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We will review chapter 9 (Map Symbolization) & chapter 10 (Map Symbol Abstraction) from the Making Maps book. Additional information and examples can be gleaned from the material below.

• Making Maps Ch 9. Map Symbolization

  
  
  

• Making Maps Ch 10. Map Symbol Abstraction

Map Symbolization and Choropleth Maps

Once decisions have been made regarding the classification of the data you have to design an appropriate graphic symbolization of your data

WWW) Visual variables: appropriate levels of meas.

The visual variables are primary means of guiding cartographic symbolization

• at points, lines, and in areas

• expand upon earlier discussion of visual variables

• briefly: point and line symbols

• more detail: symbolization for areas

Abstract and Realistic Point and Line Symbols

Abstract point/line symbols: the symbol doesn't look like what it represents

• also called geometric
  
  
  Source

  
  

  advantages: easy to remember, design, create, easy to see patterns on map

  disadvantages: harder to interpret, can be ambiguous

Realistic point/line symbols: the symbol looks like what it represents

• also called mimetic, pictorial
  
  
  Source

  
  

  advantages: easy to interpret, unambiguous

  disadvantages: difficult to design for all phenomena, more difficult to design in general

Most maps have a mixture of Abstract and Realistic Symbols: National Park Service Symbols

Source

Introduction to Symbolizing Data in Areas

Symbolizing statistical thematic map data in areas is more complicated than data at points or along lines

• first step is to determine the nature of the phenomena you are mapping

• population, tax rates, number of service workers, average income

• this is NOT the same as your data

Phenomena Space:

From MacEachren

Two dimensions in "Phenomena Space"

Discrete versus Continuous variation

• Discrete: phenomena occurs at isolated locations; areas of no occurrence between

• Continuous: phenomena occurs at all places in varying amounts

A continuum: phenomenon may fall at either end, or somewhere in between

Abrupt versus Smooth variation

• Abrupt variation: distinctive and substantial breaks possible

• Smooth variation: continuous variation over space with no breaks

Also a continuum: phenomena may fall at either end, or somewhere in between

Data examples in phenomena space:

The next step is to match this to an appropriate symbolization method...

Symbolization space: same dimensions as phenomena space graphic

Representing discrete phenomena with smooth variation: us a dot map:

Dot map of total Hispanic population in Los Angeles

Representing discrete phenomena with abrupt variation: use a graduated symbol map:

Graduated symbol map of shopping mall size in Atlanta

Representing continuous phenomena with smooth variation: use a isarithmic map (surface map):

Temperature isarithmic map of USA

Representing continuous phenomena with abrupt variation: use a choropleth map:

College basketball players per capita in US

MacEachren fig. 3.25: AIDS data mapped with four different area symbolization techniques

Source: MacEachren


Data on AIDS in PA is available by county
Thus it seems to be a case of representing continuous data with abrupt variation
BUT: the actual phenomena of AIDS is not evenly distributed in each county
There is an important distinction between phenomena and data
AIDS as a phenomena is actually less abrupt and more discreet than the data

What to do?

It is ok to map the data with a choropleth map but it reflects the way the data was collected rather than the phenomena

• Consider other methods

• Graduated circle map: more appropriate than choropleth map, as it is lest apt to imply that the phenomena of AIDS is equally distributed in each county. However, it may imply that there is NO variation at all in each county.

• Dot map: dots imply variation through county, but also implies that AIDS is discreet - and as a disease it is not (it is contageous).

• Isopleth map: implies AIDS as contageous, but also everywhere, which is not the case. Better if you are trying to scare people.

But in reality AIDS as a phenomena is not so continuous (it isn't really everywhere) and it is not so highly contagious (like influenza or measles) as to be easy to catch with casual contact.

AIDS is actually a phenomena that is
between continuous and discreet
and between abrupt and smooth

Back to symbolization space: most appropriate is the middle technique: the "chorodot"

Source: MacEachren

AIDS example is useful in that it reveals

• the difference between phenomena and the data representing the phenomena

• that you have to think about how you symbolize your data

• you have to be familiar with the phenomena

• understand how different area symbolization techniques are less or more appropriate

Problem: you may not be able to easily choose the best area mapping method

The Choropleth Map: Symbolizing Data in Areas

1. The Choropleth Map: Characteristics and History

When to use a choropleth map: symbolization space:

Choropleth map defined: "A method of cartographic representation which employs distinctive shading applied to areas other than those bounded by isolines. These are usually statistical or administrative areas." Dent.

• The choropleth (literally 'area value') technique dates from the early 1800s

  
  

  
  
  Prussian Choropleth map, 1828. Source: Robinson

  
  
  

  

  D'Angeville (French) Choropleth map, 1836. Source: Robinson

The choropleth map is now the most common way to map areal aggregated data

In most cases, the data is classified

• original choropleth maps were classless

• most choropleth maps are based on classified data

• different classifications: different maps
  

  
  

  watch out for the default classification scheme in computer mapping software:

  

Important question to ask in choropleth mapping: Is the data appropriate?

Mistake: using magnitude data for choropleth maps

• choropleth best for mapping density data
  

In most cases, the enumeration units on a map are unequal in area

• The varying sizes of the areas and their mapped values will alter the impression of the distribution

• Total values on left (above) if map totals, different sizes of units obscures fact that the densities are the same (right, above)

• In essence you want to control for area (areas are standardized over the map)

Different view of data when looking at density versus totals:

Choropleth maps are optimal for:

• densities (population per square mile, crop yield per acre)

• ratios independent of area (per capita income, infant deaths per 100,000 births)

• proportions (percentages)

2. Designing Choropleth Maps

Appropriate Legend Design

Source: Dent

Range grading

Better to assign noncontinuous than continuous values in the legend

• reduces error (eg., assumption that 5 is in the data set, or range goes down to 0)

• but: ArcGIS may choke! Change the labels only.

  
  

  

  Source: Dent

  
  

  Useful to include a frequency histogram in the legend

  Design the histogram to show class bounds and symbolization

Appropriate Symbolization for Choropleth Maps

Several visual variables which can be used to differentiate the variations in data on a choropleth map, but value is the one that you should use for typical single variable choropleth maps

Source: Cindy Brewer


More on color in future lecture

A few more choropleth map design guidelines

With printed maps most people tend to see dark as more, light as less

This may be different on computer screens, but the dark = more is a convention that you should violate with caution

Also try and avoid white and black at the extremes of your range of values

• black obscures black boundary symbols

• white may be interpreted as 'no data.'

Remember that most people can identify about 6 to 8 different values

• more: may not be able to match value on map to legend

• but can still see patterns on map

Pay attention that the map projection is appropriate

• Always use area preserving (equivalence) especially for world map

• Note Population Growth Rate map above...oops!

E-mail: jbkrygier@owu.edu

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