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Cartography

and Geovisualization

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Maps are the principle means of displaying spatial data

Exploration: visualize and hence conceptualize patterns and processes.

Communication: convey information and findings thatare difficult to express verbally.

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Therefore, it is vital that you are familiar withthe basic elements of cartography

and, in particular, map design.

http://liam.avenza.com/MPcomp/

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Map design criteria

What is the motive, intent or goal of the map?

Who will read the map (the audience)?

How will the map be used? Stand alone, in a report, or simply for your viewing.

Issues of generalization: simplification, classification,

symbolization, induction

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Three phases of the transformation of the Earth's surface can be recognized:

Primary (geometric -- map projections)

Secondary (semi-geometric -- geoid to ellipsoid)

Tertiary (generalization)Generalization is a non-reversable process, and therefore must be carefully considered.

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Generalization has four main components

Simplification: excluding unwanted, enhancing desired

Classification: reduces complexity (qualitative, quantitative)

Symbolization: implicit or explicit, mimetic or abstract

Induction: logical inference--integration of parts into a whole

The scale at which the data will be displayed is of prime consideration.

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Simplification Classification

Both processes reduce the detail

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Impact of simplification toleranceThe first image shows an ungeneralized data set, the second is generalized at an 0.1 foot tolerance,

the third is generalized at a 10 foot tolerance.

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Data classification

1) categorical (nominal, ordinal) vs

numerical (interval, ratio) data

categorical classification: mostly qualitative

numerical classification: quantitative

2) number of classes

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3) dividing up data: numerical classification

a) exogenous schemesb) arbitrary schemes (e.g., equal interval)

c) ideographic schemes (e.g., natural breaks, quantiles)d) serial schemes (e.g., standard deviation)

e) unclassified schemes

You should always explore your data-try different schemesbefore settling on one.

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Same data,equal # of classes,but different maps.

Quantiles

Jenks

Equal intervalJenks-reverse order

Manual

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Symbolization components (PGE's)

Proper use of the PGE's depends on whether the data is quantitative or qualitative

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You must alsoconsider how thespatial primitive

(point, line, area)interacts with the

PGE.

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Only certain combinationsare effective.

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Qualitative data symbolizationAbstract Mimetic

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Complex legends -- displaying 3 related datasets.

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Natural breaks

Quantile

Standard deviation

Equal interval

Same data, different classification schemes

From ArcGIS's Help file: classification, schemes

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Other cartographicconcerns, such as

the visual hierarchy,are also important.

The proper use oftype is also very

important.

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Every map must contain elements such as the title, scale, legend, source, author and north arrow.

Depending on the scale you may or may not need a grid and an inset map.

All maps should be enclosed by a neat line.

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It is easy to make a map, but making a great looking map ...

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requires consideration of all of the elements, and a sense of design.

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Dasymetric maps use the intersectionof two datasets to obtain a more

precise estimate of a spatial distribution.

For example, census tracts often containlarge tracts of land whereon people

cannot live (e.g., parks, industrial areas).

Excluding those areas where determiningvalues such as population density can

make a significant difference to the values.

Improving the quality of the dataand of the visualization.

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Cartography is both an art and a science.

Maps are fundamental to any GIS project.

Modern advances in GISoftware make itvery easy to produce good and bad maps.

Every map is just one of all possible maps.

Complex maps can be difficult to understand.

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We have only scratched the surfacewith respect to the elements of cartographythat are important in presenting the resultsof your GIS-based analyses. The quality of

the map will determine, to a large extent,the reception of your work.

A poorly produced map suggests that theanalyses were also poorly handled.

A quality map suggests that the analyseswere also done properly and with due care.

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