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									Analysis in GIS
            Analysis Components
   analyses may be applied to (use as input):
    – tabular attribute data
    – spatial data/layers
    – combination of spatial and tabular
   results may be displayed as (produce as output):
    – table subsets, table combinations, highlighted records
      (rows), new variables (columns)
    – charts
    – maps/map features:
          highlights on existing themes
          new themes/layers
    – combination
    Availability of Analytical Capabilities:
               Analysis Options
   Basic: Desktop GIS packages
    – ArcGIS ArcView
    – Mapinfo
    – Geomedia (Bentley); Geographics (Intergraph)
   Advanced: Professional GIS systems
    – ArcGIS ArcINFO, Intergraph MGE
    – provide data editing plus more advanced analysis Capabilities move
   Specialized: modelling and simulation               ‘up the chain’
    – via scripting/programming within GIS              over time.
           ArcObjects in ArcGIS
    – via specialized packages and/or GISs
           3-D Scientific Visualization packages
           transportation planning packages
           ER Mapper, PCI for raster
         Advanced and Specialized
      in comparison to basic applications
Most ‘basic’ analyses are used to create descriptive
  models of the world, that is, representations of
  reality as it exists.
Most ‘advanced’ analyses involve creating a new
  conceptual output layer, or in some cases table(s) or
  chart(s), the values of which are some
  transformation of the values in the descriptive input
  e.g. slope or aspect layer
Most ‘specialized’ applications involve using GIS
  capabilities to create a predictive model of a real
  world process, that is, a model capable of
  reproducing processes and/or making predictions or
  projections as to how the world might appear.
  e.g. fire spread model, traffic projections
       Analysis Options: Basic
   Spatial Operations         Attribute Operations
    – centroid                  – record selection
      determination                 tabular via SQL
    – spatial measurement           ‘information
    – buffer analysis                clicking’ with
    – neighborhood                   cursor
      analysis/spatial          – variable recoding
      filtering                 – record aggregation
    – geocoding                 – general statistical
    – polygon overlay             analysis
    – spatial aggregation
         redistricting

         regionalization

         classification
              Analysis Options: Advanced &
Advanced                               Specialized
   surface analysis                      Remote Sensing image
     – cross section creation              processing and
     – visibility/viewshed
                                          raster modelling
   proximity analysis
                                          3-D surface modelling
     – nearest neighbor layer
                                          spatial
     – distance matrix layer
   network analysis                       modelling
     – routing                            functionally specialized
           shortest path (2 points)
                                            – transportation
           traveling salesman (n             modelling
                                            – land use modelling
     – time districting
                                            – hydrological modelling
     – allocation
                                            – etc.
   Thiessen Polygon creation
                  Spatial operations:
                 Centroid or Mean Center                        n                   n

                                                               X       i          Y       i

   balancing point for a spatial distribution            X   i 1
                                                                            ,Y    i 1
                                                                    n                   n
    – point representation for a polygon--analogous to the mean
    – single point summary for a distribution (point or polygon)
   useful for
    – summarizing change over time in a distribution (e.g Canadian
      pop. centroid every 10 years)
    – placing labels for polygons
   for weird-shaped polygons,
    centroid may not lie within polygon

                           centroid outside
                   Spatial operations:
                   Spatial Measurement
Spatial measurements:           Comments:
                                    possible distance metrics:
 distance measures

                                     – straight line/airline
    – between points
                                     – city block/manhattan
    – from point or raster to          metric
      polygon or zone                – distance thru network
      boundary                       – time/friction thru network
    – between polygon              shape often measured by:
      centroids                       perimeter       = 1.0 for circle
   polygon area                    area x 3.54       = 1.13 for square
                                     = large number for irregular polygon
   polygon perimeter
                                   may generate output raster:
   polygon shape
                                     – value of spatial
   volume calculation                  measurement assigned to
    – e.g. for earth moving,            each pixel within a polygon
      resevoirs                         or zone
   direction determination        Projection affects values!!!

    – e.g. for smoke plumes
                 Spatial operations:
         Spatial Measurement--example

       0.265    2.729  2605  2605 Anderson           48001
       0.368    2.564  2545  2545 Andrews            48003
       0.209    2.171  2680  2680 Angelina           48005
       0.072    2.642  2899  2899 Aransas            48007
       0.233    1.941  2335  2335 Archer             48009
       0.233    1.941  2103  2103 Armstrong          48011
       0.299    2.278  2870  2870 Atascosa           48013
       0.159    2.115  2830  2830 Austin             48015
       0.203    1.806  2256  2256 Bailey             48017
       0.205    2.114  2844  2844 Bandera            48019
       0.218    1.842  2801  2801 Bastrop            48021
       0.223    1.897  2338  2338 Baylor             48023

Attributes of ..... file in ArcView provides area and perimeter
measurements automatically.
                       Spatial Operations:
                         buffer zones                        polygon buffer
     Buffer zones
         region within ‘x’ distance       Examples
          units                               200 foot buffer around
         buffer any object: point,            property where zoning
          line or polygon                      change requested
         use multiple buffers at             100 ft buffer from stream
          progressively greater                center line limiting
          distances to show gradation          development
         may define a ‘friction’ or          3 mile zone beyond city
          ‘cost’ layer so that spread is       boundary showing ETJ
          not linear with distance             (extra territorial
point                                          jurisdiction)
buffers                           line        use to define (or exclude)
                                 buffer        areas as options (e.g for
                                               retail site) or for further
                                              in conjunction with ‘friction
                                               layer’, simulate spread of
                 Spatial Operations:
       neighborhood analysis/spatial filtering
   spatial convolution or filter       low frequency ( low pass) filter:
     – value of each cell replaced       mean filter
       by some function of the
                                          – cell replaced by the mean
       values of itself and the
                                            for neighborhood
       cells (or polygons)
       surrounding it                     – equivalent to weighting
                                            (mutiplying) each cell by
     – can use ‘neighborhood’ or
                                             1/9 = .11 (in 3x3 case)
       ‘window’ of any size
                                          – smooths the data
          3x3 cells (8-connected)
                                          – use larger window for
          5x5, 7x7, etc.
                                            greater smoothing
     – differentially weight the
                                         median filter
       cells to produce different
       effects                            – use median (middle value)
                                            instead of mean
     – kernel for 3x3 mean filter:
                                          – smoothing, especially if data
         1/9 1/9 1/9
                                            has extreme value outliers
         1/9 1/9 1/9 weights must
         1/9 1/9 1/9 sum to 1.0
                   Spatial Operations:
                        Polygon Overlay
   combines two (or more)           Examples
    layers to create a third            assign environmental samples
   used to integrate attribute          (points) to census tracts to
    data having different spatial        estimate exposure per capita
    properties (point v. polygon)        (polygon on point)
    or boundaries (zip and tract)       identify tracts traversed by
   can overlay polygons on:             freeway for study of
     – points (point in polygon)         neighborhood blight (polygon
     – lines (line on polygon)           on lines)
     – other polygons                   integrate census data by
   many different Boolean logic         block with sales data by zip
    combinations possible                code (polygon on polygon)
     – Union (A or B)
     – Intersection (A and B)
     – A and not B ; not (A and B)
 Spatial Matching via Polygon Overlay:

  Land Use              a.    b.        c.
                                             The two themes (land use
                                             & drainage basins) do not
Drainage                           A.        have common
           Gulf              G.              boundaries. GIS creates
                                             combined layer
                                             permitting calculation of
Combined layer       aA bA          cA       land use by drainage
                  aG    bG          cG       basin.
   Point-point
    is within , e.g. find all of the customer points within 1 km of this retail store point
    is nearest to , e.g. find the hazardous waste site which is nearest to this
    groundwater well
   Point-line
    ends at , e.g. find the intersection at the end of this street
    is nearest to , e.g. find the road nearest to this aircraft crash site
   Point-area
    is contained in , e.g. find all of the customers located in this ZIP code boundary
    can be seen from , e.g. determine if any of this lake can be seen from this viewpoint
   Line-line
    crosses , e.g. determine if this road crosses this river
    comes within , e.g. find all of the roads which come within 1 km of this railroad
    flows into , e.g. find out if this stream flows into this river
   Line-area
    crosses , e.g. find all of the soil types crossed by this railroad
    borders , e.g. find out if this road forms part of the boundary of this airfield
   Area-area
    overlaps , e.g. identify all overlaps between types of soil on this map and types of
    land use on this other map
    is nearest to , e.g. find the nearest lake to this forest fire
    is adjacent to , e.g. find out if these two areas share a common boundary
              Spatial Operations:
   assigning spatial coordinates (explict location) to
    addresses (implicit location)
   usually assigns x,y coordinates; could be lat/long
   requires street network file with street attribute
    information (street name and number range for
    each block) for all street segments (blocks)
   precise matching of street names can be
    – completeness (esp. for ‘new’ streets) important
    – PO boxes, building names, and apartment complex
      names cause problems.
Districting: elementary school attendance zones grouped to form
junior high zones.

Regionalization: census tracts grouped into neighborhoods

Classification: cities categorized as central city or suburbs
               soils classified as igneous, sedimentary, metamorphic
                Spatial Operations:
          spatial aggregation (dissolving)
Groupings may be:
             formal (based on in situ        Examples:
              e.g. city neighborhoods
                                                 elementary school zones to high
             functional (based on flows or       school attendance zones
              links):                             (functional districting)
              e.g. commuting zones
   districting/redistricting                    election precincts (or city blocks)
     – grouping contiguous polygons               into legislative districts (formal
       into districts                             districting)
     – original polygons preserved               creating police precincts (funct.
   regionalization                               reg.)
     – grouping polygons into
       contiguous regions
                                                 creating city neighborhood map
     – original polygon boundaries                (form. reg.)
       dissolved                                 grouping census tracts into
   classification                                market segments--yuppies,
     – grouping polygons into non-                nerds, etc (class.)
       contiguous regions
     – original boundaries usually
                                                 creating soils or zoning map
       dissolved                                  (class)
     – usually ‘formal’ groupings
          Advanced Applications:
            Network Analysis
Routing                                   Districting
                                           expand from site along network
 shortest path between
                                              until criteria (time, distance, cost,
  two points                                  object count) is reached; then
      – direction instructions                assign area to district
        (locating hotel from                   – creating market areas,
        airport)                                  attendance zones, etc
    travelling salesman:
                                           assign locations to the nearest
     shortest path connecting                 center
     n points                                  – assign customers to pizza
       – bus routing, delivery                    delivery outlets
           drivers                         draw boundaries (lines of
    In all cases, ‘distance’ may be           equidistance) based on the above
    measured in miles, time, cost or           – market area creation
    other ‘friction’ (e.g pipe diameter        – example of application of
    for water, sewage, etc.).                     Thiessen polygons
    Arc or node attributes (e.g one-way
    streets, no left turn) may also be

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