Lecture 5. Symbolization and Classification MAP DESIGN: PART I. A picture is worth a thousand words

Transcription

1 Lecture 5 MAP DESIGN: PART I Symbolization and Classification A picture is worth a thousand words

2 Outline Symbolization Types of Maps Classifying Features Visualization Considerations

3 Symbolization Symbolization is the process of assigning colors, markers, sizes, widths, angles, transparency and other properties to features.

4 Symbolization Visual resources cartographers can use to symbolize information on a map. 3 Types of Symbols: Options for Symbolization: Point Line Polygon

5 Color Three "dimensions" of color: hue, lightness, and saturation. Hue: Color can be described by its wavelength; red at one end of the visible spectrum, violet on the other. A change in wavelength of visible light is manifested by a change in hue. "Red," "green," and "violet" are all hues. Lightness: Color can also be described by its shade, or its degree of lightness. a.k.a "value," or "brightness. Saturation: A third dimension of color describes how "pure" the color is. If a color is made up of only one hue, it is called "saturated." However, if you imagine mixing together many hues, the result is a desaturated color.

6 Symbolizing Values Nominal: Values are discrete (that is, mutually exclusive) and are classed according to type or quality. For example, a line could represent either a road or river, and a land use polygon could be residential, commercial, or a recreational area. Ordinal: Ordinal values determine position. These measurements show place, such as first, second, and third, but they do not establish magnitude or relative proportions. It is not possible to measure the differences between ordinal data. Ratio: The values from the ratio measurement system are derived relative to a fixed zero point on a linear scale. Mathematical operations can be used on these values with predictable and meaningful results. Examples are age and distance.

7 Types of Maps The form of your data dictates the form (type) of the map. General Reference Maps Graduated Symbol Maps (Proportional Symbol Maps) Chloropleth Maps Isopleth Maps Area Qualitative Maps Dot Density Maps

8 General Reference Maps Generalized map Examples: world maps, road maps, atlas maps, etc.

9 Graduated Symbol Maps (Proportional Symbol Maps) The proportional size of symbols represents the value of the attribute. These maps scale icons (most often circles) according to the data they represent. Proportional symbol maps are not dependent on the size of the spatial unit associated with its attribute.

10 Chloropleth Maps Numerical data is classified into categories and the categories are shaded. Polygons are often based on politically defined features. This will yield a display that puts visual emphasis on the largest area units of the map. Example: Indicate differences in population.

11 Isopleth Maps The concentration of an attribute is represented by connecting points of identical values to create lines. Ideal for continuous area data (i.e. data exists at every point) that varies smoothly over space. It does not change abruptly at any point (like tax rates do as you cross into another political zone). Examples: Temperature and Elevation Animated Example:

12 Area Qualitative Maps Shows the distribution of pre-existing geographic classes (forest types, soils, land-use, vegetation, etc.)

13 Dot Density Maps Dot maps create a visual impression of density by placing a dot or some other symbol in the approximate location of one or more instances of the variable being mapped. Used to represent themes that vary smoothly over space but are discrete. Examples: livestock farms, utility poles, and population distribution in a region.

14 Classifying Features Groups attributes into classes to help discern patterns. Make choices about not only how many different classes that the data should be categorized into, but what the value ranges of those classes should be. A slight adjustment of the "breaks" in the value ranges of ordered data, for example, might alter the map significantly and reveal trends that were not detected previously (or are not in fact there). You can define your own classes OR Use one of the standard methods: 3 Frequently Used Standard Methods: Natural Breaks Equal Interval Quantile

15 Natural Breaks (Jenks Optimization) Default method in ArcGIS (Also called Jenks Optimization classification) Identifies natural groupings of values that are inherent in your data. The features are divided into classes whose boundaries are set where there are relatively big jumps in the data values. This method works well with data that is not evenly distributed and not heavily skewed toward one end of the distribution.

16 Equal Interval This method is like a ruler: the interval between each class is the same. For example, you might have classes with intervals of 10 percent (1-10%, 11-20%, 21-30%, etc.) This method sets the value ranges in each category equal in size. The entire range of data values (max - min) is divided equally into however many categories have been chosen. This option is useful to highlight changes in the extremes of data values. It is probably best applied to continuously distributed data ranges such as percentages or temperature.

17 Quantile Each class contains an equal number of values. For example, you might have 100 counties grouped into 5 classes each class would contain 20 counties regardless of the values of the attributes. Misleading Legends: In order to have an equal number of features in each class, features with greatly different values can be placed in a single class. One solution is to manually increase the number of classes this may separate dissimilar values. Because the intervals are generally wider at the extremes, this option is useful to highlight changes in the middle values of the distribution.

18 Map Visualization Considerations A vital task in creating effective graphics for visually relating ideas is the filtering of vast fields of data into units that are simple enough to be understood quickly and intuitively but detailed enough to convey useful information Effective interpretation of the results of your analysis -- both by you and by others -- depends to a large extent on the design of the onscreen display. A slight alteration of the color scheme or classification method of a map, for example, might produce two different graphics that appear to tell two very different stories -- even though they represent the exact same data.

19 Next Week Map Design: Part II Map Layouts Graphics Text, Annotation and Labels.