MAKING A MAP
Data Types, Legends, and Classification Types
Making a Map - Overview
•Mapmaking, cartography, has been around a long time
•Cartographic principles and ‘rules of thumb’ have emerged to help create a useful map.
•GIS has changed some of the basic thoughts about mapmaking.
•Cartographers can create ‘passive’ and ‘active’ maps
–Create a map to communicate some geographic pattern to a map reader (a passive map)
–Create a map that can be easily queried and analyzed (an active map)
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•Attributes of geographic data can be displayed through thematic maps
•Map type depends on purpose of map and type of attribute data.
•Non spatial data - attribute data types
–attribute data is either qualitative or quantitative
•qualitative data tells what things exists
•quantitative data measures magnitude of things
•Data types include:
–Nominal
–Ordinal
–Interval
–Ratio
–Cyclic
•Nominal –
–NOMINAL DATA ARE USED FOR QUALITATIVE DATA
•PHENOMENA WITHIN A CLASS ARE ASSUMED TO BE RELATIVELY ‘HOMOGENEOUS’.
–LAND COVER AND LANDUSE DATA EXAMPLES OF NOMINAL DATA TYPES
–NOMINAL DATA STORED USING A STRING FIELD TYPE (‘FOREST’, ‘WETLAND’, ETC.)
–NOMINAL MAY BE STORED AS INTEGER VALUES (1,2,3 FOR STREAM ORDER OR CLASS), BUT…
•EVEN IF NOMINAL DATA STORED IN A NUMERICAL FIELD, IT IS STILL NOMINAL DATA
ArcView – Data Types
•ORDINAL
•ORDINAL DATA USED TO PARTITION FEATURES ALONG A CONTINUUM.
–ALSO KNOWN AS ‘RANKED’ DATA
–POPULATION DENSITY CLASSES SUCH AS ‘LOW’, ‘MEDIUM’, HIGH’ ARE ORDINAL DATA TYPES
–WILDFIRE HAZARDS RATINGS
–VEGETATION CONDITIONS
ArcView – Data Types
•INTERVAL
•INTERVAL DATA USED TO ORGANIZE FEATURES ALONG A CONTINUUM
•INTERVALS BETWEEN GROUPINGS HAVE MEANING – BUT NUMBERS DO NOT HAVE AN ABSOLUTE SCALE.
–TEMPERATURE IS A GOOD EXAMPLE OF INTERVAL DATA TYPE
–DIRECTION, IN DEGREES FROM 00 TO 3600, IS A SPECIAL CASE OF INTERVAL DATA
ArcView – Data Types
•RATIO
•RATIO DATA ARE USED TO ORGANIZE VALUES ALONG A CONTINUUM, WHERE VALUES HAVE AN ABSOLUTE MEANING
–DATA HAS KNOWN INTERVALS BETWEEN VALUES AND ARE BASED ON A MEANINGFUL ZERO VALUE
•POPULATION DENSITY VALUES ARE AN EXAMPLE OF RATIO DATA
ArcView – Data Types
Making a Map – Classification Types
•ArcView utilizes five methods to classify the range of values contained in an attribute field.
–Natural Breaks
–Quantiles
–Equal Interval
–Equal Area
–Standard Deviation
•Each method has its own strength and weaknesses
•The challenge is to choose the one method that shows an important feature of your data such that:
–It is easily discerned from other features
–It is easy for a map reader/user to understand
Making a Map – Classification Types
•A classification is a method that creates a group of classes
•Each class consists of a series of:
–value ranges
–labels
–symbols
•The number of classes should be between 3-10
–The human eye is limited in its ability to differentiate shades of gray and colors
Making a Map – Natural Breaks
•The Natural Breaks classification method identifies breakpoints between classes to form groups that are internally homogeneous (have similar values) – while assuring heterogeneity among classes.
–This results in classes of similar values separated by breakpoints
•This is a good choice for discovering patterns in the data, but can result in legends that are difficult to understand
•This method works well with data that is not evenly distributed and not heavily skewed toward one end of a distribution
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Making a Map – Quantiles
•In Quantile classification, breakpoints between classes is identified so that each class contains the same number of features.
•If the theme has 20 features, quantile classification will place 4 features into each of 5 classes (if 5 classes was selected)
–If the number of classes is 5 – the data is broken into quantiles
–If the number of classes is 4 - then it results in quartiles
•This classification method works well with data that is evenly (linearly..uniformly…) distributed.
Making a Map – Equal Interval
•In an Equal Interval classification method, ArcView divides the total range of feature values, from maximum to minimum, into five equal subranges.
–The features are then classified based on subranges.
•This method works best with continuously distributed data, such as precipitation amounts across an area
Making a Map – Equal Area
•The Equal Area classification method divides the range of features in a polygon theme so each subrange contains basically the same area
•The breakpoints are identified so that the total area of the polygons in each class is approximately the same.
•This classification method is useful in a limited number of situations
–Apportioning sampling areas so every observer gets an equal area
–Dividing up sales territories so each salesperson receives an equal territory
Making a Map – Standard Deviation
•In the Standard Deviation classification method, ArcView calculates the mean value of a field in a theme
•The range of feature values is divided by several deviations above and below the mean.
•The intervals will be either ¼, ½, or 1 standard deviation until all the data values are contained within the class.
•This method works well with normal distribution data
–Data that fits a standard bell curve, with most feature values falling in the center
–A few high and low feature values appearing above and below
ArcView – Understanding the Histogram
•Histograms show where class breaks fall in relation to the data.
•In Natural Breaks (Jenks), for example, breaks reflect clusters in the data.
•This method places breaks so each class has the same range of values.
ArcView - Histograms
•The x-axis shows ranges of value in the field
•The y-axis is a count of features.
•Vertical blue lines are class breaks – also shown in the Break Values box
•Gray columns represent percentages of the value range.
•The default number of gray columns is 100.
Making a Map – Symbolizing Data
•Symbology is set in Layer Properties, using the Symbology tab.
•Features Map Type
–Single Symbol - this selection will draw all features with same symbol.
•Useful to show size, shape and location of features
ArcView – Symbolizing Data
•Categories Map Type: unique values
•A different color/symbol is used to symbolize each value in an attribute. Unique values are good for mapping three types of attributes:
–Attributes that describe the name, type, condition, or category of a feature
–Attributes that contain measurements or quantities already classified
–Attributes that uniquely identify features
ArcView – Symbolizing Data
•Quantities Map Type
–Graduated Color
•Using a color ramp that changes from darker colors representing higher values to lighter colors representing lower values.
•Useful for data that is ranked or has a numerical progression.
ArcView – Symbolizing Data
Quantities Map Type -
Graduated Symbol
•Similar to graduated color, but variation is in size of point symbol, width of line symbol, etc.
•Values grouped into 5-6 discrete classes.
•Useful for showing rank or progression.
ArcView – Symbolizing Data
•Quantities Map Type
•Proportional Symbol
–Similar to graduated symbol, except symbol is drawn proportional in size to a value.
–A continuous range of symbol sizes are used from smallest to largest.
ArcView – Symbolizing Data
•Quantities Map Type
•Dot Density
–Symbolizes polygon features using dots inside the polygon to represent attribute value.
–Each dot represents a specific value.
–Used to communicate density of occurrences of a feature in addition to the quantity.
–Can be misleading – the eye infers pattern even if none exists.
ArcView – Chart Maps
•Chart Maps are useful to show multiple attributes on a single map.
•Also show the relationship between different attributes.
•Presented as a pie chart, bar chart, or stack bar chart.
•Example: Proportion of whites, Hispanics and blacks in each state relative to total population of United States.
Making a Map – Normalizing Data
Normalization of Data
Normalization of data means an attribute is expressed as a percentage of the total for that attribute – or as a ratio of another
–The Values are divided in the classification field by the values in the normalize fields (for density values)
–…Or…. The values in the classification field are divided by the sum of the values in the normalize field
–This is useful for polygon data with ‘raw’ values that need conversion to density or percentage values
•i.e., normalizing population by area to calculate population density
ArcView – Normalizing Data
•First example – Each value divided by total of all values in a single attribute column.
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•Second example – One value is divided by values from another attribute field (column)
Making a Map – Editing Symbols and Styles
•A selection of predefined sets of symbols are included in ArcMap
•There are many existing marker symbols available by clicking the More Symbols… button in the Symbologytab dialog box
Making a Map – Editing Symbols
•Additional symbols can be created using the Symbol Property Editor.
•Created symbols will be saved for future use in folders called styles
Example illustrates symbols from Crime Analysisstyle.
Making a Map – Symbology and Color
•Symbology features means assigning them colors as well as other properties for recognition a map.
•Varying symbol properties can convey information about features.
•Color hue is most associated with ‘color’.
•Hue is defined by a color’s wavelength (“rainbow colors” – ROYGBIV)
–Changes in hue are best suited for depicting nominal data
ArcView – Color and Symbology
•Color saturation is the brilliance of a hue.
•It is actually a range of wavelengths
•A narrow range equals more pure hue, while a wider range equals more cloudier colors.
•Saturation is best suited for presentation of numerical data.
•Color value is the variation in lightness or darkness of a color
•Value ranges between 0-100%.
•High color values are light, while low color values are dark
•Variations in color value are best suited for depicting quantitative data
ArcView – Color and Symbology
•Texture – describes the spatial pattern of geographic features
•Changes in texture are best used to symbolize areal features
•Using different fill patterns, texture can be applied to features.
•Orientation - is used to depict either nominal or quantitative data
•It is most often used in combination with text to create fill patterns
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