Maps are increasingly used as a means of exploring and analysing environmental health data, and these days are almost essential parts of health impact assessment.  Amongst other things, they help to check the quality of the data being used in the assessment (e.g. by revealing unexpected discontinuties or patterns in the data), to develop hypotheses about possible causal relationships or processes, and to test and validate the results of modelling. 

Maps are also important tools at the appraisal stage, for they are useful and powerful ways of presenting results of assessments.  Amongst other things, they:

• communicate information in a consistent and accessible visual form to a wide range of users, since most people have the ability to read and interpret maps;
• show spatial variations in impacts and highlight hotspots and geographic patterns or trends, thereby helping users to understand the inequalities in impact that may occur and who might be more-or-less affected;
• indicate spatial relationships between health effects and causal factors (e.g. sources, pollution levels or socio-economic conditions), and thus help users to understand how impacts develop.

Designing and interpreting maps of health impact

If mapping is to be used , however, it needs to be planned early in the analysis, in order to ensure that the data are appropriate - in particular that they are properly georeferenced (i.e. linked to recognisable geographical features, and/or to a knwon co-ordinate system).  It also has to be remembered that maps can lie (or, to be more accurate, people can easily misinterpret maps).  So great care is always needed both in designing maps (in order to minimise the risk that they will mislead), and in the actual interpretation process (especially when trying to infer patterns or spatial relationships). 

In terms of design, important issues include:

1. The choice of metric.  It rarely makes sense, for example, to map absolute measures, such as excess risk, total number of deaths or DALYs, because much of the spatial variation they show is likely to be due simply to differences in the population size.  Maps of health impact invariably make much more sense when they are based on relative measures or on rates - e.g. average life expectancy, relative risk, standardised mortality rate, or DALY rates.
2. The choice of denominator.  Point 1, above, means that most maps need a denominator.  Often this will be population (e.g. deaths or DALYs per 100,000 people), since the aim will be to compare risks across the study population.  In some cases, however. other denominators are more appropriate.  To show risks associated with road accidents, for example, it may make more sense to use road length or traffic volume (e.g. vehicle kilometres driven) in order to highlight areas where the roads are more inherently dangerous.  Likewise, it may be more meaningful to map the number of deaths per unit area if the intention is to indicate which areas contribute most to the overall burden of disease.  As these examples indicate, different denominators give different messages.  The choice of denominator must reflect the purpose of the map.
3. Zone design.  Information on health impacts is usually best expressed in the form of area data (i.e. averaged or aggregated to a defined set of zones).  In many cases, the zones used comprise irregular polygons, relating to administrative regions, such as census tracts, health authority areas or provinces.  In other cases, data can be represented as a regular grid.  The choice of zone system greatly affects the structure of the map, and the message it conveys.  Irregular polygons, for example, can be useful because they often help to ensure that each area has an approximately equivalent population, thus avoiding the so-called 'small number problem' where rates tend to be very unstable because the areas contain only a small population.  On the other hand, the zones often differ greatly in size, with the consequence that the map tends to be dominated by the larger (more sparsely populated zones).  Spatial patterns and relationships may also vary depending on the choice of zone design (see Zone design systems), so the choice of zone system must always be made on the basis of a clear understanding both of the data and of the purpose for which the map is to be used.
4. Map scale and resolution¹.  In the same way, maps are very sensitive to the spatial scale and resolution on which they are constructed.  Broad scale maps, which cover large areas at a relatively low resolution, may be useful to show general trends, but inevitably obscure much of the detail.  Fine scale maps, at high resolution, retain the detail, but in the process may become difficult to interpret because the user 'cannot see the wood for the trees'.  Fine scale maps may also exacerbate the small number problem, mentioned above, making estimates of rates unstable in many of the areas because the underlying population in each map zone will be small.    
5. Symbolisation and colour.  Care is also needed in selecting the symbols and colour schemes used in maps, for these not only affect how attaractive the map is, but also how easy it is to read.  They may also may carry implict (and unintended) messages: red, for example, is often seen as dangerous or bad, while green is seen as benign or good.  Symbols and colours thus need to be matched to the scale of the map, and the amount of detail it contains, and selected to ensure that they convey information both clearly and without bias.

Further guidance on these and other considerations in effective map design making is given via the link in the panel to te left.  A link to some standard mapping templates, for use with ESRI's ArcGIS, is also provided below.

Mapping tools

A wide range of mapping software is now available, both in the form of fully functional geographical information systems (GIS) and as more specific map tools.   An increasing number of these are designed as Web-based tools, thereby avoiding the need to purchase and instal potentially expensive systems, and facilitating sharing and collaborative analysis of spatial data.   

A tool to help map and present spatial data is also provided, via the link below. 

1.  Note that scale and resolution are different: scale refers to the ratio of the size of the features on the map to their real size; resolution refers to the amount of detail shown in the map (i.e. smallest observable size of feature).  It is thus possible (though not very helpful) to have a fine scale map with a low resolution - i.e. the map would be large, but it would only show large features.  Equally it is possible to have a broad scale map with a high resolution - in this case, the map would be relatively small, but would (try to) show a great deal of detail.  As this indicates, map scale and resolution ideally need to be balanced.  As it further indicates, care is needed in describing map scales.  The terms large and small scale, for example, are often used ambiguously (or simply wrongly).  Technically, a large scale map is one that has a large ratio between the feature size on the map and that in reality (it is large relative to real space): e.g. it may have a scale of 1:10,000, meaning that each 1 cm on the map represent 10,000 cm (0.1 km) on the ground.  A small scale map, conversely,  will be small relative to real space: e.g. it may have a scale of 1:1,000,000, meaning that each cm on the map represents 10 km on the ground.  To avoid this confusion, it is often better to refer to coarse and fine scales, or high and low levels of resolution.