Abstract
The reality of uncertain data cannot be ignored. Anytime that spatial data are used to assist planning, decision making, or policy generation, it is likely that error or uncertainty in the data will propagate through processing protocols and analytic techniques, potentially leading to biased or incorrect decision making. The ability to directly account for uncertainty in spatial analysis efforts is critically important. This article focuses on addressing data uncertainty in one of the most important and widely used exploratory spatial data analysis (ESDA) techniques—choropleth mapping—and proposes an alternative map classification method for uncertain spatial data. The classification approach maximizes within-class homogeneity under data uncertainty while explicitly integrating spatial characteristics to reduce visual map complexity and to facilitate pattern perception. The method is demonstrated by mapping the 2009 to 2013 American Community Survey estimates of median household income in Salt Lake County, Utah, at the census tract level.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1-12 |
| Number of pages | 12 |
| Journal | Professional Geographer |
| DOIs | |
| State | Accepted/In press - Mar 31 2017 |
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ASJC Scopus subject areas
- Geography, Planning and Development
- Earth-Surface Processes
Cite this
An Alternative Classification Scheme for Uncertain Attribute Mapping. / Wei, Ran; Grubesic, Anthony.
In: Professional Geographer, 31.03.2017, p. 1-12.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An Alternative Classification Scheme for Uncertain Attribute Mapping
AU - Wei, Ran
AU - Grubesic, Anthony
PY - 2017/3/31
Y1 - 2017/3/31
N2 - The reality of uncertain data cannot be ignored. Anytime that spatial data are used to assist planning, decision making, or policy generation, it is likely that error or uncertainty in the data will propagate through processing protocols and analytic techniques, potentially leading to biased or incorrect decision making. The ability to directly account for uncertainty in spatial analysis efforts is critically important. This article focuses on addressing data uncertainty in one of the most important and widely used exploratory spatial data analysis (ESDA) techniques—choropleth mapping—and proposes an alternative map classification method for uncertain spatial data. The classification approach maximizes within-class homogeneity under data uncertainty while explicitly integrating spatial characteristics to reduce visual map complexity and to facilitate pattern perception. The method is demonstrated by mapping the 2009 to 2013 American Community Survey estimates of median household income in Salt Lake County, Utah, at the census tract level.
AB - The reality of uncertain data cannot be ignored. Anytime that spatial data are used to assist planning, decision making, or policy generation, it is likely that error or uncertainty in the data will propagate through processing protocols and analytic techniques, potentially leading to biased or incorrect decision making. The ability to directly account for uncertainty in spatial analysis efforts is critically important. This article focuses on addressing data uncertainty in one of the most important and widely used exploratory spatial data analysis (ESDA) techniques—choropleth mapping—and proposes an alternative map classification method for uncertain spatial data. The classification approach maximizes within-class homogeneity under data uncertainty while explicitly integrating spatial characteristics to reduce visual map complexity and to facilitate pattern perception. The method is demonstrated by mapping the 2009 to 2013 American Community Survey estimates of median household income in Salt Lake County, Utah, at the census tract level.
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U2 - 10.1080/00330124.2017.1288573
DO - 10.1080/00330124.2017.1288573
M3 - Article
AN - SCOPUS:85016457586
SP - 1
EP - 12
JO - Professional Geographer
JF - Professional Geographer
SN - 0033-0124
ER -