Geographic Data Models

In this lesson you will learn:

• spatial data models

• raster data

• vector data

• raster vs. vector storage

• attribute data scales

• data scales & allowable operations

• types of GIS software

From features to data

“We would never have learned anything if we had never thought:

This object resembles this other, and I expect it to manifest the same properties.”

-Bertrand de Jouvenel Quoted in: R. Abler, J. Adams, and P. Gould,

Spatial Organization: The Geographer’s View of the World, Englewood Cliffs, NJ: Prentice-Hall, 1971.

The spatial data model

Definition: a data model is a logical means of organization of data for use in an information system

-from: K.C. Clarke, Getting Started with Geographic Information Systems, 2nd edn., Upper Saddle River, NJ: Prentice Hall, 1999.

Basic data models 1: raster

source: U.S. Geological Survey, Topographic Mapping, http://erg.usgs.gov/isb/pubs/booklets/topo/topo.html

“green” separate “blue” separate

Raster points, lines, polygons

b b

b b b

b b b

b b b

Resolution, scalability and quality

P

P P

P P

L L L

L L L L L

L L

L L L

L L L L L

L L L

low resolution

high resolution

point object line object

“Raster is faster, …

Raster meta-data

1. image projection

2. data image size : # columns, # rows

3. pixel size

4. geographic coordinates of first (NW corner) and last pixel (SE corner)

(x1, y1)

Raster data can be generalized and simplified, but they cannot be reprojected.

… but raster is vaster”

Quadtree structured raster data

Basic data models 2: vector

Hierarchical geometry of the vector data model

spatial object type constituent parts constituent geometry

point (x,y) coordinate pair

line segment from-node, to-node point

polygon edges line segment

surface faces polygon

Vector points, lines, polygons

(xfrom,yfrom)

(xto,yto)

Vector data storage

ID X Y

A01 1000 900

A02 3000 2000

….

….

ID from_Node to_Node

L01 P0171 P0172

L01 P0172 P0181

L02 P003 P004

L02 P004 P005

L02 P005 P006

L02 P006 P043

L02 P043 P012

….

….

ID from_Node to_Node

A01 P1181 P1182

A01 P1182 P1183

A01 P1183 P1184

A01 P1184 P1185

A01 P1185 P1186

A01 P1186 P1181

A02 P3126 P3127

A02

A02

….

….

Point data file

Line data file Polygon data file

Resolution, scalability and quality

1:24,000

1:6,000

1:12,000

Vector data are infinitely scalable, with resolution only up to the precision of the coordinate measurements

Vector to output

Mixing data models

source: Northern Illinois University, Department of Geography, GIS Lab

Mixing data models

Demonstration 3-d shaded relief image; portion of Carroll County, Illinois. Courtesy of the Advanced Geospatial Lab, Department of Geography, Northern Illinois University

Attribute data models – the measurement scales

Measurement scale (model) Example

1. NominalGender: male vs. femaleLand-use: commercial, industrial, residentialSpecie: ash, elm, hickory, locust, maple, oak, tamarack

2. OrdinalClass standing: senior, junior, sophomore, freshman Residential use: low density, medium density, high densityFlood risk: none, low, medium, high, extreme

3. IntervalTemperature: °F or °CSoil productivity potentialIQ

4. Ratio Distance between spatial objectsLength of a polygon’s perimeterArea of land parcels, in square miles, acres, or hectares

Data scales and allowable operations

Measurement scale (model)

Properties Allowable operations

1. Nominal measures “categories” count

2. Ordinalidentifies order: most to least, smallest to largest;

count, <, =, >

eye color: brown; blue; gray; hazel; green; othernominal scale

ordinal scaleclass standing: freshman, sophomore, junior, postgraduate

land use: commercial, industrial; residential; open space; other

residential density: low, medium, high

Data scales and allowable operations

Measurement scale (model)

Properties Allowable operations

3. Intervalquantitative: no true zero, but preserves equal intervals

count, <, =, >, +, -

average, range, median, standard deviation, etc.

4. Ratio quantitative: has true zero, preserves ratios

count, <. =, >, +, -, ×,÷, ln()…

average, range, median, standard deviation, etc.

interval scale ratio scale

Image scales

Types of GIS software

1. Raster GIS: designed and programmed for raster spatial databases; strengths in image processing, including image conflation and extraction; may include ability to use and create vector data and perform elementary vector operations

2. Vector GIS: designed and programmed for vector spatial databases; strengths in featuregeometry and feature-based spatial analysis, such as buffering, distance, and densityanalyses; can incorporate feature topology for advanced analysis; may include ability to use and create raster data for depiction of surfaces and graphic output

3. Web GIS: host may use raster or vector data; raster or vector-standard output to client ensures universal/multi-platform access and faster graphics rendering; analyses and spatial operations more limited than desktop/server implementation.

Spatial data vs. Mapping data

Printed USGS topographic map “color” separates

blue

blackred

brown

purple

green

source: U.S. Geological Survey; www.usgs.gov

GIS vs. Mapping vs. CAD

GIS map

graphics illustration map

CAD map

GIS vs. Mapping vs. CAD

CAD graphics & attribute data

geo- coordinates reference

GIS project

CAD to GIS

GIS map

graphicsconverter

graphics illustration

GIS to graphics

Raster/Vector: a summary

What you have learned:

In this lesson you have learned:• Spatial data are organized by feature class, with each object in a feature class having the same basic characteristics – including geometry.• A data model is a means for organizing and structuring the data used in an information system.• Geographic Information Systems employ two basic types of spatial data models: raster and vector.• The raster data model is composed of pixels. Pixels either belong to a feature or are empty. Raster data are easily rendered to screen or output, have an inherent scale resolution and projection, but are not reprojectable.• The vector data model reduces object geometry to the locational coordinates of vertices. Faces of a surface can be reduced to polygons, perimeters of polygons to edges, and edges or line segments to sets of from- and to-vertices. Vertices are point data describable by location coordinates. Vector data are infinitely scalable and reprojectable, but require additional processing to render to screen or output.• No data model is ideal for all purposes: the raster model is best suited to images and where features extensively cover geographic space; the vector model separates individual features from the background and best captures the geometry of features. • The measurement scale of attribute data determines the types of mathematical or statistical operations that can be performed with those data. Nominal and ordinal scales are qualitative; interval and ratio scales are quantitative.• Common color models for imagery are panchromatic and RGB; one brightness value is associated with each dimension of the color model for each pixel. Brightness data are interval-scaled, with values that range between 0 and 255. • GIS may be referred to as raster or vector GIS, though most software is capable of using either type of data. Web GIS outputs raster data to the client, enabling quicker rendering across multiple platforms.• Map data created in CAD and illustration software are graphics data. GIS, CAD and illustration software have similar graphics functions; GIS can input CAD data and output maps to illustration software.