Data Representation and Mapping Ming-Chun Lee

What is a map? A miniaturized and convenient representation of spatial reality A picture or diagram, usually two- dimensional, showing all or part of the Earth and is a device for transferring selected information about the mapped area to the map viewer

Types of Maps Reference Maps - Used to emphasize the location of spatial phenomenon USGS topographic maps Road maps Thematic maps - used to display the spatial pattern of a particular theme Maps of population in the United States Geological maps

Characteristics of Maps All maps are reductions of reality All maps portray data which has been generalized, classified, and simplified All maps use symbols to designate elements of reality. Data are portrayed by the use of various marks, such as dots, lines, patterns, and colors which are referred in a legend

Map Elements

Data classification and scaling Nominal Scaling Data are differentiated by qualitative or intrinsic differences between features, without a quantitative relationship. The nominal scale locates and names items and places them in exclusive categories. Ordinal Scaling The data are ranked based on some quantitative measurement. They are only ranked from lowest to highest, without defining their numerical value. Interval/Ratio Scaling Scaling adds the dimension of distance between the ranked data by employing some standard units. Scaling adds magnitude to the ranks. Interval scaling starts at some arbitrary point, such as 32°F, Ratio scaling begins with zero.

Nominal Data Examples

Ordinal Data Examples

Interval/Ratio Data Examples

The range of visual resources As cartographers reduce the world to points, lines, and areas, they use a variety of visual resources. Jacques Bertin in his book The Semiology of Graphics (1983), inventories these resources using the categories of size, shape, value, texture or pattern, hue, orientation, and shape.

To increase the legibility of figures, use different line types and colors (and labels) rather than just different colors

Use hatching and labeling

Qualitative: nominal classes Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University

Sequential: for numeric classes Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University

Qualitative sequential: numeric and nominal Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University

Interval/Ratio Data

colors are nice, but what’s wrong with this map? Primary home heating fuel. U.S. Department of Commerce

Vector Symbology: Discrete Attribute Data Single Symbol All Features Look the Same Categories Unique Values Using One Attribute, Set a Distinct Symbol for Each Value Unique Values, Many Fields Using Several Attributes, Set a Distinct Symbol for Each Combination Match to Symbols In a Style Use Preset Symbology Based on an Attribute

Vector Symbology: Discrete Attribute Data Unique Values: Arterial Class Unique Values, Many Fields: Arterial Class & Bike Class Single Symbol

Vector Symbology: Continuous Attribute Data Quantities Graduated Colors Maps Colors Along a Gradient to Discrete, Ordered Ranges of Attribute Values Graduated Symbols Maps Sizes of Symbols to Discrete, Ordered Ranges of Attribute Values Proportional Symbols Continuously Varies Symbol Sizes by Attribute Values

Vector Symbology: Continuous Attribute Data Graduated Colors Graduated Symbol Sizes Proportional Symbol Sizes

Vector Symbology: Charts Pie Bar/Column Stacked

Symbology: Raster Layers Views of Radio Towers Unique Values Discrete Data  Discrete Colors Classified Continuous Data  Discrete Colors Stretched Continuous Data  Continuous Colors Aspect Slope

Map Layout Layout View

Map Layout: Data Frames Layout View Toolbar Data Frame Object Extents of GIS Data Data Frame Edge Paper Margin Paper Edge

Map Layout: Auxiliary Elements

Map Layout: Multiple Data Frames

Map Layout: Full Map Layout Title North Arrow Scale Body Landmark Text (opt.) Legend Overview (opt.)

Normalization Set Value to One Attribute Set Normalization to Another Attribute ArcMap Calculates Color Based On: Displayed Value = Value / Normalization Examples: Density: Value / Area Proportional Growth: New Value / Old Value Proportional Population: Subgroup Population / Total Population

Total Population by Nation

Total Population by Nation

Normalization for Population Density

World Population Density by Nation

Total Population by County

Total Population by County

Normalization for Population Growth

US Population Growth by County

Classification How are Continuous Data Categorized in Symbology? Classification Methods Equal Interval/Defined Interval Place Breaks at Equal Intervals, Specifying Number or Width of Breaks Standard Deviation Place Breaks at Equal Standard Deviations From the Mean Value Quantile Place Breaks Such That Groups Have Equal Size Memberships Natural Breaks Place Breaks Between Clusters of Data Manual Breaks

Ozone Levels in California

Histogram x-Axis: y-Axis: Bars: The full range of data values, classified into narrow range categories y-Axis: Number of features/cells, or frequency Bars: The number of features in each narrow range category

Equal Interval/Defined Interval Lecture 12

Equal Interval/Defined Interval Guarantees a linear relationship between the data values and the color selected

Standard Deviation Lecture 12

Standard Deviation Similar to Equal Interval, but uses a statistical basis for determining the interval size

Quantile Lecture 12

Quantile Guarantees that each color will be assigned to approximately the same number of features Effectively divides your data into equally-sized groups Results in Greatest Overall Differentiation

Natural Breaks Lecture 12

Natural Breaks Uses an algorithm to place breaks such that: The variance within groups is minimized, and The variance between groups is maximized Results will tend to be irregularly-sized intervals This is the default in ArcMap

Manual Breaks Lecture 12

Manual Breaks Can reflect policy-based or arbitrary thresholds and categories Tedious to set up

Classifications: When to Use Equal Interval You want to be able to compare relative values using the colors Standard Deviation You expect your data to be normally distributed Quantile You want your breaks to be narrower in clusters of data Natural Breaks You want to use your data to identify clusters of values Manual Breaks You have an external source for setting breaks