Introduction to Geographic Information Systems GIS SGO1910

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Introduction to Geographic Information Systems
(GIS)SGO1910 SGO4930 Fall 2005Karen
OBrienHarriet Holters Hus, Room
215karen.obrien_at_sgeo.uio.no
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Announcements

• Questions about home pages?
• Mid-term quiz September 27
• (chapters 1, 3, 4, 5, 6)

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Review

• Spatial Data Models
• Conceptual and Digital Representations
• Discrete Objects and Fields
• Vector and Raster

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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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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
• Any field can have slope, gradient, peaks, pits

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A raster data model uses a grid

• One grid cell is one unit or holds one attribute.
  
• Every cell has a value, even if it is missing.
• A cell can hold a number or an index value
  standing for an attribute.
• A cell has a resolution, given as the cell size
  in ground units.

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Generic structure for a grid
Grid extent
Grid cell
s
w
o
R
Resolution
Columns
Figure 3.1
Generic structure for a grid.
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Legend
Urban area
Suburban area
Forest (protected)
Water
Raster representation. Each color represents a
different value of a nominal-scale field denoting
land use.
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Vector Data

• Used to represent points, lines, and areas
• All are represented using coordinates
• One per point
• Areas as polygons
• Straight lines between points, connecting back to
  the start
• Point locations recorded as coordinates
• Lines as polylines
• Straight lines between points

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Areas are lines are points are coordinates
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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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Fundamental problem in GIS

• Identifying what to leave in and what to take out
  of digital representations.
• The scale or level of detail at which we seek to
  represent reality often determines whether
  spatial and temporal phenomena appear regular or
  irregular.
• The spatial heterogeneity of data also influences
  this regularity or irregularity.

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Todays TopicThe Nature of Geographic Data
(Or how phenomena vary across space, and the
general nature of geographic variation)
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Scale

• Scale refers to the details fine-scaled data
  includes lots of detail, coarse-scaled data
  includes less detail.
• Scale refers to the extent. Large-scale project
  involves a large extent (e.g. India) small-scale
  project covers a small area (e.g., Anantapur,
  India)
• Scale can refer to the level (national vs. local)
• Scale of a map can be large (lots of detail,
  small area covered) or small (little detail,
  large area covered) (Opposite of other
  interpretations!!)

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Principal objective of GIS analysis

• Development of representations of how the world
  looks and works.
• Need to understand the nature of spatial
  variation
• Proximity effects
• Geographic scale and level of detail
• Co-variance of different measures attributes

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• Space and time define the geographic context of
  our past actions, and set geographic limits of
  new decisions (condition what we know, what we
  perceive to be our options, and how we choose
  among them)
• Consider the role of globalization in defining
  new patterns of behavior

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Geographic data

• Smoothness versus irregularity
• Controlled variation oscillates around a steady
  state pattern
• Uncontrolled variation follows no pattern
• (violates Toblers Law)

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Toblers First Law of Geography

• Everything is related to everything else, but
  near things are more related than distant things.

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Spatial Autocorrelation

• The degree to which near and more distant things
  are interrelated. Measures of spatial
  autocorrelation attempt to deal simultaneously
  with similarities in the location of spatial
  objects and their attributes. (Not to be
  confused with temporal autocorrelation)
• Example GDP data

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Spatial autocorrelation

• Can help to generalize from sample observations
  to build spatial representations
• Can frustrate many conventional methods and
  techniques that tell us about the relatedness of
  events.

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The scale and spatial structure of a particular
application suggest ways in which we should
sample geographic reality, and the ways in which
we should interpolate between sample observations
in order to build our representation.
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Types of spatial autocorrelation

• Positive (features similar in location are
  similar in attribute)
• Negative (features similar in location are very
  different)
• Zero (attributes are independent of location)

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• The issue of sampling interval is of direct
  importance in the measurement of spatial
  autocorrelation, because spatial events and
  occurrences can conform to spatial structure
  (e.g. Central Place Theorem).
• Note it is also important in the measurement of
  temporal autocorrelation

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Spatial Sampling

• Sample frames (the universe of eligible elements
  of interest)
• Probability of selection
• All geographic representations are samples
• Geographic data are only as good as the sampling
  scheme used to create them

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Sample Designs

• Types of samples
• Random samples (based on probability theory)
• Stratified samples (insure evenness of coverage)
• Clustered samples (a microcosm of surrounding
  conditions)
• Weighting of observations (if spatial structure
  is known)

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• Usually, the spatial structure is known, thus it
  is best to devise application-specific sample
  designs.
• Source data available or easily collected
• Resources available to collect them
• Accessibility of all parts to sampling

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Spatial Interpolation

• Judgment is required to fill in the gaps between
  the observations that make up a representation.
• To do this requires an understanding of the
  effect of increasing distance between sample
  observations

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Spatial Interpolation

• Specifying the likely distance decay
• linear wij -b dij
• negative power wij dij-b
• negative exponential wij e-bdij
• Isotropic (uniform in every direction) and
  regular relevance to all geographic phenomena?

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Key point

• An understanding of the spatial structure of
  geographic phenomena helps us to choose a good
  sampling strategy, to use the best or most
  appropriate means of interpolating between
  sampled points, and to build the best spatial
  representation for a particular purpose.

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Spatial Autocorrelation

• Induction reasoning from the data to build an
  understanding.
• Deduction begins with a theory or principle.
• Measurement of spatial autocorrelation is an
  inductive approach to understanding the nature of
  geographic data

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Spatial Autocorrelation Measures

• Spatial autocorrelation measures
• Geary and Moran nature of observations
• Establishing dependence in space regression
  analysis
• Y f (X1, X2 , X3 , . . . , XK)
• Y f (X1, X2 , X3 , . . . , XK) e
• Yi f (Xi1, Xi2 , Xi3 , . . . , XiK) ei
• Yi b0 b1 Xi1 b2 Xi2 b3 Xi3 . . . bK
  XiK ei

Y is the dependent variable, X is the independent
variable Y is the response variable, X is the
predictor variable
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Spatial Autocorrelation

• Tells us about the interrelatedness of phenomena
  across space, one attribute at a time.
• Identifies the direction and strength of the
  relationship
• Examining the residuals (error terms) through
  Ordinary Least Squares regression

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Discontinuous Variation

• Fractal geometry
• Self-similarity
• Scale dependent measurement
• Each part has the same nature as the whole
• Dimensions of geographic features
• Zero, one, two, three fractals

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Consolidation

• Representations build on our understanding of
  spatial and temporal structures
• Spatial is special, and geographic data have a
  unique nature
• This unique natures means that you have to know
  your application and data

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Georeferencing
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Georeferencing

• Geographic information contains either an
  explicit geographic reference (such as latitude
  and longitude coordinates), or an implicit
  reference such as an address, road name, or
  postal code.
• Geographic references allow you to locate
  features for analysis.

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Georeferencing

• Is essential in GIS, since all information must
  be linked to the Earths surface
• The method of georeferencing must be
• Unique, linking information to exactly one
  location
• Shared, so different users understand the meaning
  of a georeference
• Persistent through time, so todays georeferences
  are still meaningful tomorrow

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Uniqueness

• A georeference may be unique only within a
  defined domain, not globally
• There are many instances of Storgatas in Norway,
  but only one in any city
• The meaning of a reference to Greenwich may
  depend on context, since there are cities and
  towns called Greenwich in several parts of the
  world

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Georeferences as Measurements

• Some georeferences are metric
• They define location using measures of distance
  from fixed places
• E.g., distance from the Equator or from the
  Greenwich Meridian
• Others are based on ordering
• E.g. street addresses in most parts of the world
  order houses along streets
• Others are only nominal
• Placenames do not involve ordering or measuring

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Placenames

• The earliest form of georeferencing
• And the most commonly used in everyday activities
• Many names of geographic features are universally
  recognized
• Others may be understood only by locals
• Names work at many different scales
• From continents to small villages and
  neighborhoods
• Names may pass out of use in time
• Where was Camelot? Or Atlantis?

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Postal Addresses and Postcodes

• Every dwelling and office is a potential
  destination for mail
• Dwellings and offices are arrayed along streets,
  and numbered accordingly
• Streets have names that are unique within local
  areas
• Local areas have names that are unique within
  larger regions
• If these assumptions are true, then a postal
  address is a useful georeference

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Where Do Postal Addresses Fail as Georeferences?

• In rural areas
• Urban-style addresses have been extended recently
  to many rural areas
• For natural features
• Lakes, mountains, and rivers cannot be located
  using postal addresses
• When numbering on streets is not sequential
• E.g. in Japan

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Postcodes as Georeferences

• Defined in many countries
• E.g. ZIP codes in the US
• Hierarchically structured
• The first few characters define large areas
• Subsequent characters designate smaller areas
• Coarser spatial resolution than postal address
• Useful for mapping

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ZIP code boundaries are a convenient way to
summarize data in the US. The dots on the left
have been summarized as a density per square mile
on the right
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Linear Referencing

• A system for georeferencing positions on a road,
  street, rail, or river network
• Combines the name of the link with an offset
  distance along the link from a fixed point, most
  often an intersection

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Users of Linear Referencing

• Transportation authorities
• To keep track of pavement quality, signs, traffic
  conditions on roads
• Police
• To record the locations of accidents

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Problem Cases

• Locations in rural areas may be a long way from
  an intersection or other suitable zero point
• Pairs of streets may intersect more than once
• Measurements of distance along streets may be
  inaccurate, depending on the measuring device,
  e.g. a car odometer

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Cadasters

• Maps of land ownership, showing property
  boundaries
• The Public Land Survey System (PLSS) in the US
  and similar systems in other countries provide a
  method of georeferencing linked to the cadaster
• In the Western US the PLSS is often used to
  record locations of natural resources, e.g. oil
  and gas wells

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Portion of the Township and Range system (Public
Lands Survey System) widely used in the western
US as the basis of land ownership. Townships are
laid out in six mile squares on either side of an
accurately surveyed Principal Meridian. The
offset shown between townships 16N and 17N is
needed to accommodate the Earths curvature
(shown much exaggerated). The square mile
sections within each township are numbered as
shown in (A) east of the Principal Meridian, and
reversed west of the Principal Meridian.