2023-01-27

Overview

  • Thematic Map?

    • What is Thematic Map?
    • Types of Thematic Maps
  • Making a Map

    • Planning
    • Data Mapped: Measurement scales
    • Symbology
      • Symbols
      • Colors and Color Schemes
      • Data Normalization
      • Data Classification
  • An Example

A Typology of Maps

Source: Redrawn from Thematic Map Design, by Borden Dent.

What is the difference between these two maps?

  • General Purpose (Reference)
  • Thematic

What is thematic map?

  • Thematic map: a map that shows the spatial distribution of one or more geographic phenomena.

  • A thematic map is made up of two main components: the geographic basemap, and the overlay of the thematic data. In thematic maps, your main subject (the information you try to convey) is distinctly visible against the base map, rising to the top of your visual hierarchy.

Types of Thematic Maps

Based on how the data is mapped, the applied visual variable, and symbol dimensionality, there are different types of thematic maps:

  1. Choropleth Map (Graduated Color Map)
  2. Proportional/Graduated Symbol Map
  3. Dot Density Map
  4. Dasymetric Map
  5. Isoline/Isarithmic Map
  6. Other Types
    • Flow Map
    • Cartogram

Types of Thematic Maps

Accordingly, each thematic map suggests a different way of thinking about the same mapped phenomenon, and thus may lead to different insights and conclusions about the represented topic (MacEachren & DiBiase, 1991; Kraak et al., 2020).

Types of Thematic Maps — Three Parameters

Choropleth Map

  • A thematic map that uniformly colors each non-overlapping enumeration unit according to the represented value.
  • Choropleth maps typically receive sequential color schemes varying color value as a primary visual variable, using a “dark-is-more” order for light backgrounds and the reverse “light-is-more” order for dark backgrounds.

source

Choropleth Map

Choropleth maps are perhaps the most frequently used thematic maps for portraying statistical data!

  • Choropleth maps only represent enumerated data, often enumerated to political boundaries or a tessellation of regular shapes

source

Choropleth Map

  • Patterns in choropleth maps are strongly influenced by the distribution of the enumeration units. (Pay attention to this! When boundary changes, everything changes.)
    • Modifiable areal unit problem (“MAUP”): Differences between various types of enumeration units can change how data is aggregated, resulting in a dramatic difference in display and modifying the message of the map.
    • Ecological fallacy: also called ecological inference fallacy, in epidemiology, failure in reasoning that arises when an inference is made about an individual based on aggregate data for a group.

Proportional Symbol Maps

  • A proportional symbol map is a thematic map that scales the size of a point symbol proportional to the represented value.
  • The scaling ratio is the first important design consideration for proportional symbol maps. The symbol sizes should be large for the values of the phenomenon to be easily estimated and compared.

Proportional Symbol Maps

  • Proportional symbol map is great candidate for multivariate mapping.
  • A proportional symbol map can represent both individual or enumerated data.

Graduated Symbol Maps

  • A graduated symbol map, sometimes called range-graded symbols, is a classified proportional symbol map.
  • But you no longer get the exact value!!

Graduated Symbol Maps

Isoline Maps

  • An isoline map (contour map) is a thematic map that represents data with lines derived through interpolation that connect points of equal attribute value on a map. The lines are called isolines or isarithms.

    • Isometric maps are thematic maps that use isolines resulting from interpolating values collected at sample points. For isometric maps, sample points already may exist (e.g., weather stations).
    • Isoplethic maps are thematic maps that use isolines resulting from interpolating values enumerated across areas. (most of the raster data map we will see later)

Isoline Maps

  • The distribution of sample points: For isometric maps it can be systematic, random, stratified, or purposeful sampling whereas for isopleth maps, only the centroid is used (As shown in the figure to the left).
  • Interpolation methods: inverse distance weighting, bi-cubic spline fitting, and kriging.

Dot Density Map

  • A dot density map is a thematic map that places dots within an enumeration unit in proportion to the represented value/feature, to preserve the distribution and variation of density of a phenomenon.

Dot Density Map

  • The dot value, or number of the phenomenon that each dot represents.
  • The dot size is the diameter of the dot that is placed multiple times on the map.

Dasymetric Map

A dasymetric map is a thematic map that uses ancillary data to determine new more meaningful borders of enumeration units, improving the representation of the spatial distribution of the mapped phenomenon.

  • Two kinds of ancillary data:

    • exclusionary datasets (those that indicate where the mapped phenomenon cannot appear)
    • inclusionary datasets (those that enable readers to assume a high positive correlation between the additional variable and the mapped phenomenon) (Robinson et al., 1995; Kraak et al., 2020).

Dasymetric Map

Other Maps: Cartograms and Flow Map

Multivariate maps

It is common to plot more than one variable on a map by using two thematic map types, and these are referred to as bivariate maps.

[source: Maantay and Ziegler, 2006]

Type of Thematic Maps — A Summary

Choropleth map

Strengths:

  • allows good representation of a theme, tied to well-defined enumeration units
  • represents abrupt, continuous surfaces
  • user/reader friendly

Limitations:

  • creates the risk of unintentionally masking important details due to improper choice of the number of classes and classification methods
  • may misleadingly suggest homogeneity within the enumeration unit
  • boundaries of the enumeration units (e.g., census units) often do not associate with the distribution of the phenomenon
  • often has enumeration units of different sizes, and the largest ones visually dominate
  • does not allow exact values to be derived

Type of Thematic Maps — A Summary

Proportional / graduated symbol map

Strengths:

  • effectively represents datasets with a large range of values
  • can represent individual point data and enumerated data
  • represents discrete, abrupt surfaces

Limitations: not very reader/user friendly

  • may cause problems with overlapping symbols
  • causes a risk of underestimating symbol values as they grow larger (proportional symbol maps only)
  • does not allow exact values to be read (graduated symbol maps only)

Type of Thematic Maps — A Summary

Isoline/isarithmic map

Strengths:

  • effectively represents the arrangement of magnitudes, the orientation of surface gradients, and the main lines of distribution
  • represents smooth, continuous surfaces

Limitations:

  • does not allow exact values to be read

Type of Thematic Maps — A Summary

Dot density map

Strengths:

  • represents distribution and variations in pattern, such as clustering
  • provides an easily understood visual impression of related densities
  • represents smooth, discrete surfaces

Limitations:

  • does not allow quantities to be read

Type of Thematic Maps — A Summary

Dasymetric map

Strengths:

  • includes variations within enumeration units
  • improves other common thematic map types when enumeration units are poorly matched to geographic distribution

Limitations:

  • requires ancillary information
  • involves time consuming map making process

Before Mapmaking – Planning

Before you make any map, ask yourself these crucial questions:

  • What is the purpose of the map?
  • Who will read it?
  • What is the story in the data?
  • Where and how will you share the map?

Data Mapped: Quantitative vs. Qualitative Map

  • Quantitative maps have as their basis the numerical relationships of the variables being mapped.
  • Qualitative maps, by contrast, are based on descriptive information, and show location and boundaries of differences of kind or type.
  • Qualitative maps make use of nominal or ordinal measurement scales, while quantitative maps make use of interval or ratio measurement scales.

Qualitative Thematic Maps — Measurement scales: Nominal

  • Objects are classified to groups. The groups have names, not numeric values.

Qualitative Thematic Maps — Measurement scales: Ordinal

  • Implies a hierarchy of rank—a ranking of classes.

Quantitative Thematic Maps — Measurement scales: Interval

  • Arrange the classes in ranks, and the intervals between ranks are known.

Quantitative Thematic Maps — Measurement scales: Ratio

  • Ratio–scale magnitudes are absolute, and have a known starting point of zero.

Quantitative Thematic Maps — Measurement scales: Quiz

Each scale is represented once in the list below.

  • Favorite candy bar
  • Weight of luggage
  • Year of your birth
  • Egg size (small, medium, large, extra large, jumbo)
  • Military rank
  • Number of children in a family
  • Jersey numbers for a football team
  • Shoe size
  • N,R,I,O,O,R,N,I

Measurement scales: A Summary

Symbology

Symbols

Common Parameters (visual variables) of Symbols

Symbology

Color Scheme

  • Color Scheme:

    • Color value, color hue and color saturation.
    • Sequential versus diverging color schemes
    • Color Theory

source

Symbology

Data Normalization and Classification

Data Normalization and classification should be strongly considered in all types of aggregated mapping!!!

  • 2016 U.S. Presidential Election. Source: New York Times.

Data Normalization

  • Data normalization (or standardization) is the process of taking enumerated data and attempting to remove biases and misleading messages that are founded in differences between the enumeration units.
  • Enable us to tell a consistent and robust story across the extent of the map.
  • Two general types of normalization: statistical normalization and visual normalization (cartogram: purposefully exaggeration).

Data Normalization — Statistical normalization

  • Normalization by unit area
  • Normalization by relevant population
    • Remember: data are normalized against the same universe of values from which the enumerated phenomena was measured.
  • Normalization by summary value within a unit
  • Normalization by summary value across units (temporal normalization)

Data Normalization — Statistical normalization

  • Normalization by unit area
  • Normalization by relevant population

Data Normalization — Statistical normalization

  • Normalization by summary value within a unit
  • Normalization by summary value across units (temporal normalization)

Data Classification

  • Classification is an intellectual process that groups similar phenomena to gain relative simplicity in communication and user interpretation (Robinson et al., 1995; Longley et al., 2015).

Data Classification

  • Classification is an important and critical process in thematic storytelling

  • Changing a classification scheme can modify the message of the map entirely

  • Be objective and honest ( May the force be with map-maker)

  • Some practical guidance:

    • thematic maps should always be presented with more than one classification scheme
    • thematic map could be accompanied by an explanatory note stating that the data classification depicted on the map.

Data Classification — How to decide?

You need to do data exploration. It is a trial-and-error process!!

  • Descriptive statistics about the dataset: minimum, maximum, range, mean, median, standard deviation, and other statistical measures of the dataset.
  • Line graphs, bar charts, scatterplots, frequency histograms, and other methods of graphing the data are also usually helpful in exploration.

Data Classification — Equal Interval

  • Class breaks at regular intervals along the number line at a set equivalent range. Each class is used to represent an equivalent range of measured data values.
  • Classes are chosen regardless of the data.
  • Equal interval is easy to read and understand; but it can be misleading in that no information is given on the distribution of the data within each distinct class.
  • Suggested use: Uniformly distributed data with familiar data ranges.

Data Classification — Quantiles (Equal Count)

  • Equal numbers of data observations are placed into each category.
  • Data is classified into groups like Top 20%, Upper-Middle 20%, Middle 20%, Lower-Middle 20%, and Bottom 20%.
  • Easy for the map reader to understand. (Unable to discern outliers)
  • Suggested use: Evenly(Close to uniformly) distributed data and ordinal data.

Data Classification — Natural Breaks/Jenks

  • Algorithmically optimal breaks are placed in data based on sums of deviations of means between individual classes.
  • Maximizing between group variation while minimizing within group variation.
  • Suggested use: Clustered and Skewed data

Data Classification — Mean-Standard Deviation

  • Groups according to the distance to the mean standard deviation of the dataset.
  • Suggested use: Normally distributed data

Data Classification — Unique/Manuel

  • Breaks determined by external criteria.
  • Map-makers’ knowledge:
    • Such as the context surrounding the data, which can be provided through literature, policy reviews, or external assessment to determine key data values, then data is grouped according to its relationship to these key values.
  • Suggested use: Data where key numbers are important and map-makers know their stuff

Data Classification — Maximum Breaks

  • Breaks are placed at the largest intervals between adjacent data values.
  • Be careful with outliers
  • Suggested use: Clustered data without outliers

Data Classification — Head/Tail Breaks

  • Algorithmically optimal breaks and the number of classes are based on the dataset itself.
  • Suggested use: Heavily skewed data

Data Classification — How many classes?

  • Thematic maps use at least 5 and as many as 7 groupings, or classes, of data
  • Five is the standard number.
  • Studies have shown that the human eye cannot differentiate among more than eleven classes, and even maps with seven classes can lead to confusion.

Thematic maps and Storytelling — An example

Chetty, R., et al. (2022). Social capital I: measurement and associations with economic mobility. Nature, 1-14.

Chetty, R., et al. (2022). Social capital II: determinants of economic connectedness. Nature, 1-13.

What is the social capital that affecting economic mobility? Three types of social capital by ZIP (postal) code in the United States:

  • connectedness between different types of people, such as those with low versus high socioeconomic status (SES);
  • social cohesion, such as the extent of cliques in friendship networks;
  • civic engagement, such as rates of volunteering.

Thematic maps and Storytelling — An example

They find that the share of high-SES friends among individuals with low SES is among the strongest predictors of upward income mobility identified to date:

  • If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average.

Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality

References