Visual Variables J. Bertin: Sémiologie Graphique, 1968

Geoinformation in Environmental Modelling Communication by maps Jussi Nikander ENY-C2005 13.2.2019 Slides by Paula Ahonen-Rainio and Jaakko Madetoja Topics today

• Communication by maps – Information & aesthetics

• For whom, what, and why?

• How: elements to be designed

• Visual variables and how they link with different types of data • Visual hierarchy and colour design Examples of potential exam questions relating to this lecture (in English) • There is a poorly composed thematic map in the attachment. Which three mistakes and/or missing elements would you correct in the first place? What other mistakes there are in the map?

• Explain what it means that data to be presented in a map has to be normalized. When is the normalization necessary, and how can it be done?

• Name the visual variables that can be used on map symbols (and in information visualisation in general). How does the measurement scale (nominal, ordinal, interval, ratio) of attribute data rule which visual variables should be used? Same questions in Finnish

• Liitteenä on huonosti toteutettu teemakartta. Mitkä kolme virhettä ja/tai puutetta ensisijaisesti korjaisit kartassa? Mitä muita virheitä kartassa on?

• Selitä mitä tarkoittaa, että kartassa esitettävä tieto on normalisoitava. Milloin normalisointi on tarpeen, ja miten se voidaan tehdä?

• Nimeä visuaaliset muuttujat, joita voidaan käyttää karttamerkeissä (ja yleensäkin tiedon visualisoinnissa). Miten esitettävän ominaisuustiedon mitta-asteikko (nominaalinen, ordinaalinen, intervalli-, suhdeluku-) vaikuttaa siihen, mitä visuaalisia muuttujia tulisi käyttää? Communication by maps

Communication process:

SENDER  encoding  MESSAGE  decoding  RECEIVER

content form (visualization) … Communication by maps

• Visual presentation can reveal something that numbers don’t tell us • An image is worth a thousand words, a map perhaps even more

Visual patterns on a map represent: spatial relationships, distributions, trends, outliers,…

http://ilmatieteenlaitos.fi/kuukausitilastot … Communication by maps

“The purpose of visualization is insight, not pictures” J. Bertin Aims of map design

• Informativeness For example, attributes of a building signify… – Discrimination (residental, administrative, industrial, office) – Order (buildings of 1-2; 3-5; over 5 floors) – Emphasis (buildings with geothermal heating) Clarity, visual hierarchy, logics There are no neutral • Aesthetics choices – a factor of usability: ”satisfaction” in map desing – connotations: eg style  up-to-dateness Affect on map reading and interpretation For whom, what, and why?

Phenomenon & data

User Purpose - visual perception - specific...generic - profession, age,… - big picture…detailed study - cultural context - convincing, provoking, - motivation, expectations, exploring,… tiredness,… Important terms

Topographic map

Large scale map

Small scale map

Thematic map A bad example

Study with you neighbour the map example

Can you understand its message?

What is wrong in it? Kantasolusiirre = Stem cell transplant (blood cancer treatment) Red = export % Blue = import % Verestä elämää 1/2017 How? – Elements to be designed

Decisions on and design of…

1. Data to be included (and excluded) – Normalization, classification 2. Mapping method – Different symbolization, colours 3. Area of the map & presentation scale – Generalization of geometry 4. Background for the theme – Relevant content, visual hierarcy 5. Key for interpretation: title and legend 6. Metadata 1. Data to be presented

• What data to include, and what to exclude – Object classes and their attributes – One or more attributes per object (how many we can visualize) OR – Spatially continuous variable(s) • Data analysis – Which measurement scale (nominal, ordinal, interval, ratio) – Range and distribution of values – Counts or proportions? Need for normalization? – Unclassified or classified? – Need for preprocessing of data? Why normalization

Example of population distribution • Think of a grid with 500 people in each grid cell; that means constant population density across the whole study area • Then divide the study area into regions of different sizes so that each grid belongs to one area • Then count the number of population of each region by summing up the population in the grid cells; as a result, the number of population is the larger the larger the region, and vice versa • If we now present this information, the number of population, in a map – take one colour hue and variate it from light to dark, darker meaning a higher population – what do we get? • We have a map that communicates, that larger areas have more population than smaller ones, but not that the population density actually is the same in every region. Not very useful! Instead, eliminate the disturbing effect… …Why normalization

5 5 5 5 5 5 Example: 5 objects in each grid cell. 5 5 5 5 5 5 4 regions of varying size. 5 5 5 5 5 5 5 5 5 5 5 5

Map of non-normalized values: Map of normalized values reveals The larger the region the higher the value. the constant density across the area.

20 20/4=5 50 50/10=5 20

30 30/6=5 20/4=5 Normalization of data

…eliminate the effect of varying size of the enumeration areas - or some other distorting effects: spatial or other dependencies

Relevant with ratio scale values only • Normalization – per area • e.g. population density: number of people per area • e.g. productivity: tons of wheat harvested per area (yield) – per another variable • e.g. proportion of Swedish speaking people per population • e.g. birth rate per women of child-bearing age Example: normalization of data

Consider what is the message of the map, what makes sense. Classification of data

• Classification of data – First of all, classified or unclassified  why do we classify? – How to classify: one of the profound choices in map design

• Systematic grouping of data based on one or more attributes • For a clearer map, even if this map image is generalised – “Less is more” principle

• The classification, and the resulting map, should reflect those patterns or structures that are characteristic for the mapped phenomenon – And each class should contain its share of the observed values Example: classification of data

The map should display the characteristics of the phenomenon …Classification of data

Aim: Homogeneous classes + contrast between the classes

What is the suitable number of classes depends on... • map users: the lower the number of classes, the easier to interpret the map – but not too low, either  what is the sufficient level of information We can perceive 5 without counting, or keep in working memory about 7 meaningful chunks at a time • characteristics of the phenomenon: present essential behaviour – Avoid deciding the number of classes before studying the data • the number of objects in the map (if very small, avoid too many classes) • visualisation of classes: what we can perceive & how it is presented For example, bipolar data (diverging from a meaningful turning point, such as 0 for temperature) affords more classes: use two hues that vary in lightness What is the suitable classification method See the examples Example: classification of data

Here measured values of 20 data objects  Classified into five classes

Classification by equal intervals:

Classification by quantiles (equal count):

Classification by maximum breaks: Example: classification of data

Searching natural breaks visually by a cumulative frequency diagram

4 2. Mapping method

Nature of data, first of all, determines what is appropriate map type • Spatially: points, lines, areas (or grid), continuous field • Attribute values: – Absolute ratios (or interval) – Relative ratios Quantitative data – Ordinal – Nominal Qualitative data – Cyclic: Quantitative values on a scale that, at a certain point, is back to zero • direction of wind, time of day Notice the use of visual variables

• Intuitive interpretation • Variables in different map types Visual variables J. Bertin: Sémiologie graphique, 1968

Mapping of data variables to visual variables to encode data values visually to points, lines and areas

Kraak & Brown 2001 Web Cartography

Later additions, such as transparency, crispness, perspective hight, … Example: Relative ratio value per area / classified: ordinal data Choropleth map

Notice the projection (Lambert, conical): north direction and scale vary across the map.  no north arrow, no scale bar Display grid lines instead.

Notice the legend: visual presentation of the class intervals.

eea.europa.eu …Choropleth map

However, even if the map is correctly made: • in the interpretation: bigger areas gain more attention • intuitive assumption: map suggests that areas are homogeneous – that is seldom true – Dasymetric map corrects this drawback: If further data is available, form homogeneous areas instead of enumeration units that may have remarkable internal variation. E.g. exclude waters, mountains; divide urban and rural areas, etc. Example: Nominal value per area Chorochromatic map

Don’t follow this example! Colour here suggests and order. Provinces in Finland – commons.wikimedia.org Example: Absolute ratio value per area Dot density map & Proportional point symbols

e-education.psu.edu / Geoff Hatchard Example: Quantitive or ordinal data per point or area Proportional point symbols

Two attributes (bivariable map): population presented with size, its change with colour in the same symbol. Two attributes: Example: Nominal value per point type of service (colour) and Point symbol map accessibility (ring)

http://www.hel.fi/palvelukartta/ Example: Qualitative or quantitative data per line Flow maps

Google Ordinal data presented with associative hues

Quantitative data would be presented with Nominal data HSL varying widths of line. Example: qualitative values for a continuous field Isaritmic map

Notice the bipolar scale presented with diverging colours 3. Area and scale

• What is the area that needs to show – how large is the map – Do these lead to an appropriate map scale? – Is the map legible: limitations of human visual perception

• Generalization of data/presentation accordingly – Level of detail – suitable to the intended use and users? – Notice: geographic data is always a generalized representation

• Mark the scale with a scale bar – Also mark the orientation: North arrow or grid Illustrations from McMaster and Shea of their 10 forms of generalization The original feature is shown at its original level of detail, and below it at 50% coarser scale.

Each generalization technique resolves a specific problem of display at coarser scale and results in the acceptable version shown in the lower right.

Figure from Ch 3.8 in Longley et al. (2015)

• Simplification (yksinkertaistaminen) – E.g. buildings to squares, remove small curves from a line – It is important to save the characteristics of a shape – See Douglas-Poiker (Peucker) algorithm for line generalization: p. 73 in Longley et al. (2015) – esim. rakennus suorakulmioksi, viivan pienet mutkat pois; oleellista on muodon pääpiirteiden säilyminen • Collapse (typistys) – Area-type object is presented with line or point – E.g. airport to point symbol, wide rivers to lines • Amalgamation (alueiden yhdistäminen) – E.g. areas close to each other are combined to one • Refinement (valinta) – Complex group of objects is presented by a sample of them – E.g. branches of a river …methods of generalization

• Enhancement (symbolien korostaminen) – E.g. road line is wider than its areal geometry • Smoothing (viivan pehmennys) – E.g. coast line • Aggregation (pisteiden yhdistäminen alueeksi) – E.g. separate buildings -> built-up areas • Merge (yhteensulauttaminen) – Multiple parallel lines to one, e.g. roads • Exaggeration (korostaminen, liioittelu) – E.g. curve in the road • Displacement (siirtäminen) – Bigger distance between parallel lines (road and coastline), buildings along the road 4. Background for the theme

Role of the background map (base map) taustakartta • To provide a locational reference – what is relevant content in each case – avoid overload • Typical: waters, main transport networks (main roads, railroads), administrative areas (border lines), large cities – Place names • Always drawn on top • Not always needed, if the area is familiar – visual hierarchy: background – theme

• To visualize the heterogeneous environment – may suggest explanation for patterns Example: Reference map for thematic data

HSY Example: Reference map for continuous field data 5. Title and legend When they are missing… …Title and legend

• Check the title once more – Does it tell what the map is about, really – Is the spelling correct (yes, these mistakes happen)

• Check the text in the legend – Is it logical addition to the title – Does it make clear what the values are, incl. the units – Do not duplicate values in adjacent class ranges – The visual sample should look exactly the same as in the map

• Consider how the title and legend set in the visual hierarchy 6. Metadata

• What other information a map user would need?

• Source(s) of the data – Possible data processing • Timing of the data – If not already in the title • Reference system – Essential in topographic maps • Author, publisher • Any other relevant information Some remarks about colour in maps

• For support with colour schemes, look at ColorBrewer at http://colorbrewer2.org

• Background – theme – Cool colours for background, warm colours for theme – Less saturated (”greyish”) colours for background, bright and vivid colours for theme – Background maps that are not designed for background tend to be problematic: too much details, too many and bright colours • Try a (white) transparent layer on the base map, under the theme • Balance and order – Large objects draw attention more than small ones; you may try to balance this by colour design • Harmony – Use colours reasonably, but don’t make the map dull Do not use colours randomly

News map that was corrected soon after publication Example: Two hues when bipolar data in a choropleth map

Bipolar data: There is a meaningful turning point in the data, e.g. here ”No change” between increased and decreased values

Don’t use a perspective view when users have no means in rotating the map. Visibility is prevented by high elements in the front, and the feeling of distance confuses the interpretation of magnitudes. => the same with tilted pie charts! source: Statistics Canada (www) Visual variables of colour

• Hue (sävy) – wavelength of the light

• Value, lightness, brightness (vaaleus, kirkkaus) – intensity – “light” vs. “dark”

• Saturation, croma (kylläisyys) – decreases >> – “purity”of the colour – saturated vs. achromatic Example of colour dynamics: Perceived colour moves towards the opposite of the surrounding colour Reading for the lecture

• Longley et al. (2015): Chapter 3.8

• Scanned materials in MyCourses: – Mark Monmonier: How to Lie with Maps (2nd ed.)1996 - example of normalization and the meaning of classification – Terry Slocum et al.: Thematic Cartography and Geovisualization (3rd ed.) 2009 - classification methods