Maps are an excellent means of presenting statistical information. Not only are they visually attractive, they also:

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1 Statistical Maps: Best Practice Why draw a map? Maps are an excellent means of presenting statistical information. Not only are they visually attractive, they also: make it easier for users to relate data to location; and help users to identify geographic trends in the data, in a way which would be difficult using a chart or a table. When a map is less suitable Maps are for demonstration (i.e. display) purposes rather than as a source of reference material. They are excellent for showing geographic patterns but are poor for providing precise values with which users can do their own calculations. Such reference material is much better presented in a table (although you could do both a map and a table if required). Types of statistical map There are three main types of statistical map: Choropleth (colour shaded) maps. This is the most common type, and is especially appropriate for showing standardised data such as rates, densities or percentages. A different colour is used for each of a number of bands, allowing users to identify which areas have high, low or middling values. Proportional symbol maps. These use symbols that are proportional in size to the values they represent, such that the biggest symbol will fall in the area with the highest value. Symbols can include circles, bars, or objects indicating what is being measured. This type of map is better for count data. Dot maps. Individual events or groups of events are marked with a dot, allowing users to geographic patterns such as clusters. The most famous use of this technique was by Dr John Snow, who mapped cholera deaths in an outbreak in London in 1854 and was able to show that they were concentrated around a particular water pump.* * Note that dot maps are not generally suitable for Neighbourhood Statistics data see note later. 1

2 This document considers some general good practice in mapping, and some particulars relating to each of the main types of map. Design principles for all maps Clear title (supported by relevant footnotes if appropriate). Indicate the geographic areas used. Indicate the time period(s) both of the data and of the currency of the geographic boundaries (eg 1990 data presented to 2005 boundaries). Include a key to explain what is meant by the colours on the map, the different sizes of symbol etc. Ensure the ranges on the key do not overlap (So don t use 0-5 and 5-10, for example. Otherwise, how would you show an area with value 5? Better ranges would be 0-4 and 5-9). Indicate data source. Textual summary. The key message(s) of your map should also be summarised in words. Your map should be as large as is necessary to show everything clearly. If you are showing data for smaller areas (such as local authorities) you will probably need a larger map than if you were showing region-level data. You may not wish to label each of the areas shown on the map as this might make the page too cluttered and obscure the statistics. However, as an alternative, you may wish to consider publishing a separate reference map containing just area names and boundaries. Choropleth (colour shaded) maps Choose an appropriate number of classes for your data, and also how to divide them. There are a number of possibilities here. For example: o Equal ranges eg classes take values 0-9, etc. o Percentiles. For example, you could use quintiles whereby the bottom 20% of values fall into one class, the next 20% into another etc. o Polarised ranges, whereby categories cluster towards one (or both) ends of the range of values. For example, if you were highlighting deprivation you might decide that the most prosperous 80% of areas are one shade, but the most deprived areas are highlighted by having different shades for the worst 20%, 10% and 5% of areas. o Natural breaks in the data. If data values tend to cluster into distinct groups, you may wish to adjust the ranges such that all those areas falling into a particular group are shaded in the same colour. Most Geographic Information Systems (GIS) offer a range of ways of doing this. Make sure that the one you choose is appropriate to the data you are showing. Note that having too many classes is bad practice as it makes the map too complicated and colours harder to pick out. Five or six classes is usually ample. Appropriate choice of colours. There are a number of aspects to this too: o It is best to have a gradation of colour along a range, rather than having completely unrelated colours for each category. Note however that it is not 2

3 easy for readers to distinguish more than four shades of one colour. o In general, use light colours for low values and darker ones for high values. Also be aware that in some cases certain colours may have natural associations - for example, red and debt. o White is generally used to indicate areas where data are missing or unavailable. It is therefore inadvisable to use white to represent any part of your range of actual values. Proportional symbol maps This sort of map is good for showing big and small, and giving an idea of where events are concentrated, but is less effective for comparison across a range. This is because it is very difficult for the human eye to interpret the relative size of two-dimensional objects (although a good key will help here). It is important that the symbols you use are genuinely proportional to the value being mapped. A value twice as high needs a symbol with double the surface area, not a doubling of each dimension. Consider that if you double both the height and width of anything, the area becomes four times larger, which would give a very misleading visual impression. Dot maps Geographic Information Systems (GIS) offer aggregated dot maps, which take the aggregate statistic for a given area and randomly scatter an appropriate number of dots across it. It would be possible to do this with Neighbourhood Statistics data, but you should not do so. Such maps are misleading and are not true dot maps. A true dot map will only mark data at the exact location of occurrence. Neighbourhood Statistics datasets do not generally provide locations of individual data events, but instead aggregate them into standard areas (such as Super Output Areas and local authorities). This means that you will not be able to produce dot maps of Neighbourhood Statistics data. You may, however, produce dot maps based on the datasets containing grid references for the location of services such as doctors, dentists and schools. Map dangers Although maps are a valuable means of data presentation, there are some pitfalls in the way they may be interpreted: They may exaggerate the difference between areas. Suppose the dividing line between two classes on a choropleth map is 20%. Then suppose, for the variable in question, that area A has a rate of 19.95% and B has one of 20.05%. This is very little difference in reality, but their different colouring on the map might be interpreted otherwise. This is a reason why it is often useful to make the data available in a table too. As different geographic areas vary greatly in both size and population, presenting maps of count data (either choropleth or graduated symbol) can be misleading. The human mind will often associate dark colours or large symbols with a high likelihood of the variable in question, but they may have more to do with the simple 3

4 fact that the area has a large overall population. For this reason it is often more appropriate to map using standardised data such as rates or percentages. The Tyne & Wear example on page 7 is a good example. The fact that some geographic units are much larger than others means their colours can dominate a choropleth map. For example, rural local authorities are often large, whereas urban authorities tend to have a much smaller area. This means that if one end of a range of values is common in rural areas, and the other in urban, the colour of the typically rural characteristic might misleadingly dominate the map. Although this is inevitable you can reduce the effect by choosing nondominant colours, and your textual summary might usefully highlight the urbanrural distinction. Be aware of how the areas you use can substantially alter the visual impact of a map. For example, a neighbourhood with a particularly high crime rate might really stand out on a map if the boundaries approximate to those of the neighbourhood. However, if the boundaries used happened to combine the neighbourhood with an adjacent area of particularly low crime, the presence of the hotspot could be lost. Don t assume that the default values suggested by your GIS (for choropleth map ranges, for example) are necessarily the best. Always examine the data and its context to see if it could be presented in a better way, and adjust the settings on your GIS accordingly. Mapping Examples Choropleth map Source: Regional Trends 2004, Office for National Statistics 4

5 Population mapping using proportional symbols Source: Extract from an Office for National Statistics map Presenting count data: Choropleth vs proportional symbol The map extracts overleaf show the amount of spending by overseas residents in The graduated symbol map is preferable as it overcomes the distortion caused by large blocks of colour (eg Northern Ireland, and the Highland area of Scotland). Note too that the first map has a geographically incorrect title (Northern Ireland and the Isle of Man are not part of Great Britain). In addition the note below the key is poorly worded - how should the user interpret the shading on Shetland and Eilean Siar (Western Isles)? 5

6 Choropleth Proportional symbol 6

7 Choropleth maps: comparison of count data vs rate data: Tyne & Wear, Population by Ward, 2001 Source: Office for National Statistics The left hand map uses count data i.e. the number of people in each ward. However, the wards with larger populations are often larger in extent so no settlement pattern is obvious. Much better is the right hand map showing population density from this the settlement pattern is obvious, with towns such as Newcastle, Gateshead, South Shields and Sunderland clearly visible. Selective use of naming on a choropleth map Source: Jason Dykes, 2005 (gicentre, City University, London), with kind permission. 7

8 You wouldn t normally provide labels for areas on a choropleth map they generally add clutter and make the map harder to read. In this case, however, appropriate use of selected labels (and values) provides useful extra information drawing the reader s attention to those wards with a particular crime problem. 8