The following
text was taken from the ERSI Online Campus
website where these courses can be accessed
Turning Data into
Information Using ArcGIS 9
Module 1:
Basics of Data and Information
Representing geography
What are
geographic data?
How are
geographic data represented?
Problems with
representing geographic data
Objects and
fields
Rasters and vectors
Comparing the
discrete object and field views for lakes in
Explore
geographic data
The nature of geographic data
Spatial
autocorrelation
Spatial sampling
Distance decay
and spatial interpolation
Explore how
sampling scheme can affect spatial interpolation
Creating and visualizing
information
Visualization
and interaction
What is spatial
analysis?
Types of spatial
analysis
Spatial analysis
sampler
Uncertainty
Uncertainty in
the conception of geographic phenomena
Uncertainty in
the measurement of geographic phenomena
Uncertainty in
the representation of geographic phenomena
Uncertainty in
the analysis of geographic phenomena
Module 2:
Cartography, Map Production, and Geovisualization
GIS-based visualization
GIS-based
visualization as a decision support tool
Properties of
GIS-based visualization
Explore the power
of a GIS-based representation
Representing attributes and
spatial objects
Representing
attributes
Classifying
attributes
Representing
spatial objects
Represent
features and attributes
Simplify a line
feature
Scientific visualization
Mapping
technology and media
Purposes of
visualization
Interacting with
representations to support decisions
Interacting with
representations on the Internet
Internet GIS as
a site selection tool
Interact with a
GIS display using feature selections
Advanced methods for improving
visualizations
Dasymetric mapping
Multivariate
mapping
Multivariate
visualization in
Module 3:
Query and Measurement
Querying views of a GIS
Catalogs
Maps
Tables,
histograms, and scatterplots
Explore data
using a catalog view
Advanced queries
Linking views
Tabular queries
Create advanced
queries to get information
Querying for measurements
Distance and
length
Shape
Slope and aspect
Work with
measurements
Module 4:
Transformations and Descriptive Summaries
Buffering, point-in-polygon, and
polygon overlay
Buffering
Point-in-polygon
Polygon overlay
Perform buffer,
point-in-polygon, and polygon overlay operations
Spatial interpolation and
density estimation
What is spatial
interpolation?
Inverse Distance
Weighting
Kriging
Calculating
density
Use and compare
Inverse Distance Weighting (IDW) and Kriging
Density
Centers and dispersion
Centers
The Varignon Frame experiment
Dispersion
Calculate
measures of central tendency and dispersion
Histograms, pie charts, and scatterplots
Histograms and
pie charts
Scatterplots
Comparing
attributes when objects do not spatially coincide
Work with charts
and histograms
Spatial dependence and
fragmentation
Spatial dependence
Fragmentation
Positive vs.
negative spatial autocorrelation
Explore spatial
dependence and fragmentation
Module 5:
Optimization and Hypothesis Testing
Optimization
Point location
Routing problems
Optimum paths
Determining the
coverage area for fire stations
Finding the
shortest route to visit customers
Find the least
cost path for a power line
Hypothesis testing
Sampling
Hypothesis
testing
Hypothesis tests
on geographic data
Module 6:
Uncertainty
Measuring uncertainty of nominal
and ordinal values
Using a
confusion matrix
Summarizing a
confusion matrix
Spatial sampling
Difficulties in
sampling natural areas
Examine and
interpret a confusion matrix
Measuring uncertainty of
interval or ratio values
Accuracy and
precision
Measuring the
magnitude of errors
Describing the
distribution of errors
Uncertainty in
spatial data
Examine Root
Mean Squared Error
Uncertainty issues for spatial
data
The spatial
structure of errors
Error
propagation
Fuzzy approaches
Living with uncertainty