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Mapping Estimates and their Reliabilityfrom the American Community Survey:               One user’s struggle              ...
Charting estimates and reliability:
What does Census have to say?                   Significance Tool                   appears inactive.
What does ESRI have to say?Stoplight approach based on Coefficient of Variationhttp://www.esri.com/library/whitepapers/pdf...
Coefficient of Variation is the Standard Error as a Percentage of the Estimate             Poverty                   Stand...
CV used in a Mapping Context
Maryland State Data Center Online Mapping
Quick Fix for ArcGIS Users?David W. Wong, George Mason UniversityArcGIS that created this Cross Hatch Overlay        Latin...
Problem #1: Which Measure?• MOE• CV• Compare to Reference Geo• Others…                             Problem #2: Bivariate M...
Latino Poverty Rates by SignificanceBars on a map…
Latino Poverty Rates by SignificancePies on a map… Larger Pies Indicate Higher Pov Rate                        Black slice...
Latino Poverty Rates by SignificanceCombo…      Black slice    represents CV (SE as prop. of est.)
The Matrix…                                   Latino Poverty Rates by Significance                 CV Significance        ...
Back to basics…
Web Interactivity…   Latino Poverty Rates by Significance
Additional Reading
Thank you                    David Long                dlong@ssc.wisc.eduApplied Population Laboratory, University of Wisc...
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11D - MAPPING MEASURES OF RELIABILITY ALONGSIDE DATA FROM THE AMERICAN COMMUNITY SURVEY

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David Long, Applied Population Laboratory

With the Decennial Census’s “Long-Form” data it was sometimes easy for data users to forget there were margins of error associated with each measure.With many detailed socioeconomic characteristics now being sourced, instead, through the American Community Survey, margins of error are much larger and more difficult to ignore.This presentation explores several methods for those mapping ACS data to address the challenge of showing measures of reliability alongside the attributes of interest.

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  • How many familiar w/ ACSReplaced long formMOEs too big (and too readily available) to ignoreHigh minded goal of efficiently communicating this
  • Hopefully most of us are or could easily become somewhat adept at making sense of measures of reliability we encounter in graphs like this but what about in maps?
  • One place I hoped to find some guidance was on the Census Bureaus factfinder website.I was pretty excited when I saw the Statistical Significance toolCan’t map them simultaneously and maybe you shouldn’t!.
  • So this time I took some guidance from ESRI which has a lot more brainpower to throw at this problem than I did. Here’s what they came up with:
  • Census Bureau promotes the use of the CV statistic, but their language on it is a bit vague. Users should “use” caution with estimates that have a CV of >15%. But how you use this statistic really depends on what your needs are….Even though I don’t know what the poverty in Tract B is, I can say with relative certainty that it’s very low
  • If we chose to filter our maps using a minimum CV as a measure of reliability we end up with the map on the right.25% threshold. Arguably, tho, we know a lot about the those omitted tracts with the cross-hatch pattern. Very likely these tracts have comparatively small standard errors, it’s just that we have very low estimates of percent Asian, so the CV blows up.
  • Cross hatch pattern is not one we read intuitively in the way we do a choropleth map. No natural gradient from dots, to lines no narrow lines.
  • Wong created extension for Arc…Still hard to read
  • My woe’s are multiplying…Still I was hopeful…
  • ArcGIS arsenal of cartographic tools …allows us to do things that perhaps we shouldn’t…
  • Here’s a 3x3 Matrix All of these require users to really study them to understand them. The demands in this regard really challenge the efficiency of the map as a tool for presenting these estimates and reliability indicators.
  • Sometimes the best data representation is simply a number (not a chart/graph/map)
  • To avoid overcrowding of your data, perhaps the solution is simply to have those additional data available through mouseovereffect.
  • Thought still draft version, lots or references and options presented here:
  • Transcript of "11D - MAPPING MEASURES OF RELIABILITY ALONGSIDE DATA FROM THE AMERICAN COMMUNITY SURVEY"

    1. 1. Mapping Estimates and their Reliabilityfrom the American Community Survey: One user’s struggle David Long dlong@ssc.wisc.edu Applied Population Laboratory, University of Wisconsin –Madison
    2. 2. Charting estimates and reliability:
    3. 3. What does Census have to say? Significance Tool appears inactive.
    4. 4. What does ESRI have to say?Stoplight approach based on Coefficient of Variationhttp://www.esri.com/library/whitepapers/pdfs/the-american-community-survey.pdf
    5. 5. Coefficient of Variation is the Standard Error as a Percentage of the Estimate Poverty StandardGeography Estimate MOE Error CV Interpretation Tract A 40% +/- 3% 1.8% 5% Very Reliable Tract B 4% +/- 3% 1.8% 46% Not ReliableIs the Coefficient of Variation a good indicator of reliability?
    6. 6. CV used in a Mapping Context
    7. 7. Maryland State Data Center Online Mapping
    8. 8. Quick Fix for ArcGIS Users?David W. Wong, George Mason UniversityArcGIS that created this Cross Hatch Overlay Latino Poverty Rates by Significance
    9. 9. Problem #1: Which Measure?• MOE• CV• Compare to Reference Geo• Others… Problem #2: Bivariate Mapping  • Can be hard for viewers to decipher
    10. 10. Latino Poverty Rates by SignificanceBars on a map…
    11. 11. Latino Poverty Rates by SignificancePies on a map… Larger Pies Indicate Higher Pov Rate Black slice represents CV (SE as prop. of est.)
    12. 12. Latino Poverty Rates by SignificanceCombo… Black slice represents CV (SE as prop. of est.)
    13. 13. The Matrix… Latino Poverty Rates by Significance CV Significance Low High HighPoverty Low
    14. 14. Back to basics…
    15. 15. Web Interactivity… Latino Poverty Rates by Significance
    16. 16. Additional Reading
    17. 17. Thank you David Long dlong@ssc.wisc.eduApplied Population Laboratory, University of Wisconsin –Madison
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