1 symbology the guts of making a decent map!. 2 the module has…. lots of detail on just how to...
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Symbology
The guts of making a
decent map!
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The module has….
• Lots of detail on just HOW to symbolize your data mod 2
• BUT before you start wielding the electronic paint brush you need to know what you want to communicate to whom.
• And that is the most difficult part of making a map!
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Damn…
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About what?
• Colors – themselves and re others• Symbols – random or standard• Classifications --
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According to Brewer…
• “Many Factors affect the colors you choose.
• The perceptual structuring of the colors should correspond with the logical structuring in the data…
• Make sure the character and organization of the colors match the logic of your data…”1
1 Cynthia Brewer, Designing better maps. ESRI Press
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“When choosing map colors…
• You should not be overly concerned about which colors your audience likes.
• Everyone has an opinion• Regardless of context , it seems that most people
like blue and do not like yellow… • People like maps with many colors so focus your
attention on – presenting your data clearly and– don’t worry about whether you have picked everyone’s
favorite colors.”1
1 Cynthia Brewer, Designing better maps. ESRI Press
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BUT…
• “When choosing map colors, you should not be overly concerned about which colors your audience likes. Everyone has an opinion … Regardless of context , it seems that most people like blue and do not like yellow… People like maps with many colors so focus your attention on presenting your data clearly and don’t worry about whether you have picked everyone’s favorite colors.”1
1 Cynthia Brewer, Designing better maps. ESRI Press
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And…
• “When choosing map colors, you should not be overly concerned about which colors your audience likes. Everyone has an opinion … Regardless of context , it seems that most people like blue and do not like yellow… People like maps with many colors so focus your attention on presenting your data clearly and don’t worry about whether you have picked everyone’s favorite colors.”1
1 Cynthia Brewer, Designing better maps. ESRI Press
![Page 9: 1 Symbology The guts of making a decent map!. 2 The module has…. Lots of detail on just HOW to symbolize your data mod 2 BUT before you start wielding](https://reader030.vdocuments.us/reader030/viewer/2022032723/56649d0d5503460f949e17fd/html5/thumbnails/9.jpg)
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However
• Usually students don’t have too much trouble with making decent maps with reasonable symbolizations
• It comes naturally• But you do need to keep some
things straight when working with classifications of data
• And you usually have to classify
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Data Types• Nominal
– are categorical data where the order of the categories is arbitrary
• Ordinal– categorical data where there is a logical
ordering to the categories • Interval
– continuous data where differences are interpretable, but where there is no "natural" zero
• Ratio– continuous data where both differences and
ratios are interpretable
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ESRI talks more about…• Quantitative data is numerical
– Ratio, Interval, ordinal data types– continuous data like elevation (interval)– depth-to-bedrock (ratio)– Usually contrasting color between classes
• Qualitative data is not necessarily numeric– Nominal data – soil type, road classification– Limit of 10-12 colors (classes) and you want
contrast – 5 is better yet– Usually smooth transitions of color between
classes
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Exercise 2
• Lots of symbols for points, lines, & polys
• Labeling features – – Dynamic and Interactive– Annotation
• Symbolizing based on attribute– Category– Quantity
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Quantity
• Graduated colors– Color ramps – which work best?
• Graduated Symbols (classification)
• Editing legend entries for the TOC
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Classification
• How many classes• What method to use for placing the
values into classes• What kind of Symbology to use
(e.g., graduated colors or graduated symbols)
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Maps - Categorical
• Categorical symbolization is typically used for NOMINAL data– Quite often similar colors will be used
for related categories – You want the user to be able to
discern the categories
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Classifications
• Natural breaks : finds groupings inherent in the data. Default
• Equal interval : interval between each class is the same.
• Quantile : each class contains an equal number of values (features).
• Manual : you decide
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Quantitative maps
• Displays quantitative data – interval or ratio data and even ordinal data
• A graduated ramp or palette is used
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