GIS Data Models and

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GIS Data Models and Geographic
Representation Reading Longley Chpts 3,
9 Demers Chpts 2, 3, 4
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• Data Types and Models
• Data for a GIS comes in three basic forms
• Spatial data -What Maps are Made Of
• Spatial data, made up of points, lines, and
  areas, is at the heart of every GIS. Spatial data
  forms the locations and shapes of map features
  such as buildings, streets, or cities.  
• Tabular dataadding information to mapsTabular
  data is information describing a map feature. For
  example, a map of customer locations may be
  linked to demographic information about those
  customers.  
• Image datausing images to build mapsImage data
  includes such diverse elements as satellite
  images, aerial photographs, and scanned datadata
  that's been converted from paper to digital
  format. In addition, this data can be further
  classified into two types of data models

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• Vector data modelDiscrete features, such as
  customer locations and data summarized by area,
  are usually represented using the vector model.  
• Raster data modelContinuous numeric values, such
  as elevation, and continuous categories, such as
  vegetation types, are represented using the
  raster model. 

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This map shows vector data (the streets) laid on
top of raster data (the mountains and valley
floor).
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• Raster or Vector?
• While any feature type can be represented using
  either model, discrete features, such as customer
  locations, pole locations or others, and data
  summarized by area such as postal code areas or
  lakes, are usually represented using the vector
  model.
• Continuous categories, such as soil type,
  rainfall, or elevation, are represented as either
  vector or raster.

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• Vector Data Model
• A coordinate-based data structure commonly used
  to represent linear geographic features. Vector
  data represents each feature as a row in a table,
  and feature shapes are defined by x,y locations
  in space (the GIS connects the dots to draw lines
  and outlines.)
• Features can be discrete locations or events,
  lines, or areas.
• Locations, such as the address of a customer, or
  the spot a crime was committed, are represented
  as points having a pair of geographic
  coordinates.
• Lines, such as streams or roads, are represented
  as a series of coordinate pairs.

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• The Vector Data Model
• Represents features on the earth's surface as
  points, lines or polygons.
• Point nodes or vertices
• Line arc or chain
• Polygon area feature.
• Vector data is usually contained within the Layer
  database structure where points, lines and
  polygons are stored on separate layers
• The question is how to build intelligence into a
  computer so that it can know not only the
  individual locations of specific spatial
  entities, but where things are in relation to
  other things. This is known as defining topology
  for a spatial database

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• Vector Database Structure
• In the vector data model, xy locations are used
  to specify point locations. These point locations
  can either represent point features (eg a
  building), or they can specify the start and end
  point of line segments. (eg. Point Attribute
  Table)
• Arcs (Lines) are composed of one or more straight
  line segments which join point vertices. Lines
  are described by the start node which is the
  point of the first segment, and an end node which
  is the end point of the last segment. Since all
  area in our vector database belongs to something
  (Universe polygon), then if we travel along the
  arc in the direction it is digitized, we can
  specify a left polygon and right polygon. Arcs
  can describe line features or form the boundary
  of polygons.
• Polygons are described by a unique identifier,
  and a list of the arcs that make up the polygon
  boundary. One arc can form the boundary between
  two polygons.

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• Topology
• The actual process of defining topology is where
  enough additional information is added to a
  dataset so that the computer can calculate
  spatial analysis functions such as determining if
  two features are adjacent.
• Standing on a street corner looking at a map is a
  pretty easy way to identify intersecting streets
  and properties that are adjacent. The computer
  sees these relationships by means of topology.
• Topology explicitly defines spatial
  relationships. The principle in practice is quite
  simple spatial relationships are expressed as
  lists For example, a polygon is defined by the
  list of arcs comprising its border.

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• Advantages of Vector
• High Precision in locating where objects are
• Less Disk space
• Maps are more aesthetically pleasing
• Disadvantages of Vector
• Expensive to gather and clean data (digitizing)
• Expensive and time consuming to explicitly define
  topology
• Complex operations may be computationally time
  consuming

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• Vector GIS
• Mostly used in utilities applications,
• Government applications, such as voting districts
• Planning applications, such as parcel mapping

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• Raster Data Model
• Raster models are developed by building up a grid
  of cells, or pixels
• Divides space into a matrix of cells (pixels)
  called a raster.
• Each cell has a single value attached to it. If
  more than one value occurs at a location, must
  decide what value to store at that location
• In raster data, topology is explicitly defined by
  the matrix

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• Raster Input Methods / Sources
• Rasterization of vector data / conversion of
  other data formats
• Scanning
• Remote Sensing Imagery
• DEM's (digital elevation model)
• Raster Data
• Thematic
• Continuous

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• Advantages of Raster
• Layer overlays are fast and simple
• topology is explicitly defined
• gathering raster data from scanning or remotely
  sensed images is cheap and fast
• Disadvantages of Raster
• Resolution of dataset will determine what
  features are represented by the coarse raster
  cells
• If fine resolution, will take up large amounts of
  disk space

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• Most Raster Applications are environmental
• Natural resource management
• Deforestation

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Spatial Data--What Maps are Made Of Spatial data
includes points, lines and areas. Points
represent anything that can be described as an x,
y location on the face of the earth, such as
shopping centers, customers, utility poles,
banks, and physicians' offices. Lines represent
anything having a length, such as streets,
highways, and rivers. Areas, or polygons,
describe anything having boundaries, whether
natural, political or administrative, such as the
boundaries of countries, states, cities, census
tracts, postal zones, and market areas.
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DISCRETE DATA Discrete data, which are
sometimes called categorical or discontinuous
data, mainly represent objects in both the vector
and grid data models. -A discrete object has
known and definable boundaries. It is easy to
define precisely where the object begins and
where it ends. For example, a road has a definite
width and length, and is represented on a map as
a line. -Discrete map features may also be
thought of as thematic data. These data or map
features are easily represented in maps as
points, lines or areas.
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• Examples of Discrete data
• A lake is a discrete object within the
  surrounding landscape. Where the waters edge
  meets the land can be definitively established.
• Other examples of discrete objects include
  buildings, roads and parcels. Discrete objects
  are usually nouns.
• A landownership map shows the boundaries between
• various parcels. There are definite changes in
  characteristics (such as owner name, parcel
  number, and legal area) between each feature on
  the map.

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• CONTINUOS DATA
• A continuous surface represents phenomena where
  each location on the surface is a measure of the
  concentration, level, or its relationship from a
  fixed point in space or from an emanating source.
  
• Continuous data are also referred to as field,
  non-discrete, or surface data. One type of
  continuous surface is derived from those
  characteristics that define a surface where each
  location is measured from a fixed registration
  point.
• These include elevation (the fixed point being
  sea level) and aspect (the fixed point being
  direction, north, east, south and west).

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• Generally, the transition between possible values
  on a
• continuous surface is without abrupt or
  well-defined breaks
• between values.
• The attribute of the surface is stored as a z
  value, a single
• variable in the vertical dimension associated
  with a given x, y
• location.
• Examples of Continuous data elevation, rainfall,
  pollution
• concentration, and water tables.

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• Continuous surface includes phenomena that
  progressively vary as they move across a surface
  from a source.
• For example, continuous data could be fluid and
  air movement. These surfaces are characterized by
  the manner in which the phenomenon moves.
• The first type of movement is through dispersal,
  diffusion, or any other locomotion where the
  phenomenon moves from areas with high
  concentration to those with less concentration
  until the concentration level evens out.
• Surface characteristics of this type of movement
  include salt concentration moving through either
  the ground or water, contamination level moving
  away from a hazardous spill or a nuclear reactor,
  and heat from a forest fire.
• In this type of continuous surface, there has to
  be a source. The concentration is always greater
  near the source, and diminishes as a function of
  distance and the medium the substance is moving
  through.

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• It is important for you to understand the type of
  data you are modeling, whether it be continuous
  or discrete, when making decisions based upon the
  resulting values.
• The exact site for a building should not be
  solely based on the soils map. The square area of
  a forest cannot be the primary factor when
  determining available deer habitat. A sales
  campaign should not be based only on the
  geographic market influence of a television
  advertising spree.
• The validity and accuracy of boundaries of the
  input data must be understood.

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• All numbers cannot be treated the same. It is
  important for the GIS user to know the type of
  measurement system being used, so that the
  appropriate operations and functions can be
  implemented and the results will be predictable.

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• Measurements
• - The type of measurement system used may have a
  dramatic effect on the interpretation of the
  resulting values.
• - A distance of twenty kilometers is twice as
  far as ten kilometers,
• - Something that weighs 100 pounds is a third as
  much as something that is 300 pounds.
• - Someone who came in first place may not have
  done three times as well as someone in third
  place,
• - Soil with a pH of 3 is not half as acidic as
  soil that has a
• pH of 6.

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• Measurement values can be broken into four types
• nominal, ordinal, interval, ratio
• Nominal
• - Values associated with this measurement system
  are used to identify one instance from another.
• - They may also establish the group, class,
  member, or category with which the object is
  associated.
• - These values are qualities, not quantities,
  with no relation to a fixed point or a linear
  scale.
• - Coding schemes for land use, soil types, or
  any other attribute qualify as a nominal
  measurement.
• - Other nominal values are social security
  numbers, identification numbers, zip codes and
  telephone numbers.

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Ordinal -Ordinal values determine position.
-These measurements show place, such as first,
second, third, and so on, but they do not
establish magnitude or relative proportions.
-How much better, worse, prettier, healthier
and stronger, cannot be demonstrated from ordinal
numbers.
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• Interval
• - Time of day, years on a calendar, the
  Fahrenheit temperature scale and pH value are all
  examples of interval measurements.
• - These are values on a linear calibrated scale
  but not relative to a true zero point in time or
  space.
• - Because there is no true zero point, relative
  comparisons can be made between the measurements,
  but ratio and proportion determination are not as
  useful.

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• Ratio
• - The values from the ratio measurement system
  are derived relative to a fixed zero point on a
  linear scale.
• - Mathematical operations can be used on these
  values with predictable and meaningful results.
• - Examples of ratio measurements are age,
  distance, weight and volume.

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• The computer does not distinguish between the
  four different types of measurements when asked
  to process or manipulate the values.
• Most mathematical operations work well on ratio
  values, but when interval, ordinal, or nominal
  values are multiplied, divided, or evaluated for
  square root, the results are typically
  meaningless.
• On the other hand, subtraction, addition and
  Boolean determinations can be very meaningful
  when used on interval and ordinal values.
  Attribute handling within and between rasters is
  most effective and efficient when using nominal
  measurements.