Chorophletic maps are provided to present relations, also called ratio numbers, in between two values. In most of the instances the denominator is one area. However, proportion numbers can additionally be calculation from two non area-related values, but the pure numbers somehow need to be in relation with their recommendation area, together it is styled depending on the worth of the proportion number. An example of the first-mentioned variant is the population per square kilometre (population density) on neighborhood level. An instance for the second variant is the ratio in between protestants and also catholics on ar level Choroplethic maps (Witt 1967, p. 186).

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Choroplethic map according variant 1 (Institut für Kartografie 2004)
Choroplethic map according different 2 (Imhof 1972)

Choroplethic maps space unsuitable because that the symbolisation of graded pure values. Castle distort the blog post of the map because the map viewer instantly takes into account the dimension of the areas. In this case big areas through a low pure value acquire too much importance conversely, the prestige of little areas v a high absolute value is also small. For the explicate of absolute worths diagram maps need to be used (see the complying with section). A comparable case occurs once the denominator of proportion numbers is no the equivalent area. The figure above shows, which confession dominates in a specific area. The map tempts the viewer to analyze a high absolute worth for big areas with a high density.

Characteristics that choroplethic maps

Representation The areas, to which the ratios room related, are styled with various fill colour or patterns. The much more dense an object is spread within an area the darker the colour or the much more dense the pattern need to be.

The following figure shows an instance of a choroplethic map. Through a click the map you acquire to the matching interactive map. ~ above its basis you deserve to get acquainted with the features of choroplethic maps.

Interactive quantitative choroplethic map (Andreas Neuman, IKA ETH)

Value classes

The ratios or densities are usually classified in worth classes. Locations get the same styling if your values room in the same class. The optimal number of classes varies for each dataset. To preserve a great readability that the map, the color of the classes need to be clearly distinguishable.

Types the choroplethic maps

Choroplethic maps are classified by their recommendation areas:

Choroplethic maps based upon administrative areas. Dasymetric maps show densities that areas about geographic properties. Choroplethic maps in relation to geometric areal devices such as grid squares.

Below all three map types are defined in details.

Choropleth maps


The presented densities in these maps are family member to offered administrative areas such together communities, districts, cantons or also countries and continents. The following number is an example of this map kind and depicts the population density that Switzerland on district level.

Population thickness of Switzerland on ar level (Institute of Cartography 2004)

The choice of the bureaucratic reference area, the worth classes and also class borders play critical role throughout the mapping process. V a specific an option of this parameters the appearance and significance the a map can be highly influenced. Density differences deserve to be emphasized or suppressed.

Advantages and also disadvantages the choropleth maps


statistical data deserve to be visualised conveniently as castle are generally related to bureaucratic areas.


Choropleth maps i think a constant density within the shown area. If the actual object thickness varies, the expressiveness that the map gets distorted. For the example of the populace density this is the instance in mountain regions, whereby the lived in area is usually only a portion of the bureaucratic area. The following instance demonstrates this problem: the most thick area is no visible, since it gets cancelled the end by less dense areas within the same bureaucratic area. to compare of different datasets from different years are possibly complicated because administrative locations may vary in time (communities deserve to be consolidated or separation up etc.).

Dasymetric maps


Dasymetric maps are likewise called "choropleth maps according to the geographical method" (Imhof 1972, p. 167).

In comparison to choropleth maps this map type does no use bureaucratic but geographical areas which are created by considering the real distribution of the objects. The following part of a map depicts a dasymetric visualisation. Click the figure to watch the totality map.

Soruce: (Spiess 2004)
building methods

There exist several approaches to build dasymetric maps. Many of them usage dot maps together a basis. In-depth explanations the the different building and construction methods can be uncovered here (only accessible in German).

Advantages and also disadvantages the dasymetric maps


In comparison to choropleth maps dasymetric maps allow a an ext precise representation of locations of the same density. over there is no hazard that an ext dense and also less dense locations cancel each other out and areas through a high thickness are no much longer visible.


The resulting map depends on the cartographer The developing of dasymetric maps is time and also cost spend The boundaries of the reference areas can be uncertain. Dasymetric maps are not suitable for big scales since the uncertainties throughout the building and construction of the reference areas are as well high. direct comparison between different periods is not possible.

Choroplethic maps in relation to geometric areal units such together grid squares


Choropleth maps with continuous patterns framework are likewise called Choropleth maps follow to the geometric method (Imhof 1972, p. 171)

The reference locations are built by a consistent network of similar polygons prefer squares, triangle or hexagons. The size of the polygons may vary depending upon the scale and the dataset. Conceivable space hectare or square kilometre grids or even more comprehensive meshed grids. The tighter the grid, the an ext representative is the resulting map. If a square grid is used, it should be aligned come the national coordinate grid. Furthermore it permits an development to added areas.

The square net is the simplest and also most widely used form of grids. The following instance shows a choroplethic map utilizing a square grid and its straightforward data.

Point data with works with are preferably provided as a basis. The Swiss commonwealth Statistical Office offers some raster data to adjust (hectare net of the populace density) which facilitates the preparation of such maps.

In the following visualisation you deserve to see a choroplethic map based on a square grid. It depicts the percent of shrubland every 4 km2 in Switzerland in the years 1992/1997.

Advantages and disadvantages of choroplethic maps in relationship to geometric areal units

Below the benefits and disadvantages space listed. Advantages

that is very basic to compare certain areas because of the constant grid. The comparison of various periods is possible because the reference areas do not change in time. because of the continuous reference locations this an approach allows the visualisation of pure values.


many data are not raster data. The data collection procedure for raster data is expensive. The density values might vary depending on the placing of the raster, particularly if the mesh width is large. Thus a little mesh width is recommended. Natural borders (e.g. Wood line) are no much longer recognizable due to the raster based representation.

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Generalization that choroplethic maps

For the generalisation that choroplethic maps different methods have the right to be used individually or in combination with every other. In any kind of case it is crucial to respect the minimum dimensions, which are depending upon the following factors: form, colour, pattern and also contour of the area. The following number gives one overview.

If girlfriend get below the minimum dimensions generalisation is needed. If administrative locations are used, the is thought about to summarize the data ~ above a greater administrative level (summarize areas to districts, etcv). If inhomogeneities occur, that is also feasible to group details areas. Too small but important areas can it is in enlarged while keeping an analogous shape or aggregated with bigger neighbouring areas if the density value is just related to settlement areas. In the second case the thickness value needs to be recalculated. In the instance of choroplethic maps in relation to geometric areal systems the mesh width deserve to be increased. E.g. Four squares space summarized come one and also the value is recalculated. In addition the variety of classes can be reduced for any choroplethic map. The border of the density areas have to be simplified and smoothed according to the scale. For choroplethic maps in relation to geometric areal units this is no necessary because the geometries are currently simplified. In general it has to be taken into consideration that the fundamental type of the locations should not obtain lost during the generalisation process and that initial structures should be preserved.