Analysis of Spatial Relationships in Vector GIS

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Analysis of Spatial Relationships in Vector GIS
Proximity Finding features near or nearest
other features e.g. find all block centroid
points that are nearest to these water-supply
pipeline features Adjacency Features that
share common segments -- by definition, only
line and polygon features can be adjacent e.g.
find all counties adjacent to Fulton
County Intersection Finding features based on
spatial relationships, such that at least one
point is common to the features e.g. does I-75
cross the Chattahoochee River? Containment Findi
ng features based on spatial relationships, such
that all points of a feature form a subset of all
points of another feature e.g. select all
counties inside the state of Georgia
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Analysis of Spatial Relationships between Layer
Pairs
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Proximity Relationships -- points near points
Point nearest point e.g. to find the ATM
point feature closest to the Theater point
feature e.g. to find the Theater point feature
closest to the ATM point feature
spatial join
T 02
T 01
A
T 04
T 03
C
B
D
T 05
T 06
Theater Point Feature
ATM Point Feature
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Proximity Relationships -- line near point

• Line nearest Point
• Used often as a preliminary step in network and
  flow analyses to calculate demand
• Step I
• Line feature attributes are attached to the
  nearest point feature record
• Step II
• The point table is summarized on the line
  Identifier to create a new summary table, with
  aggregated numeric attributes
• Step III
• The summary table is joined normally to the
  line feature table, along with the aggregated
  numeric attributes, which serve as the demand
  field
• Step IV
• Network Analysis is performed, based on
  minimizing impedance and/or maximizing demand
• Note -- the spatial relationship of nearest
  works between point and line also

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Proximity Relationships -- line near point
Implementation
spatial join
normal join
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Containment -- Point-in-Polygon Analysis

• The bread and butter of vector GIS spatial
  analysis
• Essentially a counting exercise
• Point features are counted and their
  attributes aggregated based on their
  spatial groupings within the containing
  polygons
• The aggregations are then converted into
  attributes of the polygons

• Consider the following
  example
• Your neighborhood has just received a grant
  of 100,000 to tackle the problem of teenage
  pregnancy
• The neighborhood is divided into four wards
  of varying sizes
• You have a polygon data set of the wards, and
  a point data set of all teenage pregnancies
  within your jurisdiction
• What method are you going to use to allocate
  the grant among the four wards?

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Point-in-Polygon Analysis Base Data graphic
Ward A
Ward D
Ward B
Ward C
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Point-in-Polygon analysis, How to

• Point-in-Polygon
• Step I
• Ward polygon feature attributes are
  spatially joined to the teenage pregnancy
  point feature record, if the point feature lies
  within that polygon
• Step II
• The point table is summarized on the polygon
  Identifier to create a new summary table, with
  counts of teenage pregnancies and
  associated aggregated numeric attributes
• Step III
• The summary table is normally joined to the
  ward polygon feature table, along with the
  aggregated numeric attributes
• Step IV
• The grant of 100,000 is divided in the same
  proportion as that of the count of teenage
  pregnancies by ward to the total number of
  teenage pregnancies within the jurisdiction

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Point-in-Polygon Analysis Implementation
spatial join
normal join
summarize
Based on the simple point-in-polygon analysis,
the 100,000 grant should be distributed in
proportion to each wards share of the total
number of teenage pregnancies within the
jurisdiction Ward A -- 30,000 Ward B --
50,000 Ward C -- 14,000 Ward D --
6,000
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Point-in-Polygon Analysis with Numeric Aggregation
Another Example of a Point-in-Polygon Analysis


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Line-in-Polygon Analysis with Numeric Aggregation
Line-in-Polygon Analysis


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Polygon-in-Polygon Analysis

• The point-in-polygon analysis may be extended
  to the contained polygon without any
  modification
• The analysis deals with contained polygons
  as if they were points -- the transfer of
  attributes is effected in the same manner as the
  point-in-polygon analysis
• You must be careful in boundary cases, where
  polygons may not be contained and therefore
  excluded from the analysis -- an
  area-weighting scheme might be more
  appropriate

Feature Count N01 4 N02 2
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Spatial Data Management -- The Dissolve Operation

• THE DISSOLVE OPERATION
• The process by which features of a layer are
  combined into larger features, based on a common
  attribute value
• e.g. Several islands make up the state of
  Hawaii
• You might have digitized each island as a
  distinct polygon feature, and all these
  polygon features have Hawaii as the value of
  the attribute field for StateName
• The data set may then be spatially merged
  based on the coincidence of the StateName
  value being Hawaii to create one polygon in a
  new data set for the State of Hawaii
• e.g. Forming Counties from Census Tracts
• You might have a polygon data set of all census
  tracts for the State of Georgia
• One attribute field in this data set is called
  CntyName and contains the name of the
  county that the census tract lies in
• You can spatially merge the tract data set on
  the field CntyName, and create a new
  county data set
• All census tracts with the same value under
  CntyName will be combined into one county
  polygon feature -- you can thus create all
  counties for the state of Georgia

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The Dissolve Operation Inputs and Results
Input Layer
Result Layer
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Spatial Data Management -- The Merge Process or
Combining Data sets

• The Merge Process
• The Merge Process is used to create larger
  data sets from existing data sets
• All features from the two or more input themes
  are combined in extents and drawn in the
  new result theme
• If the attribute data structure of the input
  themes is the same (the field names and field
  types are identical), no data is lost and the
  result theme will have appropriate values drawn
  from the corresponding input theme fields
• If the attribute data structure of the input
  themes is not the same, you must choose one of
  the input themes to provide the attribute data
  structure for the result theme -- the result
  theme may have several null values as a
  consequence
• The coordinate system of the output dataset
  will be the same as that of the DataFrame if you
  are using ArcMap

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The Merge Operation Inputs and Results
Input Layer
Input Layer

Result Layer
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Buffering Features

• Buffering Features
• The Buffering process creates polygon features
  whose perimeters are at a constant distance
  from input features
• Several concentric buffers can be produced
  in a single operation
• Overlapping buffers may be merged, in order to
  avoid double-counting errors when doing
  point-in-polygon analyses
• Polygon buffers may be drawn at specified
  distances either inside or outside the polygon
  input feature
• The output polygon theme has the same
  coordinate system as that of the DataFrame, if
  you are using ArcMap

Multiple Ring Line Buffers
Multiple Ring Point Buffers with dissolved
overlaps
Multiple Ring Polygon Buffers Inside and Outside
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Clipping Data Sets

• The Clipping Process
• Clipping is an operation used to trim the
  extents of features in existing data sets
  using the features of other data sets
• The input layer features are clipped by
  using the overlay layer features as a sort of
  cookie cutter
• Note that all the original attributes of the
  input layer features are retained, even though
  the shape and area of the output features are
  different, and the coordinate system of the
  DataFrame is used
• Clipping may be used for aesthetic purposes to
  good effect

Overlay theme
Input theme
Before the Clip Operation
After the Clip Operation