Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features...

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Spatial data models (types) Lecture 3, 9/7/2006

Transcript of Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features...

Page 1: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

Spatial data models (types)

Lecture 3, 9/7/2006

Page 2: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

Two basic data models to represent these features

Raster spatial data model– Define space as an array of equally sized cells arranged in rows and

columns. Each cell contains an attribute value and location coordinates

– Individual cells as building blocks for creating images of point, line, area, network and surface

– Continuous raster Numeric values range smoothly from one location to another, for example,

DEM, temperature, remote sensing images, etc.– Discrete raster

Relative few possible values to repeat themselves in adjacent cells, for example, land use, soil types, etc.

Vector spatial data model– Use x-, y- coordinates to represent point, line, area, network, surface

Point as a single coordinate pair, line and polygon as ordered lists of vertices, while attributes are associated with each features

– Usually are discrete features

Page 3: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

DIGITAL SPATIAL DATA

• RASTER

• VECTOR

• Real World

Source: Defense Mapping School National Imagery and Mapping Agency

Page 4: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

Raster and Vector Data Models

Vector RepresentationX-AXIS

500

400

300

200

100

600500400300200100

Y-AXIS

River

House

600

Trees

Trees

BB

B BB

BBB G

GBK

BBB

G

G

G GG

Raster Representation

1 2 3 4 5 6 7 8 9 1012345

67

8910

Real World

G G

Source: Defense Mapping School National Imagery and Mapping Agency

Page 5: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

Example: Discrete raster

Page 6: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

Xie et al. 2005

Example: continuous raster

Page 7: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

Raster Real world Vector Heywood et al. 2006

Page 8: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

Effects of changing resolutionHeywood et al. 2006

Page 9: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

Vector – Advantages and Disadvantages

Advantages– Good representation of reality– Compact data structure– Topology can be described in a network– Accurate graphics

Disadvantages– Complex data structures– Simulation may be difficult– Some spatial analysis is difficult or impossible to perform

Page 10: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

Raster – Advantages and Disadvantages

Advantages– Simple data structure– Easy overlay– Various kinds of spatial analysis– Uniform size and shape– Cheaper technology

Disadvantages– Large amount of data– Less “pretty”– Projection transformation is difficult– Different scales between layers can be a nightmare– May lose information due to generalization

Page 11: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

GIS data formats (files)

Shapefiles Coverages TIN (e.g. elevation can be stored as TIN)

– Triangulated Irregular Network

Grid (e.g. elevation can be stored as Grid) Image (e.g. elevation can be stored as image)

Vector data

Raster data

Page 12: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

Shape Files

Nontopological Advantages no overhead to process topology Disadvantages polygons are double

digitized, no topologic data checking At least 3 files .shp .shx .dbf

Page 13: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

Coverages

Original ArcInfo Format Directory With Several Files Database Files are stored in the Info

Directory Uses Arc Node Topology

– Containment (coincident)– Connectivity– Adjacency

Page 14: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

TIN

A triangulated irregular network (TIN) is a data model that is used to represent three dimensional objects. In this case, x,y, and z values represent points. Using methods of computational geometry, the points are connected into what is called a triangulation, forming a network of triangles. The lines of the triangles are called edges, and the interior area is called a face, or facet.

While the TIN model is somewhat more complex than the simple point, line, and polygon vector model, or the raster model, it is actually quite useful for representing elevations. For example a raster grid would require grid cells to cover the entire surface of a geographic area. Also, if we wanted to show great detail we would have to have small grid cells. Now, if the land area is relatively flat, we would still need the small grid cells. However, with a TIN we would not have to include so many points on the flat areas, but could add more points on the steep areas where we want to show greater detail.

The illustration shows how we can create a TIN of the terrain around Ithaca, NY.

– First, a series of elevation points are created– Second, a TIN face is created with the elevation data– Third, the faces are shaded in to give the impression of a

3D surface

©Arthur J. LemboCornell University

Page 15: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

Components of a TIN

Nodes Edges Triangles Hull Topology

©Arthur J. LemboCornell University

Page 16: Spatial data models (types) Lecture 3, 9/7/2006. Two basic data models to represent these features Raster spatial data model – Define space as an array.

Grid Properties

Each Grid Cell holds one value even if it is empty.

A cell can hold an index standing for an attribute.

Cell resolution is given as its size on the ground.

Point and Lines move to the center of the cell.

Minimum line width is one cell.

Rasters are easy to read and write, and easy to draw on the screen.