Visualizing georeferenced data

representing reliability of health statistics

Research output: Contribution to journalArticle

107 Citations (Scopus)

Abstract

The power of human vision to synthesize information and recognize pattern is fundamental to the success of visualization as a scientific method. This same power can mislead investigators who use visualization to explore georeferenced data-if data reliability is not addressed directly in the visualization process. Here, we apply an integrated cognitive-semiotic approach to devise and test three methods for depicting reliability of georeferenced health data. The first method makes use of adjacent maps, one for data and one for reliability. This form of paired representation is compared to two methods in which data and reliability are spatially coincident (on a single map). A novel method for coincident visually separable depiction of data and data reliability on mortality maps (using a color fill to represent data and a texture overlay to represent reliability) is found to be effective in allowing map users to recognize unreliable data without interfering with their ability to notice clusters and characterize patterns in mortality rates. A coincident visually integral depiction (using color characteristics to represent both data and reliability) is found to inhibit perception of clusters that contain some enumeration units with unreliable data, and to make it difficult for users to consider data and reliability independently.

Original languageEnglish (US)
Pages (from-to)1547-1561
Number of pages15
JournalEnvironment and Planning A
Volume30
Issue number9
DOIs
StatePublished - Jan 1 1998

Fingerprint

health statistics
visualization
health
statistics
mortality
semiotics
fill

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Environmental Science (miscellaneous)

Cite this

@article{39230e22788748aa9bf022e70a5754b1,
title = "Visualizing georeferenced data: representing reliability of health statistics",
abstract = "The power of human vision to synthesize information and recognize pattern is fundamental to the success of visualization as a scientific method. This same power can mislead investigators who use visualization to explore georeferenced data-if data reliability is not addressed directly in the visualization process. Here, we apply an integrated cognitive-semiotic approach to devise and test three methods for depicting reliability of georeferenced health data. The first method makes use of adjacent maps, one for data and one for reliability. This form of paired representation is compared to two methods in which data and reliability are spatially coincident (on a single map). A novel method for coincident visually separable depiction of data and data reliability on mortality maps (using a color fill to represent data and a texture overlay to represent reliability) is found to be effective in allowing map users to recognize unreliable data without interfering with their ability to notice clusters and characterize patterns in mortality rates. A coincident visually integral depiction (using color characteristics to represent both data and reliability) is found to inhibit perception of clusters that contain some enumeration units with unreliable data, and to make it difficult for users to consider data and reliability independently.",
author = "Alan Maceachren and Brewer, {Cynthia Ann} and Pickle, {L. W.}",
year = "1998",
month = "1",
day = "1",
doi = "10.1068/a301547",
language = "English (US)",
volume = "30",
pages = "1547--1561",
journal = "Environment and Planning A",
issn = "0308-518X",
publisher = "Pion Ltd.",
number = "9",

}

Visualizing georeferenced data : representing reliability of health statistics. / Maceachren, Alan; Brewer, Cynthia Ann; Pickle, L. W.

In: Environment and Planning A, Vol. 30, No. 9, 01.01.1998, p. 1547-1561.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Visualizing georeferenced data

T2 - representing reliability of health statistics

AU - Maceachren, Alan

AU - Brewer, Cynthia Ann

AU - Pickle, L. W.

PY - 1998/1/1

Y1 - 1998/1/1

N2 - The power of human vision to synthesize information and recognize pattern is fundamental to the success of visualization as a scientific method. This same power can mislead investigators who use visualization to explore georeferenced data-if data reliability is not addressed directly in the visualization process. Here, we apply an integrated cognitive-semiotic approach to devise and test three methods for depicting reliability of georeferenced health data. The first method makes use of adjacent maps, one for data and one for reliability. This form of paired representation is compared to two methods in which data and reliability are spatially coincident (on a single map). A novel method for coincident visually separable depiction of data and data reliability on mortality maps (using a color fill to represent data and a texture overlay to represent reliability) is found to be effective in allowing map users to recognize unreliable data without interfering with their ability to notice clusters and characterize patterns in mortality rates. A coincident visually integral depiction (using color characteristics to represent both data and reliability) is found to inhibit perception of clusters that contain some enumeration units with unreliable data, and to make it difficult for users to consider data and reliability independently.

AB - The power of human vision to synthesize information and recognize pattern is fundamental to the success of visualization as a scientific method. This same power can mislead investigators who use visualization to explore georeferenced data-if data reliability is not addressed directly in the visualization process. Here, we apply an integrated cognitive-semiotic approach to devise and test three methods for depicting reliability of georeferenced health data. The first method makes use of adjacent maps, one for data and one for reliability. This form of paired representation is compared to two methods in which data and reliability are spatially coincident (on a single map). A novel method for coincident visually separable depiction of data and data reliability on mortality maps (using a color fill to represent data and a texture overlay to represent reliability) is found to be effective in allowing map users to recognize unreliable data without interfering with their ability to notice clusters and characterize patterns in mortality rates. A coincident visually integral depiction (using color characteristics to represent both data and reliability) is found to inhibit perception of clusters that contain some enumeration units with unreliable data, and to make it difficult for users to consider data and reliability independently.

UR - http://www.scopus.com/inward/record.url?scp=0031784949&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031784949&partnerID=8YFLogxK

U2 - 10.1068/a301547

DO - 10.1068/a301547

M3 - Article

VL - 30

SP - 1547

EP - 1561

JO - Environment and Planning A

JF - Environment and Planning A

SN - 0308-518X

IS - 9

ER -