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A dot distribution map (or a dot density map or simply a dot map) is a type of thematic map that uses a point symbol to visualize the geographic distribution of a large number of related phenomena. Dot maps are a type of unit visualizations that rely on a visual scatter to show spatial patterns, especially variances in density.
Multivariate thematic maps found a resurgence starting in the middle of the 20th Century, coinciding with the scientific turn in geography. George F. Jenks introduced the bivariate dot density map in 1953. [6] The first modern bivariate choropleth maps were published by the U.S. Census Bureau in the 1970s. [7]
Scrope's 1833 map of world population density, possibly the first dasymetric map. The earliest maps using this kind of approach include an 1833 map of world population density by George Julius Poulett Scrope [4] and an 1838 map of population density in Ireland by Henry Drury Harness, although the methods used to create these maps were never documented.
The most common purpose of a thematic map is to portray the geographic distribution of one or more phenomena. Sometimes this distribution is already familiar to the cartographer, who wants to communicate it to an audience, while at other times the map is created to discover previously unknown patterns (as a form of Geovisualization). [17]
One could conceive of a map being quantified by its map information density, the average number of "bits" of information per unit area on the map (or its corollary, information resolution, the average distance between bits), and by its ground information density or resolution, the same measures per unit area on the Earth. Scale would thus be ...
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 ...
The algorithm for computing a dot plot is closely related to kernel density estimation. The size chosen for the dots affects the appearance of the plot. Choice of dot size is equivalent to choosing the bandwidth for a kernel density estimate. In the R programming language this type of plot is also referred to as a stripchart [3] or stripplot. [4]
Each iso-density locus — the locus of points in k-dimensional space each of which gives the same particular value of the density — is an ellipse or its higher-dimensional generalization; hence the multivariate normal is a special case of the elliptical distributions.