• 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

• General Purpose (Reference)
• Thematic

• 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.

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

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).

• Mapping and Counter Mapping
• American Panorama
• Great Resources

• Different data mapping, applied visual variable, and symbol dimensionality.

• 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.

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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

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• 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 map is great candidate for multivariate mapping.

• A proportional symbol map can represent both individual or enumerated data.

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

• 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)

• 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.

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).

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]

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?

Common Parameters (visual variables) of Symbols

• Color Scheme:

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

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

• 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).

• 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)

• Normalization by unit area
• Normalization by relevant population

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

• 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).

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.

• 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.

• 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.

• 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

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

• 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

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

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

• 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.

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.

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

University Consortium for GIS

Maantay, J., & Ziegler, J. (2006). GIS for the Urban Environment. Redlands, CA: Esri Press.