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 Various printers & plotters Softcopy 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 Tagged Image File format BMP BitMaP Related capabilities CAD & CoGo (Coordinate Geometry)
IDRISI s Database Management System DBMS Database Workshop Traditional 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 + average income + Tree species + 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 Defined by (x,y) pairs Characteristics Brightness Color Shape 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 Layers Building owner Building age Building value 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 80 m ground resolution cells Landsat TM 30 m ground resolution cells SPOT XS 20 m ground resolution cells SPOT Pan 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 AND, OR Mathematical combinations 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 Money, time, effort Low frictional costs Valleys High frictional costs 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 Basic scientific principles Empirical Curve-fitting (e.g., regression lines)