Design principles for maps usingarchive.stats.govt.nz/~/media/Statistics/about-us/stati... · 2014-08-12 · 6 1 About Design principles for maps using New Zealand’s statistical

Design principles for maps using

New Zealand’s statistical data

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Citation

Statistics New Zealand (2014). Design principles for maps using New Zealand’s statistical

data. Available from www.statisphere.govt.nz

ISBN 978-0-478-42913-8 (online)

Published in August 2014 by

Statistics New Zealand

Tatauranga Aotearoa

Wellington, New Zealand

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Contents

List of figures ...................................................................................................................... 5

1 About Design principles for maps using New Zealand’s statistical data ............... 6

2 About maps .................................................................................................................... 7

Importance of displaying spatial relationships ................................................................. 7

Basic characteristics of maps .......................................................................................... 7

Cartographic workflow ..................................................................................................... 8

Basemaps ........................................................................................................................ 9

3 Designing a good map ................................................................................................ 11

Criteria for a good map .................................................................................................. 11

Web accessibility principles ........................................................................................... 11

A map is not the only option .......................................................................................... 12

Different types of maps .................................................................................................. 12

Map projections ............................................................................................................. 13

4 Characteristics of statistical data .............................................................................. 14

Types of data ................................................................................................................. 14

Displaying differences.................................................................................................... 14

Distinguishing classes ................................................................................................... 14

Normalising statistical data ............................................................................................ 14

Privacy ........................................................................................................................... 15

5 Classifying data ........................................................................................................... 16

Using a histogram to help decide categories ................................................................ 16

Choosing class break points .......................................................................................... 16

Classification schemes .................................................................................................. 17

6 Cartographic symbolisation ....................................................................................... 19

Visual variables .............................................................................................................. 19

Colour schemes ............................................................................................................. 20

Sequential or diverging colour schemes ....................................................................... 21

7 Mapping methods ........................................................................................................ 23

Graduated symbol maps ............................................................................................... 23

Proportional symbol maps ............................................................................................. 24

Choropleth maps ........................................................................................................... 24

Dot density maps ........................................................................................................... 25

Bivariate and multivariate maps .................................................................................... 26

Multi-panel maps and map series.................................................................................. 27

Design principles for maps using New Zealand’s statistical data

4

Continuous surface maps .............................................................................................. 28

Cartogram maps ............................................................................................................ 29

8 Generalisation and map scale .................................................................................... 30

Generalisation techniques ............................................................................................. 30

Map scale ....................................................................................................................... 30

9 Map design ................................................................................................................... 32

Legibility ......................................................................................................................... 32

Visual contrast ............................................................................................................... 32

Figure ground ................................................................................................................ 34

Visual hierarchy ............................................................................................................. 35

Balance .......................................................................................................................... 35

Map marginalia .............................................................................................................. 37

10 Web mapping ............................................................................................................... 39

Characteristics of web maps ......................................................................................... 39

Design considerations for web maps ............................................................................ 39

Making web maps .......................................................................................................... 40

Compiling web maps ..................................................................................................... 40

11 References.................................................................................................................... 43

5

List of figures

Figures by chapter

2 About maps

1 Transforming spatial data to appear on a map ............................................................ 8

2 Purpose and audience .................................................................................................. 9

3 Three basemaps used by Statistics NZ: neutral grey, photographic, and topographic ............................................................................................................... 10

3 Designing a good map

4 Thematic map showing percentage of population aged over 65 years in 2011 ........ 13

5 Classifying data

5 Histogram showing the number of times the same value occurs in the data layer ... 16

6 Examples of different classification techniques applied to the same data ................. 17

6 Cartographic symbolisation

7 Variables to consider when visualising points, lines, or polygons within a map ........ 19

8 The components of colour – hue, value, and saturation ............................................ 21

9 Statistics NZ’s colour palette ...................................................................................... 22

7 Mapping methods

10 The population of area units represented by circles sized according to their population ................................................................................................................. 24

11 Comparing population totals for area units with the population density in the Bay of Plenty .................................................................................................................... 25

12 New Zealand’s population density ............................................................................ 26

13 Bivariate mapping from the GeoVista interface. ....................................................... 27

14 A continuous surface showing the density of dwellings ........................................... 28

15 Comparing standard map and cartogram ................................................................ 29

8 Generalisation and map scale

16 Understanding map scale ......................................................................................... 31

17 StatsMaps header shows which geography will be visible at a particular scale ..... 31

9 Map design

18 Examples of legibility ................................................................................................ 33

19 Examples of contrast ................................................................................................ 33

20 Examples of figure ground ........................................................................................ 34

21 Examples of visual hierarchy .................................................................................... 36

22 Examples of map balance ........................................................................................ 37

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1 About Design principles for maps using New Zealand’s statistical data

The purpose of Design principles for maps using New Zealand’s statistical data is to help map-makers and analysts who use Statistics New Zealand’s geo-statistical information – geographic boundaries and statistical data.

If you apply these principles, your maps will have more impact, tell the right story, and be a fair and accurate representation of the data.

We examine the most common types of maps and give guidelines to help you produce good maps. The guidelines include the necessary elements of a map for informative and release purposes. It is important to note that not all elements are compulsory on every map. The choice of elements is based on the purpose of the map.

The most important factors to consider when you create a map are that the map:

is user friendly and easy to read

portrays a clear message without any confusion

is not over complicated – only include extra data sets for a defined purpose

is aesthetically pleasing and well balanced on a page.

This document has been developed in collaboration with Aileen Buckley of ESRI, Mairead de Roiste of Victoria University of Wellington and the New Zealand Geospatial Office.

The map principles are based on Buckley’s New Zealand presentation on design principles for maps using statistical data (2012a). She is the lead of the Esri Mapping Center), a website dedicated to helping users make professional-quality maps with ArcGIS. She has more than 25 years of experience in cartography and holds a doctorate in geography from Oregon State University. She has written and presented widely on cartography and GIS.

http://mappingcenter.esri.com/

http://mappingcenter.esri.com/

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2 About maps

Find out about maps and the:

importance of displaying spatial relationships

basic characteristics of maps

cartographic workflow

basemaps.

Importance of displaying spatial relationships Geographic information is an integral part of all statistical data. Geographic areas have boundaries, names, and other information that make it possible to locate them on the ground and connect statistical information to them. This spatial relationship is particularly important for displaying data from the Census of Population and Dwellings.

Statistical data is often broken down by one or more attributes, such as industry type or age group. Statistics can also be broken down by geographic area. For example, population counts are always attached to a geographic area. Industry types and age groups may also be different depending on which part of the country people live and industries are based.

Maps are the most efficient tools to visualise these spatial patterns and help people identify and highlight distributions and patterns that might not be apparent from tables and graphs.

Remember, you can use maps for more than presenting results. They can also be useful when you plan your survey and when you analyse your results.

Consider using a map if you want to:

show the geographical location and spatial distribution of your data

compare different areas

summarise a large volume of data and reduce their complexity

communicate a clear message

validate your findings

attract people’s attention

explore possible spatial patterns in your data (eg are similar values in adjoining areas)

engage people with the information presented

store spatial information in a geographical information system (GIS).

You can produce a map as your sole output but you can also provide a map in conjunction with other more traditional statistical display methods, eg tables and graphs.

Basic characteristics of maps All maps:

are concerned with two primary elements – locations and attributes

are reductions of reality – the scale of a map represents a ratio to the distance on the ground

are abstractions of reality – generalisation is required as it is not possible to represent the complexity of reality

are transformations of space – map projections and coordinate systems are used to project the earth’s surface to a flat plane

use signs and symbolism.


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Cartographic workflow Cartographers transform elements of a complex geographic environment into geographical data and information in order for map readers to understand the map’s message.

Geographic data is usually compiled with the primary goal of communication. A successful map minimises the amount of interpretation and analysis the map reader has to perform. In most cases, the data used on a map was often not collected for this specific purpose. Cartographers have to choose from a number of cartographic and data management techniques to prepare data for the map.

Figure 1 illustrates the steps taken by the data collector, cartographer, and map reader to transform spatial data to appear on a map.

Figure 1 1 Transforming spatial data to appear on a map

Transforming spatial data to appear on a map

Source: Buckley, 2012

Many maps portray statistical data. If the map is effectively executed, the map reader will intuitively and correctly understand the statistical data that has been mapped. Judging the effectiveness of a statistical map is easier if you understand the data being mapped and the method used to map it.

Consider your audience

The first thing to consider as a cartographer when you make a map with statistical data is your intended audience. Identify and understand the audience you are designing for and what types of activities the map will be used for as this makes the design process easier and more straightforward. It will also make the map more successful, easier to use, less cluttered with details, and likely be more attractive to the user.

Ask yourself or the requestor: How will the audience use your map? Will they obtain specific or general information from it? Is the purpose of the map to influence decisions? Or is it to motivate people to change their behaviour? Or is it to enable people to obtain specific measurements from it?


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No matter who you are communicating the results of an analysis to, you need to think about the number and type of features to include on the map.

Think about the nature of the phenomena you are communicating about. If you are working with topographic maps you need to understand something about the landforms, hydrography, landcover, landuse, and human features. If you are working with environmental data, you need to understand the physical phenomena and processes. If you are working with demographic data, you should know whether it is aggregated into enumeration areas or discrete samples.

Figure 2 2 Purpose and audience

Purpose and audience

Source: Buckley, 2012

Professionals need little context, as they already understand the environment you are communicating. The general public and policy makers may need more contextual information. For example, policy makers require more context in order to interpret the information because there is often less time to make decisions.

Remove all jargon or technical terms that are not used by the intended audience of the map.

Basemaps Basemaps are used to provide geographic context to the mapped statistical data. They provide a foundation or canvas for your work. They may be general-purpose, such as a topographic basemap, an imagery basemap, or a street basemap.


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Figure 3 3 Three basemaps used by Statistics NZ: neutral grey, photographic, and topographic

Three basemaps used by Statistics NZ: neutral grey, photographic, and topographic

Contextual features that are related to the statistical data can be of particular value to the reader in interpreting patterns, for example a hillshade or shaded relief basemap could be useful in interpreting the pattern of population density in relation to topography.

Ensure that the basemap is not so complex that it limits the reader’s ability to interpret the statistical data. Pick applicable basemaps that will be legible for the map size and medium. Some basemaps such as a grey hillshade layer can alter the colour of other layer classes displayed. Ensure the legend displays the colours as they appear on the map, and potentially reduce the number of classes so they can be distinguished clearly against a detailed basemap (Buckley, 2012).

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3 Designing a good map

Mapmaking is a mixture of art, science, and technology. It is a complex task with many possibilities for arranging the layout. Interactive mapping tools are available online that allow map makers to upload data and generate thematic maps instantly. The production of thematic maps has become much cheaper and faster, but it does not automatically result in well-designed maps that communicate your message accurately.

To communicate your message accurately, consider:

criteria for a good map

web accessibility principles

a map is not the only option

different types of maps

map data and projections.

Criteria for a good map Consider the following four criteria before you design and create your map:

1. The purpose of the map – What is the theme of the map? What information should it convey? Is the data fit-for-purpose? For example, a map of a walking tour will use different data and design from a map used for a street maintenance.

2. Map audience – Define your target audience: How and in which context will the map be used? Are there any accessibility constraints? Who will read the map once it is published? Will it be subject matter experts or the general public? The end audience will help determine the level of detail and depth you include in the data on the map.

3. Map situation – How will the map be used and displayed? Will it be viewed on a computer? Or viewed as a presentation at a conference? Answers to these questions help determine the features required size of fonts, colours used etc.

4. Determine the appropriate mapping technique, colours, and symbols – What is the nature of your data (quantitative or qualitative, absolute or relative values)? Is there any technical constraint (eg format or black and white reproduction)? What are the conventions for colours or classifications?

A good map:

is simple and easily understood

has a clear and objective message

gives an accurate representation of the data and does not mislead

attracts the reader’s attention to the most important information

is well presented and attractive

fits the output format and your audience

can stand by itself without further explanations

is viewable to colour-blind persons (UNECE, 2009).

Web accessibility principles To comply with the NZ Government Web Accessibility Standard 1.0, any static or dynamic map or spatial data you publish on the web needs to adhere to the following web accessibility principles:

https://webtoolkit.govt.nz/standards/web-accessibility-standard-1-0/


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1. Perceivable – Information and user interface components must be presentable to users in ways they can perceive.

This means that users must be able to perceive the information being presented (it can't be invisible to all of their senses).

2. Operable – User interface components and navigation must be operable.

This means that users must be able to operate the interface (the interface cannot require interaction that a user cannot perform).

3. Understandable – Information and the operation of user interface must be understandable.

This means that users must be able to understand the information as well as the operation of the user interface (the content or operation cannot be beyond their understanding).

4. Robust – Content must be robust enough that it can be interpreted reliably by a wide variety of user agents, including assistive technologies.

This means that users must be able to access the content as technologies advance (as technologies and user agents evolve, the content should remain accessible).

A map is not the only option Is a map the most appropriate tool to present your data? Don’t waste your time and effort if a graph or a data table can provide a better way to communicate your message. For example, the results of a spatial analysis or query could be a list of features or sum of areas.

There is no point in mapping your data if:

the data has no location references

there is no significant variation in the data

your target audience will have difficulty understanding your map

there is not enough space available to present the map so it can be properly read and understood.

Different types of maps There are many different types of maps, which are generally classified according to what they are attempting to show. One classification is to consider them as being either a topographic map or a thematic map.

Topographic maps

Topographic maps represent physical features on the surface of the land and sea floor. They help users identify the boundaries of geographic areas. They are also known as reference maps.

Reference maps generally show several types of spatial data without specific emphasis on one type over another. Reference maps can vary in their complexity and size, but generally include just the various geographic features that give a picture of the area being mapped, for instance political boundaries, cities, topographic features, and/or transportation routes.

Thematic maps

Thematic maps represent phenomenon that either unseen, or are highly generalised such as population density or land use, see figure 4.


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Thematic maps can represent qualitative and quantitative data with points, lines, areas, or volumes. They can use visual variables such as hue, lightness, pattern, and shape. A thematic map is always designed to serve a purpose and answer specific questions about political, social, cultural, economic, agricultural, or natural phenomena. Maps can also be classified according to scale, function, design, production technology, or the way they are used in a publication.

Map projections Internationally, many mapping projections are used in cartography. For statistical maps, cartographers choose a projection to reduce distortion of area. Therefore, each area on the map is relatively the same size as it is on the earth’s surface. This allows readers of the map to make correct assumptions about the relative distribution on the ground.

When you create a map of mainland New Zealand (including Stewart Island and smaller coastal islands), use the standard projection New Zealand Transverse Mercator 2000 (NZTM2000). It has a low level of distortion at its east-west extents. The Chatham Islands and Offshore Islands have their own projections (CITM2000, AKTM2000, RITM2000), which you can use, but consider producing these maps in NZTM so they display on the same map as mainland New Zealand.

You can use WGS84 Web Mercator for web maps, but this does cause some distortion in shape. This allows for interoperability internationally but means you have to re-project data held in NZTM or re-project data on the fly.

Figure 4 4 Thematic map showing percentage of population aged over 65 years in 2011

Thematic map showing percentage of population aged over 65 years in 2011, by territorial authority

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4 Characteristics of statistical data

A statistical map is more effective if the map maker understands the data being mapped and the method used to map it. This chapter covers:

types of data

displaying differences

distinguishing classes

normalising statistical data

privacy.

Types of data Maps with statistical data display only two types of data:

qualitative data differentiates between various types of things. Qualitative data are measures of 'types' and may be represented by a name, symbol, or a number code. Qualitative data are data about categorical variables (e.g. what type).

quantitative data communicates a message of magnitude. Quantitative data are measures of values or counts and are expressed as numbers. Quantitative data are data about numeric variables (e.g. how many; how much; or how often). There are two fundamental types of quantitative measurements to map: raw quantities (such as counts) or measures of intensity (such as population density or the percentage of the population who are Māori).

Displaying differences If your map displays qualitative data, use symbols that vary by colour and shape to show differences.

If your map displays quantitative data, you can use symbol variations such as orientation and pattern spacing to display differences; however, consider using colour hue and lightness, and symbol shape and size instead, as they are the most easily understood.

Distinguishing classes Most statistical data portrayed on maps is quantitative data that use absolute values to distinguish classes. Quantitative data can be as simple as a ranking scale such as small, medium, large. They can also use ratios, such as percentages or densities to aid understanding.

Normalising statistical data Statistical geographies differ in size and population, which makes it difficult for people to compare data values for neighbouring areas. For example, one area might have a higher population than its neighbour, but it may also count people over a larger area. You can choose to compare the areas using population density rather than population totals by dividing the population by the area. This is called normalisation.

There are methods within your map making tool of normalising statistical data by using ratios, densities, or absolute numbers. To help you decide the best way of normalising the data consider how the data was collected and processed and whether the data is fit


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for the purpose of your map. For example, Statistics NZ data are collected from households and businesses and then aggregated to other geographical units such as meshblocks, area units, and territorial authorities. This means that the data is then applied to the whole meshblock even if the population lives in a small corner of the area.

If you are normalising the same data for different geographies, for example, aggregating meshblock to area units, and area units to territorial authorities, make sure you use a method that does not alter the spatial pattern or data distribution. This is called the modifiable areal unit problem (MAUP) (see esri Support).

Privacy New Zealand’s Privacy Act 1993 makes it illegal to disclose data that identifies an individual. Since it is possible to use analytical techniques to reveal an individual’s data in some meshblocks (e.g. by performing multiple queries to return a single record or feature), Statistics NZ rounds or suppresses totals for meshblocks with low populations in order to maintain the privacy of people living in the meshblock.

http://support.esri.com/en/knowledgebase/GISDictionary/term/MAUP

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5 Classifying data

If the dataset you plan to use for your map has a large number or range or values, you may need to divide it into categories or classes. Categories separate and simplify the data, making geographic patterns easy to visualise and interpret. You then assign colour and symbols to each class. Consider:

using a histogram to help decide categories

choosing class break points

classification schemes.

Using a histogram to help decide categories The easiest way to classify your data is to look at the distribution of the data to determine where the class breaks should be placed (see figure 5).

Start with a standard classification such as natural breaks, and adjust the breaks to improve the map based on your knowledge of the data and the audience. Some data may have known thresholds that can be used in your classification scheme. For example, there may be a population threshold for urban areas that dictates the amount of funding that should be allocated to it.

Figure 5 5 Histogram showing the number of times the same value occurs in the data layer

Histogram showing the number of times the same value occurs in the data layer

Note: Values between the blue lines are grouped into the same class.

Choosing class break points A map’s appearance is greatly influenced by the choice of class break points, and different classification methods convey very different pictures of the data. Figure 6 shows the same thematic data classified six different ways. None of these classification methods are wrong to use, but depending on the purpose of the map, one could be more efficient or appropriate.


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Figure 6 6 Examples of different classification techniques applied to the same data

Examples of different classification techniques applied to the same data

Use the smallest number of classes that fits the data and map’s purpose. Make sure the reader can distinguish the different classes on the map. They may not be able to do this if there are too many classes and/or their colours are too similar. The Colorbrewer tool provides good colour advice for maps.

Outliers are values at the extreme ends of the data’s distribution that can significantly distort the classification scheme and the map’s look. You could include outlying values in other classes or exclude them from the classification altogether.

Some classification schemes break classes according to the number of geographic features in a dataset. There may be a significant variation in the areas of each feature. For example, you might group 20 percent of territorial authorities into a quintile (5 quantiles). On a national map, the TAs with small areas may not be visible, or be visible enough to have an impact on the map. If this is a problem, convert the polygon dataset to a raster layer – which has evenly sized cells – then reclassify the data using the raster layer.

Classification schemes You can choose to classify your quantitative data by creating your own classes, or you can let the geographic tool classify it for you using one of the standard classification schemes as explained below or see Slocum, McMaster, Kessler, and Howard (2008) for other classification schemes.

http://colorbrewer2.org/


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Natural breaks is a method of grouping thematic data based on obvious gaps in the data. Classes are created wherever the data makes a significant jump. Natural breaks minimises variation within classes and maximises variation between classes. For example, meshblocks that share a colour are statistically more similar to each other than to units in other colour classes.

Equal intervals divides data into categories with equal value ranges. This type of classification works best when the data are evenly distributed. If the data is unevenly distributed, there may be many geographic entities in just a few of the categories, and as a result, outliers may not be highlighted on a map. The equal interval method creates classes such as ‘1 to 100’ and ‘101 to 200’ with identical ranges of 100. You can also exclude the outliers from the map. This should be clearly stated in the legend.

Quantiles creates data breaks so that there is an equal number or nearly equal number of geographic entities in each category. This gives the map an even, aesthetically pleasing colour distribution. Like the equal interval scheme, this method best represents thematic data that are evenly distributed.

Standard deviations shows the reader how much a feature's attribute value varies from the mean; class breaks are created with equal value ranges that are a proportion of the standard deviation, usually at intervals of 1, 1/2, 1/3, or ¼ standard deviations.

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6 Cartographic symbolisation

Once you have divided your data into categories use colour, value, or pattern to create symbols for their values. Consider:

visual variables

colour schemes

sequential or diverging colour schemes.

Visual variables Visual variables are tools to adjust the symbology to show size or degree of difference apparent in the data.

Figure 7 indicates which visual variables are best for showing qualitative or quantitative differences.

Figure 7 7 Variables to consider when visualising points, lines, or polygons within a map

Variables to consider when visualising points, lines, or polygons within a map

Source: Cynthia Brewer, 2005


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Shapes

Use shapes to differentiate points to indicate qualitative differences. For example you can use different shaped point objects to indicate an area’s land use. This may include a sheep symbol to indicate sheep farming and a hay stalk or a warehouse to indicate crops or industrial areas.

Sizes

Adjust sizes of points or lines to differentiate magnitudes. For example, to show greater proportions of people within a region, use larger dots over the area.

Colour schemes Graphics need to be readable when printed or photocopied in black and white. There are many different colour models to choose from when creating a map. The RGB colour model (mixtures of red, green, and blue) is the standard model for viewing on a computer or projector screen. The CMYK colour model (cyan, magenta, yellow, and black) is suitable for printed output.

The same colour can look very different on different computer screens, projectors, and prints. Different, but similar, colours may be distinguishable on screen, but look the same when printed. Some professional cartographers have specially calibrated monitors that match the colours of their screen to the colours that would appear on the medium their map will be printed on – such as glossy or matte paper. They use this when they have to match map colours to a corporate colour or an existing print.

When you create your map, consider the three components of colour – hue, value and saturation see figure 8.

Colour hue

To differentiate qualitative differences where there is no area larger or smaller than the other use colour hue. Use this technique to show areas that are no more or less important than others, just different, for example, ethnic groups. The use of red, blue, green, indicates just different. For qualitative data, hue is going to help see categorical differences.

Colour value

Use colour value and colour intensity to map differences in the data – lightness to darkness. Using colour value, darker will appear to mean more to us intuitively. This is measured by taking a colour, and then adding white or black to it to make it darker or lighter. It is not changing the value of the original colour.

Texture

Texture is the amount of ink (or toner) within the symbol that is being mapped. Texture works very much like colour value, because more ink there is in a symbol gives the impression that it contains a higher numeric value or ranking. This could be such as a land area map identifying the difference between agriculture land and residential land. This may be symbolised using stripes or dots within the polygon

Colour intensity or saturation

Colour intensity (or saturation) is similar to value, in that the greater the intensity, the greater the portion it represents. You apply colour intensity by taking a colour then adding more of the pigment to the base colour, making it more intense.


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Figure 8 8 The components of colour – hue, value, and saturation

The components of colour – hue, value, and saturation

Colour hue – red, green, blue, yellow differentiation. This is what most people call colour

Value/lightness – think of this in terms of a paint bucket. The amount of black you put in to a colour hue can make it appear darker or the amount of white you put in to make it appear lighter.

Intensity/saturation/chroma – this is the amount of pigment you put into colour. For example, if you add lots of blue, this makes your colour choice very saturated. A little amount of blue added makes the colour seem faded.

Sequential or diverging colour schemes Choose either a sequential or diverging colour scheme for your map.

Sequential schemes

Sequential schemes are suited to ordered data that progress from low to high. Lightness steps dominate the look of these schemes, with light colours for low data values to dark colours for high data values. Try to choose colours so you can create differentiation between the classes. Avoid creating a scheme where there is a big difference in the colour when the differences in the data vales are not so significant.

Sequential colour schemes can be in a single hue (eg hue of blue) or they can be in hues that transition (eg transition from blue to green to yellow) – this is called a blended hue and its blending one colour to the next and its best if you choose colours that would be next to each other in the colour wheel/colour spectrum or rainbow.

Diverging schemes

Diverging schemes put equal emphasis on mid-range critical values and extremes at both ends of the data range. The critical class or break in the middle of the legend is emphasised with light colours and low and high extremes are emphasised with dark colours that have contrasting hues (Brewer, 1994).


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A great way to find colours is an online tool called ColorBrewer. This tool enables you to select good colour schemes for maps. You can also pick schemes that work well for colour blind audiences.

Colour palette

Choose a colour palette for your map. A colour palette is a limited range of colours that you choose to use on your map, or series of maps. A colour palette gives your map consistent look, for example, your map’s colours could be the same set of colours your organisation uses in its publications.

Figure 9 shows the four standard colour palettes used to express the Statistics NZ brand colours. The four colour palettes are yellow, green, blue, and grey. All four have various colours within its range that can be added as individual standard colours to represent a whole area or boundary.

Figure 9 9 Statistics NZ’s colour palette

Statistics NZ’s colour palette


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7 Mapping methods

Statistical data can be displayed on a map using a number of different mapping methods. The purpose and audience of the map will dictate which mapping method you should use. Some common mapping methods include:

graduated symbol maps

proportional symbol maps

choropleth maps

dot density maps

bivariate and multivariate maps

multi-panel maps and map series

continuous surface maps

cartogram maps.

Statistical data is often not suitable for making a good map. You may need to convert data onto a form that is suitable for the type of map that best tells the story of the data.

Choosing the best mapping method for your data is the most important decision affecting the design and usability of your map.

Graduated symbol maps Graduated symbol maps use symbol size to portray data values grouped into classes. The legend displays the exact symbols used to represent each class. The symbol is used to represent the data associated with a geographic area or point. The symbols are usually placed at the centroid of the geographic unit they represent.

Strengths of using graduated symbols on a map are that they:

show less variation between symbols, so the reader can directly relate the symbols on the map to the legend

can be used to represent total counts

represent values at a point or area

can represent data with extreme values

can be visually effective

can show data for very small areas that may be difficult to see on choropleth maps

can clearly convey varying spatial density.

Weaknesses of using graduated symbols are that:

they reduce the ability to show the range of data to the same extent as proportional maps

the human eye underestimates the size of larger circles in relation to smaller ones, though you can use a compensation method such as Flannery Compensation to increase the size of the larger symbols

larger symbols can overlap and obstruct smaller symbols, though you can minimise this effect by considering how you order the data. It is important to get the right balance between symbol size being sufficient for the reader to interpret, while not obstructing other symbols

larger symbols can obstruct the boundaries of the geographic area they represent.


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Proportional symbol maps A point or symbol on your map can indicate either the true geographic location of the geographic feature or an aggregation of multiple features (eg one symbol might represent 10 farms). In the latter example, the symbols are proportional.

Figure 10 shows a proportional symbol map, with symbols of different sizes to represent data associated with a geographic area or point.

The size of the symbol is directly in proportion to the numerical value of the data represented. Place the symbols at the centroid of the geographic unit they represent. While proportional symbol maps commonly use circles to proportionally indicate the data value, you can also use squares, where the area is proportional to the length of the side. This may be easier for the reader to interpret. Symbols with an irregular outline are more difficult for the user to interpret. Strengths of proportional symbol maps are that they:

are able to be used to represent total counts

represent values at a point or area

can represent data with extreme values

can be visually effective

may reduce the problem of very small areas being difficult to see on choropleth maps

can clearly convey varying spatial density.

Weaknesses are that:

the human eye underestimates the size of larger circles in relation to smaller ones. Compensation methods such as Flannery Compensation can be used to increase the larger symbol sizes

larger symbols can overlap and obstruct smaller symbols – although this can be minimised through ordering of the data. It is important to get the right balance between symbol size being sufficient for the reader to interpret, while not obstructing other symbols

larger symbols can obstruct the boundaries of the geographic area they represent.

Choropleth maps A choropleth map displays data for an area using colour or shading to represent the data value. This is a commonly used mapping method, but can be misused. Choropleth maps use geographic units such as meshblocks and area units.

Choose a colour scheme that reflects the theme of the map. Make sure the colours you chose are easy to distinguish. Darker colours have more emphasis than lighter ones when set against a lighter background. Using red and/or green hues imply good and bad data values.

Figure 10 10 The population of area units represented by circles sized according to their population

The population of area units represented by circles sized according to their population


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Compare the maps in figure 11, which both use the same population totals. The map on the left is the total population count and on the right is the population density. The population density map shows where people live with a greater degree of accuracy.

Figure 11 11 Comparing population totals for area units with the population density in the Bay of Plenty

Comparing population totals for area units with the population density in the Bay of Plenty

Areas with data that is not significant to the map, or null values, can be de-emphasised by using a neutral colour such as the grey in figure 11.

Strengths of choropleth maps are that they:

are familiar to most map readers

can symbolise measures of intensity to help the reader interpret the data

are widely supported by mapping software

can clearly show broad geographic patterns

can effectively display special classes of data.


they should not be used for representing total counts

the reader may assume data is uniformly spread across individual areas

large areas dominate smaller areas, influencing reader perception even if the smaller area represents data of higher magnitude

class intervals need to be carefully selected and colour choice is critical. These can significantly change the appearance of the map

they are susceptible to the modifiable areal unit problem, which can introduce statistical bias.

Dot density maps Dots can be used to represent data within a geographic area. They are randomly placed and are of uniform size with the number of dots in an area representing the data value. You specify the value that each dot represents, for example 1 dot = 100 people. The sum of dots in a geographic unit amounts to the total count for that area.


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In figure 12, for instance, 1 dot represents 2,000 people. The dot value should be large enough to cater for the range of data values while not overcrowding the map, and small enough to allow for variation to be seen.

The geographic units should be small enough to show the variation in data distribution, but not so small that there are too may geographic units where their small data values mean there are one or no dots.

Include text in the legend for dot density maps that states the value for each dot, for example ‘1 dot represents 100 people’. Also include a visual showing what a high, medium, and low density distribution looks like. This is important as readers usually underestimate densities, especially higher densities. Don’t place a population point on a lake or in a national park.

Strengths of dot density maps are that they:

are able to be used for representing absolute numbers

can be used for data with extreme values

can show geographic patterns more clearly than a choropleth map.


readers may interpret the location of the dot as being the actual location of the data value

dot size and value need to be carefully chosen so that the variation in density can be easily seen over the data range. The choice of these can strongly influence the reader’s perception of the map as to whether the mapped phenomena are sparse or plentiful

they are useful for showing geographic pattern, but it is difficult for the reader to extract the data value of a specific geographic unit.

Bivariate and multivariate maps Use a bivariate map if you want to display two variables of data on the same map such as the population size and the number of dwellings. A multivariate map uses more than two. One method of creating a multivariate map is to overlay graduated symbols on to a choropleth map.

Proportional or graduated symbols maps can use colour, size and shape to symbolise different variables. With smaller symbol sizes it can be difficult for the reader to distinguish the colour and they tend to therefore take more notice of size. Additional symbols can include proportional bar or pie graph, however, this presents a lot of information to the reader, so think about whether this is appropriate for your audience. There needs to be enough space to display the charts legibly. Limit the amount of classes shown in your charts, do not use more than five for pie or bar charts (United Nations Economic Commission for Europe, 2009).

Figure 12 12 New Zealand’s population density

New Zealand’s population density

One dot equals 2,000 people


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Bivariate choropleth maps are suitable for more advanced audiences because they are harder to interpret than choropleth maps with symbols overlaid (see figure 13Error! eference source not found.). Most readers will notice the extreme values, as the mid-range values are less distinctive.

You can create multivariate dot density maps with different colours to represent each variable. Be careful not to overlap the dots, as this can obstruct the mapped variables. Choose colours carefully, so they are easily distinguished.

An example of bivariate mapping can been seen in figure 13, where two variables are classified into three classes and assigned different hues. That combination makes a total of nine colours that appear on the map.

Strengths of bivariate/multivariate maps are that they:

can display a lot of information

are useful to highlight relationships that exist between variables.


it is more difficult to make map contents legible, especially for black and white maps

they can be harder to interpret, consider the audience when choosing if this method is appropriate

symbols can obscure the information that is underneath it.

Figure 13 13 Bivariate mapping from the GeoVista interface.

Bivariate mapping from the GeoVista interface

Multi-panel maps and map series Multi-panel maps visualise broad trends and patterns through time, rather than exploring the details of a specific area.

Multi-panel maps, or a map series, can show:

a single variable at multiple points in time

multiple variables in the same area at the same time

both of the above.

The human brain is very good at extracting patterns from visualisations like this. The graphic size for each map in the series can be very small if there are few geographic areas mapped. This still allows the reader to see the broad patterns over the series.

Any of the standard mapping methods can be used in a multi-panel map. Whatever method you use, you need to think about how you display the changes.

Strengths of a multi-panel map or map series are that they:

can display a lot of information to the reader and are easy to follow

show changes over time and or different variables for the same geographic area.


the reader can infer a relationship between variables that may not be correct

a high number of geographic units mapped can make it difficult to interpret.


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Continuous surface maps Some geographic features exhibit continual changes over an entire geographic area. For example, air temperature can be measured anywhere and its values are likely to be slightly different from the next closest sample point – depending on the precision of the instrument. Weather maps often break a continuous temperature surface into classes to group similar values into a single colour.

This is different from the way statistical geographic units operate. They hold a single value to represent an area with the implication that the value applies homogeneously to the entire area.

You can generate a surface that represents a continuous phenomenon from a number of discrete points with measurement values. This uses a technique called interpolation to assign values for all point in between the measured sample points. For example, you can create a national map of temperature by interpolating the temperatures between all the temperature gauges where data is available. If the temperature at one site is 10 degrees, and the temperature at the nearest site is 15 degrees, a simple interpolation assumes that the temperature at a site half way between them would be 12.5 degrees.

A commonly used term for a surface is a heat map. A heat map is another form of interpolated surface that highlights clusters, or hot spots, present in the data. They can highlight patterns in data that may not be easily seen in a table. They can also be a visually compelling way to present the data.

A surface can be a useful way to anonymise data in order to preserve privacy and confidentiality. Figure 14 shows a map with a continuous surface, where each cell represents the number of dwellings within a 10km radius, divided by the area.

Figure 14 14 A continuous surface showing the density of dwellings

A continuous surface showing the density of dwellings


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Cartogram maps A cartogram uses a data value to adjust the size of a geographic area relative to the other areas in the data layer. This can emphasise smaller geographic units with high values that would not be visible on a standard geographic map.

There are many different types of cartogram. Some preserve the shapes by disconnecting neighbouring polygons, whereas others keep a layer contiguous but distort the shapes.

Strengths of cartograms are that they:

have a high visual impact

normalise areas rather than attributes, so large low-value areas do not dominate the map

can use measures of intensity

can clearly show broad geographic patterns

can effectively display special classes.


they should not be used for representing total counts

the reader may assume data is uniformly spread across individual areas

they may overly distort areas so they are unrecognisable, such as area units

they don’t work if you use geographic units that are unfamiliar to the reader.

Compare the standard map of New Zealand on the left of figure 15, with the cartogram on the right. Both maps were created usi9ng the dame data – population by region (2012 population estimates).

Figure 15 15 Comparing standard map and cartogram

Comparing standard map and cartogram

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8 Generalisation and map scale

This chapter covers:

generalisation techniques

map scale.

Generalisation techniques Use any of several generalisation techniques to remove unnecessary detail so the data is more appropriate to the scale and/or the purpose of your map. Strike the balance between emphasising the important aspects of the map while faithfully representing the data.

Use generalisation to:

increase the visual clarity of a map by reducing clutter.

avoid having too many objects compressed into too small an area

avoid features that will collide with one another or clutter the map.

increase the performance of dynamic maps and spatial analysis operations by providing fewer data points to process.

Map features are generalised using one or more of the following operations (listed here with the most common features, and the easiest to use, first):

simplification – reduce the number of coordinates required to replace an object

classification – aggregate data into classes

smoothing – reduce angularity of angels between the lines

aggregation – group point locations and representing them as areas

amalgamation – group several areas into a larger element

collapse – replace a feature with symbol

merging – group a number of features to make a single feature

refinement – select specific portions of an object to represent the entire objects

exaggeration – amplify specific portion of an object

enhancement – exaggerate or enhance feature symbology to make it stand out

displacement – separate objects by reducing the overlap between features to increase legibility.

See Cartographic generalisation for more detail (Jabeur, 2006).

Map scale To be most useful, a map must show locations and distances accurately on a sheet of paper of convenient size. This means that all things included in the map ground area, rivers, lakes, roads, distances between features, and so on must be shown proportionately smaller than they really are. The proportion chosen for a particular map is its scale.

Map scale is defined as the ratio of the distance on the map to the actual distance on the ground. For example, a scale ratio of 1:50,000 means that one centimetre measured on the map is equivalent to 500 metres measured on the ground, see figure 16. Map scale affects map detail. A larger scale map can show more detail than a smaller scale map. Another way to remember relative scale is, larger scale, more detail; smaller scale, less detail.

http://theses.ulaval.ca/archimede/fichiers/23356/ch02.html#d0e457


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Figure 16 16 Understanding map scale

Understanding map scale

The choice of map scale can influence the choice of data that is used for the map. Although it might be easier to use the same data for different maps, consider the consequences first. For instance when the same data is used on a map that is a smaller scale than the data, the map can seem congested because adjacent features can be drawn too close together to be distinguishable. A small scale map is one that is zoomed out to show the whole of New Zealand. The data is created at a much larger scale and to a higher resolution than the map scale.

Sometimes when a feature is too crenulated for a particular scale, it can look like someone used a blotchy fountain pen to draw the line. If this happens, you may wish to smooth the lines into curves (see generalisation techniques for more information on generalisation).

This principle applies to online maps where a user can zoom in to an area and change the scales. In this case, you may need to create several versions of a layer that are suitable for viewing at particular scale ranges. As the user zooms in or out, a new version of the layer swaps with the old version when a scale threshold is reached. If done correctly, the user will not notice the resolution of the data changing as they interact with the map.

For example Statistics New Zealand’s StatsMaps interface uses the statistical geographies as a guide to visualise census data at different scales. The user must zoom in to see meshblock data, and out to see regional council area data. Figure 17 shows where on the header of the StatsMap a user selects their required level of detail by clicking on one of the boundaries. The data will be displayed using different scales.

Figure17 17 StatsMaps header shows which geography will be visible at a particular scale

StatsMaps header shows which geography will be visible at a particular scale

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9 Map design

You can apply any number of design principles and cartographic techniques when you compile maps and construct page layouts. Together these principles form a system for seeing and understanding the relative importance of the content in the map and on the page. How they are used will either draw the attention of map readers or potentially repel them.

The main design principles are:

legibility

visual contrast

figure ground

visual hierarchy

balance

map marginalia.

Legibility Make your map as legible as possible so your reader can easily see and comprehend the information you are presenting. To do this, choose symbols, text, and colour hue and shading that are familiar to the map audience, and of appropriate size, see figure 18.

Visual contrast Use good visual contrast to distinguish map elements from their surroundings, see figure 19.

A map with low visual contrast can create a more subtle impression but features must be able to be distinguished by the reader. Features with less contrast appear to belong together. Having a sufficient level of contrast is a particularly important design consideration when creating black and white maps.


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Figure 18 18 Examples of legibility

Examples of legibility

Source: Buckley, 2012a

Figure 19 19 Examples of contrast

Examples of contrast


http://www.esri.com/news/arcuser/0112/graphics/designprinciples_3-lg.jpg



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Figure ground Figure-ground organisation is the separation of the main figure in the foreground from background information. Use this to guide the reader to quickly focus on the main figure, see figure 20.

Figure-ground organisation can be promoted through the use of a whitewash for areas outside the area of interest, use of a drop shadow, feathering, or adding detail to the map to differentiate the foreground from the background.

Figure 20 20 Examples of figure ground

Examples of figure ground



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Visual hierarchy Visual hierarchy refers to the order that the eye focuses on the layers of information on your map, see figure 21.

Map features or elements will be seen as higher in the visual hierarchy if they are relatively larger, have a more prominent position (eg title at the top of the page), have a higher order, are of a stronger colour or contrast, or have surrounding space. Guided by the purpose of the map, select the features and elements on the map that are the most important and you want the reader to see first. Ensure these are highest in the visual hierarchy. Be careful to not increase the size of elements to solely fill in space, as this will inadvertently increase its position in the visual hierarchy.

Maintaining an appropriate visual hierarchy is generally easier for thematic maps than reference maps. This is because they usually require fewer layers and they do not all have to exist on the same visual plane, as thematic layers should be treated as more important than the basemap.

Balance Balance involves the layout of the map and elements on the page or screen. A balanced map that does not appear as heavy in one direction is primarily the result of two primary factors: visual weight, and visual direction, see figure 22.

Balance of the map is dependent on the relative location, size, shape, and colour of elements on the page. Remember that the optical centre of the page is slightly higher than the geometric centre of the page. Look at the empty spaces on the page, if these are balanced in size and location they can indicate a well-balanced map.

A balanced map is pleasing to the eye and enhances readability of the map elements.


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Figure 21 21 Examples of visual hierarchy

Examples of visual hierarchy



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Figure 22 22 Examples of map balance

Examples of map balance


Map marginalia Marginalia is a form of map metadata that gives the reader information about the map. Not all map marginalia are required on every map, but you need to be able to justify your decisions to omit any of this metadata.

Title

Create a title that gives the reader enough information to interpret the map contents and message, along with additional information included as a sub title, legend text, or additional text. If the map is part of a map atlas not all the detail (eg region of the atlas) needs to be repeated for each title. Maps can be separated from their parent document, so having a title will ensure the context of the map is not lost.

Legend

Always include a legend if there is more than one layer on the map or multiple categories within a layer. This helps the reader identify the information and interpret the map correctly. Ensure the units represented in the legend are rounded appropriately and fall into different classes. For choropleth maps, ensure the legend does not contain gaps between the class units.



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North arrow

Only include a north arrow when either north is not at the top of the map, or the map projection used does not have north in the same direction. In the latter case, add a graticule to the map.

Graticule

A graticule is the network of lines of latitude and longitude added to the map. Include a graticule when the map projection on the map would show these lines as curved rather than straight lines.

Scale bar/text

Use a scale bar or text to show distance estimates without distracting the reader from the map message. Round the units appropriately for the reader to use.

Do not use a scale bar or ratio where the scale varies across the map, such as maps in 3D or a perspective projection. This is because the scale bar measurement will be a different size at different places on the map.

Inset/locator maps

Use an insert map, or locator map, if it is not easy for the reader to determine where on earth, or New Zealand, the area the map represents. Keep inset maps very simple; generally a simple outline or shaded area is sufficient. It is best not to use connecting lines from the main map to the inset map/s, because the line can obscure information on the map. Instead, use text such as ‘Area shown on left’.

Additional/source information

Make sure you know the owner of all the data you are using on your map. Some of the data used on a map may be owned by another organisation which may need to be mentioned on the map. This can be useful for people who need to know whether the data is from a reliable source, or other map makers who may want to use the same data on their maps.

Avoid including descriptions, disclaimers, and explanations on maps. They are a map’s equivalent of fine-print that is rarely read or understood.

If a statistical map requires a substantial amount of additional text to explain the data and map contents then choose a more appropriate way to represent the data that does not require clarification. If this can’t be avoided, then leave the text off the body of the map and include it as an additional publication.

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10 Web mapping

This chapter includes the following design principles you need to consider for web maps:

characteristics of web maps

design considerations for web maps

making web maps

compiling web maps.

Characteristics of web maps We use the term web maps for maps with characteristics that make them different from static on-screen maps or printed maps. For example they:

are interactive and may allow the map user to create their own map with a limited set of tools

may be able to generate information on the fly by accepting some user input to customise a map

can display more information when the map user applies mouse-overs, tooltips, information boxes, labels, and hyperlinks

can show less on the map itself (eg labels or detailed features) and still convey useful information

can be linked to databases that report attribute information, display images, play sounds when users click related map features, or perform analyses by accessing geoprocessing functionality

can also be portals for downloading or uploading content.

Design considerations for web maps When you design a web map, as with any map you make, the first thing to determine is the purpose for this map. You also need to specify the functions that will be performed by the map user. For example, a web map may allow the user to select allocation and a buffer width to select and display the features on another layer that fall within the buffer.

On average, users spend less than two minutes interacting with a map (1min 43 sec according to Timoney, 2012). The key aspects for web maps are performance and simplicity. This applies to maps developed for internal staff as well as maps for a public audience.

Users also expect what they are viewing to be of immediate and personal use to them. Develop a specific set of requirements for your web map and if necessary split them to create separate single-purpose map applications. Single purpose maps that fulfil a specific need of your audience are increasingly preferred, as the user is able to quickly access the information they need with minimal clutter.

Web maps can be reproduced in a variety of forms – not just for on-screen viewing. They can be printed, screen-captured, projected onto a wall, exported to an image file, copied and pasted into a document or for a presentation. These present challenges for modern-day cartographers that didn’t exist for previous generations of map-makers.

Do not make web maps look like cut-down replicas of a desktop GIS!


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Making web maps The workflow for making a web map is the same as for static maps, with more emphasis on designing the user experience – how a user interacts with the map.

A generic audience will require a simple user interface. Google Earth and Maps are good examples of this.

Technical users will need more functionality, or tools to perform specific tasks such as routing or analysis. You will need to balance simplicity with functionality for your web map because it is very difficult to achieve both.

Compiling web maps A web map is different from a static image or hard copy map because a user can interact with the map by changing the zoom levels, selecting different data to display on the map, or performing some function with the map.

Before compiling a web map, determine what size to make it and what geographic extent it will show. Then determine the map scale and resolution. You need to make the same types of decisions you make for static maps. For instance: decide which map projection is best; choose the colours, fonts, and symbols; and decide what to show in the map margins.

Below are general guidelines for web map designs. While this is not an exhaustive set of recommendations, it should help you get started.

Size

Although web maps are usually designed for a 17- or 19-inch LCD monitors – because that is what most people have on their desktops – web maps can also be viewed on other devices such as Tablet PCs, smartphones, or iPads. Design your map for the device that will be most commonly used by your target audience. For example, your map might be developed for mobile devices use in field work. Sometimes a map design will work well on devices other than the primary delivery mode. Sometimes it won’t.

User interface

Design your web map to allocate as much screen space to the map as possible, but at the same time allow easy access to the controls and functions. Don’t hide critical or commonly-used functions. Automate as much as possible. Remember, when it comes to web maps, less is more. So if there is any doubt as to whether piece of functionality is useful or not, leave it out. Functionality can almost always be added in a later release.

Geographic extent

Because users can pan and zoom, the geographic extent of the map can be greater than what is initially shown on the screen. Sometimes it is useful – and necessary – to restrict the map extent. Other times, it makes more sense to provide a global view. It will depend on the map’s purpose.

Set the initial map extent to a smaller scale to save the majority of users from having to zoom out – then in – to their area of interest.

Map scale

If readers can zoom in and out, the map scale will be variable. A map layer can seamlessly change to a lower or higher resolution version of the layer as the user zooms out or in. Make sure you set the maximum and minimum display scales for each layer precisely so that there is no chance of two versions of the layer displaying at the same time, or there is no layer displaying at a particular zoom level. A dynamic table of contents and legend should display the same information when the layers switch.


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Map projection

The map projection you use depends on whether the map will be mashed up with other web maps. For example, if you want your map to overlay with maps on ArcGIS Online, Bing, or Google, you’ll have to use the web Mercator projection. If you use Eagle Technology’s community base map you will be using NZTM. A web map can only use one projection so all the data you use for it has to be stored in that projection. Anyone who wants to use your map in a mash-up will have to use that same projection.

Colour

Almost every web map is in colour. However, a colour viewed on a computer screen will appear different if it is printed. This means you need to select symbology for features and labels that will be legible on top of variety of base maps. A layer that is easily visible on top of an aerial photograph may not be equally visible on top of a topographic map.

Remember that certain features can slow the performance of the interactivity of the map. For instant, weigh up the advantages of using a halo to make text stand out or stacked symbology against the disadvantage of slower performance.

Use a neutral base map to avoid any colour clashes. Subtle shades of grey work well.

Symbols

Make symbols and text large enough to be seen against the different backgrounds that the user is able to select. Text and symbols should be at least 10 pixels high. That means using fonts 7 points or larger on a PC and 9 points or larger on a Mac. The ability to distinguish a symbol from its background is called contrast. A table of colour contrast metrics is a good guide for colour selection that will promote contrast.

Fonts

When possible, use fonts designed for the web. A recent study by Buckley (2012b) identified Arial (or Helvetica on Macintosh), Verdana, Georgia, Trebuchet, and Century Gothic (all installed on Windows systems), and Lucinda Grande and Palatino (installed on most systems) as the most popular fonts for web design. Good web fonts have a generous amount of space between characters and within characters (called the punch width).

Serifs are the small lines or decorations added to the ends of the main strokes of the character that are very popular in print. Sans-serif fonts are more suitable for web map design because serifs intrude into the space between characters.

Resolution

Computer display resolution is low compared with print maps. For desktop computers, it is common to design for a resolution of 96 dots per inch (dpi) because all LCD monitors support this resolution. Newer LCDs typically have a native pixel density of 120 dpi and 144 dpi. Choose a resolution based on the type of computer your target audience will mostly likely use.

This low resolution, coupled with the colour projection issue, will impact the cartographic design of a web map. Because screen displays are pixels, diagonal lines and sharp edges appear jagged. These jagged edges can be softened by adding pixels of intermediate colour between the object and the background (antialiasing), which fools the eye into seeing a jagged edge as a smooth one.


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Output

Provide functions that allow a map user to export your map to number of different formats – electronic or printed. This means you can control what extra information is left off or added to the output.

This is better than having the user copy and paste their screen image (called a screen dump or screen capture) into a document or presentation, as they will also capture the on-screen elements like buttons, controls, and table of contents, which they may not want, and therefore have to trim them. If the user captures a map scale ratio from the web map, it will be wrong when the map is output at a different scale.

To avoid this, you can create map templates for different sizes and orientations with additional marginalia such as a legend and corporate logo.

Provide functions that allow a map user to export their version of a map to number of different formats – electronic or printed. You can then control what extra information is left off or added to the output. Map templates can be created for different sizes and orientations with additional marginalia such as a legend and corporate logo.

Map caches

When a user views a map on a website, much of the work involved in drawing the map happens on the map supplier’s server. What generally happens is that the web browser supplies the coordinates for the area being viewed to the map server, the server extracts the geographic features from a database, and renders it on the web browser using the symbology embedded in the map document.

In most cases this happens almost instantaneously. However if performance is too slow, the map maker may need to build a map cache.

A map cache is a pre-rendered image of map layers that is created for a number of viewing scales. A cache dramatically improves the performance of a web map because the map does not need to do any graphical processing or database queries.

The downsides of a cache are they:

need to be re-created every time a data layer changes in the database

can take up a lot of disk space when they are built

can take a long time to build.

Map marginalia

It is as important for web maps as it is for static maps, to include metadata such as the author, publication date and information on the data. You can include this information either on the map or as a link to an associated web page. Users of web maps expect data to be current and accurate and sometimes expect to be able to access the data. Knowing who made the map, when it was published, and what data was used to make it helps users assess the validity of the information on or linked to the map.

Testing

Test everything – several times. To make sure buttons do what they are supposed to do, have a test plan written that documents what should happen when a user action is performed. For example, “when the user clicks the ‘+’ button, the map zooms to the next map scale”.

Use professional testers as well as user testers at various stages of the implementation project. Web map developers should make a test version of the site available for user acceptance testing (UAT). Successful completion of a user acceptance test is a major milestone in a development project.

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11 References

Brewer, C (2005). Designing better maps: A guide for GIS users. Redlands, California: ESRI.

Buckley, A (2012a). Design principles for maps using statistical data [Presentation]. Retrieved from: www.geospatial.govt.nz.

Buckley, A (2012b). Designing great web maps. Retrieved from: http://www.esri.com.

Buckley, A (2012c). Make maps people want to look at. Retrieved from www.esri.com.

Buckley, A (2013). Understanding statistical data for mapping purposes. Retrieved from: www.esri.com.

Buckley, A, Frye, C, Buttenfield, B, Hultgren, T ( 2005). An information model for maps: Towards cartographic production from GIS databases [PDF format]. Retrieved from: www.cartogis.org.

esri support. GIS dictionary, MAUP. Retrieved from http://support.esri.com.

Jabeur, N (2006). A multi-agent system for on-the-fly web map generation and spatial conflict resolution (chapter 2.1 Cartographic generalization). Retrieved from http://theses.ulaval.ca.

NZ Government Web Accessibility Standard 1.0. Retrieved from: https://webtoolkit.govt.nz.

Slocum, TA, McMaster, RB, Kessler, FC, Howard, HH (April 2008). Thematic cartography and geographic visualization. (2nd ed.) chapter 5. Data classification. Upper Saddle River, NJ: Pearson Prentice Hall.

Timoney, B (2012, August 12). How the public actually uses local government web maps: mMetrics from Denver. Mapbrief. Retrieved from http://mapbrief.com.

United Nations Economic Commission for Europe (2009): Making data meaningful, Part 2: A guide to presenting statistics. Retrieved from:www.unece.org.

Wikipedia (nd). Modifiable areal unit problem. Retrieved from http://en.wikipedia.org.

Useful links

Statistics New Zealand’s ArcGIS online site: http://statsnz.maps.arcgis.com/home/

Statistics New Zealand’s StatsMaps site: http://www.stats.govt.nz/StatsMaps/Home.aspx

ColorBrewer (http://colorbrewer2.org).

GeoVista. http://www.geovista.psu.edu/

http://www.geospatial.govt.nz/presentation-video-design-principles-for-maps-using-statistical-data

http://www.esri.com/news/arcuser/0612/designing-great-web-maps.html

http://www.esri.com/news/arcuser/0112/make-maps-people-want-to-look-at.html

http://www.esri.com/esri-news/arcuser/winter-2013/understanding-statistical-data-for-mapping-purposes

http://www.cartogis.org/docs/proceedings/2005/buckley.pdf

http://www.cartogis.org/docs/proceedings/2005/buckley.pdf

esri%20support.%20GIS%20dictionary,%20MAUP.



https://webtoolkit.govt.nz/standards/web-accessibility-standard/

http://mapbrief.com/2012/08/01/how-the-public-actually-uses-local-government-web-maps-metrics-from-denver/

http://mapbrief.com/2012/08/01/how-the-public-actually-uses-local-government-web-maps-metrics-from-denver/

http://www.unece.org/fileadmin/DAM/stats/documents/writing/MDM_Part2_English.pdf

http://www.unece.org/fileadmin/DAM/stats/documents/writing/MDM_Part2_English.pdf

http://en.wikipedia.org/wiki/Modifiable_areal_unit_problem

http://statsnz.maps.arcgis.com/home/

http://www.stats.govt.nz/StatsMaps/Home.aspx


http://www.geovista.psu.edu/

Documents

Design principles for maps usingarchive.stats.govt.nz/~/media/Statistics/about-us/stati... · 2014-08-12 · 6 1 About Design principles for maps using New Zealand’s statistical