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Dept. of Civil and Environmental Engineering and Geodetic Science College of Engineering The Ohio State University Columbus, Ohio 43210 [email protected] Data Acquisition Data Acquisition Analysis & Modeling F( )xyz=f(p1)xyz+f(p2)xyz+…+f(pN)xyz Data Organization Map Composition Analysis & Modeling F( ) =Layer1+Layer2+ … + LayerN Result Report Result Report Definition of GIS GIS Component Input Management & Analytical Modules Data Acquisition - Geodetic Positioning - Remote Sensing - Field Sampling Analog Data Conversion - Scan - Digitize Output Data Output Management - Data Storage - Data Retrieval, Expand Edit, and Update - Query Analytical Modules - Data Conversion - Data Manipulation - Modeling - Visual Presentation - Analog Map Output - Reports GIS Data Elements & Characteristics Data Types Vector - Based on mathematical function - point, line, polygon, & surface Point Polygon Lines Surface Image Grid Raster - Data present on a fixed grid structure (matrix) - image, grid Data Characteristics Space – feature locations Attribute – feature attributes, qualities & characteristics of geographic places. Relationships Between Features Time – additional spatial dimension GIS Data Layers Data Organization Research Data Layers Research Data Layers Basemap Coordinate System - Nominal Data - categorized & named – class value - relates numbers to names - ex: tree species, soil type, parcel owner names - Ordinal data - classes are in a rank or order - ex: 1 - good, 2 – moderate, 3 - poor - Interval data - intervals between data values are meaningful - quantify differences - ex: elevation, °F - Ratio data - measures a condition with a natural zero value - quantify proportions - ex: electromagnetic radiation, rainfall, slope Basemap - Provides spatial relation and geometric shape of ground features - Serves as a foundation for data rectifications Coordinate System - Geographic coordinate system for small scale research. - Plane coordinate system for large scale research. GIS Data Availability GIS DATA Remotely Sensed Data Spatial & Biophysical Spatial Only - B/W aerial photo Panchromatic images Radar image GPS Bathymetry LIDAR - Color aerial photo Multispectral images Hyperspectral image Multiband radar image Conventional Data - Data driven from maps. - Statistical data from published tables. - CAD drawings. - Data from archives using the Internet or other network. - 4Ds: DRGs, DLGs, DEMs, DOQQs. DRG DLG DEM DOQQ Inventory Operation Measurement A=0.175km2 P=1.5km Inventory operation is to obtain information from existing data layers or databases Measurement Spatial Query - Distance - Area/Size - Perimeter Spatial Query - Graphic Query - Boolean Query: AND, OR, NOT Database operations Database Operations - Lists & Reports - Relational Database Spatial Analysis Soil Soil 1 Soil 3 Landuse Hydrology Observation Urban Station 1 Corn Forest Soil 4 Soil 2 Soybean River Spatial Operations Soil type and Landuse along the river, but within 150m radius of Station 2 Station 2 Station3 Spatial analysis can be used to derive spatial relationships among data layers The basic operation involves: - Buffer operation - Overlay operation Network Analysis Service Area Routing & Network Distance Network analysis is to solve the problem or model the behavior of a network structure by connecting lines, such as a transportation network or a stream network The basic operation involves: Pollutant Transport Effective Area Time to Distribute - Locating routes - Determine which facility or feature is closest (allocation) - Modeling travel directions - Obtaining area around a site within a given distance or time 3-D Analysis 3-D analysis is to analyze spatial information in a 3-D perspective. Visualization - Provide 3-D view of spatial data Terrain analysis - Viewshed - Elevation - Slope, Aspect, Hillshading - Watershed DEM Slope Aspect Sink Stream Space-Time Analysis Time is used as a spatial dimension (t). 2-D View - The Space-Time concept is to model spatiallyrelated events by using time as one dimension. This is so that the correlation between spatial movement and time can be derived by using conventional mathematical functions, such as distance, in 3D: Distance( )x,y,t = sqrt(x2+y2) Velocity( )x,y,t= sqrt(x2+y2)/t Space-Time Distance( )x,y,t= sqrt (x2+y2+t2) X Y 3-D View Time X X Y Y Time T3 Y T2 T1 X Time Time River Boundary Change in Time X X Change in Time Y X Change in Time Observation Points Function in Time - Data sets are registered to a common coordinate system - Geospatial data sets can be stored across a distance - Analytical tools are available for modeling environmental processes - GIS & remote sensing offer a way to study latitudinal gradients effectively