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501:, in formally introducing the unclassed scheme in 1973, asserted that it was a more accurate depiction of the original data, and stated that the primary argument in favor of classification, that it is more readable, needed to be tested. The debate and experiments that followed came to the general conclusion that the primary advantage of unclassed choropleth maps, in addition to Tobler's assertion of raw accuracy, was that they allowed readers to see subtle variations in the variable, without leading them to believe that the districts the fell into the same class had identical values. Thus, they are able to better see the general patterns in the geographic phenomenon, but not the specific values. The primary argument in favor of classed choropleth maps is that it is easier for readers to process, due to the fewer number of distinct shades to recognize, which reduces
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collective entity. However, while it is possible to map an extensive variable in a choropleth map, this is almost universally discouraged because patterns can be easily misinterpreted. For example, if a choropleth map assigned a particular shade of red to total populations between 60 and 70 million, a situation in which United
Kingdom (as a single district) has 65 million inhabitants would be indistinguishable from a situation in which the four constituent countries each had 65 million inhabitants, even though these are vastly different geographic realities. Another source of interpretation error is that if a large district and a small district have the same value (and thus the same color), the larger one will naturally look like more. Other types of
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perceived order of the colors (e.g., light to dark), as this will allow map readers to intuitively make "more vs. less" judgements and see trends and patterns with minimal reference to the legend. A second general guideline, at least for classified maps, is that the colors should be easily distinguishable, so the colors on the map can be unambiguously matched to those in the legend to determine the represented values. This requirement limits the number of classes that can be included; for shades of gray, tests have shown that when value alone is used (e.g., light to dark, whether gray or any single
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of districts included, then colored with the map symbol used for that class. Alternatively, the histogram may be divided into a large number of bars, such that each class includes one or more bars, symbolized according to its symbol in the map. This form of legend shows not only the threshold values for each class, but gives some context for the source of those values, especially for endogenous classification rules that are based on the frequency distribution, such as quantiles. However, they are not currently supported in GIS and mapping software, and must typically be constructed manually.
290:: nominal, ordinal, interval, or ratio, although quantitative (interval/ratio) variables are more commonly used in choropleth maps than qualitative (nominal/ordinal) variables. It is important to note that the level of measurement of the individual datum may be different than the aggregate summary statistic. For example, a census may ask each individual for his or her "primary spoken language" (nominal), but this may be summarized over all of the individuals in a county as "percent primarily speaking Spanish" (ratio) or as "predominant primary language" (nominal).
477:
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a map of the percent Latino visualizes a narrative of composition and predominance. Failure to employ proper normalization will lead to an inappropriate and potentially misleading map in almost all cases. This is one of the most common mistakes in cartography, with one study finding that at one point, more than half of United States COVID-19 dashboards hosted by state governments were not employing normalization to their choropleth maps. This is one of many issues that contributed to the
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times since, to varying degrees of success. This technique is generally used to visualize the correlation and contrast between two variables hypothesized to be closely related, such as educational attainment and income. Contrasting but not complementary colors are generally used, so that their combination is intuitively recognized as "between" the two original colors, such as red+blue=purple. The technique works best when the geography of the variable has a high degree of
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524:, meaning that any possible value falls into exactly one class. For example, if a rule establishes a threshold at the value 6.5, it needs to be clear about whether a district with a value of exactly 6.5 will be classified into the lower or upper class (i.e., whether the definition of the lower class is <6.5 or ≤6.5 and whether the upper class is >6.5 or ≥6.5). A variety of types of classification rules have been developed for choropleth maps:
359:, represents a property that could be measured at any location (a point or small area, depending on its nature) in space, independent of any boundaries, although its variation over a district can be summarized as a single value. Common intensive variables include densities, proportions, rates of change, mean allotments (e.g., GDP per capita), and descriptive statistics (e.g., mean, median, standard deviation). Intensive variables are said to be
497:) directly assigns a color proportional to the value of each district. Starting with Dupin's 1826 map, classified choropleth maps have been far more common. It is likely that this was originally due to the greater simplicity of applying a limited set of tints; only in the age of computerized cartography have unclassed choropleth maps even been feasible, and until recently, they were still not easy to create in most mapping software.
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make the map overly complex, especially if there is not a meaningful geographic pattern in the variable (i.e., the map looks like randomly scattered colors). Although representing specific data in large regions can be misleading, the familiar district shapes can make the map clearer and easier to interpret and remember. The choice of regions will ultimately depend on the map's intended audience and purpose. Alternatively, the
168:" ("tinted maps") were soon produced in France to visualize other "moral statistics" on education, disease, crime, and living conditions. Choropleth maps quickly gained popularity in several countries due to the increasing availability of demographic data compiled from national Censuses, starting with a series of choropleth maps published in the official reports of the 1841 Census of Ireland. When
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collected, including the variable being mapped. In the other view, which may be called "variable dominant", the focus is on the variable as a geographic phenomenon (say, the Latino population), with a real-world distribution, and the partitioning of it into districts is merely a convenient measurement technique.
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sample patches of the symbol for each class, with a text description of the corresponding range of values. On an unclassed choropleth map, it is common for the legend to show a smooth color gradient between the minimum and maximum values, with two or more points along it labeled with corresponding values.
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and usually modeled as a scalar function of location. Choropleth maps are better suited to intensive variables than extensive; if a map user sees the United
Kingdom filled with a color for "100-200 people per square km", estimating that Wales and England may each have 100-200 people per square km may
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The variable to be mapped may come from a wide variety of disciplines in the human or natural world, although human topics (e.g. demographics, economics, agriculture) are generally more common because of the role of governmental units in human activity, which often leads to the original collection of
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to represent the mapped variable. While the general strategy may be intuitive if a color progression is chosen that reflects the proper order, map readers cannot decipher the actual value of each district without a legend. A typical choropleth legend for a classed choropleth map includes a series of
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These are not equivalent, nor is one better than another. Rather, they tell different aspects of a geographic narrative. For example, a choropleth map of the population density of the Latino population in Texas visualizes a narrative about the spatial clustering and distribution of that group, while
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Using pre-defined aggregation regions has a number of advantages, including: easier compilation and mapping of the variable (especially in the age of GIS and the
Internet with its many sources of data), recognizability of the districts, and the applicability of the information to further inquiry and
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showing the frequency distribution of the mapped variable (i.e., the number of districts in each class). Each class may be represented by a single bar with its width determined by its minimum and maximum threshold values and its height calculated such that the box area is proportional to the number
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The final element of a choropleth map is the set of colors used to represent the different values of the variable. There are a variety of different approaches to this task, but the primary principle is that any order in the variable (e.g., low to high quantitative values) should be reflected in the
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However, it can result in a number of issues, generally due to the fact that the constant color applied to each aggregation district makes it look homogeneous, masking an unknown degree of variation of the variable within the district. For example, a city may include neighborhoods of low, moderate,
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uses a wide range of hues (possibly the entire color wheel) without intended differences in value. This is most commonly used when there is an order to the values, but it is not a "more vs. less" order, such as seasonality. It is frequently used by non-cartographers in situations where other color
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is essentially two sequential color progressions (of the types above) joined with a common light color or white. They are normally used to represent positive and negative values or divergence from a central tendency, such as the mean of the variable being mapped. For example, a typical progression
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strategies are essentially invented by the cartographer using thresholds that have some intuitive sense. An example would be classifying incomes according to what the cartographer believes to be "rich," "middle class," and "poor." These strategies are generally not advised unless all other methods
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These issues can be somewhat mitigated by using smaller districts, because they show finer variations in the mapped variable, and their smaller visual size and increased number reduces the likelihood that the map user makes judgments about the variation within a single district. However, they can
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It is possible to represent two (and sometimes three) variables simultaneously on a single choropleth map by representing each with a single-hue progression and blending the colors of each district. This technique was first published by the U.S. Census Bureau in the 1970s, and has been used many
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geographic areas of choropleth maps. The choropleth is likely the most common type of thematic map because published statistical data (from government or other sources) is generally aggregated into well-known geographic units, such as countries, states, provinces, and counties, and thus they are
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representing a variable aggregated within each district. There are two common conceptual models of how these interact in a choropleth map: in one view, which may be called "district dominant", the districts (often existing governmental units) are the focus, in which a variety of attributes are
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rule divides the range of values so the ratio of thresholds is constant (rather than their interval as in an arithmetic progression). For example, the income range above would be divided using a ratio of 2 with thresholds at $ 40,000 and $ 80,000. This type of rule is commonly used when the
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of the United
Kingdom is 250 people per square kilometer, then it would be reasonable to estimate (in the absence of any other data) that the most likely (if not actually correct) density of each of the five constituent countries is also 250/km. Traditionally in cartography, the predominant
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over space; for example, if the population of the United
Kingdom is 65 million, it is not possible that the populations of England, Wales, Scotland, and Northern Ireland could also be 65 million. Instead, their total populations must sum (accumulate) to calculate the total population of the
873:, so that there are large regions of similar colors with gradual changes between them; otherwise the map can look like a confusing mix of random colors. They have been found to be more easily used if the map includes a carefully designed legend and an explanation of the technique.
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fades from a dark shade of the chosen color (or gray) to a very light or white shade of relatively the same hue. This is a common method used to map magnitude. The darkest hue represents the greatest number in the data set and the lightest shade representing the least
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uses a limited range of hues to add more contrast to the value contrast, enabling a larger number of classes to be used. Yellow is commonly used for the lighter end of the progression due to its natural apparent lightness. Common hue ranges are yellow-green-blue and
480:
This map of the 2004–2016 U.S. presidential elections uses county districts, a spatially intensive variable (difference in proportion) that is unclassed, and a spectral divergent color progression. Note the continuous gradient legend that reflects the lack of
222:), and thus have no expectation of correlation with the geography of the variable. That is, boundaries of the colored districts may or may not coincide with the location of changes in the geographic distribution being studied. This is in direct contrast to
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in statistics. Typically, it is accomplished by computing the ratio between two spatially extensive variables. Although any such ratio will result in an intensive variable, only a few are especially meaningful and commonly used in choropleth maps:
771:), it is difficult to practically use more than seven classes. If differences in hue and/or saturation are incorporated, that limit increases significantly to as many as 10-12 classes. The need for color discrimination is further impacted by
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when mapping temperatures is from dark blue (for cold) to dark red (for hot) with white in the middle. These are often used when the two extremes are given value judgements, such as showing the "good" end as green and the "bad" end as red.
690:, then subdividing each of the two created classes at their respective means, and so on. Thus, the number of classes is not arbitrary, but must be a power of two (2, 4, 8, etc.). It has been suggested that this also works well for highly
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Normalization is the technique of deriving a spatially intensive variable from one or more spatially extensive variables, so that it can be appropriately used in a choropleth map. It is similar, but not identical, to the technique of
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In a choropleth map, the districts are usually previously defined entities such as governmental or administrative units (e.g., counties, provinces, countries), or districts created specifically for statistical aggregation (e.g.,
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map (for a qualitative variable), in which the region boundaries are based on the data itself. However, in many cases such detailed information is simply not available, and the choropleth map is the only feasible option.
647:), then the first class would include the 785 poorest counties, then the next 785. Adjustments may need to be made when the number of districts does not divide evenly, or when identical values straddle the threshold.
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uses a scattered set of hues in no particular order, with no intended difference in value. This is most commonly used with nominal categories in a qualitative choropleth map, such as "most prevalent religion."
572:
rules look for natural clusters in the data, in which large numbers of districts have similar values with large gaps between them. If this is the case, such clusters are probably geographically meaningful.
212:
In this choropleth map, the districts are countries, the variable is spatially intensive (a mean allotment) with a modified geometric progression classification, and a spectral divergent color scheme is
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by U.S. county includes values between US$ 20,000 and $ 150,000, it could be broken into three classes at thresholds of $ 45,000 and $ 83,000. To avoid confusion, any classification rule should be
32:
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and high family income, but be colored with one constant "moderate" color. Thus, real-world spatial patterns may not conform to the regional unit symbolized. Because of this, issues such as the
592:. If natural clusters do not exist, the breaks it generates are often recognized as a good compromise between the other methods, and it is commonly the default classifier used in GIS software.
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surrounding the COVID-19 pandemic, and "might also be a subtle facilitator of the extreme political polarization surrounding measures to combat COVID that has occurred in the United States".
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A choropleth map in which the districts are U.S. counties, the variable is spatially intensive (a proportion) with a quantile classification, and uses a single-hue sequential color scheme.
864:
Bivariate choropleth map comparing the Black (blue) and
Hispanic (red) populations in the United States, 2010 census; shades of purple show significant proportions of both groups.
398:
Normalization: the map on the left uses total population to determine color. This causes larger polygons to appear to be more urbanized than the smaller dense urban areas of
1354:
Chen, Xiang; Ye, Xinyue; Widener, Michael J.; Delmelle, Eric; Kwan, Mei-Po; Shannon, Jerry; Racine, Racine F.; Adams, Aaron; Liang, Lu; Peng, Jia (27 December 2022).
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policy tied to the individual districts. A prime example of this would be elections, in which the vote total for each district determines its elected representative.
1314:
T. Slocum, R. McMaster, F. Kessler, H. Howard (2009). Thematic
Cartography and Geovisualization, Third Ed, page 252. Upper Saddle River, NJ: Pearson Prentice Hall.
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Because calculated thresholds can often be at precise values that are not easily interpretable by map readers (e.g., $ 74,326.9734), it is common to create a
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by rounding threshold values to a similar simple number. A common example is a modified geometric progression that subdivides powers of ten, such as or .
512:, a series of thresholds that partitions the quantitative range of variable values into a series of ordered classes. For example, if a dataset of annual
402:, Massachusetts. The map on the right uses population density. A properly normalized map will show variables independent of the size of the polygons.
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Choropleth maps provide an easy way to visualize how a variable varies across a geographic area or show the level of variability within a region. A
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246:(MAUP) can lead to major misinterpretations of the data depicted, and other techniques are preferable if one can obtain the necessary data.
1965:
Meyer, Morton A.; Broome, Frederick R.; Schweitzer, Richard H. Jr. (1975). "Color
Statistical Mapping by the U.S. Bureau of the Census".
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618:. For example, the income range above ($ 20,000 - $ 150,000) would be divided into four classes at $ 52,500, $ 85,000, and $ 117,500.
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choropleth map separates the range of values into classes, with all of the districts in each class being assigned the same color. An
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Olson, Judy M.; Brewer, Cynthia (1997). "An evaluation of color selections to accommodate map users with color-vision impairments".
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rules are those already in common use due to past scientific research or official policy. An example would be using government
372:, in which the variable is imagined as a third-dimension "height" above the two-dimensional space that varies continuously. In
317:) is one that can apply only to the entire district, commonly in the form of total counts or amounts of a phenomenon (akin to
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48:, the variable is spatially intensive (a proportion) which is unclassed, and a part-spectral sequential color scheme is used.
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of the data and establishes a break at each multiple of a constant number of standard deviations above and below the mean.
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rule also generates equal ranges of value, but rather than starting with the minimum and maximum values, it starts at the
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can sometimes be employed to refine the region boundaries to more closely match actual changes in the subject phenomenon.
243:
133:
45:
1796:
Jenks, George F. 1967. "The Data Model
Concept in Statistical Mapping", International Yearbook of Cartography 7: 186–190.
1861:
Brewer, Cynthia A. "Color use guidelines for mapping and visualization". In MacEachren, Alan M.; Taylor, D.R.F. (eds.).
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191:
A choropleth map brings together two datasets: spatial data representing a partition of geographic space into distinct
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maps, in which region boundaries are defined by patterns in the geographic distribution of the subject phenomenon.
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Jenks, George F.; Caspall, Fred C. (June 1971). "Error on
Choroplethic Maps: Definition, Measurement, Reduction".
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corresponding with an aggregate summary of a geographic characteristic within spatial enumeration units, such as
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2019:
Kumar, Naresh (2004). "Frequency Histogram Legend in the Choropleth Map: A Substitute to Traditional Legends".
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for automatically identifying such clusters if they exist; it is essentially a one-dimensional form of the
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1356:"A systematic review of the modifiable areal unit problem (MAUP) in community food environmental research"
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1519:"Normalizing the pandemic: exploring the cartographic issues in state government COVID-19 dashboards"
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775:; for example, color schemes that use red and green to distinguish values will not be useful for a
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585:
1896:
1691:
Dobson, Michael W.; Peterson, Michael P. (1980). "Unclassed Choropleth Maps: A Comment, A Reply".
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The most common types of color progressions used in choropleth (and other thematic) maps include:
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Broadly speaking, a choropleth map may represent two types of variables, a distinction common to
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divides the dataset so each class has an equal number of districts. For example, if the 3,141
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429:= subgroup total / grand total. Example: Wealthy households as a percentage of all households.
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and allows them to precisely match the colors in the map to the values listed in the legend.
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is similar but uses regions drawn according to the pattern of the variable, rather than the
1847:
Robinson, A.H., Morrison, J.L., Muehrke, P.C., Kimmerling, A.J. & Guptill, S.C. (1995)
1718:
Peterson, Michael P. (1979). "An Evaluation of Unclassed Crossed-Line Choropleth Mapping".
1395:
Rittschof, Kent (1998). "Learning and Remembering from Thematic Maps of Familiar Regions".
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Notes on statistical mapping, with special reference to the mapping of population phenomena
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rule is an algorithm that recursively divides the data set by setting a threshold at the
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122:
1074:"The disguised pandemic: the importance of data normalization in COVID-19 web mapping"
445:= total at later time / total at earlier time. Example: annual population growth rate.
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2048:
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1159:
Report of the commissioners appointed to take the census of Ireland for the year 1841
855:
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became widely available after 1850, color was increasingly added to choropleth maps.
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divides the range of values so that each class has an equal range of values: (
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1497:
1480:
1296:
1207:
Trewartha, Glenn T. (January 1938). "Ratio Maps of China's Farms and Crops".
1834:
1517:
Adams, Aaron M.; Chen, Xiang; Li, Weidong; Chuanrong, Zhang (27 July 2023).
932:
903:
791:
451:
338:, are designed to represent extensive variables and are generally preferred.
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274:
1745:
Muller, Jean-Claude (June 1979). "Perception of Continuously Shaded Maps".
1601:
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179:, and was in common usage among cartographers by the 1940s. Also in 1938,
17:
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1560:"Policies to influence perceptions about COVID-19 risk: The case of maps"
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rules import thresholds without regard for patterns in the data at hand.
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Engel, Claudia; Rodden, Jonathan; Tabellini, Marco (18 March 2022).
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of the United States were divided into four quantile classes (i.e.,
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Every choropleth map has a strategy for mapping values to colors. A
1228:
175:
The term "choropleth map" was introduced in 1938 by the geographer
2066:
Dent, Borden; Torguson, Jeffrey; Hodler, Thomas (21 August 2008).
1897:"The End of the Rainbow? Color Schemes for Improved Data Graphics"
1481:"Mapping COVID-19: How web-based maps contribute to the infodemic"
1449:
Jenks, George F. (1963). "Generalization in Statistical Mapping".
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reintroduced them as "ratio maps", but this term did not survive.
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31:
1072:
Adams, Aaron; Chen, Xiang; Li, Weidong; Zhang, Chuanrong (2020).
1004:
Dent, Borden D.; Torguson, Jeffrey S.; Hodler, Thomas W. (2009).
318:
1992:
Olson, Judy M. (1981). "Spectrally encoded two-variable maps".
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Because of these issues, for many variables, one may prefer an
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not be accurate, but it is possible and a reasonable estimate.
160:, depicting the availability of basic education in France by
2091:
368:
conceptual model for this kind of phenomenon has been the
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or weight in physics). Extensive variables are said to be
283:
the statistical data. The variable can also be in any of
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The earliest known choropleth map was created in 1826 by
1124:
Carte figurative de l'instruction populaire de la France
1878:"What Digital Maps Can Tell Us About the American Way"
1649:"Choropleth Maps without Class Intervals? A Comment"
1143:
Early Thematic Mapping in the History of Cartography
566:rules are based on patterns in the dataset itself.
1994:Annals of the Association of American Geographers
1823:Annals of the Association of American Geographers
1776:(2nd ed.). Prentice Hall. pp. 116–121.
1747:Annals of the Association of American Geographers
1451:Annals of the Association of American Geographers
1277:Annals of the Association of American Geographers
36:A choropleth map that visualizes the fraction of
27:Type of data visualization for geographic regions
1397:Educational Technology Research and Development
44:at the 2011 census. The selected districts are
2021:Cartography and Geographic Information Science
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1426:
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508:Classification is performed by establishing a
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1162:. Dublin: H.M. Stationery Office. p. lv.
435:= total amount / total individuals. Example:
8:
94: 'multitude') is a type of statistical
1772:Kraak, Menno-Jan; Ormeling, Ferjan (2003).
423:= total / area. Example: population density
376:, the more common conceptualization is the
363:over space; for example, if the population
2092:ColorBrewer – color advice for cartography
1774:Cartography: Visualization of Spatial Data
1030:"Choropleth Maps Without Class Intervals?"
838:progressions would be much more effective.
1923:
1672:
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1196:(2nd ed.). McGraw-Hill. p. 249.
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1021:
1019:
1017:
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935:, which are often colored as choropleths.
439:per capita (total GDP / total population)
270:Property: aggregate statistical summaries
1617:"Review of Unclassed Choropleth Mapping"
1439:. pp. 22–23. University of Chicago Press
1252:Exploring Geographic Information Systems
1067:
1065:
859:
475:
393:
273:
207:
985:
1810:. Association of American Geographers.
152:Dupin's 1826 map of literacy in France
2097:A choropleth map generator for the US
777:significant portion of the population
661:of the data has a very high positive
7:
1177:"Problems in Population Mapping" in
1863:Visualization in Modern Cartography
1647:Dobson, Michael W. (October 1973).
1254:(2nd ed.). Wiley. p. 65.
2006:10.1111/j.1467-8306.1981.tb01352.x
1759:10.1111/j.1467-8306.1979.tb01254.x
1674:10.1111/j.1538-4632.1973.tb00498.x
1463:10.1111/j.1467-8306.1963.tb00429.x
1289:10.1111/j.1467-8306.1971.tb00779.x
1055:10.1111/j.1538-4632.1973.tb01012.x
25:
1327:The Modifiable Areal Unit Problem
578:Jenks natural breaks optimization
261:(for a quantitative variable) or
77: 'area, region' and
1876:Patricia Cohen (9 August 2011).
1006:Cartography: Thematic Map Design
917:
132:relatively easy to create using
2117:Statistical charts and diagrams
2068:Cartography Thematic Map Design
1851:(6th Edition), New York: Wiley.
898:An alternative approach is the
762:Full-spectral color progression
547:when classifying income levels.
204:Geometry: aggregation districts
1145:. University of Chicago Press.
790:represents variable values as
374:Geographic information science
1:
1865:. Pergamon. pp. 123–147.
1808:Maps, Distortion, and Meaning
1544:10.1080/17445647.2023.2235385
964:Michael Peterson (geographer)
722:Qualitative color progression
313:variable (sometimes called a
244:modifiable areal unit problem
1941:"Somewhere over the Rainbow"
1485:Dialogues in Human Geography
1141:Robinson, Arthur H. (1982).
1008:(6th ed.). McGraw-Hill.
813:Partial-spectral progression
746:Partial spectral progression
702:modified classification rule
590:k-means clustering algorithm
1895:Light; et al. (2004).
1479:Mooney, Peter (July 2020).
1250:Chrisman, Nicholas (2002).
140:, or other software tools.
2138:
1979:10.1559/152304075784313250
1732:10.1559/152304079784022736
1705:10.1559/152304080784522928
1381:10.1007/s44212-022-00021-1
1090:10.1016/j.puhe.2020.04.034
880:
853:
754:Bi-polar color progression
711:
472:Statistical classification
461:
345:variable, also known as a
158:Baron Pierre Charles Dupin
80:
63:
1967:The American Cartographer
1720:The American Cartographer
1693:The American Cartographer
1621:Cartographic Perspectives
883:Page layout (cartography)
850:Bivariate choropleth maps
800:uses only shades of gray.
773:color vision deficiencies
2041:10.1559/1523040042742411
1849:Elements of Cartography.
1806:Monmonier, Mark (1977).
1498:10.1177/2043820620934926
1835:10.1111/0004-5608.00043
1324:Openshaw, Stan (1983).
1121:Dupin, Charles (1826).
974:Proportional symbol map
871:spatial autocorrelation
843:Qualitative progression
522:collectively exhaustive
1584:10.1126/sciadv.abm5106
1028:Tobler, Waldo (1973).
887:A choropleth map uses
865:
805:Single-hue progression
788:Sequential progression
763:
755:
747:
739:
738:Single hue progression
731:
723:
665:, especially if it is
659:frequency distribution
603:arithmetic progression
493:map (sometimes called
482:
437:gross domestic product
403:
279:
214:
153:
49:
46:local government areas
1653:Geographical Analysis
1615:Kelly, Brett (2017).
1192:Raisz, Erwin (1948).
1034:Geographical Analysis
863:
798:Grayscale progression
761:
753:
745:
737:
730:Grayscale progression
729:
721:
653:Geometric progression
479:
397:
295:physics and chemistry
288:levels of measurement
277:
211:
151:
35:
1925:10.1029/2004EO400002
1437:How to Lie with Maps
1173:John Kirtland Wright
949:Dot distribution map
835:Spectral progression
827:Bi-polar progression
332:proportional symbols
252:dasymetric technique
177:John Kirtland Wright
2033:2004CGISc..31..217K
1916:2004EOSTr..85..385L
1665:1973GeoAn...5..358D
1576:2022SciA....8M5106E
1535:2023JMaps..19Q...1A
1372:2022UrbIn...1...22C
1221:1938GeoRv..28..102T
1209:Geographical Review
1194:General Cartography
1046:1973GeoAn...5..262T
902:, which includes a
586:heuristic algorithm
510:classification rule
370:statistical surface
353:statistical surface
40:that identified as
1630:10.14714/CP86.1424
1409:10.1007/BF02299827
939:Chorochromatic map
866:
816:yellow-orange-red.
764:
756:
748:
740:
732:
724:
623:standard deviation
518:mutually exclusive
483:
404:
357:localized variable
280:
240:ecological fallacy
215:
154:
108:population density
50:
2077:978-0-072-94382-5
1783:978-0-13-088890-7
1360:Urban Informatics
1127:. Bruxelles: s.n.
708:Color progression
558:are not feasible.
545:Poverty threshold
170:Chromolithography
112:per-capita income
16:(Redirected from
2129:
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1564:Science Advances
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925:Geography portal
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900:histogram legend
683:Head/tail Breaks
468:Cluster analysis
303:spatial analysis
197:statistical data
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2060:Further reading
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688:arithmetic mean
627:arithmetic mean
598:Equal intervals
582:George F. Jenks
580:, developed by
499:Waldo R. Tobler
481:classification.
474:
462:Main articles:
460:
433:Mean allocation
413:standardization
392:
380:, adopted from
315:global property
272:
206:
189:
181:Glenn Trewartha
166:cartes teintées
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2086:External links
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2027:(4): 217–236.
2011:
2000:(2): 259–276.
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1973:(2): 101–117.
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1882:New York Times
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1433:Mark Monmonier
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944:Dasymetric map
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709:
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570:Natural breaks
561:
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543:or a standard
503:cognitive load
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458:Classification
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443:Rate of change
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263:chorochromatic
224:chorochromatic
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123:isarithmic map
54:choropleth map
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390:Normalization
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58:Ancient Greek
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2112:Color scales
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1948:. Retrieved
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555:Common sense
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464:Data binning
448:
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364:
361:distributive
360:
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341:A spatially
323:accumulative
322:
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309:A spatially
292:
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138:spreadsheets
126:
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96:thematic map
88:
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71:
68:
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53:
51:
29:
792:color value
671:exponential
537:Established
297:as well as
100:pseudocolor
38:Australians
2106:Categories
1945:HCL Wizard
1753:(2): 240.
1623:(86): 30.
1529:(5): 1–9.
980:References
933:Cartograms
881:See also:
564:Endogenous
487:classified
427:Proportion
336:cartograms
259:isarithmic
228:isarithmic
162:department
102:, meaning
98:that uses
56:(from
18:Choropleth
2049:119795925
1950:14 August
1457:(1): 15.
1417:145086925
1403:: 19–38.
1366:(1): 22.
1297:0004-5608
1084:: 36–37.
904:histogram
823:Divergent
667:geometric
645:quartiles
636:Quantiles
530:Exogenous
491:unclassed
452:infodemic
343:intensive
311:extensive
193:districts
187:Structure
1602:35302842
1435:(1991).
1175:(1938).
1108:32416476
959:MacChoro
954:Heat map
911:See also
641:counties
285:Stevens'
242:and the
164:. More "
128:a priori
119:heat map
42:Anglican
2029:Bibcode
1912:Bibcode
1661:Bibcode
1593:8932671
1572:Bibcode
1531:Bibcode
1368:Bibcode
1217:Bibcode
1182:, p.12.
1099:7203028
1042:Bibcode
892:symbols
808:number.
584:, is a
495:n-class
421:Density
382:Physics
365:density
144:History
89:plêthos
2074:
2047:
1780:
1600:
1590:
1415:
1337:
1295:
1258:
1237:210569
1235:
1106:
1096:
889:ad hoc
877:Legend
692:skewed
600:or an
551:Ad hoc
470:, and
400:Boston
195:, and
82:πλῆθος
72:khôros
2045:S2CID
1900:(PDF)
1413:S2CID
1331:(PDF)
1233:JSTOR
378:field
355:, or
348:field
213:used.
104:color
65:χῶρος
60:
2072:ISBN
1952:2019
1778:ISBN
1598:PMID
1335:ISBN
1293:ISSN
1256:ISBN
1104:PMID
663:skew
576:The
520:and
334:and
319:Mass
301:and
226:and
2037:doi
2002:doi
1975:doi
1920:doi
1904:Eos
1831:doi
1755:doi
1728:doi
1701:doi
1669:doi
1625:doi
1588:PMC
1580:doi
1539:doi
1493:doi
1459:doi
1405:doi
1376:doi
1285:doi
1225:doi
1094:PMC
1086:doi
1082:183
1050:doi
825:or
769:hue
680:or
669:or
612:min
608:max
553:or
411:or
134:GIS
121:or
110:or
2108::
2043:.
2035:.
2025:31
2023:.
1998:71
1996:.
1969:.
1943:.
1918:.
1908:85
1906:.
1902:.
1880:.
1827:87
1825:.
1751:69
1749:.
1722:.
1695:.
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