DOCSLIB.ORG

Learn to Create a Choropleth Map in R with Data from Eurostat (2017)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Learn to Create a Choropleth Map in R with Data from Eurostat (2017) Learn to Create a Choropleth Map in R With Data From Eurostat (2017) © 2021 SAGE Publications, Ltd. All Rights Reserved. This PDF has been generated from SAGE Research Methods Datasets. SAGE SAGE Research Methods: Data 2021 SAGE Publications, Ltd. All Rights Reserved. Visualization Learn to Create a Choropleth Map in R With Data From Eurostat (2017) Student Guide Introduction This tutorial explores creating a choropleth map by visualizing a Eurostat dataset about employment in different sectors across Europe. Choropleth maps, a subtype of the areal unit map, are used to visualize predefined contiguous geographic areas in map form and enable the reader to easily scrutinize the geographic distribution of a value over a given region. The choropleth map uses color or pattern to encode values across geographic regions. The visualization in this tutorial uses Eurostat data about employment in industry, trade, and service sectors across Europe. Each country is colored based on their proportional number of employees in a given sector (Figure 1). A choropleth map showing European countries color-coded by data value. Colors range from yellow to red in a multi-hue scale. Text at the bottom of the map reads “Source: Natural Earth, Eurostat 2017.” The map also shows Montenegro, Albania, North Macedonia, and Turkey grayed out. Figure 1. Different Areal Units: Countries and States Page 2 of 21 Learn to Create a Choropleth Map in R With Data From Eurostat (2017) SAGE SAGE Research Methods: Data 2021 SAGE Publications, Ltd. All Rights Reserved. Visualization What Is a Choropleth Map? Choropleth maps are perhaps the best known and most widely used type of areal unit map. Areal unit maps, in general, are composed of a particular geographic region divided into smaller subregions, each of which is usually assigned a color or pattern based on the value of a specific variable in that region. Choropleth maps are a subtype of areal unit maps, where the method of differentiating regions is based largely on predefined geographic regions, such as postal code areas, counties, states, or countries. Other types of areal unit maps are discussed in the Variations and Alternatives section below. The quantitative data used in these maps are most often classified into brackets Page 3 of 21 Learn to Create a Choropleth Map in R With Data From Eurostat (2017) SAGE SAGE Research Methods: Data 2021 SAGE Publications, Ltd. All Rights Reserved. Visualization (also known as bins) for visualizing on an ordinal scale, especially when color density will be the only means of depicting value. Classifying, or binning, the individual values depicted in the map can be done in a variety of ways, for instance, classifying all data points into arbitrary round number categories or into categories delineated by breaks at equal intervals, quantiles, natural breaks (Jenks) in the data, or standard deviation breaks around the mean or median value. Categorizing into quantiles, that is, dividing the dataset into classes with an equal number of items, is a good option for most datasets—though this will always depend on the particular topic and data at hand. Below in Figure 2 is an example of how different binning can affect the final result. Both choropleth maps show European countries but the one on the left uses a fixed interval method and the one on the right uses a quantile binning method. Data shown by the maps are tabulated as follows: Fixed interval percentages Countries 10 to 15 Norway, U.K., Netherlands, Greece 15 to 20 Iceland, Ireland, Sweden, Latvia, Denmark, Belgium, France, Spain 20 to 25 Finland, Lithuania, Germany, Switzerland, Austria, Portugal 25 to 30 Estonia, Poland, Hungary, Croatia, Italy, Bulgaria 30 to 35 Slovakia, Romania, Serbia, Bosnia and Herzegovina, Slovenia 35 to 40 Czech Republic Missing Montenegro, Albania, North Macedonia, Turkey Quantile percentages Countries 12.09 to 15 Norway, United Kingdom, Netherlands, Greece 15.12 to 18 Iceland, Ireland, Sweden, Denmark, Belgium, Spain Page 4 of 21 Learn to Create a Choropleth Map in R With Data From Eurostat (2017) SAGE SAGE Research Methods: Data 2021 SAGE Publications, Ltd. All Rights Reserved. Visualization 18.69 to 23 Finland, Latvia, Lithuania, France, Portugal, Switzerland, Austria 23.65 to 28 Estonia, Germany, Italy, Croatia, Bulgaria 28.03 to 35 Poland, Czech Republic, Slovakia, Hungary, Romania, Serbia, Bosnia and Herzegovina, Slovenia Missing Montenegro, Albania, North Macedonia, Turkey Text at the bottom of the map reads “Source: Natural Earth, Eurostat 2017. Figure 2. Two Choropleth Maps, Using Fixed Interval and Quantile Binning Methods Page 5 of 21 Learn to Create a Choropleth Map in R With Data From Eurostat (2017) SAGE SAGE Research Methods: Data 2021 SAGE Publications, Ltd. All Rights Reserved. Visualization Why Use a Choropleth Map? Choropleth maps are well suited for comparing differences between clearly defined geographical areas. They are often a good map choice when the dataset itself contains some clear geographic area designations. These maps are especially suited for encoding numerical data in relative form, which enables more accurate comparison across disparate regions (see Considerations and Cautions below). Choropleth maps are not just for visualizing numerical values; however, they can also encode nominal data such as political party preferences by state, country membership in the EU, adherence to international treaties, and much more in the same vein. For example, the very simple figure below (Figure Page 6 of 21 Learn to Create a Choropleth Map in R With Data From Eurostat (2017) SAGE SAGE Research Methods: Data 2021 SAGE Publications, Ltd. All Rights Reserved. Visualization 3) shows the particular countries which are included in the Eurostat Persons employed by NACE Rev. 2 dataset. The choropleth map showing countries addressed by the dataset at hand, with countries included colored in yellow.Areas not included in the dataset but pictured on the map include Russia, Ukraine, Belarus, Moldova, and Kosovo, which are grayed out. Text at the bottom of the map reads “Source: Natural Earth, Eurostat 2017.” Figure 3. Simple Choropleth Map, Showing Which Countries are Included in the Dataset Page 7 of 21 Learn to Create a Choropleth Map in R With Data From Eurostat (2017) SAGE SAGE Research Methods: Data 2021 SAGE Publications, Ltd. All Rights Reserved. Visualization Considerations and Cautions Areal unit choices: Choropleth maps are entirely dependent on areal unit boundaries, and therefore, careful consideration is needed when choosing the appropriate areal units for visualizing any particular topic. Previously defined administrative areas, for instance, can be very dissimilar when comparing the land area to population density, leaving room for visual misinterpretation where larger unpopulated areas have many times the visual weight of densely populated Page 8 of 21 Learn to Create a Choropleth Map in R With Data From Eurostat (2017) SAGE SAGE Research Methods: Data 2021 SAGE Publications, Ltd. All Rights Reserved. Visualization urban areas. There is also the larger issue in statistical analysis known as the modifiable areal unit problem (MAUP), in which bias can be unwittingly introduced by choice of areal unit. Fortunately, it is not always necessary to individually determine a suitable areal unit for visualization, as the data itself will often have some predefined area designation. In these cases, the data’s own designation should be used almost without exception. For example, polling districts used in an election and any visualizations based on this polling data should use the same areal units to avoid misrepresenting the data. Figure 4 below shows two examples of differnet areal unit choices. Two maps side-by-side, on the left titled “Areal unit: countries” and spilt into similarly colored areas by country borders. The one on the right is titled “Areal unit: states and provinces” and shows the corresponding areal units of the same region. Text at the bottom of the map reads “Source: Natural Earth.” Figure 4. Different Areal Units: Countries and States Color choices: As in other areal unit maps, color can be used in choropleth maps Page 9 of 21 Learn to Create a Choropleth Map in R With Data From Eurostat (2017) SAGE SAGE Research Methods: Data 2021 SAGE Publications, Ltd. All Rights Reserved. Visualization to encode both qualitative and quantitative data, and the same principles of color apply. On a qualitative color scale, colors usually represent objects that belong in different groups or categories. The goal with a qualitative color scale is usually to create a color palette in which different colors are easily distinguishable, that is to say, relying heavily on major differences in hue. Some care should especially be used in choosing colors for charts that will feature neighboring areas of color, as small areas can easily become lost between other shapes without a significantly differentiating color, and even larger areas can blend into each other visually if their colors are too similar. Quantitative color scales on the other hand, which can be either classified or unclassified, often make use of variation in the lightness of color to show variation in value. Generally, as the value of a variable increases, so does the contrast between the color and its background. Classifying the values depicted in the map into a predefined set of colors is usually easier for the reader to differentiate, and therefore generally preferable to an unclassified continuous color scale where classes are encoded as ordinal values. As a general rule of thumb, the human eye can reliably only distinguish 6–7 degrees of lightness in any given hue due to a phenomenon called simultaneous contrast. Consequently, the number of classes visualized using a choropleth map should also ideally be limited to 7 or fewer, depending on your choice of the color palette. A sequential single-hue color scale, where value differences are marked only by differences in hue lightness (e.g., white to red), can especially hinder the differentiation of adjoining areas in choropleth maps.
Load more

Recommended publications
  • Evaluation of Methods for Classifying Epidemiological Data on Choropleth Maps in Series
    Evaluation of Methods for Classifying Epidemiological Data on Choropleth Maps in Series Cynthia A. Brewer* and Linda Pickle** *Department of Geography, The Pennsylvania State University **National Cancer Institute Our research goal was to determine which choropleth classification methods are most suitable for epidemiological rate maps. We compared seven methods using responses by fifty-six subjects in a two-part experiment involving nine series of U.S. mortality maps. Subjects answered a wide range of general map-reading questions that involved indi- vidual maps and comparisons among maps in a series. The questions addressed varied scales of map-reading, from in- dividual enumeration units, to regions, to whole-map distributions. Quantiles and minimum boundary error classi- fication methods were best suited for these general choropleth map-reading tasks. Natural breaks (Jenks) and a hybrid version of equal-intervals classing formed a second grouping in the results, both producing responses less than 70 percent as accurate as for quantiles. Using matched legends across a series of maps (when possible) increased map-comparison accuracy by approximately 28 percent. The advantages of careful optimization procedures in choropleth classification seem to offer no benefit over the simpler quantile method for the general map-reading tasks tested in the reported experiment. Key Words: choropleth, classification, epidemiology, maps. horopleth mapping is well suited for presenta- a range of classification methods in anticipation of in- tion and exploration of mortality-rate data. Of creased production of mortality maps at the NCHS, the C the many options available, epidemiologists cus- National Cancer Institute (NCI), and other health agen- tomarily use quantile-based classification in their map- cies through desktop geographic information systems ping (see Table 5 in Walter and Birnie 1991).
    [Show full text]
  • Cartography. the Definitive Guide to Making Maps, Sample Chapter
    Cartograms Cartograms offer a way of accounting for differences in population distribution by modifying the geography. Geography can easily get in the way of making a good Consider the United States map in which thematic map. The advantage of a geographic map is that it states with larger populations will inevitably lead to larger numbers for most population- gives us the greatest recognition of shapes we’re familiar with related variables. but the disadvantage is that the geographic size of the areas has no correlation to the quantitative data shown. The intent However, the more populous states are not of most thematic maps is to provide the reader with a map necessarily the largest states in area, and from which comparisons can be made and so geography is so a map that shows population data in the almost always inappropriate. This fact alone creates problems geographical sense inevitably skews our perception of the distribution of that data for perception and cognition. Accounting for these problems because the geography becomes dominant. might be addressed in many ways such as manipulating the We end up with a misleading map because data itself. Alternatively, instead of changing the data and densely populated states are relatively small maintaining the geography, you can retain the data values but and vice versa. Cartograms will always give modify the geography to create a cartogram. the map reader the correct proportion of the mapped data variable precisely because it modifies the geography to account for the There are four general types of cartogram. They each problem. distort geographical space and account for the disparities caused by unequal distribution of the population among The term cartogramme can be traced to the areas of different sizes.
    [Show full text]
  • Numbers on Thematic Maps: Helpful Simplicity Or Too Raw to Be Useful for Map Reading?
    International Journal of Geo-Information Article Numbers on Thematic Maps: Helpful Simplicity or Too Raw to Be Useful for Map Reading? Jolanta Korycka-Skorupa * and Izabela Małgorzata Goł˛ebiowska Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, Krakowskie Przedmiescie 30, 00-927 Warsaw, Poland; [email protected] * Correspondence: [email protected] Received: 29 May 2020; Accepted: 26 June 2020; Published: 28 June 2020 Abstract: As the development of small-scale thematic cartography continues, there is a growing interest in simple graphic solutions, e.g., in the form of numerical values presented on maps to replace or complement well-established quantitative cartographic methods of presentation. Numbers on maps are used as an independent form of data presentation or function as a supplement to the cartographic presentation, becoming a legend placed directly on the map. Despite the frequent use of numbers on maps, this relatively simple form of presentation has not been extensively empirically evaluated. This article presents the results of an empirical study aimed at comparing the usability of numbers on maps for the presentation of quantitative information to frequently used proportional symbols, for simple map-reading tasks. The study showed that the use of numbers on single-variable and two-variable maps results in a greater number of correct answers and also often an improved response time compared to the use of proportional symbols. Interestingly, the introduction of different sizes of numbers did not significantly affect their usability. Thus, it has been proven that—for some tasks—map users accept this bare-bones version of data presentation, often demonstrating a higher level of preference for it than for proportional symbols.
    [Show full text]
  • Beyond Choropleth Maps: a Review of Techniques to Visualize Quantitative Areal Geodata
    INFOVIS READING GROUP WS 2015/16 Beyond choropleth maps: A review of techniques to visualize quantitative areal geodata Alsino Skowronnek– University of Applied Sciences Potsdam, Department of Design Abstract—Modern digital technologies and the ubiquity of spatial data around us have recently led to an increased output and visibility of geovisualizations and digital maps. Maps and »map-like« visualizations are virtually everywhere. While areal thematic geodata has traditionally often been represented as choropleth maps, a multitude of alternative techniques exist that address the shortcomings of choropleths. Advances of these alternative techniques derive both from the traditional domains of geography and cartography, as well as from more recent disciplines such as information visualization or even non-academic domains such as data journalism. This paper will review traditional and recent visualization techniques for quantitative areal geodata beyond choropleths and evaluate their potential and limitations in a comparative manner. Index Terms—Choropleth Maps, Geovisualization, Cartograms, Grid Maps, Spatial Treemaps ––––––––––––––––––– ✦ ––––––––––––––––––– 1INTRODUCTION choropleths exhibit a number of shortcomings (i.e. sup- port of specific data types and certain visual inadequa- cies) in different situations. eople have been fascinated with maps and geospatial This paper will briefly review the literature on choro- representationsP of the world as long as we can think. This pleth maps in the following part, before contrasting the is not only due to maps’ inherent potential for storytelling technique with three alternative approaches, their main and identification with places, but also due to humans’ characteristics and implications for visualizing areal geo- excellent spatio-cognitive abilities, which allow us to easi- data in more general terms.
    [Show full text]
  • Simple Thematic Mapping
    The Stata Journal (2004) 4, Number 4, pp. 361–378 Simple thematic mapping Maurizio Pisati Department of Sociology and Social Research, Universit`adegli Studi di Milano-Bicocca, Italy [email protected] Abstract. Thematic maps illustrate the spatial distribution of one or more variables of interest within a given geographic unit. In a sense, a thematic map is the spatial analyst’s equivalent to the scatterplot in nonspatial analysis. This paper presents the tmap package, a set of Stata programs designed to draw five kinds of thematic maps: choropleth, proportional symbol, deviation, dot, and label maps. The first three kinds of maps are intended to depict area data, the fourth is suitable for representing point data, and the fifth can be used to show data of both types. Keywords: gr0008, tmap choropleth, tmap propsymbol, tmap deviation, tmap dot, tmap label, thematic map, choropleth map, proportional symbol map, deviation map, dot map, label map 1 Introduction Thematic maps represent the spatial distribution of one or more variables of interest within a given geographical unit (Monmonier 1993; Kraak and Ormeling 1996). For example, a sociologist could use a choropleth map (a.k.a., shaded map) to show how the percent of families below the poverty line varies across the states or the provinces of a given country. In turn, a police officer could be interested in analyzing a dot map showing the locations of drug markets within a given city. As Bailey and Gatrell (1995) have put it, in a sense, a thematic map is the spatial analyst’s equivalent to the scatter plot in nonspatial analysis.
    [Show full text]
  • 1 'Intelligent' Dasymetric Mapping and Its Comparison to Other Areal
    ‘Intelligent’ Dasymetric Mapping and Its Comparison to Other Areal Interpolation Techniques Jeremy Mennis Department of Geography and Urban Studies, 1115 W. Berks St., 309 Gladfelter Hall, Temple University, Philadelphia, PA 19122 Phone: (215) 204-4748, Fax: (215) 204-7388, Email: [email protected] Torrin Hultgren Department of Geography, UCB 360, University of Colorado, Boulder, CO 80309 Email: [email protected] 1 Abstract In previous research we have described a dasymetric mapping technique that combines an analyst's subjective knowledge with a sampling approach to parameterize the reapportionment of data from a choropleth map to a dasymetric map. In the present research, we describe a comparison of the proposed dasymetric mapping technique with the conventional areal weighting and 'binary' dasymetric mapping techniques. The comparison is made using a case study dasymetric mapping of U.S. Census tract-level population data in the Front Range of Colorado using land cover data as ancillary data. Error is quantified using U.S. Census block- level population data. The proposed dasymetric mapping technique outperforms areal weighting and certain parameterizations of the proposed technique outperform 'binary' dasymetric mapping. 1 Introduction A dasymetric map depicts a statistical surface as a set of enumerated zones, where the zone boundaries reflect the underlying variation in the surface and represent the steepest surface escarpments (Dent, 1999). Dasymetric mapping has its roots in the work of Russian cartographer Semenov Tian-Shansky (Bielecka, 2005) and American J.K. Wright (1936). Later significant contributions to dasymetric mapping were made by O'Cleary (1969) and, concurrent with more recent advances in GIS and environmental remote sensing, researchers in spatial analysis (Goodchild et al., 1993; Wu et al., 2005).
    [Show full text]
  • Dasymetric Mapping
    Dasymetric Mapping Some geographical distributions are best mapped as ‘volumes’ that represent surfaces characterized by plateaus of relative uniformity separated from one another by relatively steep slopes or escarpments where there is a marked change in statistical value. There are two techniques for defining this stepped surface, the choroplethic and the dasymetric. The choroplethic technique requires only grouping of similar values, and detail is constrained by the boundaries of enumeration units which rarely have to do with the variable being mapped. In choroplethic mapping, emphasis in the graphic statement is placed upon comparing relative magnitudes across the surface of the map. In contrast, the dasymetric method highlights areas of homogeneity and areas of sudden change and is produced by refining the values estimated by the choroplethic technique. Your task is to produce a dasymetric map of cropland in south central Ohio counties, using four variables to refine the enumerated data. Prepare this map and all related maps using ArcView GIS. It might be a good idea to keep a journal log while you are working through this exercise, annotated with hardcopy maps if you prefer, personal notes describing in your own words what the commands you performed in ArcView do, for later reference. Data for the initial distribution is given on the base map on page 4. Initial files for the assignment are in the Handouts folder in the usual Csiss folder on \\ubar\labs\. Copy the folder labeled dasy_ohio from the Handouts directory to your personal directory (for example to E:). Open the file dasy.apr in your directory with ArcView.
    [Show full text]
  • An Evaluation of Visualization Methods for Population Statistics Based on Choropleth Maps
    An Evaluation of Visualization Methods for Population Statistics Based on Choropleth Maps Lonni Besanc¸on, Matthew Cooper, Anders Ynnerman, and Fred´ eric´ Vernier Fig. 1. Choropleth map (A) augmented with 3D extrusion (C), contiguous cartogram (D), and rectangular glyphs (E) at the same level of granularity and with 3D extrusion (B), Heatmap (F) and dot map (G) at a finer level of granularity. Abstract— We evaluate several augmentations to the choropleth map to convey additional information, including glyphs, 3D, cartograms, juxtaposed maps, and shading methods. While choropleth maps are a common method used to represent societal data, with multivariate data they can impede as much as improve understanding. In particular large, low population density regions often dominate the map and can mislead the viewer as to the message conveyed. Our results highlight the potential of 3D choropleth maps as well as the low accuracy of choropleth map tasks with multivariate data. We also introduce and evaluate popcharts, four techniques designed to show the density of population at a very fine scale on top of choropleth maps. All the data, results, and scripts are available from osf.io/8rxwg/ Index Terms—Choropleth maps, bivariate maps 1INTRODUCTION A perennial issue with mapping statistical information onto choro- said to be harder to understand [102]. It is thus still unclear which pleth maps is that they tend to overemphasize large, yet often strategies should be adopted to create bivariate maps to properly convey sparsely populated, administrative areas because of their strong vi- population information in addition to the measured variable usually sual weight [1, 44, 64, 88, 94].
    [Show full text]
  • Mapping Crime: Understanding Hot Spots
    U.S. Department of Justice Office of Justice Programs AUG. AUG. National Institute of Justice 05 Special REPORT Mapping Crime: Understanding Hot Spots www.ojp.usdoj.gov/nij U.S. Department of Justice Office of Justice Programs 810 Seventh Street N.W. Washington, DC 20531 Alberto R. Gonzales Attorney General Regina B. Schofield Assistant Attorney General Sarah V. Hart Director, National Institute of Justice This and other publications and products of the National Institute of Justice can be found at: National Institute of Justice www.ojp.usdoj.gov/nij Office of Justice Programs Partnerships for Safer Communities www.ojp.usdoj.gov AUG. 05 Mapping Crime: Understanding Hot Spots John E. Eck, Spencer Chainey, James G. Cameron, Michael Leitner, and Ronald E. Wilson NCJ 209393 Sarah V. Hart Director This document is not intended to create, does not create, and may not be relied upon to create any rights, substantive or procedural, enforceable by law by any party in any matter civil or criminal. Findings and conclusions of the research reported here are those of the authors and do not necessarily reflect the official position or policies of the U.S. Department of Justice. The products, manufacturers, and organizations discussed in this document are presented for informational purposes only and do not constitute product approval or endorsement by the U.S. Department of Justice. The National Institute of Justice is a component of the Office of Justice Programs, which also includes the Bureau of Justice Assistance, the Bureau of Justice Statistics, the Office of Juvenile Justice and Delinquency Prevention, and the Office for Victims of Crime.
    [Show full text]
  • Choropleth Map Accuracy and the Number of Class Intervals
    POSTER SESSIONS 173 CHOROPLETH MAP ACCURACY AND THE NUMBER OF CLASS INTERVALS. Franky A.N. Declercq Laboratory for Cartography - Katholieke Universiteit Leuven Redingenstraat 16,8-3000 Leuven (Belgium) e-mail: [email protected] Abstract For a large number of data classifications, performance of the Goodness of Variance Fit (GVF) as accuracy optimization measure was compared to two other measures. It is shown that in general a number of7 to 8 classes is preferred to generate accurate choropleth maps. Two newly developed classification methods are suboptimal solutions of the GVF optimal classification method and are designed to minimize image fragmentation and maximize rounding of class limits. Firm rounding of the optimal class limits to less than two significant digits decreases accuracy only slightly. Suboptimal classifications that yield a less fragmented image become rather inaccurate and need 9 to 10 classes to maintain classification accuracy. 1 Aim The Goodness of Variance Fit (GVF) is compared to the Goodness of Deviation around the Median Fit (GDMF) and Goodness of Absolute Deviation Fit (GAOF) as measures to optimize classification accuracy. Two fast iterative classification methods that are developed to run on PC's are introduced. One method determines class breaks with highly rounded digits, the other seeks for class limits that result in a less fragmented map image. Both methods are designed to maintain the highest possible classification accuracy. These new classification methods are applied to a variety of data sets and compared with the Jenks optimal method and the traditional equal interval method in terms of generated classification accuracy (GVF), fragmentation index <PI) and number complexity index (NCI).
    [Show full text]
  • CHOROPLETH MAPS on HIGH RESOLUTION Crts : the EFFECTS of NUMBER of CLASSES and HUE on COMMUNICATION
    CHOROPLETH MAPS ON HIGH RESOLUTION CRTs : THE EFFECTS OF NUMBER OF CLASSES AND HUE ON COMMUNICATION By: Patricia Gilmartin and Elisabeth Shelton Gilmartin, P. and E. Shelton (1990) Choropleth Maps on High Resolution CRTs: The Effects of Number of Classes and Hue on Communication. Cartographica, v. 26(2): 40-52. Made available courtesy of University of Toronto Press: http://www.utpress.utoronto.ca/ ***Reprinted with permission. No further reproduction is authorized without written permission from University of Toronto Press. This version of the document is not the version of record. Figures and/or pictures may be missing from this format of the document.*** Abstract: The research reported here was designed to determine how quickly and accurately map readers viewing choropleth maps on a high-resolution computer monitor are able to identify to which class an areal unit on the map belongs, when the map has between four and eight classes and is produced in shades of either gray, green or magenta. As expected, accuracy rates decreased and reaction times increased as the number of classes on the map increased, Accuracy rates ranged from 91.9% for four-class maps to 68.2% for eight-class maps (averaged for all three colors used in the study). Hue also affected accuracy rates and reaction times, the best results being obtained with achromatic (gray-shaded) maps: 84.5% correct, averaged over all numbers of classes. Maps shaded with magenta proved to be the least satisfactory with an accuracy rate of 72.8%. The study provides cartographers with empirical guidelines regarding what level of map-reading accuracy might be expected for choropleth maps designed with a given number of map classes, in a specific hue, and displayed on a high- resolution graphics monitor.
    [Show full text]
  • Alternative Strategies for Mapping ACS Estimates and Error of Estimation
    Alternative Strategies for Mapping ACS Estimates and Error of Estimation Joe Francis, Jan Vink, Nij Tontisirin, Sutee Anantsuksomsri and Viktor Zhong Cornell University Cornell Population Center Seminar, Ithaca, New York, February 2012 Alternative Strategies for Mapping ACS Estimates and Error of Estimation Joe Francis, Jan Vink, Nij Tontisirin, Sutee Anantsuksomsri and Viktor Zhong Cornell Program on Applied Demography The beloved “long form” is dead. Long live the American Community Survey! As Eathington (2011) recently exclaimed, “Beginning in 2011, regional scientists and other socio‐economic data users must finally come to terms with major changes in U.S. Census Bureau methodologies for collecting and disseminating socioeconomic data.” Eathington’s proclamation holds all the more true for 2012. The American Community Survey (ACS) is now the primary mechanism for measuring detailed characteristics of the population at the sub-state level, and especially smaller geographies like townships, places and tracts. It is the main vehicle for disseminating information about educational attainment, occupational status, income levels (including poverty) and much more. As Sun and Wong (2010) write “Census data have been widely used to support a variety of planning and decision making activities.” Additionally, during the past decade there is increasing interest among demographers, economists, planners and regional scientists in mapping census data including the ACS. The main reason is that a map can show the spatial distribution of demographic data better than any other medium. Maps add another tool to the demographer’s analytic toolbox. Compared to the past, mapping has become an easier and more straightforward task. The widespread availability of desktop GIS systems and trained GIS professionals assures that an increasing amount of decennial, ACS, Small Area Income and Poverty Estimates (SAIPE) and other survey data will become mapped.
    [Show full text]