research-article

Avoiding the South Side and the Suburbs: The Geography of Mobile Crowdsourcing Markets

Authors:

Jacob Thebault-Spieker,

Loren G. Terveen,

Brent HechtAuthors Info & Claims

CSCW '15: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing

Pages 265 - 275

https://doi.org/10.1145/2675133.2675278

Published: 28 February 2015 Publication History

Abstract

Mobile crowdsourcing markets (e.g., Gigwalk and TaskRabbit) offer crowdworkers tasks situated in the physical world (e.g., checking street signs, running household errands). The geographic nature of these tasks distinguishes these markets from online crowdsourcing markets and raises new, fundamental questions. We carried out a controlled study in the Chicago metropolitan area aimed at addressing two key questions: (1) What geographic factors influence whether a crowdworker will be willing to do a task? (2) What geographic factors influence how much compensation a crowdworker will demand in order to do a task? Quantitative modeling shows that travel distance to the location of the task and the socioeconomic status (SES) of the task area are important factors. Qualitative analysis enriches our modeling, with workers mentioning safety and difficulties getting to a location as key considerations. Our results suggest that low-SES areas are currently less able to take advantage of the benefits of mobile crowdsourcing markets. We discuss the implications of our study for these markets, as well as for "sharing economy" phenomena like UberX, which have many properties in common with mobile crowdsourcing markets.

References

[1]

Alt, F., Shirazi, A.S., Schmidt, A., Kramer, U., and Nawaz, Z. Location-based Crowdsourcing: Extending Crowdsourcing to the Real World. October, (2010).

[2]

American Psychological Association (APA). Violence and Socioeconomic Status. 2010.

[3]

Bernstein, M.S., Little, G., Miller, R.C., et al. Soylent: A Word Processor with a Crowd Inside. Artificial Intelligence, (2010), 313--322.

Digital Library

[4]

Bishop, B. The Big Sort: Why the Clustering of Likeminded America is Tearing Us Apart. Houghton Mifflin Harcourt, 2008.

[5]

Browne, A., Salomon, A., and Bassuk, S.S. The Impact of Recent Partner Violence on Poor Women's Capacity to Maintain Work. Violence Against Women 5, 4 (1999), 393--426.

[6]

Brunn, S.D., Williams, J.F., and Zeigler, D.J. Cities of the World: World Regional Urban Development. Rowman & Littlefield Publishers, 2003.

[7]

Buka, S.L., Stichick, T.L., Birdthistle, I., and Earls, F.J. Youth Exposure to Violence: Prevalence, Risks, and Consequences. American Journal of Orthopsychiatry 71, 3 (2001), 298--310.

[8]

Callison-Burch, C. Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon's Mechanical Turk. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1, Association for Computational Linguistics (2009), 286--295.

Digital Library

[9]

Ehrenreich, B. It Is Expensive to Be Poor. The Atlantic, 2014.

[10]

Fellmann, J.D., Getis, A., and Getis, J. Human Geography: Landscapes of Human Activity. McGraw-Hill, 2007.

[11]

Goodchild, M.F. Citizens as sensors: the world of volunteered geography. GeoJournal 69, 4 (2007), 211--221.

[12]

Haklay, M. How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environment and Planning B: Planning and Design 37, 4 (2010), 682--703.

[13]

Hecht, B. and Stephens, M. A Tale of Cities: Urban Biases in Volunteered Geographic Information. (2014).

[14]

Ipeirotis, P.G. Demographics of Mechanical Turk. 2010.

[15]

Irani, L.C. and Silberman, M.S. Turkopticon: Interrupting Worker Invisibility in Amazon Mechanical Turk. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM (2013), 611--620.

Digital Library

[16]

Kittur, A., Smus, B., Khamkar, S., and Kraut, R.E. CrowdForge: Crowdsourcing Complex Work. Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, ACM (2011), 43--52.

Digital Library

[17]

Lanegran, D.A. and Natoli, S. Guidelines for Geographic Education in the Elementary and Secondary Schools. Association of American Geographers, 1984.

[18]

Li, L., Goodchild, M.F., and Xu, B. Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr. Cartography and Geographic Information Science 40, 2 (2013), 61--77.

[19]

Little, G., Chilton, L.B., Goldman, M., and Miller, R.C. TurKit: Tools for Iterative Tasks on Mechanical Turk. Proceedings of the ACM SIGKDD Workshop on Human Computation, (2009), 29--30.

Digital Library

[20]

Mashhadi, A., Quattrone, G., and Capra, L. Putting ubiquitous crowd-sourcing into context. Proceedings of the 2013 conference on Computer supported cooperative work CSCW '13, (2013), 611.

Digital Library

[21]

Mashhadi, A., Quattrone, G., and Mooney, P. On the Sustainability of Urban Crowd-Sourcing for Maintaining Large-Scale Geospatial Databases Categories and Subject Descriptors. (2012).

[22]

McKnight, T.L. Regional Geography of the USA and Canada. Prentice Hall, 2004.

[23]

Mislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P., and Rosenquist, J.N. Understanding the Demographics of Twitter Users. ICWSM 11, (2011), 5th.

[24]

Mooney, P., Corcoran, P., and Winstanley, A.C. Towards quality metrics for OpenStreetMap. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '10, (2010), 514.

Digital Library

[25]

Musthag, M. and Ganesan, D. Labor dynamics in a mobile micro-task market. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems CHI '13, (2013), 641.

Digital Library

[26]

Pavlick, E., Post, M., Irvine, A., Kachaev, D., and Callison-Burch, C. The Language Demographics of Amazon Mechanical Turk. ACL '14, (2014).

[27]

Priedhorsky, R., Masli, M., and Terveen, L. Eliciting and Focusing Geographic Volunteer Work. CSCW '10: 2010 ACM Conference on Computer Supported Cooperative Work, (2010).

Digital Library

[28]

Quattrone, G., Mashhadi, A., and Capra, L. Mind the Map: The Impact of Culture and Economic Affluence on Crowd- Mapping Behaviours. CSCW '14, (2014).

Digital Library

[29]

Rashtchian, C., Young, P., Hodosh, M., and Hockenmaier, J. Collecting Image Annotations Using Amazon's Mechanical Turk. Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk, Association for Computational Linguistics (2010), 139--147.

Digital Library

[30]

Ross, J., Irani, L., Silberman, M.S., Zaldivar, A., and Tomlinson, B. Who Are the Crowdworkers?: Shifting Demographics in Mechanical Turk. CHI '10 Extended Abstracts on Human Factors in Computing Systems, ACM (2010), 2863--2872.

Digital Library

[31]

Sheppard, S.A., Wiggins, A., and Terveen, L. Capturing Quality: Retaining Provenance for Curated Volunteer Monitoring Data. CSCW '14, ACM (2014), 1234--1245.

Digital Library

[32]

Silverstein, A. Uber and Lyft Are Being Accused of Redlining Again, But Is That Actually Happening? Dallas Observer, 2014.

[33]

Singer, C. Ridesharing and Redlining: Uber, Lyft, Race and Class. Daily Kos, 2014. http://www.dailykos.com/story/2014/05/27/1302417/Ridesharing-and-Redlining-Uber-Lyft-Race-and-Class#.

[34]

Smith, A. Smartphone Ownership 2013. Pew Internet & American Life Project, 2013.

[35]

Teodoro, R., Ozturk, P., Naaman, M., Mason, W., and Lindqvist, J. The motivations and experiences of the ondemand mobile workforce. ACM Press (2014), 236--247.

Digital Library

[36]

USA Census. 2010 Geographic Terms and Concepts Census Tract. http://www.census.gov/geo/reference/gtc/gtc_ct.html.

[37]

Zielstra, D. and Zipf, A. A comparative study of proprietary geodata and volunteered geographic information for Germany. Conference on Geographic Information 1, (2010), 1--15.

[38]

2011 FDIC National Survey of Unbanked and Underbanked Households. Federal Deposit Insurance Corporation, 2011.

[39]

Gigwalk Announces $6 Million In Funding, Expands Relationship With Microsoft, And Names New CEO. http://gigwalk.com/press/gigwalk-announces-6-million-infunding-expands-relationship-with-microsoft-and-namesnew-ceo.php.

[40]

TaskRabbit. https://www.taskrabbit.com.

[41]

GigWalk. http://gigwalk.com.

[42]

Field Agent. http://www.fieldagent.net.

Cited By

Mourelatos E(2024)Mood matters: the interplay of personality in ethical perceptions in crowdsourcingBehaviour & Information Technology10.1080/0144929X.2024.2349786(1-23)Online publication date: 17-May-2024
https://doi.org/10.1080/0144929X.2024.2349786
Ma WXu PXu Y(2023)Fairness Maximization among Offline Agents in Online-Matching MarketsACM Transactions on Economics and Computation10.1145/356970510:4(1-27)Online publication date: 5-Apr-2023
https://dl.acm.org/doi/10.1145/3569705
Sannon SCosley D(2022)Toward a More Inclusive Gig Economy: Risks and Opportunities for Workers with DisabilitiesProceedings of the ACM on Human-Computer Interaction10.1145/35557556:CSCW2(1-31)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555755
Show More Cited By

Index Terms

Avoiding the South Side and the Suburbs: The Geography of Mobile Crowdsourcing Markets
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Computer supported cooperative work
2. Social and professional topics
  1. Professional topics
    1. Computing and business
      1. Computer supported cooperative work

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cover image ACM Conferences

CSCW '15: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing

February 2015

1956 pages

ISBN:9781450329224

DOI:10.1145/2675133

General Chairs:
Dan Cosley
Cornell University, USA
,
Andrea Forte
Drexel University, USA
,
Program Chairs:
Luigina Ciolfi
Sheffield Hallam University, UK
,
David McDonald
University of Washington, USA

Copyright © 2015 ACM.

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Published: 28 February 2015

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CSCW '15: Computer Supported Cooperative Work and Social Computing

March 14 - 18, 2015

BC, Vancouver, Canada

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CSCW '15 Paper Acceptance Rate 161 of 575 submissions, 28%;

Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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Cited By

Mourelatos E(2024)Mood matters: the interplay of personality in ethical perceptions in crowdsourcingBehaviour & Information Technology10.1080/0144929X.2024.2349786(1-23)Online publication date: 17-May-2024
https://doi.org/10.1080/0144929X.2024.2349786
Ma WXu PXu Y(2023)Fairness Maximization among Offline Agents in Online-Matching MarketsACM Transactions on Economics and Computation10.1145/356970510:4(1-27)Online publication date: 5-Apr-2023
https://dl.acm.org/doi/10.1145/3569705
Sannon SCosley D(2022)Toward a More Inclusive Gig Economy: Risks and Opportunities for Workers with DisabilitiesProceedings of the ACM on Human-Computer Interaction10.1145/35557556:CSCW2(1-31)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555755
Li TLu YClark JChen MCox VJiang MYang YKay TWood DBrockman J(2022)A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig Workers against AI InequalityProceedings of the 1st Annual Meeting of the Symposium on Human-Computer Interaction for Work10.1145/3533406.3533418(1-10)Online publication date: 8-Jun-2022
https://dl.acm.org/doi/10.1145/3533406.3533418
Fedosov ASubasi OOchsenbein LHuang EMaiden NSas C(2022)Supporting Designers in the Sharing Economy Through a Generative Design Cards ToolkitProceedings of the 14th Conference on Creativity and Cognition10.1145/3527927.3535203(498-504)Online publication date: 20-Jun-2022
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Ma NRivera VYao ZYoon D(2022)“Brush it Off”: How Women Workers Manage and Cope with Bias and Harassment in Gender-agnostic Gig PlatformsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517524(1-13)Online publication date: 29-Apr-2022
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Tushev MEbrahimi FMahmoud A(2022)A Systematic Literature Review of Anti-Discrimination Design Strategies in the Digital Sharing EconomyIEEE Transactions on Software Engineering10.1109/TSE.2021.3139961(1-1)Online publication date: 2022
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Sun YNurellari EDing WShu LHuo Z(2022)A Partition-Based Mobile-Crowdsensing-Enabled Task Allocation for Solar Insecticidal Lamp Internet of Things MaintenanceIEEE Internet of Things Journal10.1109/JIOT.2022.31757329:20(20547-20560)Online publication date: 15-Oct-2022
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Ray AChowdhury CBhattacharya SRoy S(2022)A survey of mobile crowdsensing and crowdsourcing strategies for smart mobile device usersCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-022-00110-95:1(98-123)Online publication date: 15-Jul-2022
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