Cartographic and GIS Data Structures

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Cartographic and GIS Data Structures
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Overview

• Map as an Abstraction of Space
• Database Management system
• Methods of representing geographic space
• Raster Model
• Vector Model

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Map as an Abstraction of Space

• Spatial features can be represented as point,
  lines, areas, or surfaces
• Some phenomena or objects are selected for
  inclusion, others are not spatial features and
  there attributes are simplified, aggregated, and
  classified
• When we want to enter this data into a GIS,
  certain decisions need to be made based upon how
  the data can be entered into a computer
  (geocoding vs. drawing)
• How do you get simple spatial concepts into the
  computer (e.g., a map which identifies a lake
  within an island, surrounded by ocean, covered
  by forest on north side, and a cleared beach on
  the other side)
• Inside, surrounded, by, north, south

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What is a Database?

• A database is a set of computer files that stores
  information in an organized, structured format
• The information is organized in records and
  fields
• Information in a database is related so questions
  can be asked such as
• List all of the courses that are 4000 level or
  higher
• List the name and address for all people whose
  last names begin with "T"

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Database-continue

• 4 basic types of computer database Structures for
  management of data hierarchical, network,
  relational, and object oriented
• Database Records and Fields
• Record a small group of related data items (the
  logical unit of a database)
• Field An individual item of data (contain
  information that describe records)

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Methods of representing geographic space
Vector
Raster
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• The diagram below shows how real-world objects
  can be represented on a computer monitor by x,y
  coordinates.
• The coordinate pairs 1,5 3,5 5,7 8,8 and 11,7
  represent a line (road)
• The coordinate pairs 6,5 7,4 9,5 11,3 8,2 5,3 and
  6,5 represent a polygon (lake).
• The first and last coordinates of the polygon are
  the same a polygon always closes.

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Raster Models

• Raster - from the Greek word meaning "to rake"
• Quantizes or divides space into discrete packets
  (cells), each representing a part of the whole
• Cells are of equal size square, rectangular,
  hexagon, triangles
• Loose the ability to represent exact locations
  (e.g., point represented as single cell)
• Zero dimensional object rep. with 2D feature
• Lines represented as a series of connected cells
• Multiple cells joined at edges or corners,
  usually with only 1 or 2 neighbors, 1D objects
  represented in 2D
• Areas represented as a series of connected cells
• 2D objects represented in 2D, cells distort area
  and shape - stairs-stepped appearance

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Raster Models-continue

• Two general ways of associating attribute data
  with raster entities
• 1. store an attribute for every grid cell
  problem is redundancy in storage
• 2. link cells to RDBMS
• Permits more than one attribute to be associated
  for a single cell
• Only have to store attributes once
• Cell value linked to attribute table
• Essentially many to one - "many cells being
  linked to one record in separate attribute table"

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Generic structure for a grid
Grid extent
Grid cell
s
w
o
R
Resolution
Columns
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Geographic Representations

• CELLS a representation of geographic data based
  on rows and columns (e.g.. continuous surface
  data such as elevation or temperature, and
  categorical representations derived from vector
  data)
• PIXELS a group of independent points with a
  color value but no other associated data (e.g..
  scanned documents, orthophotography, satellite
  images)

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• Like the vector data model, the raster data model
  can represent discrete point, line and area
  features.
• A point feature is represented as a value in a
  single cell, a linear feature as a series of
  connected cells that portray length, and an area
  feature as a group of connected cells portraying
  shape.

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• Because the raster data model is a regular grid,
  spatial relationships are implicit. Therefore,
  explicitly storing spatial relationships is not
  required as it is for the vector data model.

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Vector Models

• Features represented in basically the same way as
  an analog map, permits more precise
  representation than raster model, permits "empty
  space, variations of the vector model
• Spaghetti models
• Simplest of vector data structures
• Does not explicitly store spatial relationships
  (topology), essentially X,Y coordinates, and
  which should be connected by lines
• Doesnt really "know" if points and connected
  lines form a line entity or poly entity
• Topological models
• Recognizes the concept of an entity
• Stores spatial relationship information
  explicitly associated with each entity, most
  common in GIS

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Feature Geometry
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To keep track of many
features, each is assigned a unique
identification number or tag. Th
en, the list of coordinates for each feature is
associated with the features tag. The objects
you see in a vector theme are actually saved in
the theme table
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Vector Data Advantages

• Data can be represented at its original
  resolution and form without generalization.
• Graphic output is usually more aesthetically
  pleasing (traditional cartographic
  representation)
• Since most data, e.g. hard copy maps, is in
  vector form no data conversion is required.
• Accurate geographic location of data is
  maintained.
• Because it recognizes entities, model allows for
  efficient encoding of topology, and as a result
  more efficient operations that require
  topological information, e.g. proximity, network
  analysis.

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Vector Data Disadvantages

• The location of each vertex needs to be stored
  explicitly
• For effective analysis, vector data must be
  converted into a topological structure. This is
  often processing intensive and usually requires
  extensive data cleaning.
• Topology is static, and any updating or editing
  of the vector data requires re-building of the
  topology
• Algorithms for manipulative and analysis
  functions are complex and may be processing
  intensive
• Often, this inherently limits the functionality
  for large data sets, e.g.a large number of
  features.
• Continuous data, such as elevation data, is not
  effectively represented in vector form. Usually
  substantial
  data generalization or interpolation is required
  for these data layers

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Raster Data Advantages

• Due to the nature of the data storage technique
  data analysis is usually easy to program and
  quick to
  perform.
• The inherent nature of raster maps, e.g. one
  attribute maps, is ideally suited for
  mathematical modeling and quantitative analysis.
• Discrete data, e.g. forestry stands, is
  accommodated equally well as continuous data,
  e.g. elevation data, and facilitates the
  integrating of the two data types.
• Grid-cell systems are very compatible with
  raster-based output devices, e.g. electrostatic
  plotters, graphic terminals.
• Also compatible with digital satellite imagery.

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Raster Data Disadvantages

• The cell size determines the resolution at which
  the data is represented.
• Processing of associated attribute data may be
  cumbersome if large amounts of data exists.
• Raster maps normally reflect only one attribute
  or characteristic for an area.
• Since most input data is in vector form, data
  must undergo vector-to-raster conversion.
• Most output maps from grid-cell systems do not
  conform to high-quality cartographic needs.

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Vector Representation
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Vector to Raster
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Raster Representation
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The mixed pixel problem
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Vector Vs. Raster
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Exercise

• Compare between Raster and Vector Model for
  representing geographic features illustrate by
  figures