Ch 5: Simplification Flashcards
generalization
Generalization → how maps simplify the shape or number of objects on a reference map
true/false: simplification used only in reference maps
false both thematic and reference maps
dot density
Dot density maps use dots or points to show a comparative density of features over a base map. The dots are all the same size.
proportional symbol maps
Proportional symbol maps use symbols that occur at points across a map, but unlike dot maps, the symbol size varies based on the quantity or magnitude of the thing being measured. Generally, higher values get larger symbols.
choropleth maps
Choropleth maps are among the most commonly used thematic maps. They use varying colors to show measures that are for areas or regions on the map.
cartograms
Cartograms distort the shape of areas to depict the magnitude of the attribute being measured (represent some variable). A relatively high value within a typically small geographic unit like a state will be depicted as disproportionately large on a cartogram because the size of the region is based on its attributes and not its actual size.
flow maps
Flow maps show the movement of goods, people, and ideas between places. Usually they depict the size of flows by changing the width of the lines connecting places.
density maps
Density maps depict the concentration of point measures. You can think of this map showing how each location spreads out its presence beyond its immediate location to include adjacent areas.
dot map
On a dot map, each dot represents a fixed quantity. For one-to-one dot maps each dot represents one object or person
E.g. John Snow’s famous map had one dot for each reported death from cholera around the Broad Street pump.
Alternatively, one dot can stand in for multiple objects or people
dot map useful for…
Useful for visualizing patterns of clustering and density
Don’t require colour
cons of a dot map
-Privacy can be an issue for one-to-one maps.
E.g. you don’t want exact locations known when mapping sensitive subjects such as where patients with STD’s live.
To get around this, dots are often displaced from their actual location
By simplifying the number of dots on the map, many-to-one maps avoid privacy problems but instead face the challenge of where to place the dots.
Dots are generally positioned at an average location of the multiple objects represented (use equal area projection to avoid distortion)
proportional symbol map
This type of map adjusts the size of simple symbols proportionally to the data value found at that location
The larger the symbol, the “more” of something exists.
Proportional symbols can be used to represent data at precise locations (points) or data averaged over a geographic area.
pro of proportional symbol map
A key advantage of this type of map is that the perception of data value is not affected by the size of the area that the symbol represents.
In choropleth maps, states with small geographic areas (such as Rhode Island) may be overlooked even if they have a large data value.
By contrast, the sizes of symbols in a proportional symbol map are not tied to the land area.
con of proportional symbol map
The downside to this is the greater likelihood of visual clutter.
Symbols may overlap if locations with large values are close together.
As in the figure below, the relative sizes of symbols can matter
If you choose symbols that are overall too small, it will be more difficult for the map reader to see patterns in the data (top left) but if they are too large, many symbols will overlap and make it difficult to see patterns in the data (top right).
Ideally, the symbols have a slight overlap between symbols in the most crowded area of the map (bottom) without there being so much overlap that symbols are hidden.
problem with overlap
This problem of overlap in proportional symbol maps can get to the point where people have trouble accurately comparing symbol sizes
Many people underestimate differences in symbol size, especially when the difference is large.
The proportional symbol map maker must strike a balance between having a range of symbol sizes and limiting their overlap
choropleth map
-Areas are shaded using hue or value to represent different quantities
Darker hues or values signify larger quantities
Easy to make and interpret
Can be misleading if incorrectly standardized or if the geographic phenomena being mapped are not intrinsically tied to the areas being shaded
E.g. rainfall totals, soil type, and length of a commute do not vary according to county or zip code boundaries.
Phenomena being mapped rarely change abruptly at human-defined boundaries as they appear to do on a choropleth map, and there may be a lot of variation within an area symbolized with a single color.
When working with choropleth maps, a mapmaker must try to maintain important patterns while simplifying unnecessary complexity
standardization
Important consideration in thematic mapping, is whether data visualized as a count (# of people) or as a density (# of people per square mile)
Primary reason to standardize data is to allow map reader to compare places that are very different in terms of size or shape
Comparing a large place like Russia to a smaller place like Ireland is only really possible by looking at population density instead of population total
Russia has far more people than Ireland but has a lower population density because it is so large.
classification
Classification can be used to simplify a wide range of values into something that can be more easily interpreted by the map audience.
Rather than symbolizing each data value with a unique hue or size, values are grouped together into a smaller number of categories.
There are many classification schemes – methods for breaking up the data into these categories
equal interval
Using the equal interval method, data is split into classes that have an equal range of values (e.g., 0-100, 100-200, 200-300, and so on)
Equal interval is easy to interpret and to compare with other maps in a series.
However, it does not work well for all data distributions. If there are gaps in the data values, some classes may be empty
If the data are strongly skewed or has outliers, you may end up with a map where almost all areas are in a single class.
Equal interval works best when data are relatively evenly distributed between the minimum and maximum value, and there are no outliers.
quantile
-Data are split so that there is an equal number of observations in each class.
For example, if you have 100 cities and 5 classes, there would be 20 cities in each class.
This method yields attractive, visually balanced maps and can be useful if you are working with ordinal data or those that are ranked (in this case from largest to smallest).
Because it puts the same number of observations in each class without reference to the value of those observations, quantile sometimes groups very different values in the same class (e.g. 0-11,12-21,22-33,34-70,71-2961).
This effect is especially notable with outliers, or especially low or high values that are out on their own.
If one were to create a quantile classification that is technically exacting, it could put observations with the same value into different classes; however, mapmakers will often manually change the classification so that observations of equal value are not separated, which makes it more like a natural breaks classification (below).
natural breaks
The natural breaks method attempts to maximize differences between classes and minimize differences within classes
There are multiple algorithms for how to do this, usually by putting breakpoints where there are the largest gaps between observation values.
This method works especially well for data with clusters or outliers.
cons of natural breaks
One drawback to natural breaks is that it establishes unique breakpoints for each dataset and thus is difficult to use if you need to make a comparison across multiple maps (e.g. population change in a city between 1970 and 2010).
number of classes
In addition to selecting a classification method, mapmakers need to decide how many classes or categories to divide the data into
Having only a few classes can hide important details and draw attention to geographical patterns that are not actually there
Having too many classes, however, can make a map confusing
With more classes, it can be hard to distinguish between different colors, increasing the likelihood that values in the legend will be misread
There is no ideal number of classes that will work for every choropleth map. It depends on what you are trying to convey and how your data are distributed
generalization
Simplifying data and information is also important when making reference maps, a process known as generalization
Especially necessary on small-scale maps
E.g as you zoom out on Google Maps, it becomes increasingly impractical to show small details like residential streets
Even if you wanted to include every building and street name, objects eventually will be too small to be displayed on your computer screen
A mapmaker has to choose what features of the map are most important to include and what can be simplified
