Lecture 10 Mapping Quantities: Dot Density Maps

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1 Lecture 10 Mapping Quantities: Dot Density Maps Introduction Creating maps of features based on quantity are some of the most common and important types of maps. In order to create maps that show quantity you need a quantifiable attribute that is tied to some form of geometry. For the next three lectures we will be looking specifically at area based geometry. In a GIS environment, the software manages and displays the geometry and the attributes, while the cartographer determines which attributes she wants to display and determines how the geometry will be symbolized. There are many different types of maps that display quantitative information, and we will see examples of many of them during this semester, but in these lectures we will play special attention to dot density, graduated symbol, and choropleth maps. Dot density maps use dots to represent specified quantities. In the map to the right, each dot represents 50,000 persons. These dots are based on population within specified enumeration units, in this case states. The more dots, the higher the population California has more dots that Arizona because it has higher population. Graduated symbols display the same data as dot density, but by using graduate symbols within the enumeration areas. That is the bigger the dot, the larger the number they represent. So in this map, California has a larger dot than Arizona because more people live in California. 1

2 Choropleth maps use shaded areas to display quantitative data. Often in the form of color ramps, enumeration areas are shaded based on quantities. In this example, lighter colors indicate lower population and darker color higher population for each state. Again, California is darker than Arizona because it has higher population. Dot Density Maps Dot density maps are part of a larger group of maps that normalize data based on area. Called density maps, they show quantities per area unit as a way of normalizing data to facilitate direct comparison between enumeration units of different size. For example, a large enumeration area may have a larger population than a smaller one, but that population may be sparse because of the larger area. In the map to the right, the color ramp goes from green (lowest populaiton) to red (highest population). In this map population has been normalized by area to create population / square mile, a density measure. The differences between these two maps are obvious. In the map normalized by area, all the high values are attached to the smallest polygons in the center of Tucson, where population is most concentrated dense. 2

3 Dot density maps take a different approach, in that the area measure used to normalize the quantitative measure is strictly visual. That is, the quantity is not divided by the area, but displayed within the enumeration unit. The number of dots per unit provides a visual measure of density. The maps below show both types of approaches for comparison purposes. The map on the left provides absolute numbers based on total population divided by total square miles in each enumeration unit. The map on the right shows a number of dots, each representing 50,000 persons spread throughout the corresponding enumeration unit. The number of dots is based, in this case, on total population for each enumeration unit divided by 50,000. The effect is to provide the map reader a quick, visual impression of population density. Density map based on population per square mile Density map based on number of dots per enumeration area Dot density maps have a number of advantages and disadvantages. Advantages: Easily understood by reader Illustrates variation in density Original data is recoverable More than one data set may be illustrated simultaneously GIS allows the cartographer to change dot value and size combination Disadvantages: Perception is not linear (reader can not depict proportions between areas) GIS randomizes dots within enumeration units; may not be close to the phenomena Large ranges in data make it difficult to select a single dot value in areas of high and low density Of the disadvantages, the biggest problem related to the way that software applications like ArcGIS automate the process of locating dots. This is usually done randomly throughout the enumeration unit. This assumes a random distribution of the phenomenon being measured, something that is almost never true. For example, in the map below population in Pima County is shown as a dot density map, with one dot equal to 500 persons. The random distribution of the dots in this map could 3

4 easily give the wrong impression about population distribution to somebody not familiar with population in the county. Before computer software automated the placement of dots, a cartographer would base the placement of dots on spatial proximity to known data locations. This si not possible with automated approaches, but it is possible to use higher resolution spatial data to create dot patterns that more closely represent actual distribution. In the map below, dot density has been calculated for census tracts in Pima County. By tying the distribution of the dots to the smaller territorial domain of the census tracts, it is possible to see a population distribution that is closer to the actual distribution. But the boundary lines for the tracts create a level of busyness that distracts from the visual impression of the map. 4

5 By removing the boundary lines but retaining the dots, the map provides a much improved visual impression of population in Pima County. This technique of using a different resolution for creation of dot density than that used for display allows the cartographer to automate the creation of the map while retaining greater control over the placement of the dots. Conclusions Dot density maps provide the user with a quick impression of quantities tied to enumeration units. They are relatively easy to create using GIS software, but there are issues with the lack of control over how the dots are distributed. Attention to different resolution of enumeration units can allow the cartographer to create higher quality dot density maps. 5