Geographic Information Systems: an introduction Week II History of GIS Representing Geography Data m

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Geographic Information Systemsan
introductionWeek IIHistory of GISRepresenting
GeographyData models

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Geographic Information Systems an introduction

• Overview/Introduction
• What is GIS
• Geography Information Science
• What comprises a GIS
• Core geographic concepts
• GIS History/Development
• Practical applications

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A brief history of GIS
Never doubt that a small group of thoughtful,
committed citizens can change the world. Indeed,
it's the only thing that ever has.- Margaret
Mead
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Innovation diffusion want to change the world?

• Relative advantage
• Compatibility
• Complexity
• Trialability
• Observability

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A brief history of GIS
Innovators
Early adopters
Early majority
Late majority
Laggards
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A brief history of GIS

• First automated cartography in the 60s and 70s
• Remote sensing played a major role in the
  development of GIS
• Precise measurement of location
• GPS technologies
• Internet

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A brief history of GIS

• 1960 70s Innovation
• First GIS Canada Land Inventory
• DIME US Bureau of Census
• Harvard Laboratory for Computer Graphics
• Major vendors started (e.g. ESRI, Intergraph)
• Landsat satellite launched

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A brief history of GIS

• 1980s and 1990s Commercialization
• Commercial GIS software (e.g. ArcInfo)
• First GIS textbooks
• First global data sets
• Clinton Executive Order
• Open GIS consortium
• 2000s Exploitation
• More than 1 million active users
• Open source GIS
• Internet becomes major delivery vehicle

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First Internet Mapping Site
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The Web
A new channel for delivery of Products Services
Data transfer
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The Web
A new channel for delivery of Products Services
Information dissemination
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The Web
A new channel for delivery of Products Services
Decision Support
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Work Flow Real-Time Web DST
Mapserver
Data Gathering
Data Formatting
Data Processing
GRASS
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Geographic Information System

• Organized collection of
• Data
• Hardware
• Software
• Network
• People
• Procedures

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Representing Geography
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Outline

• What is representation and why is it important?
• What are digital representations
• What are the methods for representing data
• What are some of the implications
• What are the basic models used for representation

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Data Model Levels
Reality
Human-oriented
Conceptual Model
Increasing Abstraction
Logical Model
Computer-oriented
Physical Model
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Sensing the World

• Personal experience limited in time and space
• One human lifetime
• A small fraction of the planets surface
• All additional knowledge comes from books, the
  media, movies, maps, images, and other
  information sources
• From indirect or remote sensing

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Representations

• Are needed to convey information
• Fit information into a standard form or model
• Almost always simplify the truth that is being
  represented

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Representations Occur

• In the human mind, when information is acquired
  through the senses and stored in memory
• In photographs, written text, movies, stories

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The Paper Map
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The Paper Map

• A long and rich history
• Has a scale or representative fraction
• The ratio of distance on the map to distance on
  the ground
• Is a major source of data for GIS
• Obtained by digitizing or scanning the map and
  registering it to the Earths surface
• Digital representations much more powerful (? )
  than their paper equivalents

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Why Digital?

• Economies of scale
• One type of information technology for all types
  of information
• Simplicity
• Reliability
• Systems can be designed to correct errors
• Easily copied and transmitted
• At close to the speed of light

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Why Digital?

• Each capacitor on a chip requires about 40,000
  electrons to charge up.
• A typical email contains 50 kilobytes, requiring
  8 billion electrons.
• One electron weighs 2 x 10E-30 pounds so a
  typical email weighs 2.6 x
• 10E-18 ounces.
• But email is only 9 of total traffic with 75
  due to file sharing.
• Total daily internet activity ranging from love
  letters and pornography to climate studies, home
  movies, and vacation plans is 40 petabytes. A
  petabyte, incidentally, is one quadrillion bytes
  -- a 1 with 15 zeros.
• And, 40 petabytes 1.3 x 10E-8 pound, or about
  0.2 millionths of an ounce.
• By comparison, if all that information were on
  paper, it might be 6.7 million tons per day.

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Digital Representation

• Uses only two symbols, 0 and 1, to represent
  information
• The basis of almost all modern human
  communication technologies
• Many standards allow various types of information
  to be expressed in digital form
• MP3 for music
• JPEG for images
• ASCII for text
• GIS relies on standards for geographic data

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What is represented

• Geographic information links a place, and often a
  time, with some property of that place (and time)
• The temperature at 48 N, 120 W at noon local
  time on 12/2/99 was 2 Celsius
• The potential number of properties is vast
• In GIS we term them attributes
• Attributes can be physical, social, economic,
  demographic, environmental, etc.

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59N, 140W
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59N, 140W
Local noon 1 celsius
Humidity 19
Median income Population density Barometric
Pressure
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Types of Attributes

• Nominal, e.g. land cover class
• Ordinal, e.g. a ranking
• Interval, e.g. Celsius temperature
• Differences make sense
• Ratio, e.g. Population density
• Ratios make sense
• Cyclic, e.g. wind direction

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SCALES OF MEASUREMENT

• Numerical values may be defined with respect
• to nominal, ordinal, interval, or ratio scales of
• measurement
• It is important to recognize the scales of
• measurement used in GIS data as this
• determines the kinds of mathematical
• operations that can be performed on the data
• The different scales can be demonstrated using
• an example of a marathon race.

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Nominal scale

• On a nominal scale, numbers merely establish
• identity
• Example a phone number signifies only the
  unique
• identity of the phone
• In the race, the numbers issued to racers which
• are used to identify individuals are a nominal
• scale
• These identity numbers do not indicate any
  order or
• relative value in terms of the race outcome

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Ordinal scale

• On an ordinal scale, numbers establish order
• only
• Example phone number 961-8224 is not more of
• anything than 961-8049, so phone numbers are
• NOT ordinal
• In the race, the finishing places of each racer,
• i.e., 1st place, 2nd place, 3rd place, are
• measured on an ordinal scale
• However, we do NOT know how much time
• difference there is between each racer

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Interval scale

• In the race, the time of the day that each racer
• finished is measured on an interval scale
• If the racers finished at 910, 920, and 925,
  then
• racer one finished 10 minutes before racer two
  and
• the difference between racers 1 and 2 is twice
  that
• of the difference between racers 2 and 3
• However, the racer finishing at 910 did not
  finish
• twice as fast as the racer finishing at 1820

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Ratio Scale

• In our race, the first place finisher finished in
  a
• time of 230, the second in 230, and the 450th
• place finisher took 5 hours
• The 450th finisher took twice as long
• as the first place finisher (5/2.52)
• Note these distinctions, though important, are
• not always clearly defined
• Is elevation interval or ration? If the local
  base level
• is 750 ft, is a mountain at 2000 feet twice as
  high as
• one at 1000 feet when viewed
• from the valley?

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Bob, Glen and Molly run a marathon
Name
Time done
Finish order
Elapsed time
220 minutes
1st
1103 AM
Molly
232 minutes
3rd
1115 AM
Glen
224 minutes
2nd
1107 AM
Bob
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Levels of measurement
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Cyclic Attributes

• Do not behave as other attributes
• What is the average of two compass bearings, e.g.
  350 and 10?
• Occur commonly in GIS
• Wind direction
• Slope aspect
• Flow direction
• Special methods are needed to handle and analyze

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The Fundamental Problem contd.

• The number of places and times is also vast
• Potentially infinite
• The more closely we look at the world, the more
  detail it reveals
• Potentially ad infinitum
• The geographic world is infinitely complex
• Humans have found ingenious ways of dealing with
  this problem
• Many methods are used in GIS to create
  representations or data models

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The Fundamental Problem
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• Not looking to close
• Ignoring homogeneous areas
• Not describing things too well (thematic
  resolution)
• Accounting for things only at one time period
  (temporal resolution)

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Accuracy of Representations

• Representations can rarely be perfect
• Details can be irrelevant, or too expensive and
  voluminous to record
• Its important to know what is missing in a
  representation
• Representations can leave us uncertain about the
  real world

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CDFG Landing Receipts

• Thematic Variables
• Species caught
• Gear type used
• Pounds landed and price received
• Spatial Variables
• Port landed
• 10 min x 10 min block where catch occurred
• Temporal Variables
• Per fishing trip, from 1981 2004

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Data Model Levels
Reality
Human-oriented
Conceptual Model
Increasing Abstraction
Logical Model
Computer-oriented
Physical Model
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Schematic representation of the lives of three US
citizens in space (two horizontal axes) and time
(vertical axis)
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Geographic Data
Field data (forest)
Discrete data (trees)
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Discrete Objects

• Points, lines, and areas
• Countable
• Persistent through time, perhaps mobile
• Biological organisms
• Animals, trees
• Human-made objects
• Vehicles, houses, fire hydrants

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Discrete Objects
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Continuous Fields

• Properties that vary continuously over space
• Value is a function of location
• Property can be of any attribute type, including
  direction
• Elevation as the archetype
• A single value at every point on the Earths
  surface
• The source of metaphor and language
• Any field can have slope, gradient, peaks, pits

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Examples of Fields

• Soil properties, e.g. pH, soil moisture
• Population density
• But at fine enough scale the concept breaks down
• Identity of land owner
• A single value of a nominal property at any point
• Name of county or state or nation
• Atmospheric temperature, pressure

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Demo

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Difficult Cases

• Lakes and other natural phenomena
• Often conceived as objects, but difficult to
  define or count precisely

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Difficult Cases

• Weather forecasting
• Forecasts originate in models of fields, but are
  presented in terms of discrete objects
• Highs, lows, fronts

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In Summary

• The core components of geographic data include
  the linking of a place, time and some
  characteristic.
• We represent data because it is impossible to
  describe the infinite number of places, and
  characteristics
• Representations are of unique places
• Representations are selective
• In geographic scope
• Of temporal scope
• In terms of the number of things we describe
  about a place
• In terms of how much we generalize
• All geographic data can be represented as
  continuous fields or discrete objects

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Geographic data models
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Outline

• Definitions
• Data models / modeling
• GIS data models
• Examples
• Arc specific data models
• Water facility example

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Definitions

• Representation
• Focus on conceptual and scientific issues
• Data model
• set of constructs for representing objects and
  processes in the digital environment

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Data Model Levels
Reality
Human-oriented
Conceptual Model
Increasing Abstraction
Logical Model
Computer-oriented
Physical Model
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GIS Data Models Applications

• CAD
• Graphical
• Image
• Raster
• TIN
• Geo-relational
• Object

• Engineering design
• Simple mapping
• Image processing and analysis
• Spatial analysis / modeling
• Surface /terrain analysis / modeling
• Geoprocessing geometric features
• Features with behavior

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CAD and graphical models
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Triangular Irregular Network
(TIN)
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Raster and Vector Models

• Raster implementation of field conceptual model
• Array of cells used to represent objects
• Useful as background maps and for spatial
  analysis
• Vector implementation of discrete object
  conceptual model
• Point, line and polygon representations
• Widely used in cartography, and network analysis

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Rasters and Vectors

• How to represent phenomena conceived as fields or
  discrete objects?
• Raster
• Divide the world into square cells
• Register the corners to the Earth
• Represent discrete objects as collections of one
  or more cells
• Represent fields by assigning attribute values to
  cells
• More commonly used to represent fields than
  discrete objects

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Legend
Mixed conifer
Douglas fir
Oak savannah
Grassland
Raster representation. Each color represents a
different value of a nominal-scale field denoting
land cover class.
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Characteristics of Rasters

• Pixel size
• The size of the cell or picture element, defining
  the level of spatial detail
• All variation within pixels is lost
• Assignment scheme
• The value of a cell may be an average over the
  cell, or a total within the cell, or the
  commonest value in the cell
• It may also be the value found at the cells
  central point

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