Download GIS - University of Portland

A Basic Introduction to
Geographic Information Systems
(GIS)
~~~~~~~~~~
Rev. Ronald J. Wasowski, C.S.C.
Associate Professor of Environmental Science
University of Portland
Portland, Oregon
3 September 2015
Introduction to GIS
• Geographic Information Systems/Science (GIS)
– Computer–assisted system for…
•
•
•
•
Acquisition
Storage
Analysis
Display
…of geographic data
• Fundamental components of every GIS
– Spatial & attribute databases
•
•
•
•
•
•
•
Cartographic display system
Map digitizing system
Database management system
Geographic analysis system
Image processing system
Statistical analysis system
Decision support system
Introduction to GIS
The Heart of Every GIS
• Two closely related databases
– Spatial database
• Shape & location of Earth’s features
– Subsurface
– Surface
– Atmosphere
– Attribute database
• Data regarding a land parcel…
– Owner
– Value
– Use
• Basic approaches
– Completely separate spatial & attribute databases
– Completely integrated spatial & attribute databases
• IDRISI’s approach
• Option to keep some elements separate
IDRISI’s Cartographic Display System
• Display
database
input
– Existing thematic maps
– Existing
imagery
• Display
processed
output
– New
thematic maps
– Restored, enhanced & classified imagery
• Display cartographic output
– Composition
•
•
•
•
Map layers
Annotation
Scale bars
Legend
– Output
• Hardcopy
• Softcopy
Various printers & plotters
Various graphics formats
IDRISI’s Map Digitizing System
• Early
IDRISI versions
– TOSCA
• Present IDRISI version
– Existing paper maps
• Digitizing tablets
• Large-format scanners
– Images
• Traditional aerial photography
– Analog acquisition
Digital input
• Digital aerial photography & satellites
– Digital acquisition
Digital input
– Supported file formats
• TIF / GeoTIFF
• BMP
Tagged Image File format
BitMaP
• Related capabilities
– CAD & CoGo (Coordinate Geometry)
IDRISI’s Database Management System
• DBMS
– Traditional
Database Workshop
Analysis of attribute data
– Specialized utilities
• Spatial data
• Attribute data
Traditional DBMS capabilities
– Represent results as an image or map
• Related capabilities
– AM/FM (Automated Mapping/Facilities Management)
• Associated with public utilities
Water, electricity …
• Allows users to manage & analyze utility network data
IDRISI’s Geographic Analysis System
• Ability to…
– digitize spatial data
– attach attributes to stored features
– Analyze spatial data based on stored attributes
– map out the result
• Analyze joint occurrence of geographic features
– Example:
Radon risk in residential areas
• Map all bedrock types associated with high radon levels
• Map all residential areas
• Overlay these two maps
• Generate a new map for the GIS database
IDRISI’s Image Processing System
• Image restoration
– Geometry
• Not a perfectly polar orbit
• Earth rotates under the spacecraft
– Sensor banding
• E/W for cross-track scanners
• N/S for pushbroom scanners
• Image enhancement
– Contrast
– Color
– Edges
• Information extraction
– Transformations
– Multispectral classification
IDRISI’s Statistical Analysis System
• Traditional statistical analyses
– Single & multivariate statistics
– Mean, standard deviation…
• Specialized analyses for spatial data
– Changes over both space & time
– Simple distances & cost distances
IDRISI’s Decision Support System
• Decisions regarding resource allocation
– Produce models incorporating error into the process
• Usually overlooked in GIS analyses
• Increases with the number of layers and/or steps involved
– Construct multi–criteria suitability maps
• Buffer zones +
land cover/use type
+…
• Water storage + recreation + flood control + …
– Address allocation decisions with multiple objectives
• Population density +
• Tree species
average income
+…
+ tree diameter/height + …
Map Data Representation
• Two data types
– Geographic definitions
of Earth features
• Latitude / Longitude, UTM coordinates …
– Attributes / characteristics of Earth features
• Tree species, diameter, height, health, age …
• Representation of those data types
– Vector
Magnitude + direction
• Points, lines & polygons
– Scalar [Raster]
Magnitude only
• Digital numbers [DN] Integer (discrete) & real (continuous)
Vector & Raster Data Representations
Vector Data Representation
• Defined using (x,y) coordinate pairs
– Representation of points in space
• Latitude / longitude
• UTM (Universal Transverse Mercator) grid
• Interpret the coordinate pairs
– Points
Benchmarks, intersections…
– Lines
Boundaries, roads, shorelines…
– Polygons
Fields, land cover areas…
• Identify the coordinate pairs
– Simple feature identifier numbers
– Attributes identified with identical numbers
Raster Data Representation
• Areas represented by an array of pixels
– No “features” are defined
• Each cell is assigned a number
– Simple feature identifier numbers
• Spectral class numbers
– Qualitative attribute code
• Ranking from first to last
– Quantitative attribute value
• Reflectance value in some spectral band
• Pixel characteristics
– Position
– Characteristics
• Brightness
• Color
• Shape
Defined by (x,y) pairs
Raster vs. Vector
• Raster data representation
Analysis oriented
– Data intensive
• Every pixel must be represented in the spatial database
– Space is simply & uniformly represented
• Substantially increased analytical power
– Ideally suited to study of continuously changing phenomena
• Matches computer & digital image architecture
• Vector data representation
DBMS oriented
– Data conservative
• Very efficient in storing map data [boundaries]
– Can produce simple thematic maps
• Pen plotters produce traditional-style maps
– Excel at analyzing movement over networks
• IDRISI
– Elements from both data representation styles
Database Concepts: Organization
• Vectors mimic map collections
Coverages
– Vector systems come closest to this organization
• Differ from a collection of maps
– Each contains information on only one feature type
• Buildings
• Roads
• Sewers
– Each contains a series of attributes about features
• Buildings: Owner, age, value, tax rate, tax amount …
• Roads:
Width, number of lanes, paving material …
• Sewers:
Diameter, wall thickness, wall material…
• Rasters establish unitary datasets
• Building owner
• Building age
• Building value
Layers
Database Coverages / Layers
Database Concepts: Georeferencing
• Coverages (vectors) & layers (rasters)
– Reference systems
• Latitude / longitude
• UTM coordinates
Universal Transverse Mercator
• State plane coordinates
– Reference units
• Degrees / minutes / seconds
45° 34' 12"
• Decimal degrees
45.57°
– Bounding rectangles
• North, East, South & West coordinates
• Required even if coverages & layers are not rectangles
Unusual Database Characteristics
• Scale differences are gracefully handled
– Input layers with different pixel dimensions
•
•
•
•
Landsat MSS
Landsat TM
SPOT XS
SPOT Pan
80 m ground resolution cells
30 m ground resolution cells
20 m ground resolution cells
10 m ground resolution cells
– Resolution strategies
• Resample pixels to a common size
• Multiply number of pixels by a scale factor
• Map reference systems are easily changed
• Map
projections
are easily changed
– Fully automated
– Extremely fast
– Metric, British, nautical…
• Resolution remains a critical issue
Analysis In GIS
• Analytical tools
– Database query
– Map algebra
– Distance operators
– Context operators
• Analytical operations
– Database query
– Derivative mapping
– Process modeling
Analytical Tools: Database Query
• Retrieve stored information from the database
– Ask questions by location
• What is present at a particular location?
– Ask questions by attribute
• What attributes does this location have?
• Two steps involved
– Produce reclassifications from existing layers
• Combine similar
layers
– Pines, firs & cedars all classified as evergreen trees
• Produce Boolean layers
Masks
– 0 [unacceptable] or 1 [acceptable]
– Overlay the reclassifications
• Logical
combinations
• Mathematical combinations
AND, OR …
Addition, subtraction …
Reclassification & Overlay
Analytical Tools: Map Algebra
• Combine map layers mathematically
– Mathematical modeling absolutely requires this
• Mean annual temperature as a function of altitude
• Soil erosion a function of erodability, gradient & rainfall
• Three kinds of mathematical operators
– Modify
data
within a single layer
• Add, subtract, multiply or divide using a constant
– Transform data
within a single layer
• Trig functions, log transformations …
– Combine
data across multiple layers
• Snowmelt = ( 0.19 . Temperature + 0.17 . Dew Point )
Analytical Tools: Distance Operators
• Construct buffer zones
– Constant distance from a point, line or polygon
• Hard boundaries
• Evaluate distance to all features in a set
– Actual distance to various points, lines or polygons
• Soft boundaries
• Frictional effects
– Cost distances
• Low frictional costs
• High frictional costs
Money, time, effort…
“Valleys”
“Hills”
– Anisotropic costs
• Going uphill costs more than going downhill
• Barriers
– Frictional costs too high to overcome
Distance Operators
Analytical Tools: Context Operators
• Neighbors often affect one another
– Elevation layer produces both slope & aspect layers
– Digital filters change the neighborhood
• Raster systems well suited to context operators
– Surface analysis
– Digital filtering
– Contiguous areas
– Watershed analysis
– Viewshed analysis
– Supply / demand modeling
Analytical Operations: Database Query
• Database query tools for multiple variables
• Apply appropriate procedures
– Measurement
– Statistical analysis
• Key features
– Take out only what is in the database(s)
– Make a withdrawal from an existing data bank
• Key activity
– Looking for spatial patterns
Analytical Operations: Derivative Mapping
• Knowledge of relationships
• Combine selected variables into new layers
– Example:
Soil erosion potential
• Topographic elevations
– Slope aspect Compass direction toward which the slope faces
– Slope gradient Slope steepness
• Soil erodability
• Create new data from old data
– Ability to produce models
• Use map algebra tools
– Foundations for those models
• Theoretical
• Empirical
Basic scientific principles
Curve-fitting (e.g., regression lines)