Download Analysis - Mohawk College

Analysis in GIS
Analysis Components

analyses may be applied to (use as input):
– tabular attribute data
– spatial data/layers
– combination of spatial and tabular
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results may be displayed as (produce as output):
– table subsets, table combinations, highlighted records
(rows), new variables (columns)
– charts
– maps/map features:
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highlights on existing themes
new themes/layers
– combination
Availability of Analytical Capabilities:
Analysis Options
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Basic: Desktop GIS packages
– ArcGIS ArcView
– Mapinfo
– Geomedia (Bentley); Geographics (Intergraph)
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Advanced: Professional GIS systems
– ArcGIS ArcINFO, Intergraph MGE
– provide data editing plus more advanced analysis Capabilities move
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Specialized: modelling and simulation
– via scripting/programming within GIS
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ArcObjects in ArcGIS
– via specialized packages and/or GISs
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3-D Scientific Visualization packages
transportation planning packages
ER Mapper, PCI for raster
‘up the chain’
over time.
Advanced and Specialized
Applications:
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
layer.
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
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Spatial Operations
– centroid
determination
– spatial measurement
– buffer analysis
– neighborhood
analysis/spatial
filtering
– geocoding
– polygon overlay
– spatial aggregation
 redistricting
 regionalization
 classification
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Attribute Operations
– record selection
 tabular via SQL
 ‘information
clicking’ with
cursor
– variable recoding
– record aggregation
– general statistical
analysis
Analysis Options: Advanced &
Specialized
Advanced
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surface analysis
– cross section creation
– visibility/viewshed
proximity analysis
– nearest neighbor layer
– distance matrix layer
network analysis
– routing
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shortest path (2 points)
traveling salesman (n
points)
– time districting
– allocation
Thiessen Polygon creation
Specialized
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Remote Sensing image
processing and
classification
raster modelling
3-D surface modelling
spatial
statistics/statistical
modelling
functionally specialized
– transportation
modelling
– land use modelling
– hydrological modelling
– etc.
Spatial operations:
Centroid or Mean Center
n
X
n
i
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balancing point for a spatial distribution
X
i 1
n
i
,Y 
– 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
polygon
Y
i 1
n
Spatial operations:
Spatial Measurement
Spatial measurements:
 distance measures
– between points
– from point or raster to
polygon or zone
boundary
– between polygon
centroids
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polygon area
polygon perimeter
polygon shape
volume calculation
Comments:
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= 1.0 for circle
perimeter
= 1.13 for square
area x 3.54
= large number for irregular polygon
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– e.g. for earth moving,
resevoirs
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direction determination
– e.g. for smoke plumes
possible distance metrics:
– straight line/airline
– city block/manhattan
metric
– distance thru network
– time/friction thru network
shape often measured by:
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may generate output raster:
– value of spatial
measurement assigned to
each pixel within a polygon
or zone
Projection affects values!!!
Spatial operations:
Spatial Measurement--example
AREA PERIMETER CNTY_ CNTY_ID NAME
2605 Anderson
2605
2.729
0.265
2545 Andrews
2545
2.564
0.368
2680 Angelina
2680
2.171
0.209
2899 Aransas
2899
2.642
0.072
2335 Archer
2335
1.941
0.233
2103 Armstrong
2103
1.941
0.233
2870 Atascosa
2870
2.278
0.299
2830 Austin
2830
2.115
0.159
2256 Bailey
2256
1.806
0.203
2844 Bandera
2844
2.114
0.205
2801 Bastrop
2801
1.842
0.218
2338 Baylor
2338
1.897
0.223
FIPS
48001
48003
48005
48007
48009
48011
48013
48015
48017
48019
48021
48023
Attributes of ..... file in ArcView provides area and perimeter
measurements automatically.
Spatial Operations:
buffer zones
Buffer zones
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point
buffers
region within ‘x’ distance
units
buffer any object: point,
line or polygon
use multiple buffers at
progressively greater
distances to show gradation
may define a ‘friction’ or
‘cost’ layer so that spread is
not linear with distance
line
buffer
polygon buffer
Examples
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200 foot buffer around
property where zoning
change requested
100 ft buffer from stream
center line limiting
development
3 mile zone beyond city
boundary showing ETJ
(extra territorial
jurisdiction)
use to define (or exclude)
areas as options (e.g for
retail site) or for further
analysis
in conjunction with ‘friction
layer’, simulate spread of
fire
Spatial Operations:
neighborhood analysis/spatial filtering

spatial convolution or filter
– value of each cell replaced
by some function of the
values of itself and the
cells (or polygons)
surrounding it
– can use ‘neighborhood’ or
‘window’ of any size
 3x3 cells (8-connected)
 5x5, 7x7, etc.
– differentially weight the
cells to produce different
effects
– kernel for 3x3 mean filter:
1/9 1/9 1/9
1/9 1/9 1/9 weights must
1/9 1/9 1/9 sum to 1.0
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low frequency ( low pass) filter:
mean filter
– cell replaced by the mean
for neighborhood
– equivalent to weighting
(mutiplying) each cell by
1/9 = .11 (in 3x3 case)
– smooths the data
– use larger window for
greater smoothing
median filter
– use median (middle value)
instead of mean
– smoothing, especially if data
has extreme value outliers
Spatial Operations:
Polygon Overlay
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combines two (or more)
layers to create a third
used to integrate attribute
data having different spatial
properties (point v. polygon)
or boundaries (zip and tract)
can overlay polygons on:
– points (point in polygon)
– lines (line on polygon)
– other polygons

many different Boolean logic
combinations possible
– Union (A or B)
– Intersection (A and B)
– A and not B ; not (A and B)
Examples
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assign environmental samples
(points) to census tracts to
estimate exposure per capita
(polygon on point)
identify tracts traversed by
freeway for study of
neighborhood blight (polygon
on lines)
integrate census data by
block with sales data by zip
code (polygon on polygon)
Spatial Matching via Polygon Overlay:
example
Land Use
Drainage
Basins
a.
Atlantic
Gulf
Combined layer
c.
b.
A.
G.
aA bA
bG
aG
cA
cG
The two themes (land use
& drainage basins) do not
have common
boundaries. GIS creates
combined layer
permitting calculation of
land use by drainage
basin.
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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:
Geocoding
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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
problemmatic
– 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:
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formal (based on in situ
characteristics)
e.g. city neighborhoods
functional (based on flows or
links):
e.g. commuting zones
districting/redistricting
– grouping contiguous polygons
into districts
– original polygons preserved
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regionalization
– grouping polygons into
contiguous regions
– original polygon boundaries
dissolved
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Examples:
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classification
– grouping polygons into noncontiguous regions
– original boundaries usually
dissolved
– usually ‘formal’ groupings
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elementary school zones to high
school attendance zones
(functional districting)
election precincts (or city blocks)
into legislative districts (formal
districting)
creating police precincts (funct.
reg.)
creating city neighborhood map
(form. reg.)
grouping census tracts into
market segments--yuppies,
nerds, etc (class.)
creating soils or zoning map
(class)
Advanced Applications:
Network Analysis
Routing
 shortest path between
two points
– direction instructions
(locating hotel from
airport)
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travelling salesman:
shortest path connecting
n points
– bus routing, delivery
drivers
In all cases, ‘distance’ may be
measured in miles, time, cost or
other ‘friction’ (e.g pipe diameter
for water, sewage, etc.).
Arc or node attributes (e.g one-way
streets, no left turn) may also be
critical.
Districting
 expand from site along network
until criteria (time, distance, cost,
object count) is reached; then
assign area to district
– creating market areas,
attendance zones, etc
Allocation
 assign locations to the nearest
center
– assign customers to pizza
delivery outlets
 draw boundaries (lines of
equidistance) based on the above
– market area creation
– example of application of
Thiessen polygons