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Urban Science
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Crowdsourcing Urban Data
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Crowdsourcing Urban Data

A special issue of Urban Science (ISSN 2413-8851).

Deadline for manuscript submissions: closed (31 December 2017) | Viewed by 30488

Special Issue Editors

Prof. Dr. Elizabeth Wentz

E-Mail Website
Guest Editor
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USA
Interests: shape and pattern analysis; geographic information science; applications of GIS to urban environment; urban remote sensing; water resource management
Special Issues, Collections and Topics in MDPI journals
Ms. Lindsey Conrow

E-Mail Website
Guest Editor
School of Geographical Sciences and Urban Planning, Arizona State University, Coor Hall, 5th Floor, 975 S. Myrtle Ave., Tempe, AZ 85287, USA
Interests: space-time analytics; human mobility dynamics; GIScience; transport geography
Ms. Heather Fischer

E-Mail Website
Guest Editor
School of Geographical Sciences and Urban Planning, Arizona State University, Coor Hall, 5th Floor, 975 S. Myrtle Ave., Tempe, AZ 85287, USA
Interests: citizen science; volunteered geographic information; biogeography; archaeology

Special Issue Information

Dear Colleagues,

Authoritative data sources (e.g., national censuses, municipal records, federal mapping agencies, macroeconomic records) historically provided the backbone for quantifying, analyzing, and understanding mechanisms that operate in a city. While these data sources continue to provide valuable information, non-authoritative data sources are growing in volume and availability. Mobile devices, social media, and the underlying telecommunications infrastructure, have resulted in a global trend where individuals are increasingly volunteering to collect and share observations of the world around them. These crowdsourced data, as images, text, time, and location, are an invaluable resource because they provide spatially and temporally continuous observations that would otherwise go unrecorded. The increased volume of these data make it difficult for the scientific community to ignore even though these data are viewed with skepticism about their scientific validity. In this Special Issue, we seek to engage with scholars to better understand the possibilities, opportunities, and limitations of crowdsourced urban data. We therefore invite manuscript submissions on theoretical and empirical research on a range of themes related to crowdsourced data, including, but not limited to:

analytics
validation
visualization
ethics
accessibility
content analysis
uncertainty

Prof. Dr. Elizabeth Wentz
Lindsey Conrow
Heather Fischer
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Urban Science is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Crowdsourcing

  • Citizen science

  • Social data

  • Non-authoritative data sources

  • Networked science

  • Volunteered geographic information

  • PPGIS (Public Participation Geographic Information Systems)

Published Papers (4 papers)

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4043 KiB  
Article
Citizen Science for Urban Forest Management? Predicting the Data Density and Richness of Urban Forest Volunteered Geographic Information
by Alec Foster, Ian M. Dunham and Charles Kaylor
Urban Sci. 2017, 1(3), 30; https://doi.org/10.3390/urbansci1030030 - 19 Sep 2017
Cited by 12 | Viewed by 5035
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Crowdsourcing Urban Data)
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4878 KiB  
Article
Building a National-Longitudinal Geospatial Bicycling Data Collection from Crowdsourcing
by Simone Z. Leao, Scott N. Lieske, Lindsey Conrow, Jonathan Doig, Vandana Mann and Chris J. Pettit
Urban Sci. 2017, 1(3), 23; https://doi.org/10.3390/urbansci1030023 - 28 Jun 2017
Cited by 8 | Viewed by 7102
Abstract
To realize the full potential of crowdsourced data collected by smartphone applications in urban research and planning, there is a need for parsimonious, reliable, computationally and temporally efficient data processing routines. The literature indicates that the opportunities brought by crowdsourced data in generating [...] Read more.
To realize the full potential of crowdsourced data collected by smartphone applications in urban research and planning, there is a need for parsimonious, reliable, computationally and temporally efficient data processing routines. The literature indicates that the opportunities brought by crowdsourced data in generating low-cost, bottom-up, and fine spatial and temporal scale data, are also accompanied by issues related to data quality, bias, privacy concerns and low accessibility. Using an exemplar case of RiderLog, a crowdsourced GPS tracked bicycling data, this paper describes and critiques the processes developed to transform this urban big data. Furthermore, the paper outlines the important tasks of formatting, cleaning, validating, anonymizing and publishing this data for the capital cities of each state and territory in Australia. More broadly, this research contributes to the foundational underpinnings of how to process and make available crowdsourced data for research and real world urban planning purposes. Full article
(This article belongs to the Special Issue Crowdsourcing Urban Data)
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524 KiB  
Article
Promoting Crowdsourcing for Urban Research: Cycling Safety Citizen Science in Four Cities
by Colin Ferster, Trisalyn Nelson, Karen Laberee, Ward Vanlaar and Meghan Winters
Urban Sci. 2017, 1(2), 21; https://doi.org/10.3390/urbansci1020021 - 21 Jun 2017
Cited by 15 | Viewed by 5623
Abstract
People generate massive volumes of data on the Internet about cities. Researchers may engage these crowds to fill data gaps and better understand and inform planning decisions. Crowdsourced tools for data collection must be supported by outreach; however, researchers typically have limited experience [...] Read more.
People generate massive volumes of data on the Internet about cities. Researchers may engage these crowds to fill data gaps and better understand and inform planning decisions. Crowdsourced tools for data collection must be supported by outreach; however, researchers typically have limited experience with marketing and promotion. Our goal is to provide guidance on effective promotion strategies. We evaluated promotion efforts for BikeMaps.org, a crowdsourced tool for cycling collisions, near misses, hazards, and thefts. We analyzed website use (sessions) and incidents reported, and how they related to promotion medium (social, traditional news, or in-person), intended audience (cyclists or general), and community context (cycling mode share, cycling facilities, and a survey in the broader community). We compared four Canadian cities, three with active promotion, and one without, over eight months. High-use events were identified in time periods with above average web sessions. We found that promotion was essential for use of the project. Targeting cycling specific audiences resulted in more data submitted, while targeting general audiences resulted in greater age and gender diversity. We encourage researchers to use tools to monitor and adapt to promotion medium, audience, and community context. Strategic promotion may help achieve more diverse representation in crowdsourced data. Full article
(This article belongs to the Special Issue Crowdsourcing Urban Data)
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12519 KiB  
Article
Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX)
by Benjamin Bechtel, Matthias Demuzere, Panagiotis Sismanidis, Daniel Fenner, Oscar Brousse, Christoph Beck, Frieke Van Coillie, Olaf Conrad, Iphigenia Keramitsoglou, Ariane Middel, Gerald Mills, Dev Niyogi, Marco Otto, Linda See and Marie-Leen Verdonck
Urban Sci. 2017, 1(2), 15; https://doi.org/10.3390/urbansci1020015 - 09 May 2017
Cited by 65 | Viewed by 11611
Abstract
The World Urban Database and Access Portal Tools (WUDAPT) is a community initiative to collect worldwide data on urban form (i.e., morphology, materials) and function (i.e., use and metabolism). This is achieved through crowdsourcing, which we define here as the collection of data [...] Read more.
The World Urban Database and Access Portal Tools (WUDAPT) is a community initiative to collect worldwide data on urban form (i.e., morphology, materials) and function (i.e., use and metabolism). This is achieved through crowdsourcing, which we define here as the collection of data by a bounded crowd, composed of students. In this process, training data for the classification of urban structures into Local Climate Zones (LCZ) are obtained, which are, like most volunteered geographic information initiatives, of unknown quality. In this study, we investigated the quality of 94 crowdsourced training datasets for ten cities, generated by 119 students from six universities. The results showed large discrepancies and the resulting LCZ maps were mostly of poor to moderate quality. This was due to general difficulties in the human interpretation of the (urban) landscape and in the understanding of the LCZ scheme. However, the quality of the LCZ maps improved with the number of training data revisions. As evidence for the wisdom of the crowd, improvements of up to 20% in overall accuracy were found when multiple training datasets were used together to create a single LCZ map. This improvement was greatest for small training datasets, saturating at about ten to fifteen sets. Full article
(This article belongs to the Special Issue Crowdsourcing Urban Data)
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