Geog 258: Maps and GIS

January 11 (Wed)

Cartographic abstraction

What is cartographic abstraction?


The process of transforming reality to a map

This process involves far more than simple reduction

Maps are like caricatures in that they emphasize certain features and de-emphasize others. Similarly, a map maker attempts to portray the essence of a situation, as defined by the maps purpose


Cartographic abstraction process is made up of

1. Selection

2. Simplification

3. Classification

4. Symbolization


We will focus on classification and symbolization



Map makers decide where (regions), when (time frame), and what (variables to be mapped) guided by purposes


Which variables should be included?

Q. Maps for canoeist

Q. Hiking map


Insets can be used if maps in different scales are needed




Determine important characteristics of feature attributes and eliminate unwanted detail

What to keep and what to eliminate?

1) Relative importance of feature/attribute

2) Relation of that class of feature to the maps purpose

3) Graphic consequences of retaining the feature




Needs for data classification 

  • For better recognition of meaningful and revealing pattern
  • For better management of symbol selection
  • For map readability

You may think data classification simply results in a loss of detail, but data classification offers more through an enhanced interpretive power. Its a kind of data preprocessing.


Consequences of different classification methods

Different classification scheme leads to different conclusion.

Therefore, it is important to consider which method is appropriate to different data sets. Understanding the distribution of data sets as well as underlying phenomenon is a must to be a wise map author.


What does data classification do? 

  • Summarizes data before being portrayed in graphic form,
  • Reduces data into forms more suitable for straightforward communication
  • Promotes clarity of purpose or meaning by filtering out details irrelevant to the maps function or theme


Methods of data classification 

In particular, grouping numerical values into class: there are no absolute rules for qualitative classification, thus we only discuss quantitative classification

Determining class intervals and class boundaries affects map interpretation

Number of classes: usually 4 to 6 classes due to the limitation of human perception

Classification schemes can be grouped into four types:

1.    Not related to the distribution

a)      Exogenous: use values not related to the way the data are arrayed (e.g. disease-incidence rate)

b)      Arbitrary: use rounded numbers having no particular relevance to the distribution (e.g. equal step)

2.    Relevant to the distribution

c)      Idiographic: determined by particular events (e.g. natural breaks)

d)      Serial: use descriptive statistics (e.g. quartiles, standard deviation, equal interval)


1. Exogenous schemes 

Class boundaries defined by criteria external to distribution of data

e.g. income data classified into tax brackets

Useful when map should be matched to external criteria 


2. Arbitrary schemes 

Class boundaries are set by arbitrary criteria

e.g. 0-20, 20-40, 40-60, 60-80, 80-100

Useful when a series of maps need to be compared

Works best with data that has a rectangular distribution


Equal Intervals

The whole range of value is divided by the number of class to return class interval. Each class has the equal intervals


3. Idiographic schemes

Class boundaries defined by the shape of the data distribution


Natural Breaks

Form groups that are inherently homogeneous while assuring heterogeneity among classes



Put an equal number of values in each class; can show flattened pattern of skewed data sets 


4. Serial schemes

Class boundaries are defined by statistical or mathematical functions


Standard deviations

Obtain mean and standard deviation, and then determine class boundaries by adding or subtracting the deviation from the mean

Useful when you are interested in areas that deviate the most or least from average


Illustration: lets map median home value by census tract in Seattle, and see how the results look differently



Map uses symbols. Maps use different kinds of symbols as follows:

Kinds of symbol (or visual variables)





        Value (brightness)

        Saturation (purity)






These symbols have inherent logics; they are either distinguishing or ordering.


Logics behind symbols should be matched to the measurement scale of data mapped.

Appropriate choice of symbols by dimensionality of features


Q. What kinds of symbols are used in the following maps, and how do they match the level of measurement of data mapped?


Shape is used to distinguish between coffee production and consumption

Size is used to indicate magnitude of coffee production and consumption


Hue is used to distinguish different origins (Europe, Asian, Latin America, Northern)

Within each map, value is used to indicate graded percentage

In the lab next week, you will be asked about


         Level of measurement {Nominal, Ordinal, Interval, Ratio}

         Kinds of symbols {Shape, Size, Color Hue, Color Value, Color Saturation, Pattern, Pattern Arrangement, Pattern Orientation, Pattern Texture}