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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11D - MAPPING MEASURES OF RELIABILITY ALONGSIDE DATA FROM THE AMERICAN COMMUNITY SURVEY
1. Mapping Estimates and their Reliability
from the American Community Survey:
One user’s struggle
David Long
dlong@ssc.wisc.edu
Applied Population Laboratory, University of Wisconsin –Madison
3. What does Census have to say?
Significance Tool
appears inactive.
4. What does ESRI have to say?
Stoplight approach based on Coefficient of Variation
http://www.esri.com/library/whitepapers/pdfs/the-american-community-survey.pdf
5. Coefficient of Variation is the Standard Error as a Percentage of the Estimate
Poverty Standard
Geography Estimate MOE Error CV Interpretation
Tract A 40% +/- 3% 1.8% 5% Very Reliable
Tract B 4% +/- 3% 1.8% 46% Not Reliable
Is the Coefficient of Variation a good indicator of reliability?
8. Quick Fix for ArcGIS Users?
David W. Wong, George Mason University
ArcGIS that created this Cross Hatch Overlay Latino Poverty Rates by Significance
9. Problem #1: Which Measure?
• MOE
• CV
• Compare to Reference Geo
• Others…
Problem #2: Bivariate Mapping
• Can be hard for viewers to decipher
17. Thank you
David Long
dlong@ssc.wisc.edu
Applied Population Laboratory, University of Wisconsin –Madison
Hinweis der Redaktion
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: