Introduction to Geographic Information Systems
Fall 2013 (INF 385T-28620)
Dr. David Arctur
Research Fellow, Adjunct Faculty
University of Texas at Austin
Lecture 9
October 24, 2013
Spatial Analysis
Outline

Proximity buffers

Site suitability

Multiple ring buffers; gravity modeling

Data mining with cluster analysis
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Lecture 9
PROXIMITY BUFFERS
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Proximity buffers

Points
 Circular buffers with user-supplied radius

Lines
 Has rounded ends where radius sweeps around
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Proximity buffers

Polygons
 Extends polygons outward and rounds off
corners
 Created by assigning a buffer distance around
polygon
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Point buffer example

Polluting company buffers
 Added schools
 Added population
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Point buffer example

Drug-free zones: criminal penalties are higher for
drug dealing within 1,000 feet of a school
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Line buffer example

Affected businesses within 0.25 miles of a
selected street being paved
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Select features in buffer
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Polygon buffer example

Parcels within 150′ of selected property
 Need to send notifications to adjacent land owners in
regard to requested zoning variance at selected property
(commercial land use in residential area)
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Select features in buffer
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Lecture 9
SITE SUITABILITY
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Locate satellite police stations

Criteria
 Must be centrally located in each car beat (within
a 0.33-mile radius buffer of car beat centroids)
 Must be in retail/commercial areas (within 0.10
mile of at least one retail business)
 Must be within 0.05 mile of major streets
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Starting map

Lake Precinct of the Rochester, New York,
Police Department
 Police car beats
 Retail business
points
 Street centerlines
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Create car beat centroids

X,y centroids of police beats
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Buffer car beat centroids

.33 mile buffer
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Buffer retail businesses

0.1 mile buffer
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Buffer major streets

0.05 mile buffer
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Intersect buffers

Can only intersect two at a time
 A = Car beat buffer and businesses buffer
 A and Streets buffer
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Site suitability result

Map showing possible sites for satellite
police station in car beat 251
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Lecture 9
MULTIPLE RING BUFFERS
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Gravity model

Newton’s Law of Gravity
 The attraction between two objects falls off by the
inverse of the distance between them

The analogy in human behavior is similar
 The further a potential client from a service, the
less likely he/she is to travel to use it
 You can use multiple ring buffers to estimate the
fall off of attraction
 Need residence locations of clients (have a
random sample in this case) and data on the
target population
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Case study

Youths’ use of public pools in Pittsburgh
 Officials closed half of Pittsburgh’s public pools
during a budget crisis

Questions
 Did the remaining pools provide good access for
the city’s youths?
 How quickly does intended use of pools fall off
with distance to the nearest pool?
 Owning a pool tag, needed for admission to a
pool, indicates intention to use a pool
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Map of pools and youth population
How can you tell if open pools provide good accessibility by
Pittsburgh’s youth?
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Steps
1.
2.
3.
4.
5.
6.
Build multiple ring buffers of pools
Intersect buffer rings with random sample of residences
of youths with pool tags and sum youths in each ring
Adjust sum of youths with pool tags in rings to total
population size (e.g., if sampled 10%, multiply by 10)
Intersect buffer rings with block centroids and sum
youth population in each ring
Divide adjusted sum of youths with pool tags by youth
population in each ring: yields fraction of youth who
intended to use pools
Plot graph of the fraction versus average distance of
ring from nearest pool
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Multiple ring buffer of open pools
Each distance produces a separate polygon.
There’s a 0.25 bright green polygon, a 0.5
light green polygon, etc. for a total of 6 polygons.
If you’re in the 0.25 polygon, the nearest pool is
at most 0.25 miles away. Same with other polygons.
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Spatial joins aggregate data

Join adjusted youths with tags and youth
population to buffer rings
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Create graph
While 87% of youths have
pool tags at average distance
of 0.125 miles from nearest pool,
only 41% do at 1.25 miles average
distance.
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Lecture 9
DATA MINING WITH CLUSTER
ANALYSIS
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What can you learn about tornadoes?
There are a lot of tornadoes! Data mining, searching for patterns,
is possible with cluster analysis.
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k-means clustering

Search algorithm that creates groups based
on attributes
 Goal: minimize differences of members in a
group and maximize differences between groups
 You specify
 The attributes to be used for clustering
 The number of groups to find

Clustering is a hard computational problem
 Algorithm is heuristic (approximate)
 Needs starting data record for each cluster
(seed)
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Clustering tool
Report on results:
FATAL = number of fatalities
F_Scale = index of tornado
destructive power
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Clusters of tornadoes with fatalities
Appears to be an area with most severe and fatal tornadoes from
Nebraska and Iowa diagonally down through Missouri, Kentucky,
Tennessee, Alabama, Georgia, and Florida
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Summary

Proximity buffers

Site suitability

Multiple ring buffers

Data mining with cluster analysis
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