A research project – Justin Longo, David M. Hondula, Evan R. Kuras, and Erik W. Johnston
For presentation at Policy-Making in the Big Data Era: Opportunities and Challenges, June 15-17 2015, University of Cambridge
Status: field research currently underway. See also
The “big data” movement has quickly emerged in recent years and is poised to have a profound impact on policy making. As governance systems have greater access to more data, the ability to adapt policy interventions based on fine-grained evidence collected from ubiquitous sources will dramatically change the nature of policy formulation, implementation and evaluation. Big data accumulated through social media, electronic transactions, measurement by in situ and personal sensors, counters and smart meters, interactions with devices and control technology, and the use of network-connected mobile technology can provide important insights in policy areas relevant to human behavior. The accumulation of these data and associated metadata such as geolocation information and time and date stamps results in a previously unimagined amount of data, measured with phenomenal precision, taken from multiple perspectives. Advances in data storage technologies now make it possible to preserve this flood of data. And advances in data mining, analysis, and visualization technologies and techniques can yield valuable new insights.
But what happens in policy areas where the evidence collected fails to detect key targets that are invisible to the network of sensors, card readers, cell towers and servers? A new digital divide may be emerging in the presence of big data, with those on one side increasingly invisible when that data is used as the basis for policy analysis purposes (Haklay 2012). For those without a smartphone, without a bank account or credit card, living beneath and beyond the network of sensors, monitors and data-capture points, their existence is being rendered increasingly invisible. In such cases, policy making is oblivious to their existence and does not reflect their reality.
One important population likely living outside the realm of big data is the homeless. While we have managed to make modern homelessness largely invisible through public policies and personal avoidance (Waldron 1991), big data is compounding those choices by rendering those living at the margins of our societies digitally invisible. Increasingly sensitive and precise policies are biased in favor of the digitally connected, though blind to the digitally invisible (boyd and Crawford 2011). This study is designed to investigate how interventions can begin to lessen and bridge this big data digital divide, while protecting the privacy rights of the digitally invisible, addressing concerns about the surveillance state and engaging participants as partners in the research initiative.
This study will build on a previous experimental approach (Kuras, Hondula and Brown-Saracino 2015) in which research participants were equipped with a Thermochron iButton, a small and lightweight mobile sensor that measures and records instantaneous air temperature at 5-minute intervals. Data is uploaded from the device when connected to a computer, not captured in real-time (i.e., the iButton does not transmit data wirelessly when being carried). Participants clipped their iButtons to a belt loop or bag such that the device was continuously exposed to the surrounding air as they went about their daily lives, and returned the device to the study team after one week. This design has been implemented in two pilot studies to date enlisting over 100 individuals from diverse neighborhoods in Phoenix and Boston.
This study will repeat this method with individuals living in the Phoenix area who self-identify as homeless, persons especially important to consider in the design of heat-health programs and policies as they disproportionately experience adverse health outcomes associated with exposure to hot weather (MCDPH 2014). We will ask ten participants to carry an iButton for in May 2015 (when temperatures in Phoenix are already approaching dangerous levels) and attempt to collect the device from them at the end of the study period. In addition, pre- and post-test interviews will be conducted with study participants to understand their perceptions and concerns with respect to privacy, obtrusiveness of the device, their experiences as a homeless person, and scope for more complex data capture protocols and greater researcher/participant collaboration.
Past pilot studies with non-homeless individuals did not reveal significant concerns about privacy and tracking using the iButton method, though we will explore this in greater depth in this study for the category of homeless individuals. The issue and measurement of individually experience temperatures is not the primary focus of this research – rather the procedure of distributing a data collection device to a homeless population, understanding the acceptability of and their experience in participating in the study, and collecting the device and learning more about the research participants’ perspectives at the end of the study period are the main objectives.
- Is digitally invisible a real phenomenon? If so what explains it?
- What challenges exist in engaging a homeless population in research supporting “big data”-informed policy making?
- How can the research participants collaborate in the co-production of the research such that they shape future initiatives and benefit from the findings?
boyd, d. and Crawford, K. (2011). Six Provocations for Big Data. A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society. Oxford Internet Institute, September 2011. Available at SSRN: http://ssrn.com/abstract=1926431
Haklay, M. (2012). ‘Nobody wants to do council estates’ – digital divide, spatial justice and outliers. Paper Session: 2121 Information Geographies: Online Power, Representation and Voice. New York: Association of American Geographers Annual Meeting. http://meridian.aag.org/callforpapers/program/AbstractDetail.cfm?AbstractID=44781
Harlan, S.L., A. Brazel, G.D., Jeanerette, N.S., Jones, L. Larsen, L. Prashad, W.L. Stefanov. (2008). In the Shade of Affluence: The Inequitable Distribution of the Urban Heat Island. Research in Social Problems and Public Policy. 15: 173-202
Kuras, E. R., Hondula, D. M., & Brown-Saracino, J. (2015). Heterogeneity in individually experienced temperatures (IETs) within an urban neighborhood: insights from a new approach to measuring heat exposure. International journal of biometeorology, 1-10.
Maricopa County Department of Public Health (MCDPH) (2014). Heat-Associated Deaths in Maricopa County, AZ. Final Report for 2013. May 7 2014. http://www.maricopa.gov/publichealth/Services/EPI/pdf/heat/2013annualreport.pdf
Morais, C. D. (2014). Using GIS to Identify Milwaukee’s Homeless Population. GIS Lounge. December 11 2014. http://www.gislounge.com/using-gis-identify-milwaukees-homeless-population/
Overeem, A., R Robinson, J. C., Leijnse, H., Steeneveld, G. J., P Horn, B. K., & Uijlenhoet, R. (2013). Crowdsourcing urban air temperatures from smartphone battery temperatures. Geophysical Research Letters, 40(15), 4081-4085.
Waldron, J. (1991). Homelessness and the Issue of Freedom. UCLA Law Review, 39: 295-324.