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Flow maps are a type of thematic map used in cartography to show the movement of objects between different areas. These types of maps can show things like the movement of goods across space, the number of animal species in a specific migration pattern, as well as traffic volume and stream flow. They can also show both qualitative and quantitative data. Flow maps usually represent the movement of goods, weather phenomena, people and other living things with line symbols of different widths. Thus, the use of lines on a flow map is similar to the use of graduated symbols on other types of thematic maps (Chang, 2012).
When properly designed, flow maps are beneficial because they allow cartographers, GIS analysts and map users alike to easily see the differences in magnitude of a wide variety of items across space with very little map clutter (Phan, et al). This in turn allows businesses to see where the majority of their products are going, commuters to see traffic patterns, and meteorologists to see wind patterns.
This article provides a basic overview of flow maps and a description of how they work. It also describes the three different types of flow maps and explains the characteristics and components of a good flow map. Finally it presents several examples of different types of flow maps.
<b>Flow maps</b> in cartography are a mix of maps and flow charts, that “show the movement of objects from one location to another, such as the number of people in a migration, the amount of goods being traded, or the number of packets in a network”
Flow maps can be used to show movement of almost anything, such as:<sup id=”cite_ref-Har99_2-0″ class=”reference”>[2]</sup>
- What it is that flows, moves, migrates, etc.
- What direction the flow is moving and/or what the source and destination are.
- How much is flowing, being transferred, transported, etc.
- General information about what is flowing and how it is flowing.
In contrast to route maps, flow maps show little aside from the paths from one point to another.<sup id=”cite_ref-Har99_2-1″ class=”reference”>[2]</sup>
Beside the flow maps in cartography there are several other kind of flow maps:
- Baker flow map of fluid flows
- Blood flow maps, see history of neuroimaging
- Flow map or <i>solution operator</i>, see random dynamical system
- Process flow map of a manufacturing process
- Sankey diagram in petroleum engineering
- Traffic Flow Maps
- XSL flow maps, see XSL Formatting Objects
A <b>choropleth map</b> (from Greek χῶρος “area/region” and πλῆθος “multitude”) is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income.
Choropleth maps provide an easy way to visualize how a measurement varies across a geographic area or show the level of variability within a region. A heat map is similar but does not use geographic boundaries.
The earliest known choropleth map was created in 1826 by Baron Pierre Charles Dupin.<sup id=”cite_ref-MF08_1-0″ class=”reference”>[1]</sup> They were first called “<i>cartes teintées</i>” (<i>coloured map</i> in French). The term “choroplethe map” was introduced in 1938 by the geographer John Kirtland Wright in “Problems in Population Mapping”.<sup id=”cite_ref-JKW_2-0″ class=”reference”>[2]</sup>
Choropleth maps are based on statistical data aggregated over previously defined regions (e.g., counties), in contrast to area-class and isarithmic maps, in which region boundaries are defined by data patterns. Thus, where defined regions are important to a discussion, as in an election map divided by electoral regions, choropleths are preferred.
Where real-world patterns may not conform to the regions discussed, issues such as the ecological fallacy and the modifiable areal unit problem (MAUP) can lead to major misinterpretations, and other techniques are preferable.<sup id=”cite_ref-3″ class=”reference”>[3]</sup> Similarly, the size and specificity of the displayed regions depend on the variable being represented. While the use of smaller and more specific regions can decrease the risk of ecological fallacy and MAUP, it can cause the map to appear to be more complicated. Although representing specific data in large regions can be misleading, it can make the map clearer and easier to interpret and remember.<sup id=”cite_ref-4″ class=”reference”>[4]</sup> The choice of regions will ultimately depend on the map’s intended audience and purpose.
The dasymetric technique can be thought of as a compromise approach in many situations. Broadly speaking, choropleths represent two types of data: spatially extensive or spatially intensive.
- Spatially extensive data are things like populations. The population of the UK might be 65 million, but it would not be accurate to arbitrarily cut the UK into two halves of equal area and say that the population of each half of the UK is 32.5 million.
- Spatially intensive data are things like rates, densities and proportions, which can be thought of conceptually as field data that is averaged over an area. Though the UK’s 60 million inhabitants occupy an area of about 240,000 km<sup>2</sup>, and the population density is therefore about 250/km<sup>2</sup>, arbitrary halves of equal area would not both have the same population density.
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