Home / Tutorials

Creating Thematic Area Maps with ArcGIS Pro

Rev. 23 March 2025

This tutorial covers the basic steps for creating thematic maps of areas in ArcGIS Pro.

Polygons

An area is "a particular extent of space or surface" (Merriam-Webster 2020).

Areas are usually represented in GIS with vector polygons.

Polygons are formed by connecting node points at specific latitudes and longitudes with edge lines that form boundaries.

Figure
Polygon nodes and edges

Curved boundaries are usually stored in GIS as polygons using closely spaced nodes that appear as curves when viewed. Conventional GIS data models do not have the ability to represent complex geometric representations (such as Bézier curves) that are available in graphic design programs like Illustrator.

Figure
Polygon curves

Individual features sometimes consist of multiple polygons that are treated a single entity in GIS. In the example below, the border of England includes islands off the coast. In the second example, the country of South Africa wraps around the country of Lesotho, and the border of Lesotho is a second polygon carving out a hole in the contiguous area of the country.

Figure
Multiple polygons per feature: Islands and holes

Human-defined areas such as political boundaries or building footprints are discrete phenomena with clearly defined edges that are usually best represented as vector polygon objects.

However, some phenomena, especially environmental phenomena, are continuous phenomena that often do not have clear boundaries and are best represented as rasters. In the example raster below, there are clearly areas of high and low vegetation across Africa, but those levels vary continuously across the landscape.

Figure
Vegetation levels as a continuous phenomenon (NASA 2005)

Types of Areas

Getis et al. (2014, 14) defined a taxonomy of four different types of regions that expanded on Hartshorne's (1959) original three-part taxonomy. In the world of GIS, this taxonomy is useful for understanding these different types of areas and how information about them can best be captured, analyzed, and communicated.

Administrative Areas

Administrative areas are areas that are "created by laws, treaties, or regulations" and are usually associated with government, military, or business control or operation (Getis et al. 2014, 14).

Administrative areas have clear, rigorously surveyed boundaries that are well-represented by discrete object polygons.

In the United States, there is a rough nested hierarchy of administrative areas that divide the country into areas that are managed by different levels of government:

Figure
The hierarchy of administrative areas containing Chicago, IL, USA

Local Administrative Areas

At the local level, there is a much wider range of different areas used to divide cities into manageable chunks overseen by separate administrators and departments, such as:

Land use zones are an especially complicated type of administrative area that govern the types of buildings and businesses that can operate in particular areas of cities. The boundaries are dictated by a variety of political and economic factors.

Figure
City of Chicago zoning web map

Cadastres

A cadastre is "an official register of the quantity, value, and ownership of real estate used in apportioning taxes" (Merriam-Webster 2020).

Data commonly included in a cadastre includes:

Assessed values are used for calculating property taxes and often vary significantly from market value (Bond 2022). Homes of low-income residents are often assessed more aggressively, resulting in higher tax rates for those residents relative to residents of wealthier neighborhoods (Srikanth 2021).

Figure
President Obama's Hyde Park house in the Cook County property information map

County Maps

The US Census Bureau (USCB) is the part of the US federal government responsible for collecting data about people (demographics) and the economy in the United States. The Census Bureau has its roots in Article I, section 2 of the US Constitution, which mandates an enumeration of the entire US population every ten years (the decennial census) in order to set the number of members from each state in the House of Representatives (the lower house of the US Congress) and Electoral College (that selects the US President) (USCB 2017).

Numerous authors publish subsets of USCB data as feature services in ArcGIS Online. You should always use caution when accessing data from non-authoritative ArcGIS Online sources as the data is commonly work from student projects that is often of uncertain vintage and reliability.

The Minn 2019-2023 ACS feature service in the University of Illinois ArcGIS Online organization features a wide variety of commonly-used demographic variables from the 2019-2023 ACS five-year estimates data profile (DP) tables at state, county, and census tract aggregation levels. The data has full metadata and is also available as downloadable GeoJSON for use in R or Python.

Use the Export Features tool to copy the data from the feature service into the project geodatabase.

Importing county ACS data using an precompiled layer

Census Tract Maps

To preserve the privacy of people who respond to the census or other surveys run by the USCB, the USCB only releases data to the public that has been aggregated to areas. The smallest areas of data that the USCB releases data in are census tracts and, sometimes, block groups. Accordingly, data by census tract provides a fine-grained GIS view of community demographics that is very useful in social and economic research.

Census tracts are subdivisions of counties that are drawn based on clearly identifiable features to ideally contain around 4,000 residents, although in practice the range of population is usually between 1,200 and 8,000 (USCB 2019).

Tract data for specific counties can be filtered using GEOIDFQs which are based on FIPS codes.

  1. Google the county name and FIPS code to find the five-digit FIPS code for the desired county. For this example, the FIPS code for Cook County, IL is 17031.
  2. Construct a GEOIDFQ prefix for the desired tracts. The GEOID for tracts begins with 1400000US, followed by the five digit county FIPS code, followed by the tract ID. Given the FIPS code found above, tract GEOIDs in Cook County begin with 1400000US17031.

  3. Use the Export Features tool to copy the tracts data from the feature service.
  4. Symbolize to make sure you have the variable you need.
Downloading census tract ACS data for Cook County, IL

Formal Areas

Formal areas (uniform regions) are areas that each have a common set of physical or social characteristics.

Although Hartshorne's (1959) original taxonomy considered administrative areas to simply be a specific type of formal areas, this tutorial follows Getis et al. (2014, 14) in seeing formal areas as defined by their contents rather than by the decisions of governing authorities.

Unlike administrative areas that are rigorously surveyed and defined by the state, formal areas of both social and environmental phenomena often do not have geometrically simple, clear, and consistent borders, so the boundaries can be ambiguous and/or contested.

Physiographic regions are formal regions classified by common geological structures and histories (Fenneman 1917).

The ArcGIS Online Living Atlas of the World provides a layer of Named Landforms of the World which can be used for mapping physiographic region data imported using the Export Features tool:

Importing and mapping physiographic regions

Functional Areas

Functional areas (nodal regions) are focused on a central point, with diminishing influence the further you go away from that central point.

Metropolitan areas are functional areas that include "a major city together with its suburbs and nearby cities, towns, and environs over which the major city exercises a commanding economic and social influence" (Encyclopedia Britannica 2020). Metropolitan functional area boundaries often cross multiple city, county, and state administrative boundaries.

For tabulating purposes, the US Census Bureau defines a set of administrative areas as core-based statistical areas (CBSA) that include metropolitan statistical areas (big cities) and micropolitan statistical areas (small cities).

Although the USCB distributes information about metropolitan areas as clear boundaries in shapefiles, different types of influence (such as access to health care or commuting distances) can have different extents, and ties to the global economy can spread influence worldwide. Accordingly, you should interpret maps of functional areas with this ambiguity in mind.

For example, the Minn 2019-2023 ACS CBSA feature service in the University of Illinois ArcGIS Online organization contains selected demographic variables from the USCB's American Community Survey 2019-2023 five-year estimates for CBSAs that can be imported using the Export Features tool:

Importing and mapping CBSAs

Vernacular Areas

Vernacular areas (perceptual regions) are areas that are socially-defined by shared history and common identities (Wikipedia 2020).

An example of a vernacular area is a neighborhood, which is "a residential section of a city" (Merriam-Webster 2020).

City governments often provide maps or data showing clear neighborhood boundaries for planning purposes, but some residents of those neighborhoods may have a different opinion of which neighborhood they belong in.

For example, the City of Chicago provides neighborhood boundary data in the Chicago Data Portal based on a field survey from 1978 asking randomly selected residents what neighborhood they lived in.

  1. Download and unzip a shapefile of the data.
  2. Run the Export Features tool:

    • Input Features: Browse to find the .shp file.
    • Output Feature Class: Browse to the project geodatabase and provide a name (Neighborhoods). Note that you need to browse rather than just entering a name so the data is copied into the project geodatabase rather than just being copied into another shapefile.
  3. Adjust the map Coordinate System if the data comes in as unprojected lat/long.
  4. Symbolize as Unique Values on the mame to create a chorochromatic map, or use no fill to show borders only.
  5. Add name labels if desired.
Importing and mapping neighborhoods

Area Identifiers

Names

Once a set of areas have been defined, you need a way of identifying individual areas. Perhaps the most common way of identifying specific areas is with names. Countries have names, cities have names, and neighborhoods have names.

A challenge of using names with geographic information systems is that GIS uses coordinate systems (usually latitude and longitude) to represent locations. Names often do not uniquely identify a specific latitudes and longitudes on the surface of the planet.

The process of converting place names to latitudes and longitudes is called geocoding. This requires large databases and complex algorithms to deal with the idiosyncrasies of place names, such as:

Figure
The 28 different counties named Washington across the US

Latitudes and Longitudes

The polygons used to represent discrete areas are created using points at specific latitudes and longitudes that are connected using lines. In some cases, one feature may contain multiple polygons for things like separate islands, or to represent holes in polygons, such as for bodies of water.

To simplify identification of areas, these collections of points are often generalized into a single point. This is a common practice with GPS apps when giving directions from one area to another.

These points can be placed at entrances to areas, such as doorways or front gates. However, a more common technique is to place points at centroids, which are locations within polygons that are the geometric center of mass of the polygon.

Figure
Centroids

Linear Addresses

Linear addresses are location identifiers based on a named streets and additional identifier(s) that specify specific locations on those streets. Locations in urban areas are commonly identified with linear addresses, although the standard formats of these addresses vary widely by country, and addressing standards within countries can have a significant amount of variation.

In the United States:

As with place names, street addresses have multiple challenges that make the geocoding of addresses into latitudes and longitudes difficult and imperfect:

Figure
Linear addressing

Standardized Codes

The ambiguity associated with place names and linear addresses can be resolved by using standardized coding systems that uniquely identify areas and other types of locations.

ISO Country Codes

At the international level, the International Organization for Standardization (ISO) has defined two-letter and three-letter country codes (ISO 3166-1 alpha-2 and alpha-3) that uniquely identify past and current country boundaries. These codes mitigate some of the challenges associated with country names as place names that were described above.

Figure
ISO country codes (large countries)

Zip Codes

The US Postal Service divides the country into delivery service administrative areas that are identified with five-digit zip codes. Postal systems in most countries of the world also have similar areas, albeit with different ways of identifying those areas. These codes are used when physically mailing letters or packages.

Figure
Zip codes in Chicago, IL, USA (City of Chicago 2020)

The ZIP in zip code is an acronym for Zone Improvement Plan that was "introduced July 1, 1963, as part of a larger Postal Service Nationwide Improved Mail Service (NIMS) plan to improve the speed of mail delivery" (Library of Congress 2021). Acronyms commonly shift to lower case after a period of frequent use (CMS 2017, 10.6), and Merriam-Webster (2022) and the Chicago Manual of Style (2017, 10.29) favor fully-lowercase. However, the US Government Publishing Office Style Manual (2016) retains the fully-uppercase acronym capitalization.

FIPS Codes

The United States Federal Information Processing Standards (FIPS) included a set of two-digit state codes (FIPS 5-1 from 1970 and FIPS 5-2 from 1987) and five-digit county codes (FIPS 6-4 from 1990). In 2008, the management of the standard moved from the National Institutes of Standards to ANSI's InterNational Committee for Information Technology Standards, with the county code standard becoming INCITS 31-2009 (US Census Bureau 2020).

The US Census Bureau's GEOID coding system is used to uniquely identify various geographic units in its data files based on these standards, and preserves the FIPS acronym (US Census Bureau 2020).

Figure
State FIPS Codes

Local Codes

Local governments use a wide variety of idiosyncratic identifier codes for parcels and structures.

For example, the City of New York's Property Land Use Tax lot Output (PLUTO) system organizes parcels (individual areas of owned land) by block and lot numbers.

Figure
PLUTO listing for New York City's Empire State Building (The City of New York 2020)

Types of Area Maps

A thematic map "is used to display the spatial pattern of a theme or attribute" ( Slocum et al. 2004, 1).

Example Electoral Data

The following examples use electoral data from the 2012 US presidential election available in the Minn 2024 Electoral Counties feature service from the University of Illinois ArcGIS Online organization. The data was originally sourced from state secretaries of state offices, and metadata is available here.

Run the Export Features tool:

Importing the electoral county data

Categorical Choropleth

A choropleth is a type of thematic map where areas are colored based on a single variable that describes some characteristic of those areas. Choropleths can be used to visualize both categorical and quantitative variables.

The following video shows how to create a choropleth using a categorical variable.

  1. Modify the symbology for Unique Values based on the categorical variable you want to map (Winner_2012).
  2. Choose colors for the categories. In this case we use the standard highly-saturated red / blue palette common for maps of this type in the media since 2000.
  3. Remove the all other values entry.
  4. Under the Feature Layer ribbon, select Layer Blend and Multiply so the choropleth colors the base map and allows the base map symbols to be visible through the choropleth polygons.
Creating a choropleth with a categorical variable

Single-Color Quantitative Choropleths

Choropleths can also be used to visualize quantitative variables. When displaying a single variable, it is common to use a sequential color scheme with a range of lightness or saturation of a single hue that clearly conveys high versus low.

This example uses the percentage of the Democratic vote in the 2012 election. In contrast to the stark, divisively categorical red-state / blue-state maps, this type of map shows that there are Democratic voters in all 50 states.

While this map is not as effective for communicating election results as the red-state / blue-state map (where there is indeed only one winner), this map is more effective at communicating the complexities of the US electorate.

  1. Right click the layer to modify the Symbology for Graduated Colors and select the variable you are going to map (Percent_Dem_2012).
  2. Choose an appropriate classification method. The default of Natural Breaks (Jenks) is usually a safe choice.
  3. Choose a Color Scheme for the categories.
  4. Remove the unnecessary decimal points from the legend entries.
  5. Under the Feature Layer ribbon, select Layer Blend and Multiply so the choropleth colors the base map and allows the base map symbols to be visible through the choropleth polygons.
Creating a single-color choropleth with a quantitative variable

Two-Color Quantitative Choropleths

There are situations where the purpose of the map is to show divergence above or below a central value.

A diverging color scheme uses a range of colors between two opposite hues separated by a neutral color like white or gray. The use of the two different hues makes it easier to distinguish between areas with lower and higher values while still being able to see intermediate values between the extremes of the range.

An example of this is US election data, where most voters choose between two candidates from two opposing parties. Using the percentage of the Democratic vote by state, red for low values represents more people voting for Republican candidates, while blue for high values represents more people voting for Democratic candidates. The unsaturated grey in the middle indicates areas that are balanced between the two parties.

To make it easier compare and contrast groups of similar values, quantitative variables are often classified into ranges of values (classes) that are assigned to a limited number of different colors.

Like the single-color map, this map offers a nuanced view of the electoral landscape. However, the two-color map also points out balanced "swing" areas where efforts at political persuasion can be effective for winning elections.

  1. Right click the layer to modify the Symbology to Graduated Colors and select the variable you are going to map.
  2. Choose an appropriate classification method. The default of Natural Breaks (Jenks) is usually a safe choice.
  3. Create a custom Color Scheme with two colors at the extremes and grey in the middle.
  4. Remove the unnecessary decimal points from the legend entries.
  5. Under the Feature Layer ribbon, select Layer Blend and Multiply so the choropleth colors the base map and allows the base map symbols to be visible through the choropleth polygons.
Creating a two-color choropleth with a quantitative variable

Graduated Symbol Maps

One approach for mapping quantitative values for irregularly sized areas (like states) is to use a graduated symbol map rather than a choropleth. A common example of this is the "bubble" map that uses differently sized circles based on the variable being mapped. Although circles are most common, other types of icons can be used for aesthetic variety.

Graduated symbol maps are also more appropriate than choropleths when mapping counts rather than amounts (rates). Counts are variables that indicate size, such as the size of the population. With choropleth maps our eyes see the land area as the size, and when the sizes indicated by the variable are not the same as the sizes of the areas, we get an incorrect impression of where the larger and smaller values are located.

  1. Right click the layer to modify the Symbology to Graduated Symbol and select the variable you are going to map.
  2. Choose an appropriate classification method. The default of Natural Breaks (Jenks) is usually a safe choice.
  3. Choose an appropriate color and bubble size scaling.
  4. Remove the unnecessary decimal points from the legend entries, or add thousands separators as needed..
  5. Under the Feature Layer ribbon, select Layer Blend and Multiply so the bubbles color the base map and allows the base map symbols to be visible through the bubbles.
Creating a graduated symbol (bubble) map

Dot Density Maps

Another approach for mapping counts is the dot density map, where individual dots represent a certain portion of the overall count.

In this example, this allows us to map the counts of both Republican and Democratic votes simultaneously.

The disadvantage with a dot density map is that dots imply specific locations. Because the dots are distributed randomly across the area, this map does not accurately convey the exact spatial distribution of the voters. This can be remedied by using data for smaller areas (like counties), although data for smaller areas can sometimes be more difficult to acquire and less accurate for sparsely-populated areas where people are difficult to poll.

  1. Rename the map with a meaningful name (Dot Density 2012 )
  2. Change the symbology type to Dot Density.
  3. Add fields (Votes_Dem_2012 and Votes_Rep_2012).
  4. Change the dot colors as needed.
  5. Adjust the dot value if one layer of dots overwhelms the other (5000).
Creating a Dot-Density Map

Cartograms

Another solution to the irregular area problem is to create a map where the colored polygons are resized and reshaped based on population. This creates significant geographic distortion and is less of a map than a map-like graphic.

This example uses polygons for a continuous cartogram of US states sized by population in the Minn 2020 Cartogram State Continuous layer in ArcGIS Online. This and other cartograms are available here.

  1. Rename your map to something descriptive (Cartogram Dem 2012).
  2. Run the Feature Class to Feature Class tool to copy the cartogram polygons into a new feature class in the project geodatabase (Cartogram_States).
  3. Turn off the base map since a cartogram distorts geography.
  4. Run the Join Field tool to join the electoral data to the cartogram polygons.
  5. Adjust the Symbology to use the display variable.
Creating a cartogram

Uncertainty

Data is a simplified abstraction that represents the infinitely-complex real world. The process of capturing data by translating the real world into an abstraction always introduces some level of uncertainty about the correspondence of our data to the facts on the ground.

Uncertainty means that something is "not clearly identified or defined" (Merriam-Webster 2021). While the rigorous computational technology of GIS implies absolute truth, uncertainty means that you need to interpret geospatial data with an understanding that the data may deviate from the actual ground truth, or the representations of that data may give a mistaken impression of the actual ground truth.

Since the outputs of GIS are often used to guide decision-making, it is important for the GIS professional to communicate uncertainty so the decision-makers understand the limitations of the information they have been given. Failure to do this can be an ethical issue that can result in harm. Negligence in adhering to professional protocols can result in legal liability.

Longley et al. (2015, 99 - 127) define three levels of uncertainty in geospatial data that are particularly relevant when working with areas: conception, representation, and analysis.

Conceptual Uncertainty

The abstract polygons used to represent areas have exact nodal coordinates and clear lines. Administrative areas usually have stable, rigorously surveyed borders that both lend themselves to representation in GIS and demand use of GIS to preserve accuracy.

However, the borders of formal, functional, and vernacular areas are often less clear. In order to draw borders around a phenomenon you need to be able to classify it into specific categories that will form clear boundaries. Such clarity is often unavailable because categories all have some measure of the following qualities:

Representational Uncertainty

The way in which geospatial data is stored in GIS can add additional uncertainty.

Figure
Topology: clean (left), gap / sliver (center), overlap (right)

Analytical Uncertainty

Different choices of what types and boundaries of areas you used to aggregate spatial data can result in radically different results.

Figure
The modifiable areal unit problem

The modifiable areal unit problem (MAUP) is "a source of statistical bias that is common in spatially aggregated data, or data grouped into regions or districts where only summary statistics are produced within each district, especially where the districts chosen are not suitable for the data" (wiki.gis.com 2022).

For example, maps of electoral results give a very different impression depending on whether you map by state or by county.

Figure
2016 percent Democratic presidential vote aggregated by state
Figure
2016 percent Democratic presidential vote aggregated by county

There is no universally applicable solution to the MAUP. In cases where accuracy is not essential (such as with data exploration) or possible (such as with data that has been aggregated for privacy reasons), the uncertainty presented by the MAUP may be acceptable as long as caveats are provided along with the analysis. In other cases, more-sophisticated analytical approaches may be more appropriate.

In drawing electoral boundaries, gerrymandering is an intentional political use of the MAUP where specific groups of voters are either "packed" into a small number of homogeneous districts or "cracked" across multiple districts in order to reduce the power and representation available to those groups.

You can create a map of a gerrymandered district using the Minn 2024 Electoral Districts feature service in the University of Illinois ArcGIS Online organization.

  1. Use the Export Features tool to copy the district data from the feature service into your project geodatabase.
  2. Symbolize as a border.
  3. Add names if desired.
  4. Use a Definition Query to isolate a specific district if desired.
Mapping a gerrymandered district