The increasing use of geographic information system (GIS) technology in various fields suggests the need for professionals, including those in Extension, to be mindful of communicating data accurately and effectively. This article examines approaches to creating classes or groupings within data as well as the weaknesses of each approach. Data break units discussed in the article include equal intervals, quantiles, and units resulting from natural breaks. Ideal situations for each type of data break are presented. The article emphasizes the need for Extension professionals to consider the effects of data grouping to avoid miscommunication when using GIS mapping

Cantarero, Rodrigo

Choi, Jeong-Kyun

deGuzman, Maria

Hong, Soo-Young

Taylor, Sarah

English

UNL | Libraries

University of Nebraska - Lincoln 
DigitalCommons@University of Nebraska - Lincoln 
Community and Regional Planning Program: 
Faculty Scholarly and Creative Activity Community and Regional Planning Program 
4-2019 
(Mis)Communicating with Geographic Information System 
Mapping: Part 2—Determining Data Cutoffs 
Rodrigo Cantarero 
University of Nebraska - Lincoln, rcantarero1@unl.edu 
Maria deGuzman 
University of Nebraska-Lincoln, mguzman2@unl.edu 
Sarah Taylor 
California State University, Long Beach, sarah.taylor@csulb.edu 
Soo-Young Hong 
University of Nebraska-Lincoln, shong5@unl.edu 
Jeong-Kyun Choi 
University of Nebraska-Lincoln, jchoi@unl.edu 
Follow this and additional works at: https://digitalcommons.unl.edu/arch_crp_facultyschol 
 Part of the Urban, Community and Regional Planning Commons 
Cantarero, Rodrigo; deGuzman, Maria; Taylor, Sarah; Hong, Soo-Young; and Choi, Jeong-Kyun, 
"(Mis)Communicating with Geographic Information System Mapping: Part 2—Determining Data Cutoffs" 
(2019). Community and Regional Planning Program: Faculty Scholarly and Creative Activity. 27. 
https://digitalcommons.unl.edu/arch_crp_facultyschol/27 
This Article is brought to you for free and open access by the Community and Regional Planning Program at 
DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Community and Regional 
Planning Program: Faculty Scholarly and Creative Activity by an authorized administrator of 
DigitalCommons@University of Nebraska - Lincoln. 
April 2019
Volume 57
Number 2
Article # 2TOT4
Tools of the Trade
(Mis)Communicating with Geographic Information System
Mapping: Part 2—Determining Data Cutoffs
Abstract
The increasing use of geographic information system (GIS) technology in various fields suggests the need for
professionals, including those in Extension, to be mindful of communicating data accurately and effectively. This
article examines approaches to creating classes or groupings within data as well as the weaknesses of each
approach. Data break units discussed in the article include equal intervals, quantiles, and units resulting from
natural breaks. Ideal situations for each type of data break are presented. The article emphasizes the need for
Extension professionals to consider the effects of data grouping to avoid miscommunication when using GIS
mapping.
Keywords: geographic information system (GIS), misrepresentation in GIS use, Extension and geospatial
technologies
   
Given the increasing popularity of geographic information system (GIS) technology in various fields,
including Extension, it is imperative that professionals be aware of factors that maximize its communicative
potential. More importantly, knowledge of these factors can help one avoid miscommunicating or
misrepresenting data. In Part 1 of this two-article set, also published in this issue of the Journal of Extension,
we discuss the importance of choosing the appropriate unit of data representation (i.e., count, percentage,
location quotient) and potential implications for data interpretation (see
https://joe.org/joe/2019april/tt3.php). In this article, we turn our attention to an often overlooked but very
important aspect of mapping—namely, how data are organized or broken into classes. How data are broken
Rodrigo Cantarero
Associate Professor
University of
Nebraska–Lincoln
Lincoln, Nebraska
rcantarero1@unl.edu
Maria Rosario T. de
Guzman
Associate Professor
and Extension
Specialist in
Adolescence
University of
Nebraska–Lincoln
Lincoln, Nebraska
mguzman2@unl.edu
Sarah Taylor
Assistant Professor
California State
University, Long
Beach
Long Beach, California
sarah.taylor@csulb.ed
u
Soo-Young Hong
Associate Professor
University of
Nebraska–Lincoln
Lincoln, Nebraska
shong5@unl.edu
Jeong-Kyun Choi
Associate Professor
and Early Childhood
Extension Specialist
University of
Nebraska–Lincoln
Lincoln, Nebraska
jchoi@unl.edu
down into classes in maps can lead to communication of very different stories; accordingly, it is imperative
that those who use GIS mapping be mindful of this issue.
In mapping, data typically are organized into discrete categories (e.g., 0%–25%, 26%–50%, 51%–75%,
76%–100%). The most popular approaches to classifying data are the equal intervals, quantiles, and natural
breaks methods (with the last being the default setting in most GIS programs). Although seemingly a minor
detail, the choice of methodology for determining breaks can result in drastically different maps. Herein, we
illustrate this concept by using the three approaches to represent the concentration of ethnic minorities by
county in Nebraska using county data from the 2010–2014 U.S. Census (U.S. Census Bureau, 2014).
Equal Intervals
The term equal intervals simply means that all data classes are about the same size (e.g., 0%–25%, 26%–
50%, 51%–75%, 76%–100%). Figure 1 exemplifies the use of the equal intervals method, with classes
calculated by dividing the highest count by the number of categories we want. Some scholars published in
the Journal of Extension have used equal intervals in mapping various data (e.g., Veregin, 2015). In our
example, Douglas County had the highest number of minorities (156,144). Thus, we divided this figure by
the number of classes we had predetermined (156,144 ÷ 4), creating intervals of approximately 39,000. One
county falls in the class with the highest minority population range, one falls in the class with the third
highest range, and the remaining counties are in the class with the lowest range.
The equal intervals approach (Figure 1) highlights the influence of high concentrations of population
(skewness). Because Nebraska's population distribution is highly concentrated in two counties (Douglas
County, where Metro Omaha is located, and Lancaster County, where the capital of Lincoln is located), so is
its minority population.
Figure 1.
Concentrations of Ethnic Minorities in Nebraska (Equal Intervals)
Quantiles
Tools of the Trade (Mis)Communicating with Geographic Information System Mapping: Part 2 JOE 57(2)
©2019 Extension Journal Inc. 1
In the quantiles approach, the number of groups is predetermined. Cases (e.g., counties) are then sorted by
the target variable (e.g., number of ethnic minorities) and then divided into equal groups, each containing
the same number of cases. Because there are 93 counties in Nebraska and we had predetermined four data
classes, each class has approximately 23 counties. As indicated by the breaks in the data in Figure 2, 25% of
the counties fall within the 2–101 range for number of minorities, 25% in the 102–411 range, 25% in the
412–494 range, and 25% in the 995–156,144 range.
Figure 2.
Concentrations of Ethnic Minorities in Nebraska (Quantiles)
The stories from the two maps shown in Figures 1 and 2 could not be more different despite the fact that
both represent the same data and the same unit of representation (i.e., counts/frequencies). Someone
examining Figure 1 might surmise that there is ethnic minority concentration in only two counties, whereas
someone examining Figure 2 would likely think that there is great dispersion of ethnic minority populations
across the state.
An important factor to consider in choosing whether to use equal intervals or quantiles is whether the
population distribution is skewed. In our example, one county in Nebraska has only two ethnic minorities,
and another has 156,144. In addition, there are two counties with somewhat large populations and
correspondingly large ethnic minority populations. All other counties have low numbers of ethnic minorities
(less than 39,000). Thus, based on use of equal intervals, Figure 1 has only two counties highlighted. In
contrast, the use of quantiles (Figure 2) forces cases into each category. Thus, the highest quantile group is
overrepresented (weighted in favor of the group with the highest minority population range)—making it
seem as though there are many "hot spots" of minority population.
Natural Breaks
The natural breaks approach ameliorates the extremes affecting the equal intervals and quantiles methods as
they are based on the average. Natural breaks in the data are determined through an algorithm that
Tools of the Trade (Mis)Communicating with Geographic Information System Mapping: Part 2 JOE 57(2)
©2019 Extension Journal Inc. 2
maximizes between-class variance and minimizes within-class variance. In other words, the natural breaks
method makes classes where there are clusters in the data (population) distribution. The natural breaks
method is the default setting in many mapping programs, and its calculation can be found in earlier scholars'
descriptions (e.g., Jenks, 1967). Several examples of the use of the natural breaks approach can be found in
articles published in the Journal of Extension (e.g., Harris, Aboueissa, Jacobus, Dharod, & Walter, 2010;
Rebori & Burge, 2017). Regarding our example of mapping ethnic minority concentrations in Nebraska,
Figure 3 shows one county in the group with the highest minority population range; two in the group with
the second highest range; 13 in the group with the third highest range; and the rest in the group with the
lowest range.
Figure 3.
Concentrations of Ethnic Minorities in Nebraska (Natural Breaks)
Conclusion
Reflecting on such issues as the purpose of a mapping exercise, the geographic spread of the data (e.g., are
the data of interest highly concentrated in a handful of areas?), and existence of outliers can guide Extension
professionals in their interpretation and development of maps for programming. In our example, if our
intention is to highlight locations with high concentrations of ethnic minorities, the equal intervals approach
(Figure 1) may be most helpful. However, equal intervals would be a poor choice if we want to communicate
where in the state we can find more ethnic minorities. If our need is to highlight counties in which special
attention should be paid to new audiences or topics relevant to ethnic minorities, the natural breaks
approach (Figure 3) might be most useful. As for quantiles (Figure 2), it is difficult to envision a situation in
which this would be most useful. Given the nature of our data, the use of quantiles (Figure 2) is in fact quite
misleading, exaggerating the presence of minorities and suggesting greater dispersion across the state.
Nonetheless, quantiles may be useful in other scenarios, particularly if one is mapping data that are more
evenly distributed across the range. Understanding issues around the seemingly small detail of determining
data breaks is extremely important in effectively communicating with maps and avoiding miscommunication
Tools of the Trade (Mis)Communicating with Geographic Information System Mapping: Part 2 JOE 57(2)
©2019 Extension Journal Inc. 3
of data.
Acknowledgment
Our study was funded by a Research and Engagement Grant from the Rural Futures Institute at the
University of Nebraska.
References
Harris, D. E., Aboueissa, A. M., Jacobus, M. V., Dharod, J., & Walter, K. (2010). Mapping food stores &
people at risk for food insecurity in Lewiston, Maine. Journal of Extension, 48(6), Article 6RIB3. Available at:
https://joe.org/joe/2010december/rb3.php
Jenks, G. F. (1967). The data model concept in statistical mapping. International Yearbook of Cartography,
7, 186–190.
Rebori, M. K., & Burge, P. (2017). Using geospatial analysis to align Little Free Library locations with
community literacy needs. Journal of Extension, 55(3), Article 3TOT3. Available at:
https://www.joe.org/joe/2017june/tt3.php
U.S. Census Bureau. (2014). 2010–2014 American Community Survey 5-year estimates. Retrieved from
https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF
Veregin, H. (2015). Using maps in web analytics to evaluate the impact of web-based Extension programs.
Journal of Extension, 53(3), Article 3IAW2. Available at: https://www.joe.org/joe/2015june/iw2.php
Copyright © by Extension Journal, Inc. ISSN 1077-5315. Articles appearing in the Journal become the
property of the Journal. Single copies of articles may be reproduced in electronic or print form for use in
educational or training activities. Inclusion of articles in other publications, electronic sources, or systematic
large-scale distribution may be done only with prior electronic or written permission of the Journal Editorial
Office, joe-ed@joe.org.
If you have difficulties viewing or printing this page, please contact JOE Technical Support
Tools of the Trade (Mis)Communicating with Geographic Information System Mapping: Part 2 JOE 57(2)
©2019 Extension Journal Inc. 4


(Mis)Communicating with Geographic Information System Mapping: Part 2—Determining Data Cutoffs

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1029&amp;context=arch_crp_facultyschol

(Mis)Communicating with Geographic Information System Mapping: Part 2—Determining Data Cutoffs

Abstract

Similar works

Full text

Available Versions

UNL | Libraries