Proper communication of spatial distributions, trends, and patterns in data is an important component of a cartographers work. Geospatial data is often large and complex, and due to inherent limitations of size, scalability, and sensitivity, cartographers are often required to work with data that is abstracted, aggregated, or simplified from its original form. Working with data in this manner serves to clarify cartographic messages, expedite design decisions, and assist in developing narratives, but it also introduces a degree of abstraction and subjectivity in the map that can make it easy to infer false messages from the data and ultimately can mislead map readers. This entry introduces the core topics of statistical mapping around cartography. First, we define enumeration and the aggregation of data to units of enumeration. Next, we introduce the importance of data normalization (or standardization) to more truthfully communicate cartographically and, lastly, discuss common methods of data classification and how cartographers bin data into groups that simplify communication.
Thematic maps cover a wide variety of mapping solutions, and include choropleth, proportional symbol, isoline, dot density, dasymetric, and flow maps as well as cartograms, among others. Each thematic map type requires a different data processing method and employs different visual variables, resulting in representations that are either continuous or discrete and smooth or abrupt. As a result, each solution highlights different aspects of the mapped phenomena and shapes the message for the map readers differently. Thematic maps are tools for understanding spatial patterns, and the choice of thematic map type should support this understanding. Therefore, the main consideration when selecting a thematic map type is the purpose of the map and the nature of the underlying spatial patterns.
This entry reviews the common types of thematic maps, describes the visual variables that are applied in them, and provides design considerations for each thematic map type, including their legends. It also provides an overview of the relative strengths and limitations of each thematic map type.
A common goal in spatial analysis is the identification of regions containing unusually high or low values. These areas may be called hot spots if the values are high and cold spots if the values are low. These hot/cold spots indicate where the effects of spatial heterogeneity are greatest. Point density, heat, and choropleth maps all highlight these areas in one way or another. However, due to the limitations of subjective symbolization, statistical methods of hot spot detection are common. Some, like Moran’s I, simply identify the pattern for the entire study area. Local methods display the location and magnitude of individual high and low clusters. Getis-Ord Gi* analysis is the local method most associated with the term hot spots and it is the focus of the second half of the article. Getis-Ord Gi* combines the logic of a probability map with moving windows, kernels and/or adjacency weights. The result is an output surface showing neighborhoods with means significantly above or below the global mean. A primary concern is the correct parameterization, especially the correct conceptualization of spatial relationships. Spatiotemporal variants, limitations, and future directions of hot spot analysis are briefly discussed.