Mathieu Cain’sGeographic Information SystemsSkills Portfolio             Prepared for Pamela Wilson                 Geomat...
Table of ContentsIntroduction                                                  3Database Design                           ...
GIS serves to:        Capture                        Hardware                                                    Geographi...
Database design (withMicrosoft Access):- Developing table                 To Table  layouts based on                  Layo...
Exploring theGeodatabase model:- Making valid edits to  features through                            ∞  attribute domains  ...
Data Collection via aGlobal PositioningSystem:- Familiarization with  product selection- Understanding the  basic function...
Remote Sensing –satellite imagery:- Presentation case-   study on the Ikonos   satellite and   evaluation of its   imagery...
Image Processing:                                             Key Diagnostic Characteristics- Aerial photo  interpretation...
Image Processing:- Digital image                                   Georegistration  rectification  • using a world file   ...
Unsupervised image                                      LandSat Imageryclassification- Use of a   multispectral image   da...
Supervised imageclassification- Defining class   training areas:   informed decision   making (i.e., use of   prior knowle...
Normalized DifferenceVegetation Index:- Calculating  vegetation/amounts  of biomass, and  spatial and temporal  evaluation...
Distance and DensityAnalysis:- The airport must be  more than 150 km  from a current  airport- The airport must be  locate...
Spatial-TemporalAnalysis:- Identification of  communities that  have a higher than  normal risk of a West  Nile outbreak i...
Site Analysis:- Accessibility for fire   and ecology   managers: i.e., within   200 meters of a   “major” road- Accessibil...
Suitability Analysis:- In an area with at  least a “good” wind  farm resource  potential: i.e., within a  Wind Power Class...
Suitability Analysis:- Raster analysis- Use of weighted  criteria    Raster Data Model                        Suitability ...
Linking ElementalStatistics with RenderingSchemes for digitalelevation models:- Evaluating error and  data squewness  • Eq...
Measuring GeographicDistribution:- Data outliers/trend  skewing- Measuring change  over time (e.g.,  population)- Determin...
Spatial Autocorrelation& Cluster Analysis:- All natural objects are  related, while closer  ones are more so- Cluster anal...
Surface Interpolations:- Exploring Trend  Surface Interpolation  • Spline surface     creation  • Some raster cell     val...
DeterministicInterpolators:- Creates surface from  measured points- Surfaces based on:  • Extent of     similarity (e.g., ...
GeostatisticalInterpolators:- Creates surfaces  through spatial  autocorrelation of  random processes  (i.e., to model spa...
Application of SpatialStatistics &Geostatistical Analysis:        Histogram     (test of normality)   Standard Deviation  ...
Statistical Surfaces:- Isarithmic map –  using delauny  triangular net to  linear interpolate  isolinear contour  interval...
Triangular IrregularNetwork:- Creating Triangular  Irregular Network  from a Digital  Elevation Model   Creation of DEMs  ...
3D Modeling:- Creating a 3D model  fly-through in  ArcScene- Draping layers over a  3D surface and  extruding features    ...
Project Management:- Identifying and                                 Identifying & Describing Information Products  descri...
Precision Agriculture:                                        Evaluating Agricultural Capabilities at a site   Critiquing ...
Cartographic use ofmedium:- Exploring the use of  alternative mapping  mediums to convey a  picture (e.g., Lego)- Using me...
Cartographic use ofcolour:- Exploring the use of  colour and how it  influences the  viewer’s perceptions.- Black on yello...
Projections and Datums: - Evaluating   projections’ merits   and their effects (e.g.,   distortion) on shape,   area, dire...
Knowledge Sharing:        Criteria     (ven-diagram)      Metadata    (data about data)      Approach      (flow-chart) De...
“The good cartographer is both a scientist andan artist. He must have a thorough knowledgeof his subject and model, the Ea...
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Skills portfolio

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A portfolio of skills developed through Assiniboine Community College

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Skills portfolio

  1. 1. Mathieu Cain’sGeographic Information SystemsSkills Portfolio Prepared for Pamela Wilson Geomatics Applications
  2. 2. Table of ContentsIntroduction 3Database Design 4Geodatabase Model 5Data Collection & Global Positioning Systems 6Remote Sensing – Satellite Imagery 7Imagery Processing 8-9Image Classification 10-12Unsupervised Classification 10Supervised Classification 11Normalized Difference Vegetation Index 12Analysis 13-17Distance and Density Analysis 13Spatial-Temporal Analysis 14Site Analysis 15Suitability Analysis 16-17Elemental Statistics & Rendering Schemes 18Measuring Geographic Distribution 19Spatial Autocorrelation & Cluster Analysis 20Surface Interpolation 21Deterministic Interpolators 22Geostatistical Interpolators 23Application of Spatial Statistics & Geospatial Analysis 24Statistical Surfaces 25Triangular Irregular Network 263D Modeling 27Project Management 28Precision Agriculture 29Cartography 30-32Knowledge Sharing 33
  3. 3. GIS serves to: Capture Hardware Geographic Data Store 2004 2008 Update Knowledge Manipulate Analize Software People Display Geographic Information SystemsReferenced Geographic The complex interaction of multiple components Information
  4. 4. Database design (withMicrosoft 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. 5. Exploring theGeodatabase 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. 6. Data Collection via aGlobal PositioningSystem:- 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. 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. 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. 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. 10. Unsupervised image LandSat Imageryclassification- 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. 11. Supervised imageclassification- 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. 12. Normalized DifferenceVegetation 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. 13. Distance and DensityAnalysis:- 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. 14. Spatial-TemporalAnalysis:- 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. 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. 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. 17. Suitability Analysis:- Raster analysis- Use of weighted criteria Raster Data Model Suitability Analysis Archaeological Potential
  18. 18. Linking ElementalStatistics with RenderingSchemes for digitalelevation 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. 19. Measuring GeographicDistribution:- 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. 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. 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. 22. DeterministicInterpolators:- 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. 23. GeostatisticalInterpolators:- 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. 24. Application of SpatialStatistics &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. 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. 26. Triangular IrregularNetwork:- 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. 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. 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. 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. 30. Cartographic use ofmedium:- 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. 31. Cartographic use ofcolour:- 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. 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. 33. Knowledge Sharing: Criteria (ven-diagram) Metadata (data about data) Approach (flow-chart) Detailed Instructions (step-by-step)
  34. 34. “The good cartographer is both a scientist andan artist. He must have a thorough knowledgeof his subject and model, the Earth…. He musthave the ability to generalize intelligently andto make a right selection of the features toshow. These are represented by means of linesor colors; and the effective use of lines orcolors requires more than knowledge of thesubject – it requires artistic judgement.” –Erwin Josephus Raisz (1893 – 1968)“If you want a map or database that haseverything, you’ve got it. It’s out there. It’scalled Earth.” –Scott Morehouse, Director of Software Development, ESRI “Here Be Dragons”

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