A portfolio of skills developed through Assiniboine Community College

1. Mathieu Cain’s
Geographic Information Systems
Skills Portfolio
Prepared for Pamela Wilson
Geomatics Applications

2. Table of Contents
Introduction 3
Database Design 4
Geodatabase Model 5
Data Collection & Global Positioning Systems 6
Remote Sensing – Satellite Imagery 7
Imagery Processing 8-9
Image Classification 10-12
Unsupervised Classification 10
Supervised Classification 11
Normalized Difference Vegetation Index 12
Analysis 13-17
Distance and Density Analysis 13
Spatial-Temporal Analysis 14
Site Analysis 15
Suitability Analysis 16-17
Elemental Statistics & Rendering Schemes 18
Measuring Geographic Distribution 19
Spatial Autocorrelation & Cluster Analysis 20
Surface Interpolation 21
Deterministic Interpolators 22
Geostatistical Interpolators 23
Application of Spatial Statistics & Geospatial Analysis 24
Statistical Surfaces 25
Triangular Irregular Network 26
3D Modeling 27
Project Management 28
Precision Agriculture 29
Cartography 30-32
Knowledge Sharing 33

3. GIS serves to:
Capture
Hardware Geographic Data
Store
2004
2008
Update
Knowledge
Manipulate
Analize
Software People
Display Geographic Information Systems
Referenced Geographic The complex interaction of multiple components
Information

4. Database design (with
Microsoft Access):
- Developing table To Table
layouts based on Layout
historic records
- Creating database
tables and queries in
via SQL commands To SQL
- Populating database
tables from multiple
input formats (e.g.,
manual, Excel, text) Adding
and creation of Data
masks and table
lookups
- Defining table
relationships (e.g.,
one-to-one; one-to-
Table
many)
Lookup
- Designing data entry
forms
- Creating reports via
database queries
Mask
Relationships
Forms
To Report

5. Exploring the
Geodatabase model:
- Making valid edits to
features through ∞
attribute domains
(i.e., validation) and
Linked
subtypes (default field 1
values based a
particular field entry)
- Using utility network Map Topology Utility Network Analyst:
analyst to test (maintaining testing network connectivity
network connectivity Spatial relationships)
through trace
operations
- Editing feature sets Data structures
based on
simple/composite
relationship classes
- Creating feature
datasets/raster
catalogues and
feature classes/raster Domains
(Range vs. Coded Values)
datasets; and
importing structures
(i.e., schema)
- Working with
personal (i.e., *.mdb)
Feature Class
and file (i.e., *.gdb)
geodatabases
- Creating/editing
features and map
topology (e.g.,
shared boundaries)
- Using labels and
annotation

6. Data Collection via a
Global Positioning
System:
- Familiarization with
product selection
- Understanding the
basic functionality of
a hand held GPS
- Identification of
limitations and
sources of error
- Exploration of uses
in the professional
and social domain,
including geocaching
Surface data collection
Using technology legacy
of a Cold War race to
find a film canister GPS Accuracy and Tracks Study
geocache in a tree City of Waterloo, Ontario Province, Canada

7. Remote Sensing –
satellite imagery:
- Presentation case-
study on the Ikonos
satellite and
evaluation of its
imagery products
• Historical
perspective (e.g.,
regulations,
competition,
failures/successes)
• Technical
specifications (e.g.,
orbit, revisit time,
type of system,
type of scanner)
• Imagery product
(e.g.,
panchromatic/
multispectral/pan-
sharpened,
resolution)
• Target markets
and use

8. Image Processing: Key Diagnostic Characteristics
- Aerial photo
interpretation using 9
key diagnostic
characteristics
- Photogrammetry (i.e.,
science of making
measurements from
photos) – photo scale
and image
interpretation
- Using a mirror Tone/Colour Size Shape
stereoscope to (e.g., Grate Lakes) (e.g., Great Pyramid) (e.g., capsized ocean liner)
evaluate depth
between separate
offset images
Texture Pattern Site
(e.g., tree cover) (e.g., fields) (e.g., rail cars)
Association Shadows Height
(e.g., Skydome next to CN Tower) (e.g., water tower & ferns) (e.g., house vs. lamp post)
Area Grid

9. Image Processing:
- Digital image Georegistration
rectification
• using a world file
to georegistre a
topographic tiff
image to a MrSID
aerial photo
• georeferencing
using orthophoto
ground control
points
- On-screen
digitization of digital
imagery (point, line,
and polygon features)
Georeferencing Digitization

10. Unsupervised image LandSat Imagery
classification
- Use of a
multispectral image
data analysis system
(i.e., MultiSpec)
- e.g., 6-channel image
of the Deloraine,
Boissevain area in
Manitoba, Canada
Original False Colour Composite (4-3-2)
Unsupervised Classification Unsupervised Classification
(MultiSpec - 10 clusters) (Class Identification)

11. Supervised image
classification
- Defining class
training areas:
informed decision
making (i.e., use of
prior knowledge)
- Use of a
multispectral image
data analysis system
(i.e., MultiSpec)
- e.g., 6-channel image
of the Deloraine,
Boissevain area in
Manitoba, Canada
Supervised Land Cover Classification
Deloraine, Boissevain area, Manitoba Province, Canada

12. Normalized Difference
Vegetation Index:
- Calculating
vegetation/amounts
of biomass, and
spatial and temporal
evaluation (i.e. NDVI
as a ratio) using
MuliSpec
- e.g., 6-channel image
of the Deloraine,
Boissevain area in
Manitoba, Canada
Normalized Difference Vegetation Index (NDVI)
South Western Manitoba Province, Canada

13. Distance and Density
Analysis:
- The airport must be
more than 150 km
from a current
airport
- The airport must be
located near a high
density of smaller
sized communities
(i.e., less than 5,000
people)
Distance and Density Analysis - Evaluating potential sites for an airport

14. Spatial-Temporal
Analysis:
- Identification of
communities that
have a higher than
normal risk of a West
Nile outbreak in the
future based on the
spatial distribution of
previous years
- Determining the top
25 communities and
associated Regional
Health Authorities
that have a higher
than normal risk of
having a West Nile
outbreak in 2007
- Identifying
communities that
have had previous
outbreaks of West
Nile virus and are
within 1 kilometer of
any standing water
Spatial-Temporal Analysis - Assessing risk of West Nile Virus

15. Site Analysis:
- Accessibility for fire
and ecology
managers: i.e., within
200 meters of a
“major” road
- Accessibility to a
water supply for
potential firefighting:
i.e., within 2,000
meters of a “major”
river
- Maximised viewshed
to increase site of
terrain from a tower:
i.e., on an elevation
of over 840 meters
- Minimised
construction
problems: i.e., on a
slope of no more
than 5%
- Maximum proximity
to grasslands as these
are ones of the most
concern
- Use of Geometric
Mean Centre to
determine point of
relative equi-distance
- Use of Euclidean
Distance to
determine location’s
relative distance to all
other points Site Analysis - Siting a Fire Tower

16. Suitability Analysis:
- In an area with at
least a “good” wind
farm resource
potential: i.e., within a
Wind Power Class
(WPC) of at least 4
- Accessibility to
highway for potential
maintenance crews:
i.e., within 5 miles of
a Highway
- Accessible to a
nearby target market:
i.e., within 50 miles
of a city of no less
than 25,000 people
- Not on Federal Land:
i.e., not in national
parks, forests,
grasslands, etc.
- On a large enough
area for a wind farm:
i.e., within an area of
at least 1 km²
Suitability Analysis - Proposed Wind Farm Sites

17. Suitability Analysis:
- Raster analysis
- Use of weighted
criteria
Raster Data Model
Suitability Analysis
Archaeological Potential

18. Linking Elemental
Statistics with Rendering
Schemes for digital
elevation models:
- Evaluating error and
data squewness
• Equal Intervals
(divides the range of values
(i.e., between maximum
and minimum values) into
equally spaced groups
based on the number of
specified intervals)
• Quantile (divides the
total number of values
(i.e., the count) into equal
numbers of values based
on the number of specified
intervals)
• Natural Breaks
(identifies variation in the
dataset and classifies
values into groups of
varying sizes based on
maximizing variability
within the number of
specified intervals)
• Standard
Deviation (identifies
the amount of variation of
values with respect to the
mean. Interval values are
classified as within a set
standard deviation above
the mean (i.e., positive
value), or within a set
standard deviation below Rendering Scheme Comparison – MZTRA Field 201 – Elevation
the mean (i.e., negative Manitoba Province, Canada
value))

19. Measuring Geographic
Distribution:
- Data outliers/trend
skewing
- Measuring change
over time (e.g.,
population)
- Determining the
Weighted Geographic
Centre (i.e.,
geographic mean
centre) – the point
determined by the
average of the other
point features’
geographic
coordinates
- Determining
accessibility
With outlier datum
Measuring Geographic Distribution
Manitoba Province, Canada
Without outlier datum

20. Spatial Autocorrelation
& Cluster Analysis:
- All natural objects are
related, while closer
ones are more so
- Cluster analysis over
time
- E.g., Invasive species
and water resource
management
Spatial Autocorrelation & Cluster Analysis
for Zebra Mussels in North America
North America

21. Surface Interpolations:
- Exploring Trend
Surface Interpolation
• Spline surface
creation
• Some raster cell
values lie outside
of sample range
• Note stiffer
tension vs. more
gradual Weighted
regularized Surface
- Exploring Weighted Interpolation
Surface Interpolation
• Inverse Distance
Weighted surface
• Increase the
number of points
increases the
neighbourhood
radius on which
each cell is
interpolated
decreases potential
variability Trend
smoother looking Surface
surface (less direct Interpolation
influence by any
one point)
- Using ESRI’s Spatial
Analyst Extension

22. Deterministic
Interpolators:
- Creates surface from
measured points
- Surfaces based on:
• Extent of
similarity (e.g., Inverse
Distance Weighting)
• Amount of
smoothing (e.g.,
Radial Basis Functions or
Spline)
- Methods of
calculating prediction:
• Global – uses full
dataset
• Local – uses
measured points
within specified
neighborhoods
- Interpolators:
• Exact – preserve
all measured
values in the
prediction (e.g.,
IDW, Radial Basis
Functions)
• Inexact – use
predicted values
based on the
overall set of
measured points
(e.g., Global Polynomial
Interpolation, Local Deterministic Interpolators – Ozone Levels
Polynomial Interpolation) State of California, United States

23. Geostatistical
Interpolators:
- Creates surfaces
through spatial
autocorrelation of
random processes
(i.e., to model spatial
variation of natural
phenomena)
- Types of surfaces:
• Prediction (e.g.,
Kriging, Cokriging)
• Error/uncertainty
(e.g., standard error
surface, quantile surface,
probability surface)
- Steps:
• Quantifying the
data’s spatial
structure (i.e.,
variography – fits
a spatial-
dependence model
to the dataset)
• Producing a
prediction (i.e.,
based on fitted
variography
model, spatial data
configuration, and
values of
measured sample
points around Geostatistical Interpolation Method: Kriging – Ozone Levels
prediction State of California, United States
locations)

24. Application of Spatial
Statistics &
Geostatistical Analysis:
Histogram
(test of normality)
Standard Deviation
Classification scheme
(measure of average variation
with respect to the mean)
Normalized change Comparing Interpolation Methods – Gravity Levels
between Min & Max Manitoba Province, Canada
values

25. Statistical Surfaces:
- Isarithmic map –
using delauny
triangular net to
linear interpolate
isolinear contour
intervals
- Cross section profile
– calculating vertical
exaggeration
Representations
Vertical Exaggeration Contour Mapping - Triangulation
(cross section) Manitoba Province, Canada

26. Triangular Irregular
Network:
- Creating Triangular
Irregular Network
from a Digital
Elevation Model
Creation of DEMs
Using Hillshade
on a DEM and Triangular Irregular Network - Riding Mountain National Park
semi-transparencies Manitoba Province, Canada

27. 3D Modeling:
- Creating a 3D model
fly-through in
ArcScene
- Draping layers over a
3D surface and
extruding features
3D Model
surface fly-through
Environmentally Sensitive Areas Study
3D Model Ecosystem Community Modeling
subdivision Bechtel Park, City of Waterloo, Ontario Province, Canada

28. Project Management:
- Identifying and Identifying & Describing Information Products
describing the
components of
planning a GIS
implementation
- Identifying GIS
information products
and defining
Information Product
Descriptions (e.g.,
intended user descriptions, map
and report requirements,
document and image Identifying Functional Requirements Prioritizing Information Products
requirements, definition of
error tolerance)
- Defining the system
scope and assigning
priorities to
information products
for a Master Input
Data List (MIDL)
- Cost-benefit analysis Identifying Costs and Calculating Benefits
• Identifying point
of positive cash
flow
• Balancing
cumulative costs
and benefits
• Computing
benefit to cost
ratio
- Considering risks and
implementation
(evaluation &
monitoring)

29. Precision Agriculture:
Evaluating Agricultural Capabilities at a site Critiquing GPS Product Brochure
- Using Agri-Maps to
find (e.g., legal land
description,
acres/municipality, orthophoto
description) and critically
assess soil
information mapped
in a specific area (e.g.,
detailed or reconnaissance, Analysing Soil through
agricultural capabilities classes, Infrared Imagery &
soil drainage and salinity,
surface texture, soil landscape
Electrical Conductivity
type), and make some
precision farming
decisions (e.g., type of
crop, type of machinery and
precision agriculture equipment
being used, and being planned
for)
- Evaluating Precision
Agriculture GPS
units (e.g., accuracy,
pass to pass or static)
- Analyzing soil
through remote
sensing (e.g., moisture
through infrared imagery)
and field tests (e.g.,
electrical conductivity;
comparing salinity at depths
and over time)
- Using Ag Leader
Technology SMS
Advanced to map and Mapping & Analyzing
analyze farm data Farm Data with SMS

30. Cartographic use of
medium:
- Exploring the use of
alternative mapping
mediums to convey a
picture (e.g., Lego)
- Using mental maps to
“tell a story”
Mind Mapping – Local Transportation Routes
City of Waterloo, Ontario Province, Canada

31. Cartographic use of
colour:
- Exploring the use of
colour and how it
influences the
viewer’s perceptions.
- Black on yellow has
the best visible
contrast. As a result it
is most often
associated with
warning signs.
- As reflected in the
red-yellow-green
traffic light, red draws
our attention, while
green denotes trust.
- Many natural features
have traditionally
associated colours,
such as blue water.
Site Suitability for a Hog Barn
Manitoba Province, Canada

32. Projections and Datums:
- Evaluating
projections’ merits
and their effects (e.g.,
distortion) on shape,
area, direction and
distance
(no distortion at tangent)
Planar
(i.e., azimuthal)
Cylindrical
(e.g., UTM)
Conical
(e.g., lambert conformal conic)

33. Knowledge Sharing:
Criteria
(ven-diagram)
Metadata
(data about data)
Approach
(flow-chart)
Detailed Instructions
(step-by-step)

34. “The good cartographer is both a scientist and
an artist. He must have a thorough knowledge
of his subject and model, the Earth…. He must
have the ability to generalize intelligently and
to make a right selection of the features to
show. These are represented by means of lines
or colors; and the effective use of lines or
colors requires more than knowledge of the
subject – it requires artistic judgement.”
–Erwin Josephus Raisz
(1893 – 1968)
“If you want a map or database that has
everything, you’ve got it. It’s out there. It’s
called Earth.”
–Scott Morehouse, Director of
Software Development, ESRI
“Here Be Dragons”