Volunteered geographic information, urban forests, &amp; environmental justice

Foster, Alec; Dunham, Ian M.

doi:10.1016/j.compenvurbsys.2014.08.001

Cited by 32 publications

(16 citation statements)

References 47 publications

(60 reference statements)

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“…Through an examination of the spatial distribution and the data richness of volunteered data generated by tree mapping efforts in Philadelphia and San Francisco, our analyses conclude that sociodemographic and environmental indicators are predictive of characteristics of both densities of attributed trees and data richness. Contributing to the recent call for research on the implications of the democratization of data gathering operations through crowdsourcing [5], these findings suggest potential differentials in digital literacy and citizenship by neighborhood type, raising concerns about a potential digital divide in the utilization of VGI tree mapping applications.…”

Section: Discussionmentioning

confidence: 91%

“…Planners recognize that official data often does not include information about marginalized citizens and neighborhoods, resulting in their needs not being considered when making urban policy deliberations [4]. VGI is seen as a potential remedy to this issue, but there remain concerns surrounding the barriers to participation in VGI creation that may result in further exclusion [4,5,22,24,25].…”

Section: Literature Review: Vgi Planning and Urban Forestsmentioning

confidence: 99%

“…The choice of demographic variables for analysis is based on an existing precedent in environmental justice and similar equity examinations, especially those regarding urban forest distribution and the digital divide that have found race and class to be strong predictors of urban form and function [5,37,38]. It includes: income (median household income), education (percentage of the population age 25 or above with a high school diploma), age (percentage of population over age 65), and ethnicity (percentage of African American, Latino, and Asian residents).…”

Section: Datamentioning

confidence: 99%

“…This paper examines the spatial distribution of tree mapping efforts and the richness of data contributed to VGI urban tree mapping applications in two study areas, Philadelphia, Pennsylvania and San Francisco, California, USA. This research contributes to recent interest in the implications of the democratization of data-gathering operations, and the use of data-driven computational approaches to influence policy decisions [4,5]. We specifically address how interpretations of urban forestry analysis and ecosystem services calculations may help to shape decision-making processes and approaches to community engagement, especially surrounding urban environmental amenities, and ultimately improve the environmental and social conditions of urban residents.…”

Section: Introductionmentioning

confidence: 99%

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Citizen Science for Urban Forest Management? Predicting the Data Density and Richness of Urban Forest Volunteered Geographic Information

2017

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Volunteered geographic information (VGI) has been heralded as a promising new data source for urban planning and policymaking. However, there are also concerns surrounding uneven levels of participation and spatial coverage, despite the promotion of VGI as a means to increase access to geographic knowledge production. To begin addressing these concerns, this research examines the spatial distribution and data richness of urban forest VGI in Philadelphia, Pennsylvania and San Francisco, California. Using ordinary least squares (OLS), general linear models (GLM), and spatial autoregressive models, our findings reveal that sociodemographic and environmental indicators are strong predictors of both densities of attributed trees and data richness. Although recent digital urban tree inventory applications present significant opportunities for collaborative data gathering, innovative research, and improved policymaking, asymmetries in the quantity and quality of the data may undermine their effectiveness. If these incomplete and uneven datasets are used in policymaking, environmental justice issues may arise.

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Section: Discussionmentioning

confidence: 91%

Section: Literature Review: Vgi Planning and Urban Forestsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Citizen Science for Urban Forest Management? Predicting the Data Density and Richness of Urban Forest Volunteered Geographic Information

2017

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“…With the advent of Web 2.0 technologies, some efforts have been made on developing in methods for collecting LC sample data over the web [5,[15][16][17]49,50]. These efforts focus on two fronts: new cost-free sample data sources and collaborative volunteer-based collection methods.…”

Section: Web-based Sample Data Collection For Land Cover Map Validationmentioning

confidence: 99%

Active Collection of Land Cover Sample Data from Geo-Tagged Web Texts

Hou

Chen

et al. 2015

Remote Sensing

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Sample data plays an important role in land cover (LC) map validation. Traditionally, they are collected through field survey or image interpretation, either of which is costly, labor-intensive and time-consuming. In recent years, massive geo-tagged texts are emerging on the web and they contain valuable information for LC map validation. However, this kind of special textual data has seldom been analyzed and used for supporting LC map validation. This paper examines the potential of geo-tagged web texts as a new cost-free sample data source to assist LC map validation and proposes an active data collection approach. The proposed approach uses a customized deep web crawler to search for geo-tagged web texts based on land cover-related keywords and string-based rules matching. A data transformation based on buffer analysis is then performed to convert the collected web texts into LC sample data. Using three provinces and three municipalities directly under the Central Government in China as study areas, geo-tagged web texts were collected to validate artificial surface class of China's 30-meter global land cover datasets (GlobeLand30-2010). A total of 6283 geo-tagged web texts were collected at a speed of 0.58 texts per second. The collected texts about built-up areas were transformed into sample data. User's accuracy of 82.2% was achieved, which is close to that derived from formal expert validation. The OPEN ACCESSRemote Sens. 2015, 7 5806 preliminary results show that geo-tagged web texts are valuable ancillary data for LC map validation and the proposed approach can improve the efficiency of sample data collection.

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