UNITED NATIONS STATISTICAL COMMISSION CRP.1 and ECONOMIC COMMISSION FOR EUROPE ENGLISH ONLY

CONFERENCE OF EUROPEAN STATISTICIANS

UNECE Work Session on Communication and Dissemination of Statistics (13-15 May 2009, Warsaw, Poland)

Guidelines on the Presentation of Statistical

Prepared by Thomas Schulz, Swiss Federal Statistical Office

1. Why use maps?

An introduction All numbers that statistics meticulously measure and the findings of its many surveys and censuses do not happen just somewhere, in an empty space. They might take “place” in a specific country, in a region, in a city, in a commune, in a city block, in a building, or at some precise spot on earth – but all of them have one thing in common: a spatial relation, which is stored in statistical databases. Sometimes, this relation is more apparent, while using sophisticated spatial data bases in a geography department. Sometimes, this dimension is but one out of many dimensions in a general publication database.

In fact, geography has always played an important role in the collection and dissemination of statistical data. It has even contributed to the emergence of the modern day census. With new technologies for convenient spatial data collection and storage and an increasing awareness of an interested public that uses navigational tools and Internet applications like Google Earth basically every day and at ease, geography has gone beyond scientific inventories and school books. You only need to switch on your T.V. or browse the Internet for election results, weather forecasts or the last aviation accident – geographic location and knowledge are part of our life and thus belong into every statistical publication.

Maps and atlases convey geography and describe these spatial relationships. When carefully designed and adequately presented, they are more than just a decorative element in a publication. They serve as valuable decision-making tools for experts, politicians and the general public alike and meet a growing demand in all parts of society. Just as the adage “a picture is worth a thousand words” portrays, it can be stated that “a is often worth a thousand numbers”. In our visual era, maps can become a very powerful means in the information process and have advantages over other elements of statistical dissemination.

1 The power of maps: 4 elements of statistical publications showing the same topic.

Source: Swiss Federal Statistical Office (FSO). Apart from showing spatial patterns and locations (their primary function), which might not be apparent from looking at data tables alone, they are very concise and easy to comprehend. They help to understand an ever growing volume of information – at a glimpse, within seconds of watching it. Maps are a synopsis. They sum up voluminous data from tables. They translate long and complicated textual explanations. And they can integrate dozens of graphs (e.g. pie or age pyramids) for single regions or periods into one clear image. During the age of paper publications, maps have often been neglected by statisticians for not portraying exact numbers. This disadvantage has been overcome by modern electronic displays. Nowadays, interactive maps and mapping tools allow everyone to retrieve the actual data “behind the map” by clicking on or passing over tinted areas with a mouse. And multimedia animations have opened every possibility to combine time and space in map time series as well.

Characteristics and purposes of map use in statistics Maps can serve various purposes in the preparation of censuses and surveys as well as in analysis and reporting of the results. They can be an inventory and support description, exploration, analysis, confirmation, tabulation or decoration. If at least one of the following applies to your situation, the usage of a map in a publication should be considered:

• Maps locate statistical results and give a clear image of the spatial distribution of a phenomenon. Where was the highest temperature recorded last summer? In which county did the Democratic Party get its best results 2008? • Maps enable comparisons and answer multiple questions: between different areas in one map, between different topics in different maps, between variables for the same area in one map or between time periods. Has the population density increased in my district over the last 50 years? Have other districts been affected differently over time? • Maps confirm and validate statistical findings. Is it true that our population is aging, as the report says? • Maps and geographic information systems store spatial knowledge. • Maps support textual and tabular information that are only difficult to explain. • Maps have a synoptic character and summarize large amounts of information. They reduce complexity. It can not be assumed that any reader will ever go through a table

2 with 5000 population data values for a large country like the U.K. or Germany? There is less value in the rows of numbers shown by a table than possibly in one single map. Here, users can easily detect patterns and clusters of high and low concentrations. • Maps convey a concept or an idea. They are a democratic means, easy to understand and can help to better communicate a whole statistical programme and thus contribute to the overall acceptance of for example a population census. • Maps are colourful and have a general positive image. They appeal to the user’s curiosity and attract attention to a publication. They are hung up walls or handed over as gifts in the form of paper or DVD atlases.

Situations when not to use a map Nevertheless, there are circumstances when maps should be omitted in publications. This is the case, when there are other favourable solutions that can portray the same information. Or when technical restraints (space, colour, electronic technologies used) hinder the map from communicating properly. Some examples:

Not suited for map display: Data only on national level.

Source: FSO.

• Data is available only for one or a small amount of spatial units. For example, it is not advisable to create a map if the number for the gross domestic product exists only for the whole country, without any regional breakdown. This is, unless one has other country data to compare it with and can put it into an international context.

Good example: Graphs for 26 cantons exist and can be converted into a map.

Source: Swiss Federal Statistical Office (FSO).

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• There is no significant variation in the data. When figures for almost all districts show that the death rate last year has only been between 1 and 1.5%, then it is fairly stable all over the country. Specific spatial patterns for analysis will not be found. • The user groups have not been defined or it can not be assumed that potential users understand maps or graphs easily. Sometimes also accessibility (esp. for visually impaired persons) can be a reason to do without maps. • The publication will only appear in black & white. Maps do only unfold their real power once they can use colours. We live in a digital age, and this should apparently no longer be a problem for Internet and electronic dissemination. Surprisingly, even on the Internet, many black & white maps can be found. • There is not enough space in a publication. Maps portray space and they do need space themselves. Containing a hundred regions or more, they can take up to half a page in an A4 publication. If you have only a few centimetres left at the bottom of a page or 200 pixels in width on your web page, then maps should not be integrated.

Bad example – where a map is not the best solution: Proportion of native Hawaiian and other Pacific Islanders in the total population of the United States (map below).

Not even 10% of the counties contain detectable information. 90% of the map is virtually empty. Apart from one single county (green), all other areas belong in one class (0.1 – 0.9%) and can not be differentiated visually. Solutions: use a table for the few counties or a regional map for the Western part of the Unites States.

Source: U.S. Census Bureau, Mapping Census 2000.

4 2. Maps and Atlases

A short functional classification Maps are one of the oldest means of communication. Over time, a large variety of different styles and methods have been developed by cartographers and other scientist. They can be classified according to scale, function, design, production technology, or the way they are used in a publication. In general, there exist two functional types of maps:

• General reference (topographic) maps : support orientation in space and show locations of a variety of different features, such as water bodies, mountains, coastlines, roads. They help readers to detect the boundaries of specific geographic areas, such as provinces, cities, counties or health regions. • Thematic (statistical) maps : are used to show the spatial distribution of one or more geographic attributes. A is always designed to serve some purpose and answer specific questions. Readers can view all imaginable data themes in thematic maps such as political, social, cultural, economic, agricultural or natural phenomena that exist on the earth’s surface or even below or above in space (e.g. lunar maps).

General reference maps are used in statistics only for the preparation of censuses or the attribution of data – whenever something has to be oriented in space. Parts of their content are also used as base information for thematic maps. Maps that display statistical data are more relevant for publications and called thematic maps . In fact, statistics and censuses have largely stimulated the development of new methods in thematic mapping. The terms statistical and thematic maps can be used almost synonymously. It is estimated that 85% of the existing maps are thematic maps.

General reference map Thematic map

Source: swisstopo. Source: FSO.

Technological change Maps and map production have been at the forefront of a substantial technological change during the past two decades. It was not long ago that maps have been available only as static images on paper and could be produced by only a handful of experts in an administration. This situation has changed completely. Today, almost all maps are computer-based, designed, stored and disseminated electronically by using more or less sophisticated software. In general, this change has had several impacts for itself. Just some keywords: the development of expert systems and content management systems for atlases, time animations, multimedia atlases, Internet access and the proliferation of colour in maps.

5 Cartographic tools are available to everyone and every institution at sometimes high cost or sometimes just for free. There is a plethora of tools that a dynamic cartography society offers to the interested audience. Almost week by week new applications with new features appear on the scene. There is on the one hand the classic geographic information system (GIS), which offers a broad range of analytical functions and integrates map components, which many students get to know (or fear) already at school or universities. There are statistical software packages that contain (more or less rudimentary) mapping functions – often more suited for quick sketch display than quality publications. And, especially for thematic maps, there is a bundle of high-end cartographic information systems (CIS) on the market that aim directly at the professional map and atlas maker in statistical offices. Following the introduction of Web 2.0 concepts, numerous interactive mapping tools are available online, based on Flash, SVG or Java technologies. They allow users to upload their data and retrieve thematic maps instantly. These functions are also offered by more and more statistical offices, who open their tools.

There is no tool that is suited for every situation. The decision for a tool depends on many factors, including already available infrastructure and data bases, the desired output (print, DVD, Internet), the range of possible uses within an office, or just how many staff is going to be involved in the preparation of maps or atlases. But it can be concluded: while the production of thematic maps is now cheaper and faster, there is no automatic guarantee that the resulting map is well designed and communicates your message accurately. In fact, the increase in available software and the number of maps produced has lead to some poor designs and an increase in mistakes in maps. Even professional GIS packages do sometimes not chose the correct representation method for some data types.

A short technological classification The map output (as the user can view it) can also be distinguished by technical criteria. It is important to have the differences in mind, as the desired format often influences design concepts and the choice of a tool. Unfortunately, it is often the other way around. Firstly, maps can be static or interactive . Static maps – on paper or as electronic images – cannot be edited by the user. They are consumed as the mapmaker produced them. Interactive maps offer flexibility and give the user a far more active role with all possibilities to alter the design, select and retrieve data, animate the map, change the topic or focus on points that really interest him. Ultimately, the information gain is much higher.

Maps can also be image or data based . If a map is solely image based, the map is pre- produced and static in display. Modern data based maps store all relevant information that are necessary for the creation of a map in a data base, i.e. data and relevant metadata. The map is latent and only calculated once the user orders it via Internet. This has the advantage that both, data and map parameters, can be updated and changed continuously without going through the time consuming mapmaking process every time.

An important difference for map output is also the decision between raster image formats and vector file formats . Raster image formats, such as JPEG, GIF, TIFF, have for long dominated the available online maps, as they are often small in size and can be attached to an email or universally be integrated in web browsers. Their disadvantage: they store all image information in tiny dots in a grid – no matter what the original object looked like. Thus, they could hardly be used for intelligent interactive maps. With the advances in browser technologies and preinstalled plugins (Acrobat reader, Adobe Flash player) vector file formats, such as SWF, SVG, PDF, began to dominate the scene. They can store object information and their attributes for every single map element and reproduce it at every scale without loss of information. Vector formats paved the way for modern interactive maps and atlases.

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Raster image map Vector image map

Source: FSO.

Statistical atlases Is the spatial component the centre of your publication? Do you intend to publish more than, lets say 10 maps about a certain theme? Do you want to show spatial patterns on various analytical levels and developments over a long period of time? Do you want to promote your theme or the results of your survey with a remarkable publication that is well received and used by a broad public and will also be remembered for more than 5 years? Then you should consider the preparation of a thematic or statistical atlas.

Atlases have been widely used already in the 19 th century by statistical offices to give the publication of their results a boost and make them more user-friendly. The classic statistical atlas comprises the census atlas or the graphical-statistical atlas (Central Europe). After a long break and decline in maps caused by WWI and WWII, statistical atlases have re-emerged in the latter part of the 20 th century. Many offices have seen their potential and issued popular census atlases or special topic atlases (on population, health or economy).

In its simplest form, an atlas is a bound collection of maps. Mostly prepared in a didactic way with the help of experts – this separates them from dashboards or online data bases. From the beginning, thematic atlases have presented statistics in a comprehensive way and have been accompanied and enriched by valuable information in the form of text, graphs and tables.

Atlas technologies have significantly improved over the past 10 years or so and are nowadays true multimedia champions that combine all output elements used in statistical publications and can even include animations, sound, video and virtual reality. Modern online atlas information systems (AIS) allow the user to fully explore the data behind the maps, click on regions, “tailor” their own maps, integrate their own data and communicate with the map author or office. Behind the scenes, also new production processes have evolved that make it easier to integrate different professions like mapmakers, graphics designers, text authors, translators, or data specialists by offering atlas modules and creating a hybrid environment for both, printed and online atlases. For example, the Swiss Federal Statistical Office has recently introduced an Atlas Content Management System (CMS) that allows the fast production and up-dating of a variety of multilingual online and offline atlases for the office. For more information: http://www.flashmapped.com .

7 Some examples of modern statistical atlases:

Canada - Atlas of Canada: http://atlas.nrcan.gc.ca/site/english/index.html

France – Discover France: http://www.geoclip.com

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Switzerland – Atlas of European Regions: http://www.atlasofeurope.bfs.admin.ch

Austria – Online Atlas: http://www.statistik.at/OnlineAtlasWeb/start?action=start&lang=EN

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IMF Data Mapper: http://www.imf.org/external/datamapper/index.php

3. Types of thematic maps Thematic cartography knows a great bundle of different map types. These include area, dot, proportional symbols, diagram and flow maps. Some of the types more commonly in statistics are explained in more detail below.

The nature of statistical data in maps Before choosing from any of the proposed types of maps it is crucial to make sure of the nature of the underlying data. Some types of maps are only suited for discrete values, others may be favorable for showing relations, while multiple variables can demand for a or diagram map. In any case, mapmakers must be careful with too much information on a map or suggest phenomena with variables that have no correlation.

A first distinction can be made for discrete or continuous phenomena . Most statistical findings have a discrete character. These are aggregated data assigned to a point or area. Most phenomena influenced or caused by man, e.g. population, economic or social figures, contain discrete values. Most natural geographic phenomena, however, are continuous. Elevation, precipitation or temperature, for example demand isarithmic maps.

Another distinction comes from relative and absolute data . Absolute data are count data, such as 1242 people counted in a certain district. They can best be displayed with any kind of symbol maps. Relative data are standardized ratios or rates, derived from raw/count data. Examples are population density or any growth rates. They are best displayed in area maps.

Another important type of data should be clearly pointed out as well: nominal data . Nominal data represent qualities or features that cannot be counted. Typical examples are geological patterns, types of communes, language areas or types of farms. They are represented by areas without using classes or by symbols without applying a proportional scale.

10 Last but not least, the number of variables used for a map has to be thought over. Maps with one subject are called univariate . Examples: biological farms, deaths or rainfall. Bivariate mapping portrays the correlation of two variables. For example, population density can be combined with the work possibilities or the number of houses. The use of more than two datasets leads to multivariate mapping. An example for a is the positioning of a municipality in the context of the three economic sectors.

Example: a trivariate (multivariate) map showing 3 economic sectors.

Source: Swiss Federal Statistical Office (FSO).

The above map shows the correlation of the primary, secondary and tertiary sector for all 2715 Swiss communes by using three clearly distinguishable colours for each sector. Is a commune tinted in green, it is predominantly agricultural. Red indicates a prevailing industrial sector, while blue hints at a service oriented structure. By using gradual shades, communes with no clear attribution to either sector receive another colour in between.

Area maps Area maps are used to show the distribution of data within an area, defined by either natural, geographical or administrative boundaries. The most common thematic area maps are "choropleth" maps (Greek: choros=area; pleth=value/multiple). In choropleth maps, areas are shaded in proportion to the value of the statistical data being shown, such as population densities, unemployment growth, election results or per capita income. Data used in choropleth maps comes from derived (relative) data. They provide an easy way to visualize patterns across space once classes and colour shades are defined.

Unfortunately, choropleth maps are so common with cartographic software and GIS, that they are frequently used inappropriately or even wrongly. Choropleth maps are NOT suited for real- world continuous phenomena that have no underlying boundaries. Furthermore, choropleths are not to be used for raw or count data (absolute values) as arbitrary sizes and shapes of some areas lead to the misconception of values on the user-side (cf. Section 5.).

11 Examples: informative choropleth maps of the United Kingdom.

Source: Cancer Atlas, Office for National Statistics, United Kingdom.

Nominal data can also be displayed with area maps. These maps often depict typologies or area qualities and are less frequently used than the ordinary . The legend shows no classes, but often detailed text information to describe the nature of a shaded area. Another example would be language or ethnic maps. Languages or ethnic groups can not be scaled or classed. In these maps usually different colours define areas with predominant language or ethnic groups. As these are very sensitive topics, one should pay enough attention not to use strong or weak colours for one group. Manipulating their visual impact.

12 Example for a nominal data map: the 22 structural types of Swiss communes in 2000.

Source: Swiss Federal Statistical Office (FSO).

The 22 types are all assigned with different colours. No classes or shades can be visually derived. It is the intention of the map to show that some communes have e.g. an agricultural, some a touristic or others a clearly urban structure. None of theses “qualities” can be measured or scaled using statistical numbers.

Isarithmic and isopleth maps These types of maps, also known as contour maps, differ from choropleth maps in that the data are not grouped according to a pre- defined spatial unit. They depict continuous data and are ideal for showing gradual change over space. These maps can take two forms:

• Lines of equal value are drawn such that all values on one side are higher than the "isoline" value and all values on the other side are lower (isarithmic map), or • Ranges of similar value are filled with similar colours or patterns (isopleth map).

Temperature, for example, is a phenomenon that should be mapped using isoplething, since temperature exists at every point in nature (is continuous). Yet it does not change abruptly at any point, as population density may as you cross into another census zone or state.

13 Example : Isopleth map, precipitation 10 th June 2000.

Source: Barcelona Field Studies Centre.

Dot maps Dot or point maps show the location and density of numerical values using symbols. They allow readers to quickly comprehend the general magnitude of the numerical data and the concentrations and dispersions of the data. The dot symbol represents a discrete value, usually one entity. For example, one dot stands for one death or one divorce. But dots are not required to show single values only. They can also stand for a larger number of entities, such as a 100 people, 200 telephones or 500 trees. Note, that automatically designed dot maps, especially for aggregated values, distribute dots at random and do never give the exact position of data but merely an approximation.

Example : Dot maps from the U.S. 2000 Census.

Source: U.S. Census Bureau.

The three maps of the North Carolina coastal area (United States) are common dot maps. The dots for population, housing units and seasonal housing in the 50-mile coastal are distributed on the base maps as closely as possible to the actual location of the numerical data they represent. The maps show that the seasonal housing units are tightly concentrated along the coastline, while the population and housing units are much more disbursed. This is a good example of how well-constructed statistical maps can reveal insightful geographic patterns.

14 Proportional symbol maps Also known as graduated symbols, these maps represent data with proportionally sized symbols that show their differences in magnitude as existing in the real world. Proportional symbols are ideally used to display raw or count data, such as the number of deaths, the number of unemployed or the number of votes for a certain party. Using relative, derived data by applying different colours or shading for symbols, presents no imminent danger to this method. But it is more common to use choropleth maps for these purposes.

Basically any geometric shape can be used to create proportional symbols. Apart from well-known circles, also squares, triangles or even pictograms, such as telephones or cars, can be varied in size. The shape can also be used to indicate two different directions. For example, a triangle up indicates growth, a triangle pointing down indicates decline. All symbols are either attached to s specific point or an area (usually the centre or capital). They can also be split up into more complex symbols  chart or diagram maps.

Example for a pictogram map: Fixed line telephone subscriptions in Macedonia 2001.

Source: State Statistical Office of Macedonia.

15 Example : Population growth in the Swiss cantons 1992-2002.

Source: Swiss Federal Statistical Office (FSO).

This map uses proportionally sized triangles that represent the absolute change in population in a canton over ten years. Triangles in red and pointing upwards indicate a growing population. Triangles in blue and pointing down indicate a shrinking population.

Figures in 3D: Some new software, like Google Earth, thoughtlessly allows the creation of 3D symbols as well. Usually proportions for symbols are calculated by applying the numbers to the radius, diameter or area of the different symbols. For example: 100 people equal 5 mm in radius or 200 people equal 10 mm in radius. 3D figures use the volume of cubes or bowls to scale figures. Caution should be exercised here. The human perception for different volume sizes is poor. Most people can precisely say that one rectangle is twice as large as another rectangle in surface. But they cannot interpret two cubes. Volume symbols are cool and definitely raise some attraction for your publication. But they do not convey statistical content correctly. You can play with these methods to analyze user perception, but they should always be in the context of other, more exact representation methods.

Bad example for showing correct proportions: 3D or prism map.

Source: Google Earth.

16 Chart or diagram maps Diagram maps combine individual graphs and maps and have become popular and easy to design with modern cartographic and geographic information systems. They are used for multivariate data and wherever charts or graphs (chapter 5) are available on a regional breakdown, so that they can be located and combined to a map. Diagram maps are also referred to as complex symbol maps. Common types of diagram maps are pie charts, bar charts or age pyramids. Legends for diagram charts are more detailed. They have to indicate the variable responsible for the size of the chart and every single variable use in the pie or for each bar. Diagram maps are more often used to represent areas instead of point phenomena.

Note: complex diagrams on maps should be used with great care. Apply them only if there is a small number (10-20) of geographical regions in your map and if the number of categories within the chart is small. Maps and legends become easily overloaded using this method.

Example : Agricultural accounts 2008.

This interactive map portrays agricultural accounts for the Swiss cantons in 2006. The total value for each canton is represented by proportional symbols. Adding information on the categories of animal production (cattle, pigs, egg and dairy production), the symbols have been turned into pie charts. The exact values behind the map can be retrieved by passing over the individual regions with a mouse.

Source: FSO.

Flow maps Statistical phenomena are in reality often not bound to a location, where we want to count it. People are on the move. Commuters travel every day to work, goods are transported along railway lines, planes fly to exotic destinations. And, more often, there are slow long-term movements, which want to be put on a map: migrations. The ideal method for all these topics is a . Arrows or lines are used to show where movements take place. They can be varied in size or shaded with colours to show the magnitude of each movement.

Flow maps are not easy to design, not all software packages can handle the often complex data structures behind these seemingly simple thematic maps. And as with diagram maps, the same guidelines apply: flow maps can rapidly become overloaded with information if one tries to show all movements. For example, showing all possible commuter traffic between the 3000 Austrian communes would result in billions of arrows. A careful selection of the relevant movements must be made before starting with this map.

17 Example: Truck flows to Texas in 1998.

Source: U.S. Department of Transportation.

Combinations of map types Map types can under certain circumstances also be combined. But this should only be done, if it facilitates or increases the message and improves the understanding for the user. This is usually only the case when area maps are combined with dot, proportional symbol or diagram maps, so that the symbols can overlay shaded areas. It should not be applied if there is merely too much data around and too little space for more maps in a publication. And it should not be applied if both topics have nothing in common or if the graphical representations conflict visually with each other and the clarity and legibilty of the map is at risk. In this case, two single maps would often be the better option.

One scenario, where it makes sense to combine two methods, is the supplementing of choropleth maps with proportional symbols. For example, absolute population figures (symbols) can be added to a population density (area) map. This helps interpret the density map. Area maps have the disadvantage, that they portray events in often given and heterogeneous regions. Human visual perception of dark or light colours showing ratios/rates can be misled by the size and shape of regions. To avoid misinterpretation, absolute symbols can be added and thus show the actual importance or relevance of the tinted areas. There are two ways of combining absolute and relative data: by either superimposing symbols to the areas, or by integrating the colours and shades into the symbols itself and leaving the areas without colours at all. The latter method is preferable. It does not cover area information but still gives a precise image of distributions and their relevance. It can also be applied to nominal data. Unfortunately, gradual shading of symbols is often awkward with some tools.

The maps below show the same: the outcome of a federal vote for Northeastern Switzerland. Green indicates approval, purple disapproval. Both combine proportional symbols (number of valid votes) with relative data (approval rate in colours). In the first map the symbols for the number of votes simply overlie the coloured regions. This is already better than just displaying areas. In the second map, the approval rate has been directly integrated into the symbols. This example shows how carefully you should design your map and how large the room for misleading is. By interpreting the first image, one might come to the conclusion, that there was no clear decision during the Sunday vote. But the second image shows the actual result: an overwhelming approval by the majority of people (not the majority of regions).

18 Example: two methods of combining absolute and relative data in a map.

Source: FSO.

Example: combination of absolute and nominal data

Source: Swiss Federal Statistical Office (FSO).

This map is another good example and proves how nominal data (qualities) can well be added to proportional symbols. It shows the 4 types of agglomerations or rural areas according to their size and relevance, given by the absolute population figures (circles).

4 Thematic map design

How to design a “good” map? To disappoint and encourage readers equally: the one “good” map does not exist. But there are many good approaches, and we can try to get as close as possible to a correct map and avoid the most obvious mistakes. Mapmaking is a mixture of art, science, and technology and, just like taking a photograph, it is not a mechanical procedure. Maps are comprehensive graphic essays subject to individual creativity and technical restraints. Map design is a complex

19 task, when taken seriously, since there are unlimited possibilities for organizing the visual display, and compromises are inevitable.

Before beginning overhastily with a map or an atlas project, you should take yourself enough time and consider all possible constraints which the production process and the final product are likely to face. This is easy to say, but try to anticipate all steps during the production, develop an imagination of the result and what the audience should really find useful about your map. Nothing is more disappointing than investing time and producing the map just for your own interest without being able to communicate its intended message. The following questions can be a guidelines to what external controls exist in map production:

• What is the purpose, the message of the map? • How and in what situations will the map be used? • Who is the audience? • Are there accessibility constraints for the audience? • What is the nature of the data (nominal, relative, absolute)? • Are all data available and accurate enough? • How many variables should be mapped? • Is there a temporal dimension attached to the data? • Existing technical restraints, such as black&white reproduction or format? • Existing conventions, such as for colours or classifications? • Are the symbols and colours aesthetic and generally well conceived? • Time and cost restraints for data collection, production and dissemination?

Criteria for a “good” map Empirical studies on map use and perception have helped to establish some principles and recommendations for good maps over the last decades. Quality in thematic maps can be identified in general when:

• The map is has a clear and concise message. • The message is understood easily and fast by everyone. • The map is simple and contains no redundant elements. • The map can stand for itself without further explanations. • The map is correctly adapted for its output format and audience. • The data has been translated correctly. • The data is current and correct. • The adequate representation method was chosen and applied. • The visual hierarchy of important and less important objects has been achieved. • The map is as objective and neutral as possible. • The map is aesthetical and attractive. • The map uses a standard layout (esp. in atlases or map series). • The map layout is coherent with other elements in a publication. • The map is accessible for visually impaired persons.

General design tips When it comes to the overall composition of the map, all these principles apply together. Maps are made of several components. Already in the beginning, while still collecting data, you should outline a sketch of the final layout and make sure you know all components. To know the titles, legend titles, the nature of the data, the sources or regional structures before commencing production will save a lot of time and prevent you from last minute stress before the final stage of dissemination. Never change fundamental features of the layout within one map series or atlas.

Thematic maps in presentations are designed to complement a text, a graph or a table (or the other way around) by illustrating the key findings as they relate to real distributions and patterns. The most important recommendation is, as for all visualizations: Keep it simple .

20 This can not be overemphasized. Keep the content and the layout simple to get your message understood. Too much information on a map or too many visually conflicting elements can confuse the reader and distract him from your map.

Secondly, as mentioned before, know the audience well . What is their background? Are they familiar with maps in their every day life? Can they access the information physically? Could your map offend them in any way? It is your responsibility to know the sensitivities of different user groups. The visual impact is strong, and colours or symbols might have negative connotations for some people. An appropriate map language should try to avoid these.

Maps should be designed so they have context without being dependent on the story text or the statistical tables. They should speak for themselves. It is wrong to believe that your map will always remain part of the collection or presentation you make it for. Once your map has been published, journalists, scientists, or interested people will scan your map, download it, use it for a Powerpoint presentation. They will take it out of its context for sure. Therefore, it is extremely important to add all necessary components described below.

Example for a map that can easily be understood without further reference.

Source: U.S. Census Bureau.

This map is well designed for highlighting the projected path and flood potential of Hurricane Ike, which struck the United States on September 13, 2008. It can be understood without further explanation. However, there are several improvements that could have been made. The map title would have been improved by adding "Hurricane" before "Ike's." The Census Bureau word mark in the lower left corner is distracting readers from the content and could have been omitted. Finally, there is no scale bar, and no legend symbol for the storm's track. This all explains how the following map could have been better, but the map itself isn't labeled as a bad example or a map that could have been improved.

Elements of thematic maps An effective thematic map is made up of several components that play well together. The central part of any layout is the actual map information and a carefully base map containing boundaries, water bodies or elevation information. The map usually requires 80-85% of the total surface and should be centred in the layout. Other essential elements are:

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Source: State Statistical Office of Macedonia.

1. Titles : there are main titles, subtitles and legend titles. The lower the titles are in hierarchy, the longer they can be. A main title is short and concise. 2. Legends : are indispensable for the comprehension of any map content. They are the guide to understanding what transformation the actual values have undergone during the process of symbolization. 3. Dates, sources, credits : give information on the accuracy and credibility of the map. 4. Footnotes, explanations : can give additional information for interested users about other sources, calculation methods or about anything else that has no place in a title. All these information should be legible, but not too prominent in the map. 5. Regional labels : place names attached to points or areas support orientation, but should not be too prominent as to avoid distraction. 6. Geographic units : should be mentioned, so that the user clearly knows if he sees data for state, district, county or any other possible level. 7. Copyright : information should indicate the author and responsibility for the content. 8. Map scale : helps the user to measure distances and compare different maps. 9. Frames, lines : separate all map elements but should be attached with caution. Too many lines and boxes tear the content apart and distract the reader.

Some elements add extra information, but are not indispensable:

• North arrows : are only necessary if the map is not oriented towards north. It is a convention that, unless otherwise stated, all maps face north. • Grid, graticule : with latitudes and longitudes are only necessary on small scale world or continental maps. • Location maps : are small replicas of base maps and position the actual map content in a larger context for users who are not familiar with the geography of a region. • Graphs : such as histograms, age pyramids, or pie charts can be added, if they improve the understanding of the map and if there is enough space available.

22 Text Although maps communicate visually, the few text lines they contain are of great importance. A few key words in the title or legend can be everything or nothing and decide if a user understands a map or not. With little space available they should be thought over carefully. Some recommendations for the usage of text in maps:

• Again: keep it simple and be precise. 5-6 words are enough for a good map title. • Use a language that everyone in the country can understand. • Use a text, so that the map can stand alone and is always understood. • If you need more text to explain your titles, create a footnote using smaller font. • Avoid redundancies: don’t repeat the same words in the title, legend and notes. • Avoid things that go without saying and are clear, for example “Map of …” or “Legend”. • Leave extra space if you plan a translation. Text in other languages can be longer. • Make sure translations are coherent and say the same. • Avoid abbreviations that are not commonly used. • Be neutral and avoid adjectives that classify messages. • Start with capital letters.

Bad example: Death rate from brain deaths in Europe 2003/2005.

Source: Swiss Federal Statistical Office (FSO).

The map above is a bad example for a few things that can go wrong with text. The title, simply translated from the French original text, is just incomprehensible and aims at doctors or specialist – but not the general public. It is doubtful that more than 5% of the population has ever heard of cerebrovascular diseases. Most dictionaries haven’t. Furthermore, the title is too long. A short title like “Brain deaths” would have been enough. And parts of the title are repeated twice: in the legend and the footnotes in the box to the left.

Legends There are whole books written about rules for data classifications and correct map legends. A few basic principles for the construction of the most common legends in area, symbol and diagram maps shall be mentioned:

23 Area and proportional symbol legends

• If you have two legends or more (combined maps), begin with the more important, dominant variable. • Leave space between legends and between their components. Text should not overlay areas. • Many software, especially common GIS programmes, will get this wrong: but start with the lowest class from the bottom and pile up the boxes until reaching the top. Most users familiar with graphs and charts imagine lows at the bottom and highs at the top, just as on thermometers or stock charts. • As to how many classes your map should have, there is no clear answer. It mainly depends on the number of spatial units and the frequency range of all values. Use rather less than too many. • Class borders should be unambi- guous. One value can only belong to one class. Avoid ranges like 100- 200 and 200-300. Imagine the user guessing for hours, which actually colour equals 200 people! • Avoid inexplicable interruptions unless special analysis demands it. If a class range ends at 9.9, the next class should start at 10.0. • Legends for discrete values show ranges (10.0 - 14.9) – the boxes / areas in the legend are labeled. Boxes have spaces inbetween. • Legends for continuous values stress class borders (10.0). The boxes are continuous as well (no spaces) and the borders labeled. • In class legends the lowest or highest value can be indicated at the bottom or top boxes. • Symbol legends should at least display the maximum value. They can also show the minimum value. • Indicate areas with no available data of, if you can’t show them (for examples out of data protection).

Sources: FSO.

24 Diagram map legends

• Do not use more than 5 or 6 variables in one chart. The human eye is normally incapable of differentiating more colours or the proportions of pies and bars. • Colours should alternate visibly – don’t let orange follow red and then use yellow for the next. Stronger colours (red, purple, brown etc.) should be used for small pies. Lighter colours for larger pies. • Use colours and not black & white textures. • If using pie charts, start with the first pie at 12 o’clock. Readers know clocks and can more easily measure the proportions then. • Don’t create any subcategories. If you want to split one category, create a new chart for this purpose. • And finally a simple rule for statisticians: all pies should add up to 100%. You wonder in how many maps they don’t!

Source: FSO.

Colour Colour is one of the most powerful graphic variables and omnipresent in today’s electronic maps. The human eye can distinguish extremely well between them, but colours can also influence and mislead human perception easily. The choice of colour in thematic maps depends on the type of data and map, as well as to how much a variable can be measured.

Continuously scaled variables such as population themes, economic developments, ratios and percentages are shown with a distinct ordering (ordinal values). Using the same hue (A), differences in colour value, from light to dark, are associated with magnitudes of a dataset. This is the most common usage for colour scales. Too many classes can lead to the use of a second or third colour (B) at the other end of the scale. 10 values or more of the same colour could not be differentiated any more. But still, these colours are continuous from bottom to top

25 and more or less belonging to one “family”. Two different, indeed opposite colours are used for clearly diverging data that has a break in its range (C). Examples are developments that can have a positive and negative magnitude such as regional migration figures or votes that have a breaking line at 50%, when public opinion switches from Yes to No. Nominal data showing qualities demand clearly distinguishable colours with no order but an equal intensity in the eye of the reader (D). No differences shall be made between various languages, ethnic groups or religion. Each of them is represented by one “neutral” colour.

Note: before applying colour schemes all too quickly, think of two aspects: are there conventions or associations with certain colours? And can all readers access the message with the colours used? In fact, map authors are never completely free to choose just any colour they like. There are hundreds, if not thousands of sociological conventions that we learned from parents or school atlases. And each of us bears associations in mind with colours.

For example, we are not free to use a colour for the Democratic Party that we like: the convention is blue. While the Republican Party is always represented by red. These are man- made conventions that all maps follow, but cartographers have to be aware of them. And other countries have other conventions. In most European countries red stands for social democratic or labour parties. In historical maps it represents the British Empire, in elevation maps it shows mountains, and in economic maps it usually indicates decline.

One consideration when using colours is colour blindness. Many people cannot distinguish between some colours. The most common case is red-green blindness. So, if you use these two colours to show a difference between regions such as growth and decline, colour blind people will not see it. An easy solution: replace red by purple, and these users can see the difference again.

5 Avoidable mistakes – some poor map examples Wrongly designed mapping software, inappropriate data, human error or unawareness can lead to poorly designed maps and violate the quality criteria set up – under favourable circumstances. In more severe cases they destroy the message of a map, lead to wrong economical or political decisions or have serious consequences for a map user, if, for example, his navigational software guides him to the wrong street. Many mistakes are avoidable. The following examples show some common mistakes in thematic map construction.

“Absolutely” wrong – count data in area maps

26 As simple as it looks, the population map above contains many mistakes. The most dangerous is the use of count (absolute) data in areas where it should use symbols. Choropleth (area) maps cannot adequately portray absolute numbers, as arbitrary and random sizes and shapes of given areas lead to the misconception of values. Choropleth maps are suited for rates or percentages, such as population density. Rates help to standardize data, often in relation to area or area-related variables, and help to make them comparable.

For example, Algeria or Morocco look heavily populated compared to countries like the U.K. in fact, the Algerian population is only about half of the U.K. population. But Algeria is much larger in size and filled with the same hue, which leads to misinterpretations. On the other hand, Switzerland, with one of the highest population densities in Europe, seems to be very empty. Other mistakes are in the legend. There is no concept for class borders visible, and it can be doubted that the largest European nation has 983 million inhabitants. This value rather refers to India than to this map title.

The following illustration shows what happens, if the same data (12 and 7 values in two different areas) are represented using a relative or an absolute method.

Redundancy – the information “overkill”

27 Some people need to view data twice to understand its message. Maybe also in two different ways. But three times? The map above clearly shows an important topic. But it manages to portray the same information three times: it appears in the area, in proportional symbols, and finally, the numbers are written down. Why not adding rates or percentages instead?

Empty maps - missing information

Where there is too much information in one map, others leave the user with a guess. What does is mean? Is there no data available? Or does the author try to hide it from us?

The map of Poland (although it doesn’t say it) on the left has no title, no legend title, no scale, no copyright. But its legend is definitely rich. It abounds in classes and colours. But another guess: why do two of them not appear?

The life expectancy map below does not really lack data or information. It even contains a north arrow! Interestingly, it points the user to the missing elements by even emphasizing them: it is not to scale. Ok. But worse: if you expected life expectancy data for Antarctica, you will be disappointed – no data available!

28 “Classical” problems

The population density map on the right has but one mistake: it suggests that population is a continuous phenomenon by labeling class borders. This is not the case – population figures are discrete values. Correctly designed, it would indicate ranges and leave spaces between the colour boxes.

The life expectancy map below is an interesting example for unfortunate colour schemes and for problems with designing a class legend. The legend shows no clear order. But what remains in the dark is the method with which the classes where selected. They show all different intervals from 7.1, 1.0, 0.5, 0.4, 0.7, to 8.2. This makes a statistical comparison between the coloured areas almost impossible.

Colour playground

The intention of the map to the right is good. It wants to use a bivariate colour scheme for diverging data – data that has two directions: positive and negative. Unfortunately, colour perception was not thought over. The colours for the highest and lowest values are so dark, that they seem to be the same. Yet in the middle of the scheme an abrupt break becomes evident, when blue turns into yellow.

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These two maps feature the same problem: colours for discrete values are not gradually changing from one class to the other. In the map of India above, purple suddenly turns into green, before switching to light orange. The map of California on the left has so many different colours, it could well be used for showing nominal data, such as geology.

Problems combined

The map above seems to combine most mistakes previously mentioned. It is missing important information such as a grid or a scale. On the other hand, texts are redundant (same title and legend title). But more severe is the designing of the legend: there are no precise class borders. Many values appear in two classes (100, 200, 300 …). The classes also start too

30 high. 80% of all countries are in the lowest class. And here comes the worst and fatal mistake: this very class is coloured in grey (usually reserved for “no data”), which presents a visually empty map to the user, giving him the impression, that population density is only a problem in Central Europe and Eastern Asia. Last but not least, the green colour for Bangladesh is striking the eye of the observer – as apparently the only country with more than 500 people per square kilometer (which is incorrect). Contrary to all conventions, green is used here to show a higher density (500-1000) than red (200-300).

Composition of elements – visual disorder On June 15, 2008, the U.S. Federal Emergency Management Agency (FEMA) designated 24 counties in Iowa as flood disaster areas. Without this context, the map is unreadable. The map has no title, legend, scale bar, source or explanatory footnotes. Because the text does not provide any contextual details, one has to assume the purple lines are the city boundaries of Des Moines. Or a highway? The only unambiguous design elements for American map users are the lakes and the red Interstate highways with route number shield symbols. But something went wrong with map. Where are the disaster areas? Why are the Interstate highways so prominent that they overwhelm the rest of the map? Because a clear visual hierarchy is missing.

Thomas Schulz / 05.05.2009 ThemaKart, Swiss Federal Statistical Office.

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