Spatial Data Model: Basic Data Types 2 basic spatial data models exist vector: based on geometry of points lines Polygons raster: based on geometry of grid cells (images, bitmaps, DEMs)
Vector Data Vector Data Model Points: represent discrete point features each point location has a record in the table
airports are point features each point is stored as a coordinate pair Vector Data Model Lines: represent linear features
each road segment has a record in the table roads are linear features Vector Data Model node
vertex vertex node vertex vertex Lines start and end at nodes
line #1 goes from node #2 to node #1 Vertices determine shape of line Nodes and vertices are stored as coordinate pairs Vector Data Model Polygons: represent bounded areas each bounded polygon
has a record in the table landforms and water are polygonal features Vector Data Model Points are discreet
Nodes Vertices Lines Nodes Vertices Arcs
Closed area (Lines + points ) = polygons Vector Data Model Vector data formats available in ArcGIS ESRI GeoDatabases ESRI shapefiles ArcInfo coverages and libraries CAD files (AutoCAD DWG, DXF; microstation
DGN) StreetMap files Spatial Database Engine (SDE) data ASCII point coordinate data Linear measure (route) data Vector Data Model ESRI Geodatabases
Geodatabase can store many files from many source formats 1st preferred vector format in ArcGIS Rapid display Fully editable (coordinate and tabular) in ArcGIS Convenient storage format Data sets are either point or line or polygon Vector Data Model
ESRI shapefiles 2nd preferred vector format in ArcGIS Rapid display Fully editable (coordinate and tabular) in ArcGIS Simple in structure Do not use arc-node topology Connected lines do not necessarily share a common node
Adjacent polygons do not share common bounding arcs Data sets are either point or line or polygon Vector Data Model Shapefile polygon spatial data model less complex data model
polygons do not share bounding lines Vector Data Model ArcInfo coverages Commonly found format (due to ArcInfo market dominance) Data model more complex Display more slowly in ArcGIS
Coordinate data not editable in ArcGIS Polymorphic (point/line/polygon/route/annotation/ ) Problematic OS file structure Vector Data Model ArcInfo coverage spatial data model
polygons share bounding lines same topological rules can be built into Geodatabase Vector Data Model ASCII coordinate data Easy to obtain from a variety of sources GPS
Traverse (survey) Direct reading OS and application independent Vector Data Model
Characteristics + Features are positioned accurately + Shape of features can be represented correctly + Features are represented discretely (no fuzzy boundaries) Not good for representing spatially continuous phenomena Potentially complex data structure (especially for polygons); - can lead to long processing time for analytical
operations Raster Data Raster Data Model origin is set explicitly cell size is always known cell references
(row/column locations) are known cell values are referenced to row/column location values represent numerical phenomena or index codes for non-numerical phenomena
Raster Data Model A few different types of raster data digital orthophoto digital elevation model (DEM)
Raster Data Model Characteristics: Rectangular grid of square cells Shape of discrete polygonal features generalized by cells + Continuous (surface) data represented easily + Simple data structure
Raster Data Model Good at representing continuous phenomena, e.g., Wind speed Elevation, slope, aspect Chemical concentration Likelihood of existence of a certain species Electromagnetic reflectance (photographic or
satellite imagery) Ecological Applications: Vol. 17, No. 2, pp. 508526. LINKING OCCURRENCE AND FITNESS TO PERSISTENCE: HABITAT-BASED APPROACH FOR ENDANGERED GREATER SAGE-GROUSE Cameron L. Aldridge and Mark S. Boyce
Well_dst 10m cont. distance to nearest standing energy well Rd_dst
10m cont. distance to nearest road SB 10m cont.
% sagebrush cover determined from a.p. Crop_dst 10m cont. distance to nearest cultivated land
pCrop 10m cont. prop. of crop in a 1-km moving window Guest Speaker Line-up
Matthew Parsons, UW, Map Library Peter Singleton, USFS & UW Theresa Nogeirie, UW Evan Girvetz, TNC & UW Jesse Langdon, UW Chad Wilsey, UW Cheryl Wilder, King County
Homework Read getting data into ArcGIS Maps, displaying layers, changing layer display properties, project and data management, data export Assignment 2. Introduction to GIS (due April 11)
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Computational Methods in Physics PHYS 3437 Dr Rob Thacker Dept of Astronomy & Physics (MM-301C) [email protected] Today's Lecture Use MC techniques to simulate a random walk A few slides on random number generators Mobility of molecules Consider a single molecule...
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