How Should Urban Planners Be Trained to Handle Big Data?
Historically urban planners have been educated and trained to work in a data poor environment. Urban planning students take courses in statistics, survey research and projection and estimation that are designed to fill in the gaps in this environment. For
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Abstract Historically urban planners have been educated and trained to work in a data poor environment. Urban planning students take courses in statistics, survey research and projection and estimation that are designed to fill in the gaps in this environment. For decades they have learned how to use census data, which is comprehensive on several basic variables, but is only conducted once per decade so is almost always out of date. More detailed population characteristics are based on a sample and are only available in aggregated form for larger geographic areas. But new data sources, including distributed sensors, infrastructure monitoring, remote sensing, social media and cell phone tracking records, can provide much more detailed, individual, real time data at disaggregated levels that can be used at a variety of scales. We have entered a data rich environment, where we can have data on systems and behaviors for more frequent time increments and with a greater number of observations on a greater number of factors (The Age of Big Data, The New York Times, 2012; Now you see it: simple visualization techniques for quantitative analysis, Berkeley, 2009). Planners are still being trained in methods that are suitable for a data poor environment (J Plan Educ Res 6:10–21, 1986; Analytics over large-scale multidimensional data: the big data revolution!, 101–104, 2011; J Plan Educ Res 15:17–33, 1995). In this paper we suggest that visualization, simulation, data mining and machine learning are the appropriate tools to use in this new environment and we discuss how planning education can adapt to this new data rich landscape. We will discuss how these methods can be integrated into the planning curriculum as well as planning practice. Keywords Big data • Urban planning • Analytics • Education • Visualization
S.P. French, Ph.D., F.A.I.C.P. (*) College of Architecture, Georgia Institute of Technology, Atlanta, GA 30332-0155, USA e-mail: [email protected] C. Barchers • W. Zhang School of City and Regional Planning, Georgia Institute of Technology, Atlanta, GA 30332-0155, USA e-mail: [email protected]; [email protected] © Springer International Publishing Switzerland 2017 P. Thakuriah et al. (eds.), Seeing Cities Through Big Data, Springer Geography, DOI 10.1007/978-3-319-40902-3_12
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Planning methods have been the source of much discussion over the past few decades. Practitioners and researchers have examined what methods planning schools teach and how these methods are used in practice. The suite of traditional methods courses taught in planning programs—inferential statistics, economic cost-benefit analysis, sampling, and research design for policy evaluation—remains largely stagnant, despite the rapidly changing reality in which planners are expected to work. Although the focus of this paper is on the impact of big data for planning methods, other variables have also contributed to the need for additional methods to tackle planning problems. The rise of ubiquitous computing
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