An Alternative Classification Scheme for Uncertain Attribute Mapping

Research output: Contribution to journalArticle

2 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalProfessional Geographer
DOIs
StateAccepted/In press - Mar 31 2017

Fingerprint

spatial data
uncertainty
decision making
saline lake
household income
spatial analysis
homogeneity
census
attribute
data analysis
planning
ability
community
method
policy
protocol

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 journalArticle

@article{c5acb25364db4a9282db3da2296c3dee,
title = "An Alternative Classification Scheme for Uncertain Attribute Mapping",
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.",
author = "Ran Wei and Anthony Grubesic",
year = "2017",
month = "3",
day = "31",
doi = "10.1080/00330124.2017.1288573",
language = "English (US)",
pages = "1--12",
journal = "Professional Geographer",
issn = "0033-0124",
publisher = "Taylor and Francis Ltd.",

}

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.

UR - http://www.scopus.com/inward/record.url?scp=85016457586&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85016457586&partnerID=8YFLogxK

U2 - 10.1080/00330124.2017.1288573

DO - 10.1080/00330124.2017.1288573

M3 - Article

SP - 1

EP - 12

JO - Professional Geographer

JF - Professional Geographer

SN - 0033-0124

ER -