Next Article in Journal
Using OpenStreetMap Data and Machine Learning to Generate Socio-Economic Indicators
Previous Article in Journal
Research of Automatic Generation for Engineering Geological Survey Reports Based on a Four-Dimensional Dynamic Template
Open AccessArticle

The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets

1
School of Planning, University of Waterloo, Waterloo, ON N2L 3G1, Canada
2
Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
3
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(9), 497; https://doi.org/10.3390/ijgi9090497
Received: 25 July 2020 / Revised: 14 August 2020 / Accepted: 19 August 2020 / Published: 21 August 2020
Social media and other forms of volunteered geographic information (VGI) are used frequently as a source of fine-grained big data for research. While employing geographically referenced social media data for a wide array of purposes has become commonplace, the relevant scales over which these data apply to is typically unknown. For researchers to use VGI appropriately (e.g., aggregated to areal units (e.g., neighbourhoods) to elicit key trend or demographic information), general methods for assessing the quality are required, particularly, the explicit linkage of data quality and relevant spatial scales, as there are no accepted standards or sampling controls. We present a data quality metric, the Spatial-comprehensiveness Index (S-COM), which can delineate feasible study areas or spatial extents based on the quality of uneven and dynamic geographically referenced VGI. This scale-sensitive approach to analyzing VGI is demonstrated over different grains with data from two citizen science initiatives. The S-COM index can be used both to assess feasible study extents based on coverage, user-heterogeneity, and density and to find feasible sub-study areas from a larger, indefinite area. The results identified sub-study areas of VGI for focused analysis, allowing for a larger adoption of a similar methodology in multi-scale analyses of VGI. View Full-Text
Keywords: citizen science; user-generated content; volunteered geographic information; spatial scale; data quality; Voronoi diagrams; quadtree; optimized extents; RinkWatch; FrogWatch citizen science; user-generated content; volunteered geographic information; spatial scale; data quality; Voronoi diagrams; quadtree; optimized extents; RinkWatch; FrogWatch
Show Figures

Figure 1

MDPI and ACS Style

Lawrence, H.; Robertson, C.; Feick, R.; Nelson, T. The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets. ISPRS Int. J. Geo-Inf. 2020, 9, 497.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop