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©2005 Austin Troy. All rights reserved Lecture 3: Introduction to GIS Part 1. Understanding Spatial Data Structures by Austin Troy, University of Vermont.

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Presentation on theme: "©2005 Austin Troy. All rights reserved Lecture 3: Introduction to GIS Part 1. Understanding Spatial Data Structures by Austin Troy, University of Vermont."— Presentation transcript:

1 ©2005 Austin Troy. All rights reserved Lecture 3: Introduction to GIS Part 1. Understanding Spatial Data Structures by Austin Troy, University of Vermont

2 ©2005 Austin Troy. All rights reserved Perception, Semantics, and Space How do we deal with representing semantic constructions of spatial objects, like “mountain,” “river,” “street,” “city,” How about representing more conceptual semantic constructions like “temperature,” “migration pattern,” “traditional homeland,” “habitat,” “geographic range,” etc? Answer: we have various data models which use different abstractions of reality Introduction to GIS

3 ©2005 Austin Troy. All rights reserved Entities and Fields There are two general approaches for representing things in space: –Entities/ Objects: precise location and dimensions and discrete boundaries (remember, points are abstractions). –Fields, or phenomena: a Cartesian coordinate system where values vary continuously and smoothly; these values exist everywhere but change over space

4 ©2005 Austin Troy. All rights reserved Entities and Boundaries There are two general types of boundaries, bona fide and fiat (D. Mark, B. Smith, A. Varzi) Pure bona fide boundaries represent real discontinuities in the world, like roads, faults, coastlines, power lines, rivers, islands, etc. Pure Fiat boundaries are a human cognitive or legal construction, based on a categorization, such as administrative unit, nation state, hemisphere Some have elements of both, like soil type areas Introduction to GIS

5 ©2005 Austin Troy. All rights reserved Two major data models Entity approach roughly corresponds with the vector model Field approach roughly corresponds with raster model Any geographic phenomenon can be represented with both, but one approach is usually better for a particular circumstance Introduction to GIS

6 ©2005 Austin Troy. All rights reserved Raster Spatial features modeled with grids, or pixels Cartesian grid whose cell size is constant Grids identified by row and column number Grid cells are usually square in shape Area of each cell defines the resolution Raster files store only one attribute, in the form of a “z” value, or grid code. Consider the contrary…. Introduction to GIS

7 ©2005 Austin Troy. All rights reserved Vector layers either represent: –Points (no dimensions) –Lines, or “arcs” (1 dimension) or –Areas, or “polygons” (2 or 3 dimensions) Points are used to define lines and lines are used to scribe polygons Each point line or polygon is a “feature,” with its own record and its own attributes Introduction to GIS Vector

8 ©2005 Austin Troy. All rights reserved Raster and Vector representations of the same terrain Introduction to GIS Raster: great for surfacesVector: limited with surfaces

9 ©2005 Austin Troy. All rights reserved Introduction to GIS Raster and Vector representations of the same land use

10 ©2005 Austin Troy. All rights reserved Introduction to GIS Raster and Vector representations of the same land use: closer in

11 ©2005 Austin Troy. All rights reserved Vector vs. Raster: bounding Introduction to GIS Raster: bad with boundingVector: boundary precision

12 ©2005 Austin Troy. All rights reserved Introduction to GIS Vector vs. Raster: Sample points Cancer rates across space

13 ©2005 Austin Troy. All rights reserved In Arc View and Arc GIS, we can covert vector layers to grids, based on an attribute, or grids to vector layers The disadvantage of vector to raster is that boundaries can be imprecise because of cell shape Each time you convert, you introduce more error too Moving between vector and raster Introduction to GIS

14 ©2005 Austin Troy. All rights reserved WHEN TO USE RASTER OR VECTOR??? Introduction to GIS

15 ©2005 Austin Troy. All rights reserved where boundaries are not precise that occur everywhere within a frame and can be expressed as continuous numeric values where change is gradual across space where the attribute of a cell is a function of the attributes of surrounding cells Raster data analysis is better for representing phenomena: Introduction to GIS

16 ©2005 Austin Troy. All rights reserved Simple file structure Simple overlay operations Small, uniform unit of analysis Raster technical advantages : Introduction to GIS Raster technical disadvantages : Big file size, especially for fine-grained data Difficult and error-prone reprojections Square pixels are unrealistic

17 ©2005 Austin Troy. All rights reserved Vector analysis is better : Where there are definable regions Where the relative position of objects is important Where precise boundary definition is needed Where multiple attributes are being analyzed for a given spatial object For modeling of routes and networks For modeling regions where multiple overlapping attributes are involved EG: units with man-made boundaries (cities, zip codes, blocks), roads, rivers Introduction to GIS

18 ©2005 Austin Troy. All rights reserved Smaller file size (in general) More graphically interpretable Allows for topology (see further on) Vector technical advantages : Introduction to GIS Vector technical disadvantages : Complicated file structure Minimum mapping units are inconsistent between overlapping layers

19 ©2005 Austin Troy. All rights reserved In many cases, though, the choice between raster and vector may not be so clear. Often it depends on the application The following are some examples where you could go either way: Terrain, soils, temperature, urbanized areas, water bodies Tossups Introduction to GIS


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