Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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)