Are the Poor Digitally Left Behind? Indications of Urban Divides Based on Remote Sensing and Twitter Data

Every city is—quoting Plato—divided into two, one city of the poor, the other of the rich. In this study we test whether the economic urban divide is reflected in the digital sphere of cities. Because, especially in dynamically growing cities, ready-to-use comprehensive data sets on the urban poor, as well as on the digital divide, are not existent, we use proxies: we spatially delimit the urban poor using settlement characteristics derived from remote sensing data. The digital divide is targeted by geolocated Twitter data. Based on a sample of eight cities across the globe, we spatially test whether areas of the urban poor are more likely to be digital cold spots. Over the course of time, we analyze whether temporal signatures in poor urban areas differ from formal environments. We find that the economic divide influences digital participation in public life. Less residents of morphological slums are found to be digitally oriented (“are digitally left behind”) as compared to residents of formal settlements. However, among the few twitter users in morphological slums, we find their temporal behavior similar to the twitter users in formal settlements. In general, we conclude this discussion, this study exemplifies that the combination of both heterogeneous data sets allows for extending the capabilities of individual disciplines for research towards urban poverty.

[1]  Paul A. Zandbergen,et al.  Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning , 2009 .

[2]  David Harvey,et al.  Rebellische Städte : vom Recht auf Stadt zur urbanen Revolution , 2013 .

[3]  Grant Blank The Digital Divide Among Twitter Users and Its Implications for Social Research , 2017 .

[4]  Janpeter Schilling,et al.  Developing risk or resilience? Effects of slum upgrading on the social contract and social cohesion in Kibera, Nairobi , 2017 .

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

[6]  Anil M. Cheriyadat,et al.  Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  Monika Kuffer,et al.  Slums from Space - 15 Years of Slum Mapping Using Remote Sensing , 2016, Remote. Sens..

[8]  M. Migliavacca,et al.  Phenopix: A R package for image-based vegetation phenology , 2016 .

[9]  Víctor Soto,et al.  Characterizing Urban Landscapes Using Geolocated Tweets , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[10]  David W. S. Wong,et al.  An approach to differentiate informal settlements using spectral, texture, geomorphology and road accessibility metrics , 2013 .

[11]  Matthew E. Kahn,et al.  Why do the poor live in cities? The role of public transportation ✩ , 2008 .

[12]  Rebeca P. Díaz Redondo,et al.  Sensing the city with Instagram: Clustering geolocated data for outlier detection , 2017, Expert Syst. Appl..

[13]  Norman M. Sadeh,et al.  The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City , 2012, ICWSM.

[14]  Matthew Zook,et al.  Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb , 2013 .

[15]  Ryan N. Engstrom,et al.  Determining the Relationship Between Census Data and Spatial Features Derived From High-Resolution Imagery in Accra, Ghana , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Michael Wurm,et al.  Ich weiß, dass ich nichts weiß – Bevölkerungsschätzung in der Megacity Mumbai , 2015 .

[17]  David Nemer,et al.  Online Favela: The Use of Social Media by the Marginalized in Brazil , 2016, Inf. Technol. Dev..

[18]  Qin Yu,et al.  Mapping slums using spatial features in Accra, Ghana , 2015, 2015 Joint Urban Remote Sensing Event (JURSE).

[19]  A. R.,et al.  Review of literature , 1969, American Potato Journal.

[20]  Hannes Taubenböck,et al.  TanDEM-X mission—new perspectives for the inventory and monitoring of global settlement patterns , 2012 .

[21]  Ellen Wratten,et al.  Conceptualizing urban poverty , 1995 .

[22]  L. Anselin,et al.  Digital neighborhoods , 2016 .

[23]  Michael F. Goodchild,et al.  The quality of big (geo)data , 2013 .

[24]  S. Bibi Measuring Poverty in a Multidimensional Perspective: A Review of Literature , 2005 .

[25]  Chaogui Kang,et al.  Social Sensing: A New Approach to Understanding Our Socioeconomic Environments , 2015 .

[26]  Hannes Taubenböck,et al.  Slum mapping in polarimetric SAR data using spatial features , 2017 .

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

[28]  Andrea Forte,et al.  Hustling online: understanding consolidated facebook use in an informal settlement in Nairobi , 2013, CHI.

[29]  Hannes Taubenböck,et al.  Digital deserts on the ground and from space , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).

[30]  H. Taubenböck,et al.  Detecting social groups from space – Assessment of remote sensing-based mapped morphological slums using income data , 2018 .

[31]  Julie Hersberger Are the economically poor information poor? Does the digital divide affect the homeless and access to information? , 2013 .

[32]  P. Norris Digital Divide: Civic Engagement, Information Poverty, and the Internet Worldwide , 2001 .

[33]  Huan Liu,et al.  Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose , 2013, ICWSM.

[34]  Ryan Engstrom,et al.  Poverty from Space: Using High-Resolution Satellite Imagery for Estimating Economic Well-Being , 2017, The World Bank Economic Review.

[35]  Hannes Taubenböck,et al.  How good is the map? A multi-scale cross-comparison framework for global settlement layers: Evidence from Central Europe , 2016 .

[36]  Alfred Stein,et al.  An ontology of slums for image-based classification , 2012, Comput. Environ. Urban Syst..

[37]  Shaowen Wang,et al.  Mapping the global Twitter heartbeat: The geography of Twitter , 2013, First Monday.

[38]  J. Perlman,et al.  Marginality: from myth to reality in the favelas of Rio de Janeiro, 1969-2002. , 2003 .

[39]  Benjamin R. Barber,et al.  If Mayors Ruled the World: Dysfunctional Nations, Rising Cities , 2013 .

[40]  John M. Chambers,et al.  Analysis of Variance; Designed Experiments , 2017 .

[41]  H. Taubenböck,et al.  The morphology of the Arrival City - A global categorization based on literature surveys and remotely sensed data , 2018 .