Dept. of Civil and Environmental Engineering and Geodetic Science College of Engineering The Ohio State University Columbus, Ohio

Data Acquisition Result Map Composition Analysis & Modeling F ( ) xyz =f (p1) xyz +f (p2) xyz + … +f (pN) xyz Report Data Acquisition Result Data Organization F ( ) =Layer 1 +Layer 2 + … + Layer N Report Analysis & Modeling

Definition of GIS

InputManagement & Analytical ModulesOutput Data Acquisition - Geodetic Positioning - Remote Sensing - Field Sampling Analog Data Conversion - Scan - Digitize Management - Data Storage - Data Retrieval, Expand Edit, and Update - Query Analytical Modules - Data Conversion - Data Manipulation - Modeling Data Output - Visual Presentation - Analog Map Output - Reports GIS Component

GIS Data Elements & Characteristics PointPolygon SurfaceLines GridImage GIS Data Layers Data Characteristics Space – feature locations Attribute – feature attributes, qualities & characteristics of geographic places. Relationships Between Features Time – additional spatial dimension Data Types Vector - Based on mathematical function - point, line, polygon, & surface Raster - Data present on a fixed grid structure (matrix) - image, grid

- Provides spatial relation and geometric shape of ground features - Serves as a foundation for data rectifications - 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 Data Organization Coordinate System Research Data Layers Basemap - Geographic coordinate system for small scale research. - Plane coordinate system for large scale research. Research Data Layers Basemap Coordinate System

GIS Data Availability GIS DATA Conventional Data Remotely Sensed 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. Spatial Only - B/W aerial photo - Panchromatic images - Radar image - GPS - Bathymetry - LIDAR Spatial & Biophysical - Color aerial photo - Multispectral images - Hyperspectral image - Multiband radar image DRGDLGDEMDOQQ

Inventory Operation Measurement - Distance - Area/Size - Perimeter Spatial Query - Graphic Query - Boolean Query: AND, OR, NOT Database Operations - Lists & Reports - Relational Database Inventory operation is to obtain information from existing data layers or databases A=0.175km 2 Measurement Database operations Spatial Query P=1.5km D=0.975km

Spatial Analysis The basic operation involves: - Buffer operation - Overlay operation Spatial Operations SoilObservation Hydrology Landuse Soil 1 Corn Soil 3 Soil 2 Soil 4 Soybean Forest Urban Station3 Station 2 Station 1 River Soil type and Landuse along the river, but within 150m radius of Station 2 Spatial analysis can be used to derive spatial relationships among data layers

Network Analysis The basic operation involves: - Locating routes - Determine which facility or feature is closest ( allocation ) - Modeling travel directions - Obtaining area around a site within a given distance or time Service Area Routing & Network Distance Pollutant Transport Time to Distribute Effective Area 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

3-D Analysis Visualization - Provide 3-D view of spatial data Terrain analysis - Viewshed - Elevation - Slope, Aspect, Hillshading - Watershed DEM Aspect Slope Sink Stream 3-D analysis is to analyze spatial information in a 3-D perspective.

Space-Time Analysis 2-D View 3-D View River Boundary Change in Time X Y Time X Y Observation Points Function in Time T3 T2 T1 Time Y X Time is used as a spatial dimension (t). - The Space-Time concept is to model spatially- related 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(x 2 +y 2 ) Velocity( ) x,y,t = sqrt(x 2 +y 2 )/t Space-Time Distance( ) x,y,t = sqrt (x 2 +y 2 +t 2 ) X X XX Y Y Time Change 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