Identifying optimal study areas and spatial aggregation units for point-based VGI from multiple sources

Haydn Lawrence, Colin Robertson, Rob Feick, Trisalyn Nelson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we introduce a new metric for evaluating feasible VGI study areas and the appropriateness of different aggregation unit sizes through three different components of data quality: coverage, density, and user-heterogeneity. Two popular sources of passive VGI are used for initial testing of the metric: Twitter and Flickr. We compare the component and aggregate measures for different simulated point processes and demonstrate the properties of this metric. The three components are assessed iteratively for the point user generated data (tweets and photos) on a local basis by altering grain sizes. We demonstrate the application of this metric with Flickr and Twitter data obtained for three Canadian cities as initial study areas, including Vancouver, Toronto, and Moncton. The utility of the metric for discriminating qualitatively different types of VGI is evaluated for each of these areas based on a relative comparison framework. Finally, we present a use-case for this metric: identifying the optimal spatial grain and extent for a given data set. The results of this analysis will provide a methodology for preliminary evaluation of VGI quality within a given study area, and identify sub-areas with desirable characteristics.

Original languageEnglish (US)
Title of host publicationAdvances in Spatial Data Handling and Analysis - Select Papers from the 16th IGU Spatial Data Handling Symposium
Publisherspringer berlin
Pages65-84
Number of pages20
Volume19
ISBN (Print)9783319199498
DOIs
StatePublished - 2015
Externally publishedYes
Event16th International Symposium on Spatial Data Handling, SDH 2014 - Toronto, Canada
Duration: Oct 6 2014Oct 8 2014

Other

Other16th International Symposium on Spatial Data Handling, SDH 2014
CountryCanada
CityToronto
Period10/6/1410/8/14

Fingerprint

aggregation
Agglomeration
Testing
data quality
twitter
grain size
agricultural product
methodology
coverage
evaluation
analysis
comparison
city

Keywords

  • Optimal grain
  • Social media
  • VGI

ASJC Scopus subject areas

  • Information Systems
  • Civil and Structural Engineering
  • Geography, Planning and Development

Cite this

Lawrence, H., Robertson, C., Feick, R., & Nelson, T. (2015). Identifying optimal study areas and spatial aggregation units for point-based VGI from multiple sources. In Advances in Spatial Data Handling and Analysis - Select Papers from the 16th IGU Spatial Data Handling Symposium (Vol. 19, pp. 65-84). springer berlin. https://doi.org/10.1007/978-3-319-19950-4_5

Identifying optimal study areas and spatial aggregation units for point-based VGI from multiple sources. / Lawrence, Haydn; Robertson, Colin; Feick, Rob; Nelson, Trisalyn.

Advances in Spatial Data Handling and Analysis - Select Papers from the 16th IGU Spatial Data Handling Symposium. Vol. 19 springer berlin, 2015. p. 65-84.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lawrence, H, Robertson, C, Feick, R & Nelson, T 2015, Identifying optimal study areas and spatial aggregation units for point-based VGI from multiple sources. in Advances in Spatial Data Handling and Analysis - Select Papers from the 16th IGU Spatial Data Handling Symposium. vol. 19, springer berlin, pp. 65-84, 16th International Symposium on Spatial Data Handling, SDH 2014, Toronto, Canada, 10/6/14. https://doi.org/10.1007/978-3-319-19950-4_5
Lawrence H, Robertson C, Feick R, Nelson T. Identifying optimal study areas and spatial aggregation units for point-based VGI from multiple sources. In Advances in Spatial Data Handling and Analysis - Select Papers from the 16th IGU Spatial Data Handling Symposium. Vol. 19. springer berlin. 2015. p. 65-84 https://doi.org/10.1007/978-3-319-19950-4_5
Lawrence, Haydn ; Robertson, Colin ; Feick, Rob ; Nelson, Trisalyn. / Identifying optimal study areas and spatial aggregation units for point-based VGI from multiple sources. Advances in Spatial Data Handling and Analysis - Select Papers from the 16th IGU Spatial Data Handling Symposium. Vol. 19 springer berlin, 2015. pp. 65-84
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