Dataset Types

MapPoint datasets can be plotted using a range of different map and chart types. This page illustrates each chart type, along with their strengths and possible weaknesses.

MapPoint datasets are plotted either for data points (e.g. pushpins at street addresses or map coordinates), or for areas (e.g. shaded areas for zipcodes or counties).

Shaded Areas

Shaded area datasets are one of the most popular dataset types. These shade areas (e.g. zipcodes) with different color shades, according to a value. For example, the following map shades US States according to their total population (in 2007):

As you can see, the shaded area map is generally clear and easy to understand. Sometimes known as choropleth maps, care should sometimes be taken in their interpretation. For example a large area shape can appear to dominate a map, when it in reality this might not be the case.

Shaded Circles

Shaded Circle data maps plot a circle of fixed size for each point or area. The circle’s color shade is defined using a data value from the dataset. Here is the same population data plotted using a Shaded Circle data map:

As you can see, shaded circle cmaps tend not to be as clear as shaded area maps. They are useful when you wish to overlay one dataset on top of an existing shaded area map without obscuring it.

Sized Circles

Sized circles area similar to Shaded Circles, except the data value is used to set the circle’s size instead of its color:

Sized circle maps can be clearer than shaded circle maps, but you have to be careful when setting their scaling. Incorrect scaling can cause some circles to be too large. These will crowd out other circles and have the potential to obscure some map details. A logarithmic scale is recommended for these situations.

Multiple Symbol

Multiple symbol maps are very similar to pushpin dataset maps (see below), except one of the dataset fields is used to choose each pushpin symbol. This is useful if you wish to plot points (eg. customers) according to a category. The major limitation is that this dataset is limited to eight categories / symbols.

Multiple symbols can also be used for numeric values, eg:

Although the pushpins are not very clear in this example, it is possible to choose different symbols. Generally this map type is better suited to point data and not area data (as above).

Pushpins

Pushpins datasets are similar to the manually-located pushpins we have seen already, but they can include various data fields instead of just “Title” and “Note”. Note, however, that their data fields cannot be edited after they are imported.

The above example shows restaurant locations in the UK. One pushpin “balloon” has been opened up so that you can see the imported data fields.

Pie Charts

Pie chart maps plot multiple percentage (or proportion) values as pie charts for each point or area in the dataset. These are good for percentage or proportion data. The following example demonstrates multiple categories to illustrate the breakdown of US State population by age category:

These charts are ideal when you wish to show how a particular value is broken up into categories, eg. number of flights that are on time; customers in each region by type

Sized Pie Charts

Size pie charts are very similar to conventional pie charts, except their size (diameter) is determined by the total of the slices. They are ideal for situations where you wish to illustrate both proportions and the size of the total. This means they require absolute values (e.g. actual populations) and not pure percentage values.

For our population example, sized pie charts illustrate both the total population and the age proportions for each US State:

As you can see, this can be a very useful map type. As with the sized circle maps, care has to be taken with regard to the size of the largest pie charts. A logarithmic scale is recommended for datasets where there is the potential for a large variation in size.

Column Chart

Column charts are also known as bar charts or bar graphs. These are used to plot multiple values at each data point. However, unlike pie charts, these values do not have to be proportions or percentages.

Although they are ideal for categories (e.g. products sold per region), here we plot the same age data from above:

Although potentially very useful, column charts can create very ‘busy’ looking maps. They are best used in situations where there is a lot of separation between each data point, so that there is plenty of space surrounding each chart.

Series Column Chart

Series column charts are similar to conventional column charts but they are intended for series data and not categories. Hence the individual columns do not have their own colors. They are typically used for time series data – e.g. sales per year.

Here we plot population for the years 1990, 2000, and 2007:

Series column charts suffer from the same ‘busy’ problems as conventional column charts.