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

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.

[1]  Lydia B. Chilton,et al.  TurKit: Tools for iterative tasks on mechanical turk , 2009, 2009 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC).

[2]  Adam C. Winstanley,et al.  Towards quality metrics for OpenStreetMap , 2010, GIS '10.

[3]  David A. Lanegran,et al.  Guidelines for Geographic Education in the Elementary and Secondary Schools , 1984 .

[4]  Mor Naaman,et al.  The motivations and experiences of the on-demand mobile workforce , 2014, CSCW.

[5]  Gerald C. Davison,et al.  American Psychological Association (APA) , 2015 .

[6]  Deepak Ganesan,et al.  Labor dynamics in a mobile micro-task market , 2013, CHI.

[7]  J. Fellmann Human Geography: Landscapes of Human Activities , 1990 .

[8]  Sune Lehmann,et al.  Understanding the Demographics of Twitter Users , 2011, ICWSM.

[9]  M. Haklay How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets , 2010 .

[10]  Giovanni Quattrone,et al.  Mind the map: the impact of culture and economic affluence on crowd-mapping behaviours , 2014, CSCW.

[11]  A. Browne,et al.  The Impact of Recent Partner Violence on Poor Women's Capacity to Maintain Work , 1999 .

[12]  F. Earls,et al.  Youth exposure to violence: prevalence, risks, and consequences. , 2001, The American journal of orthopsychiatry.

[13]  Sriram Subramanian,et al.  Talking about tactile experiences , 2013, CHI.

[14]  Brent J. Hecht,et al.  A Tale of Cities: Urban Biases in Volunteered Geographic Information , 2014, ICWSM.

[15]  Loren G. Terveen,et al.  Capturing quality: retaining provenance for curated volunteer monitoring data , 2014, CSCW.

[16]  Aniket Kittur,et al.  CrowdForge: crowdsourcing complex work , 2011, UIST.

[17]  Alireza Sahami Shirazi,et al.  Location-based crowdsourcing: extending crowdsourcing to the real world , 2010, NordiCHI.

[18]  R. Porter The United States Census , 1890, Nature.

[19]  M. Goodchild Citizens as sensors: the world of volunteered geography , 2007 .

[20]  Panagiotis G. Ipeirotis Demographics of Mechanical Turk , 2010 .

[21]  Cyrus Rashtchian,et al.  Collecting Image Annotations Using Amazon’s Mechanical Turk , 2010, Mturk@HLT-NAACL.

[22]  Tom L. McKnight Regional geography of the United States and Canada , 1970 .

[23]  Chris Callison-Burch,et al.  Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon’s Mechanical Turk , 2009, EMNLP.

[24]  Mikhil Masli,et al.  Eliciting and focusing geographic volunteer work , 2010, CSCW '10.

[25]  Michael S. Bernstein,et al.  Soylent: a word processor with a crowd inside , 2010, UIST.

[26]  A. Zipf,et al.  A Comparative Study of Proprietary Geodata and Volunteered Geographic Information for Germany , 2010 .

[27]  Bill Tomlinson,et al.  Who are the crowdworkers?: shifting demographics in mechanical turk , 2010, CHI Extended Abstracts.

[28]  M. Goodchild,et al.  Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr , 2013 .

[29]  E. R. White,et al.  Cities of the World: World Regional Urban Development , 1992 .

[30]  Matt Post,et al.  The Language Demographics of Amazon Mechanical Turk , 2014, TACL.

[31]  Giovanni Quattrone,et al.  On the accuracy of urban crowd-sourcing for maintaining large-scale geospatial databases , 2012, WikiSym '12.

[32]  Giovanni Quattrone,et al.  Putting ubiquitous crowd-sourcing into context , 2013, CSCW '13.

[33]  Bill Bishop,et al.  The Big Sort: Why the Clustering of Like-Minded America Is Tearing Us Apart , 2008 .

[34]  A. Callinicos Race And Class , 1993 .

[35]  M. Six Silberman,et al.  Turkopticon: interrupting worker invisibility in amazon mechanical turk , 2013, CHI.