VGDI – Advancing the Concept: Volunteered Geo‐Dynamic Information and its Benefits for Population Dynamics Modeling

Aubrecht, Christoph; Aubrecht, Dilek Ozceylan; Ungar, Joachim; Freire, Sérgio; Steinnocher, Klaus

doi:10.1111/tgis.12203

Cited by 19 publications

(15 citation statements)

References 43 publications

(57 reference statements)

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“…A more common approach is comparison with quantitative data, often gathered administratively at coarser resolution than the incidental data or for only a portion of the full data set. Examples of this approach applied to social media include the validation of geotagged Flickr photos as a measure of visitation to tourist attractions by comparing it to empirical visitor surveys from each site (Wood et al ), the validation of Twitter as a source of data on mobility patterns by comparing it with tourist arrivals by country (Hawelka et al ), and the comparison of population estimates generated using Foursquare data to rasterized census data (Aubrecht, Ungar, and Freire ).…”

Section: Introductionmentioning

confidence: 99%

Fast Food Data: Where User‐Generated Content Works and Where It Does Not

Folch

Spielman

Manduca

2017

Geographical Analysis

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As big urban data usage expands in the social sciences, there remain real concerns about fidelity to on the ground conditions. In this paper, we examine the correspondence between Phoenix metro area restaurants identified by a social media source (http://yelp.com) and those from an administrative source (Maricopa Association of Governments [MAG]). We find that they capture largely disjoint subsets of Phoenix restaurants, with only about one‐third of restaurants in each data set present in the other. Point pattern analyses indicate that the Yelp data is significantly clustered relative to the MAG data. Specifically, restaurants in Yelp are concentrated in certain parts of metro Phoenix, most notably the downtowns of Phoenix, Scottsdale, and Tempe. Further analysis indicates that areas with more Yelp than MAG restaurants tend to have more college‐educated workers and workers employed in the Arts, Entertainment, and Recreation sector. Our comparison highlights the strengths and weaknesses of each data source: Yelp data is far more detailed and comprehensive in certain locations, while MAG data is more consistent across the entire region due to its systematic construction. When combined, administrative and user generated databases seem to provide a more holistic and comprehensive picture of the world than either would provide by itself.

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Section: Introductionmentioning

confidence: 99%

Fast Food Data: Where User‐Generated Content Works and Where It Does Not

Folch

Spielman

Manduca

2017

Geographical Analysis

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“…Some examples include geotagged tweets from Twitter, a micro‐blogging social network; geotagged photos on Instagram, a photo‐sharing social network; or GPS tracks uploaded to Strava, a social network for athletes. VGI data can be actively or passively contributed by the public (Aubrecht et al, ). Active VGI is data which are knowingly provided.…”

Section: Five Participatory Geospatial Data Classesmentioning

confidence: 99%

“…Recent research is using the APIs from LBSNs to gather location data and make inferences about human behaviour. Examples are Twitter (Crampton et al, 2013;Korson, 2015;Stephens & Poorthuis, 2015), Foursquare (Aubrecht et al, 2016;Sun & Li, 2015), and Strava (Jestico, Nelson, & Winters, 2016). This move from PPGIS/PGIS to VGI, however, is increasing the disconnection between researchers and contributors.…”

Section: Comparing Spatial Data Collection Processesmentioning

confidence: 99%

“…Brown, Corbett, Haklay, and McCall Volunteered Geographic Information VGI VGI is any information created by the public which can be located on the Earth. VGI data can be actively or passively contributed by the public (Aubrecht, Özceylan Aubrecht, Ungar, Freire, & Steinnocher, 2016) and has come to mean a broad range of citizen-based activities which create geographic information (Sabri, Rajabifard, Ho, Amirebrahimi, & Bishop, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Five classes of geospatial data and the barriers to using them

Reynard

2018

Geography Compass

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Geospatial technologies implemented through the World Wide Web (Geoweb) have improved steadily over the last decade. This Geoweb has the potential to allow citizens to collect and use geospatial data in an effort to influence urban planning in a bottom‐up manner—a stark departure from traditional top‐down public consultation in urban planning. This article reviews five classes of geospatial data available to citizens trying to influence urban planning. These classes have been reviewed in the academic literature before, but not at the same time and not with respect to urban planning. Looking at them together allows for examination of the interconnection as well as the boundaries between them. This paper establishes an understanding of these Geoweb data classes and examines the main barriers citizens face when trying to use geospatial data—with a focus on technological and financial barriers. Despite improvements in the Geoweb that help in reducing these barriers, preliminary evidence suggests that the voices of citizens are still not being fully heard in urban planning. This article tells the story of a Geoweb project by Paths for People, a biking and walking advocacy group in Edmonton, Canada. Paths for People initiated a Public Participatory Geographic Information Systems (PPGIS) project to inform the development of biking infrastructure in Edmonton, but this was largely ignored by city council, which adopted a top‐down cycling plan. This paper highlights the large gap between the promises of the Geoweb, the current practice of urban planning, and grassroots capabilities of this form of community engagement.

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“…Hybrid time-use profile using Foursquare (FSQ) data extracted for the Lisbon Metro Area, and Portuguese time-use survey (TUS) data (adapted from Aubrecht et al, 2016).…”

Section: Figurementioning

confidence: 99%

Volunteered Geo-Dynamic Information for Health-Risk Exposure Assessment - A FRESHER Case Study

Aubrecht¹,

Steinnocher²

2016

giforum

Self Cite

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In this paper we discuss the use of volunteered geo-dynamic information (VGDI) for assessing exposure to health risks and improving analysis of associated dynamic aspects in urban settings. VGDI is introduced as an alternative and complementary data source to traditional geodata whereby both spatial and temporal aspects are highlighted. Within the FRESHER project several health-related parameters are modelled, including air pollution and access to fast food restaurant locations. We discuss how Foursquare data (and VGDI in general) can benefit integrative smart urban analytics and provide sample illustrations for a test case area in Lisbon, Portugal. Keywords:foursquare, spatio-temporal analysis, population dynamics, health-risk exposure IntroductionVolunteered geographic information (VGI) has attracted increased attention in the past couple of years in the context of integrative spatial analytics, especially in relation to urban areas. When the term VGI was coined by Goodchild (2007), widespread citizen participation and engagement in the voluntary creation of geographic information was still considered an "exotic" prospect. Things have clearly changed in that regard (Goodchild et al., 2016) and the notion of ubiquitous sensing (Gartner et al., 2007) is becoming more and more today's reality. Data from different sensors are analysed to extract additional multi-level information and improve our understanding of inter-relations in the environment. This approach, recently referred to as Urban Informatics -or Urban Geoinformatics when focusing mainly on spatial aspects -is driving the agenda forward regarding big urban data analysis and advancing the smart city vision. With regard to VGI, urban areas are of particular relevance as levels of use and contribution are highest there. However, the issue of representativeness is still considered one, if not the major, factor impacting applicability of VGI for operational use and input in decision support systems. Nevertheless, it has been widely agreed that the usefulness of VGI can in many ways outplay its inherent weaknesses. Addressing those weaknesses and capitalizing on the unique potential of VGI are crucial focuses of scientific research in the domain of VGI and Urban (Geo-)Informatics.

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VGDI – Advancing the Concept: Volunteered Geo‐Dynamic Information and its Benefits for Population Dynamics Modeling

Cited by 19 publications

References 43 publications

Fast Food Data: Where User‐Generated Content Works and Where It Does Not

Fast Food Data: Where User‐Generated Content Works and Where It Does Not

Five classes of geospatial data and the barriers to using them

Volunteered Geo-Dynamic Information for Health-Risk Exposure Assessment - A FRESHER Case Study

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