Abstract
As you may know, the American Community Survey (ACS) is the replacement of the census “Long Form” and the only source of detailed socioeconomic and demographic data for small areas, such as place and tract levels. This paper is about exploring ways to improve communication of the estimates and reliability of ACS estimates in map making and in our published map products, whether in static “printable” form (e.g. a pdf) or in web-based interactive delivery format (html, JavaScript, KML). First, we summarize geo-visualization developments over the past two decades on ways to present uncertain data. Second, we present selected works of others more directly related to the ACS situation—polygons with attributes derived from a continuous monthly sampling activity and updated periodically. Third we present some of our own work at the Cornell Program on Applied Demographics exploring how to communicate simultaneously both the ACS estimates and the margins of error for polygons at the county and sub-county levels of geography.
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References
Beard, M. K., & Buttenfield, B. P. (1991). NCGIA Research Initiative 7: Visualization of Spatial Data Quality. NCGIA, Technical Paper, pp. 91–26.
Burrough, P. A., & Frank, A. U. (1997). Geographic objects with indeterminate boundaries. Transactions in GIS, 2(4), 377–378.
Burrough, P. A., & McDonald, R. A. (1998). Principles of geographical information systems. Oxford: Oxford University Press.
Cedilnik, A., & Rheingans, P. (2000). Procedural Annotation of Uncertain Information. Proceedings of visualization 00, pp. 77–84. IEEE Computer Society Press, 2000.
Chrisman, N. (1995). Beyond Stevens: A revised approach to measurement for geographic information. http://mapcontext.com/autocarto/proceedings/auto…/pages281–290.
Dent, B. D. (1993). Cartography: Thematic map design. Dubuque: William C. Brown Publishers
Dutton, G. (1992). Handling Positional Uncertainty in Spatial Databases. Proceedings 5th international symposium on spatial data handling, pp. 460–469. University of South Carolina, August 1992.
De Gruijter, J. J., & McBratney, A. B. (1988). A modified fuzzy k-means method for predictive classification. In H. H. Bock (Ed.), Classification and related methods of data analysis (pp. 97–104). Amsterdam: Elsevier.
Eathington, L. (2011). Holy MOE! Don’t Marginalize the Error in the American Community Survey. Paper was prepared for presentation at the 2011 annual meeting of the Southern Regional Science Association in New Orleans, LA.
Fairbairn, D., Andrienko, G., Andrienko, N., Buziek, G., & Dykes, J. (2001). Representation and its relationship with cartographic visualization. Cartography and Geographic Information Science, 28(1), 13–28.
Goodchild, M. F. (1994). Spatial Accuracy. Proceedings, 22nd annual conference, Australasian Urban and Regional Information Systems Association, Sydney, addendum. [216].
Hengl, T., Walvoort, D. J. J., & Brown, A. (2002). Pixel and Colour Mixture: GIS Techniques for Visualisation of Fuzzyness and Uncertainty of Natural Resource Inv entories. In G. Hunter (ed), Proceedings: International symposium on spatial accuracy in natural resources and environmental science10–12 July 2002, Melbourne, Australia.
Hootsmans, R. M. (1996). Fuzzy sets and series analysis for visual decision support in spatial data exploration. PhD Thesis, University of Utrecht, Utrecht, 167 pp.
Kardos, J. D., Moore, J. A., & Benwell, G. L. (2003). The Visualization of Uncertainty in Spatially-Referenced Attribute Data Using Trustworthy Data Structures. Proceeding of 15th annual colloquium of the Spatial Information Research Centre (SIRC 2003: Land, Place and Space).
Li, X. R., & Zhao, Z. (2005). Relative Error Measures for Evaluation of Estimation Algorithms. Paper presented at IEEE conference, 2005.
Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2001). Geographic information systems and science. New York: Wiley.
MacEachren, A. M. (1992). Visualizing uncertain information. Cartographic Perspective, 13(Fall), 10–19.
MacEachren, A. M., & Kraak, M. J. (1997). Exploratory cartographic visualization: Advancing the agenda. Computers & Geosciences, 23(4), 335–343.
MacEachren, A. M., & Kraak, M. J. (2001). Visualization in modern Cartography. Cartography and Geographic Information Science, 28(1), 3–12.
Monmonier, M. (1990). Strategies for the Interactive Exploration of Geographic Correlation. Proceedings of the 4th international symposium on spatial data handling, Vol. 1, pp. 512–521. IGU, July 1990.
Pang, A. (2001). Visualizing Uncertainty in Geo-Spatial Data. Paper prepared for a committee of the computer science and telecommunications board.
Pang, A., Furman, J., & Nuss, W. (February 1994). Data quality issues in visualization. In J. Robert, I. I. Moorhead, D. E. Silver, & S. P. Uselton (Eds.), SPIE Vol. 2178 Visual data exploration and analysis (pp. 12–23). Bellingham: SPIE.
Sun, M., & Wong, D. S. (2010). Incorporating data quality information in mapping American community survey data. Cartography and Geographic Information Science, 37(4), 285–300.
Torrieri, N., Wong, D., & Ratcliffe, M. (2011). Mapping American Community Survey Data. Paper dated September 2011.
Wieczorek, J. (2012). (http://civilstat.com/?p=50) in his March 9, 2012 posting.
Xiao, N., Calder, C. A., & Armstrong, M. P. (2007). Assessing the effect of attribute uncertainty on the robustness of choropleth map classification. International Journal of Geographical Information Science, 21(2), 121–144.
Yee, L., Goodchild, M., & Lin, C.-C. (1992). Visualization of Fuzzy Scenes and Probability Fields. Proceedings 5th international symposium on spatial data handling, pp. 480–490. University of South Carolina, August 1992.
Zhang, J. X., & Goodchild, M. F. (2002). Uncertainty in geographic information. New York: Taylor and Francis.
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Francis, J., Tontisirin, N., Anantsuksomsri, S., Vink, J., Zhong, V. (2015). Alternative Strategies for Mapping ACS Estimates and Error of Estimation. In: Hoque, M., B. Potter, L. (eds) Emerging Techniques in Applied Demography. Applied Demography Series, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8990-5_16
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