Price Ch. 2 Mapping GIS Data ‣ GIS Concepts GIS Concepts Ways to map data Displaying rasters Classifying numeric data
Map Types and Data Types ‣ Single symbol maps ‣ Unique values maps ‣ Quantities maps Graduated color Graduated symbol Dot density ‣ Nominal data ‣ Categorical data ‣ Ordinal data ‣ Interval and Ratio data
Nominal data ‣ Names or uniquely identifies objects State names Owner of parcel Tax ID number Parcel ID Number ‣ Each feature likely to have its own value ‣ Usually portrayed on a map as labels
Single symbol maps ‣ Display all features with the same symbol ‣ Combine with labels to portray nominal data
Categorical data ‣ Places features into defined number of distinct categories ‣ Category names may be text or numeric ‣ Portrayed by different symbol for each category
Unique values maps ‣ Different symbol for each category or value Geologic unitsVolcano typesRoad types
Ordinal data ‣ Type of categorical data ‣ Ranks categories along an arbitrary scale Low, Medium, High slope Village, Town, City Grades: A, B, C, D, F Rank of Best City to Live In: 1, 2, 3… A 0-40% B 40-70% C % Portrayed as categories but choosing variations in symbol size or color to indicate increase
Interval or Ratio data ‣ Interval data places values along a regular numeric scale Supports addition/subtraction Temperature, pH, elevation ‣ Ratio data places values along a regular scale with a meaningful zero point Supports addition, subtraction, multiplication, division Population, rainfall, median rent
Mapping numeric data ‣ Interval and ratio data must be divided into classes before mapping ‣ Mapped using variations in symbol size, thickness, or hue
Classed maps Graduated color map (choropleth map) Graduated symbol map
Colors for choropleth maps ‣ Generally use change in saturation or close hues to indicate increase ‣ Avoid using too many colors which tend to mask increase
Normalizing classed maps ‣ If the size of the sample impacts the measured value, data should be normalized By percent of total - Percent of farms in each state - Percent of mobile homes in each state By another field - Farms divided by area - Mobile homes divided by total housing units 2-12 Number of farms Number of farms per sq. mile
Unclassed maps Proportional symbol mapDot density map
Chart Maps Proportional chart map
Symbol psychology Where is the water? Where is there less rain? Which towns have more people? What’s there? Where’s the danger?
Displaying rasters
Raster types ‣ Discrete data Represents discrete objects such as lines or polygons Takes on relatively few values Adjacent cells often have same values Values may change abruptly at boundaries ‣ Continuous data Thousands or millions of potential values Few adjacent cells have same values Values may change rapidly from cell to cell
Raster types ‣ Thematic rasters Contain quantities that represent map data such as land use or rainfall May be continuous or discrete ‣ Image rasters Contain satellite or air photo data Generally represent brightness Usually continous
Displaying thematic rasters Unique values Discrete color Interval/Ratio data Classified Categorical/Ordinal data Stretched
Slicing 256 colors Bins raster values from 0-255, to match color ramp values
Stretching 256 colors After slicing, stretching enhances display by removing less common values at the tails Original sliceStandard deviation stretch
Image display Single band image Three band composite image Image values represent brightness as digital numbers (DN)
Stretching images Images usually contain range values already, but may not utilize full range. Stretching maximizes brightness and contrast Different stretches: Min-Max, Standard Deviation, Equalize… No stretchStandard deviation stretch 0255
Effects of stretching No stretch Min-MaxStandard deviation
RGB Color composites Image bands Composite color image
Landsat Band Combinations True color R-G-B False color Other Bands 1-7 represent different wavelengths of light
Indexed color rasters Common for scanned maps More efficient way to store colors for scanned rasters than RGB bands Each color on the map is indexed to a special unique values palette Can modify the color choices individually Colormap
Nodata 0 is another common nodata value
Transparency Geology Hillshade Layer Properties: Display tab
Classifying numeric data
Classifying Data Applies to both vector and raster maps Different classification methods available Choice impacts map appearance and validity Best method depends on data distribution and objective of map Same data, different classifications
Common data distributions Value Number of samples Normal Uniform Skewed Bimodal
Jenks Natural Breaks Exploits natural gaps in the data Good for unevenly distributed or skewed data Default method, works well for most data sets Class breaks
Equal Interval Specify number of classes Divides into equally spaced classes Works best for uniformly distributed data
Defined interval User chooses the class size Data determines number of classes Works best for uniformly distributed data
Quantile Same number of features in each class May get very unevenly spaced class ranges Results depend on data distribution
Geometrical Interval Multiplies each succeeding class boundary by a constant Works well for normal and skewed distributions
Standard Deviation Shows deviation from mean User chooses units e.g. 0.5 standard deviations Assumes data are normally distributed