Quantitative Data Analysis of Choropleth Maps

Choropleth maps are a type of thematic maps that uses a colour scheme of darker and lighter colours to communicate certain information over a location. Today this is a popular map to use. Reasoning to this, this is a visual representation of data, helping viewers understand the data to its geographic area. Although choropleth maps do have numerous ways in classifying data. To comprehend the different classification methods of choropleth maps, for each method explained an example map will be provided of Napier residents who are on the Sole Parent Support benefit and what are the highest qualifications.

The unclassified method involves no quantitative classification measure within the map. Classification here can be understood by seeing what side of the colour spectrum the data is placed. The data is arranged relative to the colour gradient. Viewers can easily interpret areas of the map having data either side of the spectrum, due to their bright colours. Take below for example. The red and purple data are very easy to see and to comprehend its meaning, and to therefore comprehend the situation of the geographic area. Thus, this being a map letting the data speak for itself. However, due to a range of colours possible to be on the map, it can be difficult to translate specific shades and therefore understand the map.

Natural breaks is a optimisation method in which groups are organised so that there is less variation in each class, thus optimising between-class differences. However, limitations occur if wanting to compare this type of map to another. With datasets creating a unique classification solution, difficulties will rise with comparing maps having different data distributors.

Equal intervals divide the data into classes of equal size. Thus, avoid this style if the dataset is skewed to one side or has clear outliers. Data not divided into similar sized groups, empty classes likely will be produced from this, wasting class space with little information inside them. Though do use this method if the data is spread wide across the dataset.

Quantiles create classes of equal numbers of observations. This meaning, below in the map, each colour rank has the same number in their dataset as the other colours. Producing a tidy and evenly distributed map. Although quantiles can tend to group together dissimilar values. Different classes may have a value extremely different to what it should be. It is important to keep the same values together.

Manual classification allows control to the map maker. Anything the map maker wants to be created, changed or deleted is in their hands. The opportunity to make the map however they intend. Yet due to the most amount of personal change, also comes the possibility of manipulation. Important to recognise personal interests of this method.