Choropleth map in the context of "Local government in Australia"

A cartogram (also called a value-area map or an anamorphic map, the latter common among German speakers) is a thematic map of a set of features (countries, provinces, etc.), in which their geographic size is altered to be directly proportional to a selected variable, such as travel time, population, or gross national income. Geographic space itself is thus warped, sometimes extremely, in order to visualize the distribution of the variable. It is one of the most abstract types of map; in fact, some forms may more properly be called diagrams. They are primarily used to display emphasis and for analysis as nomographs.

Cartograms leverage the fact that size is the most intuitive visual variable for representing a total amount. In this, it is a strategy that is similar to proportional symbol maps, which scale point features, and many flow maps, which scale the weight of linear features. However, these two techniques only scale the map symbol, not space itself; a map that stretches the length of linear features is considered a linear cartogram (although additional flow map techniques may be added). Once constructed, cartograms are often used as a base for other thematic mapping techniques to visualize additional variables, such as choropleth mapping.

A thematic map is a type of map that portrays the geographic pattern of a particular subject matter (theme) in a geographic area. This usually involves the use of map symbols to visualize selected properties of geographic features that are not naturally visible, such as temperature, language, or population. In this, they contrast with general reference maps, which focus on the location (more than the properties) of a diverse set of physical features, such as rivers, roads, and buildings. Alternative names have been suggested for this class, such as special-subject or special-purpose maps, statistical maps, or distribution maps, but these have generally fallen out of common usage. Thematic mapping is closely allied with the field of Geovisualization.

Several types of thematic maps have been invented, starting in the 18th and 19th centuries, as large amounts of statistical data began to be collected and published, such as national censuses. These types, such as choropleth maps, isarithmic maps, and chorochromatic maps, use very different strategies for representing the location and attributes of geographic phenomena, such that each is preferable for different forms of phenomena and different forms of available data. A wide variety of phenomena and data can thus be visualized using thematic maps, including those from the natural world (e.g., climate, soils) and the human world (e.g., demographics, public health)

The urbanization of the United States has progressed throughout its entire history. Over the last two centuries, the United States of America has been transformed from a predominantly rural, agricultural nation into an urbanized, industrial one. This was largely due to the Industrial Revolution in the United States (and parts of Western Europe) in the late 18th and early 19th centuries and the rapid industrialization which the United States experienced as a result. In 1790, only about one out of every twenty Americans (on average) lived in urban areas (cities), but this ratio had dramatically changed to one out of four by 1870, one out of two by 1920, two out of three in the 1960s, and four out of five in the 2000s.

A Chorochromatic map (from Greek χώρα chóra 'region' and χρώμα chróma 'color'), also known as an area-class, qualitative area, or mosaic map, is a type of thematic map that portray regions of categorical or nominal data using variations in color symbols. Chorochromatic maps are typically used to represent discrete fields, also known as categorical coverages. Chorochromatic maps differ from choropleth maps in that chorochromatic maps are mapped according to data-driven boundaries instead of trying to make the data fit within existing, sometimes arbitrary units such as political boundaries.

The modifiable areal unit problem (MAUP) is a source of statistical bias that can significantly impact the results of statistical hypothesis tests. The MAUP affects results when point-based measures of spatial phenomena are aggregated into spatial partitions or areal units (such as regions or districts) as in, for example, population density or illness rates. The resulting summary values (e.g., totals, rates, proportions, densities) are influenced by both the shape and scale of the aggregation unit.

For example, census data may be aggregated into county districts, census tracts, postcode areas, police precincts, or any other arbitrary spatial partition. Thus, the results of data aggregation are dependent on the mapmaker's choice of which "modifiable areal unit" to use in their analysis. A census choropleth map calculating population density using state boundaries will yield radically different results from a map that calculates density based on county boundaries. Furthermore, census district boundaries are also subject to change over time, meaning the MAUP must be considered when comparing past to current data.