Geographic Information System unit 4

OCE 552 - Geographic
Information System
UNIT - IV

2
Vector Data Analysis
Vector data analysis uses the geometric objects of point, line,
and polygon.
The accuracy of analysis results depends on the accuracy of
these objects in terms of location and shape.
Topology can also be a factor for some vector data analyses
such as buffering and overlay.
Buffering
Based on the concept of proximity, buffering creates
two areas: one area that is within a specified distance
of select features and the other area that is beyond.
The area that is within the specified distance is called
the buffer zone.
There are several variations in buffering. The buffer
distance can vary according to the values of a given
field. Buffering around line features can be on either
the left side or the right side of the line feature.
Boundaries of buffer zones may remain intact so that
each buffer zone is a separate polygon.

Figure 11.1
Buffering around points, lines, and areas.
Figure 11.2
Buffering with different buffer distances.
3

Figure 11.3
Buffering with four rings.
Figure 11.4
Buffer zones not dissolved
(top) or dissolved (bottom).
4

Overlay
An overlay operation combines the geometries
and attributes of two feature layers to create the
output.
The geometry of the output represents the
geometric intersection of features from the input
layers.
Each feature on the output contains a
combination of attributes from the input layers,
and this combination differs from its neighbors.
Figure 11.5
Overlay combines the geometry and attribute data from two layers into a single layer. The
dashed lines are not included in the output.
5

Feature Type and Overlay
Overlay operations can be classified by feature type into
point-in-polygon, line-in-polygon, and polygon-on-
polygon.
Figure 11.6
Point-in-polygon overlay. The input is a point layer (the dashed lines
are for illustration only and are not part of the point layer). The output
is also a point layer but has attribute data from the polygon layer.
6

Figure 11.7
Line-in-polygon overlay. The input is a line layer (the dashed lines are for illustration
only and are not part of the line layer). The output is also a line layer. But the output
differs from the input in two aspects: the line is broken into two segments, and the line
segments have attribute data from the polygon layer.
Figure 11.8
Polygon-on-polygon overlay. In the illustration, the two layers for overlay have
the same area extent. The output combines the geometry and attribute data
from the two layers into a single polygon layer.
7

Overlay Methods
All overlay methods are based on the Boolean connectors of AND, OR, and XOR.
An overlay operation is called Intersect if it uses the AND connector.
An overlay operation is called Union if it uses the OR connector.
An overlay operation that uses the XOR connector is called Symmetrical Difference
or Difference.
An overlay operation is called Identity or Minus if it uses the following expression:
[(input layer) AND (identity layer)] OR (input layer).
Figure 11.9
The Union method keeps all areas of the two input layers in the output.
8

Figure 11.10
The Intersect method preserves only the area common to the two input layers
in the output. (The dashed lines are for illustration only; they are not part of the
output.)
Figure 11.11
The Symmetric Difference method preserves only the area common to only
one of the input layers in the output. (The dashed lines are for illustration only;
they are not part of the output.)
9

Figure 11.12
The Identity method produces an output that has the same extent as the input layer. But
the output includes the geometry and attribute data from the identity layer.
10
Slivers
A common error from overlaying polygon layers is slivers,
very small polygons along correlated or shared boundary
lines of the input layers.
To remove slivers, ArcGIS uses the cluster tolerance,
which forces points and lines to be snapped together if
they fall within the specified distance.

Figure 11.13
The top boundary has a series of slivers. These slivers are formed
between the coastlines from the input layers in overlay.
Figure 11.14
A cluster tolerance can remove many
slivers along the top boundary (A) but can
also snap lines that are not slivers(B).

Areal Interpolation
One common application of overlay is to help solve the areal
interpolation problem. Areal interpolation involves transferring known
data from one set of polygons (source polygons) to another (target
polygons).
Figure 11.15
An example of areal interpolation. Thick lines represent census tracts and thin lines
school districts. Census tract A has a known population of 4000 and B has 2000. The
overlay result shows that the areal proportion of census tract A in school district 1 is
1/8 and the areal proportion of census tract B, 1/2. Therefore, the population in
school district 1 can be estimated to be 1500, or [(4000 x 1/8) + (2000 x 1/2)].
12

Pattern Analysis
Pattern analysis refers to the use of quantitative methods for
describing and analyzing the distribution pattern of spatial features.
At the general level, a pattern analysis can reveal if a distribution
pattern is random, dispersed, or clustered.
At the local level, a pattern analysis can detect if a distribution
pattern contains local clusters of high or low values.
Pattern analysis includes point pattern analysis (nearest neighbor,
Ripley’s K-function), Moran’s I for measuring spatial autocorrelation,
and G-statistic for measuring high/low clustering.
Figure 11.16
A point pattern showing deer locations.

Figure 11.18
A point pattern showing deer locations and the number of
sightings at each location.
Figure 11.19
Percent Latino
population by block group in Ada
County, Idaho. Boise
is located in the upper center
of the map with small
sized block groups.

Feature Manipulation
Tools are available in a GIS package for manipulating
and managing maps in a database.
These tools include Dissolve, Clip, Append, Select,
Eliminate, Update, Erase, and Split.
Figure 11.22
Dissolve removes boundaries of polygons that have the same
attribute value in (a) and creates a simplified layer (b).

Figure 11.23
Clip creates an output that contains only those features of the input layer that fall within the area extent of
the clip layer. (The dashed lines are for illustration only; they are not part of the clip layer.)
Figure 11.24
Append pieces together two adjacent layers into a single layer but does not remove the shared boundary
between the layers.

Figure 11.25
Select creates a new layer (b) with selected features from the input
layer (a).
Figure 11.26
Eliminate removes
some small
slivers along the top
boundary (A).

Figure 11.27
Update replaces the input layer with the update layer and its features. (The dashed
lines are for illustration only; they are not part of the update layer.)
Figure 11.28
Erase removes features from the input layer that fall within the area extent of the erase layer.
(The dashed lines are for illustration only; they are not part of the erase layer.)

Figure 11.29
Split uses the geometry of the split layer to divide the input layer into four separate
layers.

CHAPTER 12 RASTER DATAANALYSIS
12.1 Data Analysis Environment
Box 12.1 How to Make an Analysis Mask
12.2 Local Operations
12.2.1 Local Operations with a Single Raster
12.2.2 Reclassification
12.2.3 Local Operations with Multiple Rasters
Box 12.2 Local Operations in ArcGIS
12.2.4 Applications of Local Operations
12.3 Neighborhood Operations
12.3.1 Neighborhood Statistics
Box 12.3 Neighborhood Operations in ArcGIS
12.3.2 Applications of Neighborhood Operations
Box 12.4 More Examples of Neighborhood Operations
12.4 Zonal Operations
12.4.1 Zonal Statistics
Box 12.5 Zonal Operations in ArcGIS
12.4.2 Applications of Zonal Operations
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

12.5 Physical Distance Measure Operations
12.5.1 Allocation and Direction
Box 12.5 Distance Measure Operations in ArcGIS
12.5.2 Applications of Physical Distance Measure Operations
12.6 Other Raster Data Operations
12.6.1 Raster Data Management
12.6.2 Raster Data Extraction
12.6.3 Raster Data Generalization

Raster Data Analysis
Raster data analysis is based on cells and rasters.
Raster data analysis can be performed at the level of
individual cells, or groups of cells, or cells within an entire
raster.
Some raster data operations use a single raster; others
use two or more rasters.
Raster data analysis also depends on the type of cell
value (numeric or categorical values).

Raster Analysis Environment
The analysis environment refers to the area for
analysis and the output cell size.

Local Operations: Single Raster
Given a single raster as the input, a local operation computes
each cell value in the output raster as a mathematical function
of the cell value in the input raster.

Figure 12.1
Arithmetic, logarithmic, trigonometric, and power functions for
local operations.

Figure 12.2
A local operation can convert a slope raster from percent (a) to
degrees (b).

Local Operations: Multiple Rasters
A common term for local operations with multiple input rasters is
map algebra, a term that refers to algebraic operations with raster
map layers.
Besides mathematical functions that can be used on individual
rasters, other measures that are based on the cell values or their
frequencies in the input rasters can also be derived and stored on
the output raster of a local operation with multiple rasters.

Figure 12.3
The cell value in (d) is the mean
calculated from three input rasters (a,
b, and c) in a local operation. The
shaded cells have no data.

Figure 12.5
Each cell value in (c) represents a unique combination of cell values in (a)
and (b). The combination codes and their representations are shown in (d).

Neighborhood Operations
A neighborhood operation involves a focal cell and a set of
its surrounding cells. The surrounding cells are chosen for
their distance and/or directional relationship to the focal cell.
Common neighborhoods include rectangles, circles,
annuluses, and wedges.

Figure 12.6
Four common
neighborhood types:
rectangle (a), circle
(b), annulus (c), and
wedge (d). The cell
marked with an x is
the focal cell.

Figure 12.7
The cell values in (b)
are the neighborhood
means of the shaded
cells in (a) using a 3 x 3
neighborhood. For
example, 1.56 in the
output raster is
calculated from (1 +2 +2
+1 +2 +2 +1 +2 +1) / 9.

Figure 12.8
The cell values in (b) are
the neighborhood range
statistics of the shaded
cells in (a) using a 3 x 3
neighborhood. For
example, the upper-left
cell in the output raster
has a cell value of 100,
which is calculated from
(200 – 100).

Figure 12.9
The cell values in (b) are
the neighborhood
majority statistics of the
shaded cells in (a) using
a 3 x 3 neighborhood.
For example, the upper-
left cell in the output
raster has a cell value of
2 because there are five
2s and four 1s in its
neighborhood.

Zonal Operations
A zonal operation works with groups of cells of same values
or like features. These groups are called zones. Zones may be
contiguous or noncontiguous.
A zonal operation may work with a single raster or two rasters.
Given a single input raster, zonal operations measure the
geometry of each zone in the raster, such as area, perimeter,
thickness, and centroid.
Given two rasters in a zonal operation, one input raster and
one zonal raster, a zonal operation produces an output raster,
which summarizes the cell values in the input raster for each
zone in the zonal raster.

Figure 12.10
Thickness and centroid for two large watersheds (zones). Area is
measured in square kilometers, and perimeter and thickness are
measured in kilometers. The centroid of each zone is marked with an x.

Figure 12.11
The cell values in (c)
are the zonal means
derived from an input
raster (a) and a zonal
raster (b). For
example, 2.17 is the
mean of {1, 1, 2, 2, 4,
3} for zone 1.

Physical Distance Measure
Operations
The physical distance measures the straight-line or
euclidean distance.
Physical distance measure operations calculate straight-
line distances away from cells designated as the source
cells.

Figure 12.12
A straight-line distance is measured from a cell center to another
cell center. This illustration shows the straight-line distance
between cell (1,1) and cell (3,3).

Figure 12.13
Continuous distance measures from a stream network.

Allocation and Direction
Allocation produces a raster in which the cell value
corresponds to the closest source cell for the cell.
Direction produces a raster in which the cell value
corresponds to the direction in degrees that the cell is
from the closest source cell.

Figure 12.14
Based on the source cells denoted as 1 and 2, (a) shows the physical
distance measures in cell units from each cell to the closest source cell; (b)
shows the allocation of each cell to the closest source cell; and (c) shows
the direction in degrees from each cell to the closest source cell. The cell in
a dark shade (row 3, column 3) has the same distance to both source cells.
Therefore, the cell can be allocated to either source cell. The direction of
2430 is to the source cell 1.

Other Raster Data Operations
1. Operations for raster data management include Clip
and Mosaic.
2. Operations for raster data extraction include use of a
data set, a graphic object, or a query expression to
create a new raster by extracting data from an
existing raster.
3. Operations for raster data generalization include
Aggregate and RegionGroup.

Figure 12.15
An analysis mask (b) is used to clip an input raster (a). The output
raster is (c), which has the same area extent as the analysis mask.

Figure 12.16
A circle, shown in white, is used to extract cell values from the input
raster (a). The output (b) has the same area extent as the input raster
but has no data outside the circular area.

Figure 12.17
An Aggregate operation creates a lower-resolution raster (b) from
the input (a). The operation uses the mean statistic and a factor of 2
(i.e., a cell in b covers 2 x2 cells in a). For example, the cell value of
4 in (b) is the mean of {2, 2, 5, 7} in (a).

Figure 12.18
Each cell in the output (b) has a unique number that identifies the
connected region to which it belongs in the input (a). For example,
the connected region that has the same cell value of 3 in (a) has a
unique number of 4 in (b).

Network
A network is a system of linear features that has the
appropriate attributes for the flow of objects.
A network is typically topology-based: lines (arcs) meet at
intersections (junctions), lines cannot have gaps, and lines
have directions.
Attribute data of a road network include link impedance,
turns, one-way streets, and overpasses/underpasses.
Explain the difference between a
network and a line shapefile.
Unlike a line shapefile, a network is
typically topology-based: lines (arcs) meet
at intersections (nodes), lines cannot have
gaps, and lines have directions.

Link and Link Impedance
A link refers to a road segment defined by two end points.
Links are the basic geometric features of a network.
Link impedance is the cost of traversing a link.
What is link impedance?
Link impedance is the cost of
traversing a link.
A simple measure of the cost is
the physical length of the link.

Junction and Turn Impedance
A junction refers to a street
intersection.
A turn is a transition from one
street segment to another at a
junction.
Turn impedance is the time it
takes to complete a turn, which
is significant in a congested
street network. Turn
impedance is directional.
A turn table assigns the turn
impedance value to each turn
in the network.
What is turn impedance?
Turn impedance is the time it takes to
complete a turn, which is significant in a
congested street network

Figure 17.6
Possible turns at node 341.

Figure 17.7
Node 265 has stop signs for
the east–west traffic. Turn
impedance applies only to
turns in the shaded rows.

Restrictions
Restrictions refer to routing requirements on a network.
One-way or closed streets are examples of restrictions.
What are considered restrictions in network
analysis?
One-way or closed streets are considered restrictions in
network analysis

Putting Together a Network
1. Gathering linear features from a network data source
2. Editing and building network
3. Attributing the network features

Network analysis
A network with the appropriate attributes can be a variety of
applications including shortest path analysis, traveling salesman
problem, vehicle routing problem, closest facility, allocation, and
location-allocation.

Shortest Path Analysis
Shortest path analysis finds the path with the minimum
cumulative impedance between nodes on a network. The path
may connect just two nodes—an origin and a destination—or
have specific stops between the nodes.

Figure 17.8
Link impedance
values between cities
on a road network.

TABLE 17.1 The Impedance Matrix among Six Nodes in Figure 17.11
(1) (2) (3) (4) (5) (6)
(1) ∞ 20 53 58 ∞ ∞
(2) 20 ∞ 39 ∞ ∞ ∞
(3) 53 39 ∞ 25 ∞ 19
(4) 58 ∞ 25 ∞ 13 ∞
(5) ∞ ∞ ∞ 13 ∞ 13
(6) ∞ ∞ 19 ∞ 13 ∞

TABLE 17.2 Shortest Paths from Node 1 to All Other Nodes in Figure 17.11
From-
node
To-node Shortest
Path
Minimum Cumulative
impedance
1 2 p12 20
1 3 p13 53
1 4 p14 58
1 5 p14 + p45 71
1 6 p13 + p36 72

Traveling Salesman Problem
The traveling salesman problem is a routing problem that adds two
constraints to the shortest path analysis: The salesman must visit
each of the select stops only once, and the salesman may start
from any stop but must return to the original stop.

Vehicle Routing Problem
Given a fleet of vehicles and customers, the main objective of the
vehicle routing problem is to schedule vehicle routes and visits to
customers in such a way that the total travel time is minimized.
Additional constraints such as time windows, vehicle capacity, and
dynamic conditions (e.g., traffic congestion) may also exist.

Closest Facility
Closest facility finds the closest facility, such as a hospital,
fire station, or ATM, to any location on a network.

Figure 17.9
Shortest path from
a street address to
its closest fire
station, shown by
the square symbol.

Allocation
Allocation measures the efficiency of public facilities, such
as fire stations, or school resources, in terms of their service
areas. The result of an allocation analysis is
typically presented as service areas.
Why?
Allocation analysis works with the spatial
distribution of resources such as fire
stations and schools. Therefore, the result
of an allocation analysis is typically
presented as service areas, indicating the
area extents of services that can be
rendered by these resources.

Figure 17.10
Service areas of
two fire stations
within a 2-minute
response time.

Figure 17.11
Service areas of
two fire stations
within a 5-minute
response time.

Location-Allocation
Location–allocation solves problems of matching
the supply and demand by using sets of objectives
and constraints.
Define location-allocation
analysis.
Location–allocation analysis solves
problems of matching the supply
and
demand by using sets of objectives
and constraints.

Figure 17.12
The two solid squares
represent existing fire
stations, the three open
squares candidate facilities,
and the seven circles
nursing homes. The map
shows the result of
matching two existing fire
stations with nursing
homes based on the
minimum impedance
model and an impedance
cutoff of 4 minutes on the
road network.

Figure 17.13
The map shows the result
of matching three fire
stations, two existing ones
and one candidate, with
seven nursing homes
based on the minimum
impedance model and an
impedance cutoff of 4
minutes on the road
network.

Figure 17.14
The map shows the result
of matching three fire
stations, two existing
ones and one candidate,
with seven nursing
homes based on the
minimum impedance
model and an impedance
cutoff of 5 minutes on the
road network.

Explain the difference between the minimum distance model and the
maximum covering model in location-allocation analysis.
The minimum distance model minimizes the total distance traveled from
all demand points to their nearest supply centers. The maximum covering
model maximizes the demand covered within a specified time or distance.

What is network?
• A network is a system of interconnected elements, such
as edges (lines) and connecting junctions (points), that
represent possible routes from one location to another.
• People, resources, and goods tend to travel along
networks: cars and trucks travel on roads, airliners fly on
predetermined flight paths, oil flows in pipelines. By
modeling potential travel paths with a network, it is
possible to perform analyses related to the movement of
the oil, trucks, or other agents on the network.
• The most common network analysis is finding the
shortest path between two points.
• ArcGIS groups networks into two categories:
– geometric networks, and
– network datasets.

Types of network analysis
layers
• ArcGIS Network Analyst allows you to solve
common network problems, such as finding the
best route across a city, finding the closest
emergency vehicle or facility, identifying a service
area around a location, servicing a set of orders
with a fleet of vehicles, or choosing the best
facilities to open or close. 6 types or solvers:
– Route
– Closest facility
– Service areas
– OD cost matrix (an origin-destination (OD) cost matrix
from multiple origins to multiple destinations)
– Vehicle routing problem
– Location-allocation

Geographic Information System unit 4

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Geographic Information System unit 4