected for measuring the logical consistency of OSM data for navigability. For empirical validation, OSM Punjab data are compared with authoritative data from HERE (proprietary) and the Remote Sensing Centre (RSC), Punjab, India. The results conclude that even the proprietary road data sets are not free from logical inconsistencies and data contributed by the masses are credible and navigable. OSM has produced better results than the RSC, but needs more crowd contributions to improve its quality. K E Y W O R D SLogical consistency, OpenStreetMap, PyQGIS, QGIS | 45 SEHRA Et Al. | INTRODUC TI ONThe technological stack of Web 2.0 has enabled the voluminous production of crowdsourced data and, in particular, geospatial data. Volunteered geographic information (VGI), supported by the technological stack of Web 2.0 and contributed by the crowd, offers an alternative intuitive method for the collection of geographic information through volunteering at little cost. VGI produces highly diversified, elaborated, topical, and contextualized spatial data. Probably one of the best-known examples of VGI is the OpenStreetMap (OSM) project, as OSM data are free to use and reduce information gaps for the availability of recent map data.During the last decade, OSM has gained maturity and numerous articles have been published. Furthermore, a research drift toward analysis and fitness for use of OSM in various application domains has been witnessed (Sehra, Singh & Rai, 2013. The OSM project produces a huge amount of labeled spatial data contributed by users. Barrington-Leigh and Millard-Ball (2017) stated that more than 40% of countries have 83% complete OSM data (fully mapped street network), among them are several developed countries.

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Section: Introductionmentioning

confidence: 99%

Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data

Sehra

Singh

et al. 2019

Transactions in GIS

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“…To ensure a robust semantic analysis, a term frequency-inverse document frequency (TF-IDF) weighting scheme (Sehra, Singh, and Rai 2017) was applied to the terms. This resulted in a term weight-LULC matrix in which the rare terms were promoted, and the common terms were discounted based on term frequencies.…”

Section: Term Weighting and Latent Semantic Analysismentioning

confidence: 99%

“…each column of the term weight matrix) was able to describe a unique type of the LULCs. LSA is a natural language processing technique which automatically organizes, understands, and summarizes a textural dataset (Sehra, Singh, and Rai 2017). The foundation of LSA is the singular value decomposition (SVD) which can create a low dimensional space of a term weight matrix while preserving the meanings of the original matrix if there are semantic similarities among the multiple sets of weighted terms (Li, Goodchild, and Raskin 2014); each dimension represents a specific semantic direction or topic.…”

Section: Term Weighting and Latent Semantic Analysismentioning

confidence: 99%

An exploratory analysis of usability of Flickr tags for land use/land cover attribution

Yan

Schultz

Zipf

2019

Geo-spatial Information Science

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This study explored the land use/land cover (LULC) separability by the machine-generated and user-generated Flickr photo tags (i.e. the auto-tags and the user-tags, respectively), based on an authoritative LULC dataset for San Diego County in the United States. Ten types of LULCs were derived from the authoritative dataset. It was observed that certain types of the reclassified LULCs had abundant tags (e.g. the parks) or a high tag density (e.g. the commercial lands), compared with the less populated ones (e.g. the agricultural lands). Certain highly weighted terms of the tags derived based on a term frequency-inverse document frequency weighting scheme were helpful for identifying specific types of the LULCs, especially for the commercial recreation lands (e.g. the zoos). However, given the 10 sets of tags retrieved from the corresponding 10 types of LULCs, one set of tags (all the tags located at one specific type of the LULCs) could not fully delineate the corresponding LULC due to semantic overlaps, according to a latent semantic analysis.

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“…Being a potentially useful source of geospatial information for many disciplines, OSM has attracted as well an increasing interest from the academic and scientific community [7]. Not surprisingly, the topic which so far has been most investigated by researchers is OSM quality assessment [8], since crowdsourced geographic information suffers by definition from a general lack of quality assurance [9]. OSM quality has been traditionally assessed using the standard quality parameters available for geospatial datasets, e.g.…”

Section: Introductionmentioning

confidence: 99%

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

Minghini

Francesco

2019

Open geospatial data, softw. stand.

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OpenStreetMap (OSM) is a well-known crowdsourcing project which aims to create a geospatial database of the whole world. Intrinsic approaches based on the analysis of the history of data, i.e. its evolution over time, have become an established way to assess OSM quality. After a comprehensive review of scientific as well as software applications focused on the visualization, analysis and processing of OSM history, the paper presents "Is OSM up-to-date?", an open source web application addressing the need of OSM contributors, community leaders and researchers to quickly assess OSM intrinsic quality based on the object history for any specific region. The software, mainly written in Python, can be also run in the command line or inside a Docker container. The technical architecture, sample applications and future developments of the software are also presented in the paper.

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Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap

Cited by 26 publications

References 218 publications

Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data

Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data

An exploratory analysis of usability of Flickr tags for land use/land cover attribution

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

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