Uncertainty Problems and Census Data: The 2020 Census & Exurbanization Example

Last year, I had opportunities to learn about the 2020 Census from a research seminar and a professional meeting to promote Complete Count of the 2020 Census. As you may have heard already, there are some new characteristics in the 2020 Census as below (U.S. Census Bureau, n.d.-a, n.d.-c, n.d.-b):

  • The 2020 Census will be the first to offer options for internet and phone responses.
  • There will be a greater reliance on technology to prepare for and execute the count.
  • The 2020 Census will update its Master Address File (MAF) and ensure that every living quarter in the U.S. is included in the census universe by collaborating with state and local governments and using aerial imaging software.
  • For enhanced enumeration, Census takers will be equipped with smart devices, and data will be collected digitally in real-time.
  • There are no questions about citizenship on the 2020 Census.
  • Responses for the Census will never be shared with agencies of immigration or law enforcement.
  • The country is experiencing a period of heightened fear and deliberate misinformation.

Potential Uncertainty in the 2020 Census

Most of the characteristics above seem to be helpful to produce more accurate Census data. On the other hand, there might be some potential uncertainty in the 2020 Census data. First, there are some challenges to being counted on the Census data, including language barriers, mistrust in government, privacy/cybersecurity concerns, physical barriers such as inaccessible multifamily units, untraditional living arrangements, and lack of reliable broadband or internet access. Second, there may exist hard-to-count (HTC) groups for the Census, including children under five years old, racial and ethnic minorities, limited English proficiency households, immigrants, renters and residents who often move, alternative or overcrowded housing units, gated communities and publicly inaccessible multifamily units, persons displaced by natural disasters, persons experiencing homelessness, young mobile adults, and single-parent headed households (The City of Stillwater, Payne County, OK, n.d.). Thus, the 2020 Census may provide enhanced accuracy of the data and also uncertain data for some criteria of the Census.

Example of Uncertainty in Visualizing Exurbanization

Due to the potential uncertainty in the Census data, some geographic inquiries that utilize the Census data may reveal the uncertainty problems, such as visualizing the location of exurban areas. Simply speaking, the exurban areas have characteristics between urban and suburban areas. There are multiple different definitions of exurbanization in literature, and the location of certain exurban areas on maps may vary depending on the definition (Ban & Ahlqvist, 2009). In specific, you can visualize the exurbanization of certain areas by using the Census data, geospatial data, and fuzzy-set approach (Ban & Ahlqvist, 2009; Fisher, 2000; Wechsler et al., 2019; Zadeh, 1965), and could create a map that represents different degrees of exurbanization (Figure 1). In Figure 1, the degree of exurbanization of Los Angeles County, CA is visualized based on the definition of exurbanization in (Daniels, 1999). According to Daniels (1999), the exurban areas are defined using value ranges of some attributes, including population, distance from a major urban center, commuting distance, and population density. The definitions of Daniels (1999) themselves include semantic uncertainty due to the vagueness and the ambiguity (see Ban & Ahlqvist (2009) for details). However, in this blog, we will focus on the population attribute of the exurbanization definition. As mentioned above, the potential uncertainty in 2020 Census data may introduce another type of uncertainty, the error (Fisher, 2000). Most of the definitions of exurbanization use the population attribute (Berube et al., 2006). It is likely that the results of the visualization of exurbanization may present uncertainty in the locations of exurban areas.

Figure 1. Visualization of the degree of exurbanization of Los Angeles County, CA based on the exurban definition from Daniels (1999). (A) shows boundaries of exurban areas in crisp, non-fuzzy membership and (b) in the fuzzy-set membership (reproduced from Figure 16.5 of Wechsler et al. (2019)).

There would exist other geographical inquiries that might introduce uncertainty when dealt with the 2020 Census data. What would be the examples? Then how could the uncertainty problems be resolved? Things to ponder remains, and indeed, the initial process of thinking could be definitely uncertain.

 

References

  1. Ban, H., & Ahlqvist, O. (2009). Representing and negotiating uncertain geospatial concepts – Where are the exurban areas? Computers, Environment and Urban Systems, 33(4), 233–246. https://doi.org/10.1016/j.compenvurbsys.2008.10.001
  2. Berube, A., Singer, A., Wilson, J. H., & Frey, W. H. (2006). Finding Exurbia: America’s Fast-Growing Communities at the Metropolitan Fringe. 48.
  3. Daniels, T. (1999). When City and Country Collide: Managing Growth In The Metropolitan Fringe. Island Press.
  4. Fisher, P. (2000). Sorites paradox and vague geographies. Fuzzy Sets and Systems, 113(1), 7–18. https://doi.org/10.1016/S0165-0114(99)00009-3
  5. The City of Stillwater, Payne County, OK. (n.d.). Historically Hard to Count Populations. Retrieved November 30, 2020, from http://www.paynecountycensus.org/page/home/your-community-s-info/historically-hard-to-count-populations
  6. US Census Bureau. (n.d.-a). About the 2020 Census. The United States Census Bureau. Retrieved November 30, 2020, from https://www.census.gov/programs-surveys/decennial-census/2020-census/about.html
  7. US Census Bureau. (n.d.-b). Census.gov. Census.Gov. Retrieved November 30, 2020, from https://www.census.gov/en.html
  8. US Census Bureau. (n.d.-c). What Is the 2020 Census? 2020Census.Gov. Retrieved November 30, 2020, from https://2020census.gov/en/what-is-2020-census.html
  9. Wechsler, S., Ban, H., & Li, L. (2019). The Pervasive Challenge of Error and Uncertainty in Geospatial Data: Volume Eight (pp. 315–332). https://doi.org/10.1007/978-3-030-04750-4_16
  10. Zadeh, L. A. (1965). Information and control. 8(3), 338–353.

Hyowon Ban

Class of 2009, Associate Professor

Department of Geography

California State University, Long Beach

 

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