Identifying Optimal Study Areas and Spatial Aggregation Units for Point-Based VGI from Multiple Sources

Lawrence, H Lazarus; Robertson, Colin; Feick, Rob; Nelson, Trisalyn A.

doi:10.1007/978-3-319-19950-4_5

Cited by 3 publications

(4 citation statements)

References 22 publications

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“…As suggested in other studies [77,78], this unevenness may limit the use of geosocial media to certain areas. However, we were able to identify places outside high-interaction areas that were associated with particular topics or emerged at specific time periods.…”

Section: Implications For Using Geosocial Media To Understand Public mentioning

confidence: 75%

Understanding Public Opinions from Geosocial Media

Zhang

Feick

2016

IJGI

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Increasingly, social media data are linked to locations through embedded GPS coordinates. Many local governments are showing interest in the potential to repurpose these firsthand geo-data to gauge spatial and temporal dynamics of public opinions in ways that complement information collected through traditional public engagement methods. Using these geosocial data is not without challenges since they are usually unstructured, vary in quality, and often require considerable effort to extract information that is relevant to local governments' needs from large data volumes. Understanding local relevance requires development of both data processing methods and their use in empirical studies. This paper addresses this latter need through a case study that demonstrates how spatially-referenced Twitter data can shed light on citizens' transportation and planning concerns. A web-based toolkit that integrates text processing methods is used to model Twitter data collected for the Region of Waterloo (Ontario, Canada) between March 2014 and July 2015 and assess citizens' concerns related to the planning and construction of a new light rail transit line. The study suggests that geosocial media can help identify geographies of public perceptions concerning public facilities and services and have potential to complement other methods of gauging public sentiment.

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Section: Implications For Using Geosocial Media To Understand Public mentioning

confidence: 75%

Understanding Public Opinions from Geosocial Media

Zhang

Feick

2016

IJGI

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“…Previous research has demonstrated that analysis of VGI at different grains can result in different outcomes [29,56]. Here, we aimed to use the data quality index described in Lawrence et al [15] to define a study extent based on the spatial characteristics inherent in VGI point patterns. The results showed that the methods were able to find sub-areas that matched the predefined criteria for the VGI case study of skateability of RinkWatch and for conservation efforts for FrogWatch.…”

Section: Discussionmentioning

confidence: 99%

“…These components are expressed on a scale of 0 to 1 and combined multiplicatively to yield a final overall S-COM index. The relative weightings of the components can be varied to reflect differences in study parameters [15], such as a lack of unique user data. The first component of the S-COM index, coverage, assesses candidate extents and grains of the overall study area for desired spatial qualities in terms of observations, such as clustering of similar data or dispersion of attribute-less points.…”

Section: Methodsmentioning

confidence: 99%

“…This is an especially important consideration when the geographic phenomena being investigated are in themselves fuzzy and uncertain such as what individuals consider as a city's "downtown". We have previously shown how the choice of spatial grain for analysis can be optimized with respect to the general data quality parameters for VGI [15]. Here, we build on that work to demonstrate how spatial extent can also be optimized based on a predefined index consistent with a study's overarching goals.…”

Section: Introductionmentioning

confidence: 95%

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

Lawrence

Robertson

Feick

et al. 2020

IJGI

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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.

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