Choropleth Map Data Classification
A Choropleth Map is also known as a Shaded Map and a Thematic Map
Today, I am going to talk about Choropleth Map Data Classification. This is a very important part of GIS, and this article will help you gain better insight into the process.
Be sure to comment below, if this article was helpful or if you have any questions.
The Problem With Choropleth Maps
Although choropleth maps are appealing to teh casual observer, when they’re created using automated classification tools that exist in most (if not all) GISs these days, they can be meaningless. In order for a map to be meaningful, it must be classified to show data, and the main problem your map was built around.
Let’s say you wanted to create a map showing the percentage of people in towns affected by poverty.
To do this, you found household income information from the census.
Now, in order to get started you may want to use the data classification algorithms found in all GISs these days. These algorithms break the data into classes based on equal data intervals, equal number of towns in each class, etc.
While this is a good starting point to understanding your data, you’ll want to take it 1 step further. In order for the map to be sensitive to your problem – % of people in towns affected by poverty – you’ll need a good understanding of the income figures that define a household as being in poverty.
EXAMPLE
Imagine that in a region of 10 towns (shown below in table 1), the government statistics which determine a poverty stricken town, eligible for funding, was 10% or more of the town’s population with $500 or less weekly household income.
Table 1 shows ten towns, the percentage of households in poverty for each, and 3 classification techniques if the data were to be broken into 5 class intervals.
Table 1: Of the ten towns in a region, four qualify for poverty relief because >10% of households there have low incomes. The double line indicates the government’s 10% cut-off. The colors in this table would be the colors used if the table were to become a Choropleth Map.
The Three Classification Schemes
Equal Interval: There are many examples of a Choropleth Map being represented as percentage statistics that ignore the underlying data. Commonly, people would divide a map into five equal intervals of 20% each. The problem with this scheme is that Hewe and Gillamoor would be included in the first class, Murrayfield and Zumka in the second class, but the remaining three classes would contain no data.
Equal numbers: This classification scheme looks at the total number of towns and puts equal numbers of towns in each class. In our case we have 10 towns and 5 classes, so there will be 2 towns in each class. The problem with this scheme is that it creates three classes for towns that we’re not interested in, and only two classes for the towns that we are interested in.
Custom: Although it’s important for you to experiment with the automated classification techniques when creating a map, custom classifications are usually best. In this case, you’d have one class showing those towns with Very Low levels of poverty (0-9% of households), and four classes (10%-14%, 15%-19%, 20%-24% and 25%-29%). The benefit of this approach is that the towns with >10% of households in poverty are in one class, and there’s detailed classes for the towns that are experiencing higher levels of poverty.
Conclusion
Becoming an expert at classifying a choropleth map takes both time and patience. With the right formula and strategy, it will soon become second nature to you.
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