Price Ch. 2 Mapping GIS Data

‣ 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 types Road 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 70-100%

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 mapsGraduated 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 map Dot 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 valuesDiscrete color

Interval/Ratio data

Classified

Categorical/Ordinal data

Stretched

Slicing

256 colorsBins raster values from 0-255, to match color ramp values

Stretching

256 colorsAfter slicing, stretching enhances display by removing less common values at the tails

Original slice Standard deviation stretch

Image display

Single band image Three band composite image

Image values represent brightness as 0-255 digital numbers (DN)

Stretching images

Images usually contain 0-255 range values already, but may not utilize full range.

Stretching maximizes brightness and contrast

Different stretches: Min-Max, Standard Deviation, Equalize…

No stretch Standard deviation stretch

0 255

Effects of stretching

No stretch Min-Max Standard deviation

RGB Color composites

Image bands

Composite color image

Landsat Band Combinations

True color3-2-1R-G-B

False color4-3-1

Other7-4-1

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

Nodata0 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

Num

ber o

f sam

ples

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