introduction to geographic information systems spring 2013 (inf 385t-28437) dr. david arctur...
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Introduction to Geographic Information Systems Spring 2013 (INF 385T-28437)
Dr. David ArcturLecturer, Research Fellow
University of Texas at Austin
Lectures 8 & 9Feb 28, 2013
8 - Spatial Analysis9 - Geocoding
ArcInfo coverages (from Lecture 5)
Created using ESRI’s ArcInfo software (prior to version 8)
Older format (import/export as “.e00”) Set of files within a folder or directory called
a workspace Files represent different types of topology or
feature types Coverages have geometry: Arcs (lines), Nodes
(points), or Polygons, and associated attribute tables
Coverages also have Tics (spatial registration points), and may have Labels and Annotation
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Review
Inside a coverage…
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View from the operating system:
Coverage attribute table
Area and perimeter
Coverage_ and Coverage_ID
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5
Labels vs. Annotation
Labels are based on one or more attributes of features.
Annotation is a way to store text to place on your maps
independent of features. Each piece of text stores its
own position, text string, and display properties.
Annotation can also be linked to individual features, for
positional or existence dependency.
If the exact position of each piece of text is important, you
should store your text as annotation in a geodatabase.
Annotation provides flexibility in the appearance and
placement of your text because you can select individual
pieces of text and edit them.
You can convert labels to create new annotation features.INF385T(28437) – Spring 2013 – Lecture 8
Spatial Analysis Outline (Tutorial Ch.9)
Proximity buffers
Site suitability example
Basic apportionment (on your own)
Advanced apportionment (on your
own)
Then… Geocoding (Tutorial Ch.7)
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Lecture 8
PROXIMITY BUFFERSLecture 8
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Proximity buffers Points
Circular buffers with user supplied radius
Lines Looks like worm based on line feature
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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
Crimes near schools
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Line buffer example Businesses within .25 miles of a
selected street
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Select features in buffer
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Spatial join to count
Join business points to buffer polygon
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Polygon buffer example River buffer to analyze environmental
conditions, flooding, etc.
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Polygon buffer example
Parcels within 150′ of selected property
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Select features in buffer
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SITE SUITABILITYLecture 8
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Locate new police station
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
XY centroids for 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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Select major streets
Select by attribute
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Buffer major streets
0.05 mile buffer
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Intersect buffers
Can only intersect two at a time Car beat and businesses Streets
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Site suitability result Map showing possible sites for police
station
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Spatial Analysis Summary
Proximity buffers (Tutorial exercise 9-1)
Site suitability example (Tutorial exercise
9-2)
Basic apportionment (optional)
Advanced apportionment (optional)
Assignments: 9-1, 9-2 (9-3 optional)
Next up today - Geocoding28INF385T(28437) – Spring 2013 – Lecture 8
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BASIC APPORTIONMENTLecture 8
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Apportionment example
Population by voting district You want to know the population of a
voting district but only have census tracts Voting districts and census tracts are not
contiguous Approximate the population of voting using
census tracts and blocks
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Population by voting district
Start with census tracts
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Population by voting district Overlay voting districts (not contiguous
with tracts)
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Population by voting district Better to use block centroids for population
Smaller than tracts
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Spatially join centriods
Join centroids to voting districts
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Other simple apportionments
Population by
Neighborhoods
Zip Codes
Historic sites
Others?
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Census data to apportion Short form SF1 data (tract, block group,
block) Population Age Race Housing Units Others?
Long form SF3 data (tract and block group) Educational attainment Income Poverty status Others?
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ADVANCED APPORTIONMENTLecture 8
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Advanced Apportionment
Chapter 9 example Police want to know the number of under-
educated persons in their car beats Under-educated data is located SF3 tables,
census tracts or block groups (not car beat polygons)
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Data to apportion
Car beats
Census tracts
Beats and tracts
Not contiguous
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Beats and tracts zoomed
Tracts clearly cut across beats
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Tract attribute table Tracts contain undereducated data
No high school degree
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Math of apportionment Simple census data (e.g. population) is
not a problem Can use block centroids
Problem Block centroids don’t
contain undereducatedpopulation
Tracts contain thisinformation
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Math of apportionment Tract 360550002100 Car beats 261 and 251
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Math of apportionment
One approach Assume that the target population is
uniformly distributed across the tract You could split undereducated population
up by the fraction of the area of the tract in each car beat
What if, however, the tract has a cemetery, park, or other unoccupied areas? Then the apportionment could have sizable errors
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Math of apportionment A better approach
Use a block-level, short-form census attribute as the basis of apportionment
Assume that the long-form attribute of interest is uniformly distributed across the short-form population (accounts for unoccupied areas)
One limitation of the block-level data is that the break points for age categories do not match those of the educational attainment data (persons 25 or older)
The best that can be done with the block data is to tabulate persons aged 22 or older
Close enough for approximationINF385T(28437) – Spring 2013 – Lecture 8 46
Math of apportionment Tract 360550002100 has 39 block
centroids that span 2 beats
Of the 26 blocks making up the tract, the 13 that lie in car beat 261 have 1,177 people aged 22 or older.
The other 13 blocks in car beat 251 have 1,089 such people for a total of 2,266 for the tract.
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Math of apportionment Apportionment assumes that the
fraction of undereducated people aged 25 or older is the same as that for the general population aged 22 or older This fraction, called the weight, is 1,177 ÷
2,266 = 0.519. For the other car beat, the weight is 1,089 ÷ 2,266 = 0.481
Thus, we estimate the contribution of tract 36055002100 to car beat 261’s undereducated population to be (1,177 ÷ 2,266) × 205 = 106. For car beat 251, it is (1,089 ÷ 2,266) × 205 = 99
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Math of apportionment Eventually, by apportioning all tracts,
we can sum up the total undereducated population for car beats 261 and 251
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BACKGROUND STEPSLecture 8
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Background steps1.) Download census data
Download census block and tract polygons from the census Web sites for the county containing the administrative area polygons
Download the short-form census data for blocks that are the basis of apportionment, in this case the population of age 22 and greater
Download the long-form census attribute(s) at the tract level that you wish to apportion to the administrative area, in this case the population aged 25 or greater with less than high school education
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Background steps2.) Create new tract layer
That intersects administrative boundaries
If a tract is only partially inside the administrative area, you must include the entire tract for apportionment to work correctly
An example tract is the southerly-most tract in Tutorial9-3.mxd
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Background steps
3.) Prepare block centroids Create a new centroid point layer for blocks Clip the centroids with the new intersected tract
layer Join census short-form data to the clipped block
centroids This is the layer that is the basis for apportionment
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Background steps4.) Sum the short-form census attributes in age
categories to create Age22Plus in the clipped block centroids table
This step is unique to this problem Also, this table has a new TractID attribute which
concatenates FIPSSTCO & TRACT2000 to create an ID matching the Tracts map layer
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Background steps5.) In the attribute table for block centroids,
sum the field for persons aged 22 or older by TractID to create a new table, SumAge22Plus. This table provides the denominator for the weight used in apportionment
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APPORTIONMENT STEPSLecture 8
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Apportionment steps1.)Intersect tracts and car beats to create new
polygons that each have a tract ID and car beat number
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Apportionment steps2.) Spatially join the new layer of tracts and car
beats with the block centroids to assign all the tract attributes (including the attribute of interest: undereducated population) and car beat attributes to each block’s centroid
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Apportionment steps
2.)
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Apportionment steps3.) Join SumAge22Plus to block centroids to
make the apportionment weight denominator, total population aged 22 or older by tract, available to each block centroid
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Apportionment steps3.) Export the join as a precaution
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Apportionment steps4.) For each block centroid, create new fields to
store apportionment weight and apportioned undereducated population values, then calculate these values
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Apportionment steps4.) Calculate values
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Apportionment steps5.) Sum the apportionment weights by tract as
a check for accuracy (they should sum to 1.0 for each tract)
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Apportionment steps5.) Each tract that is totally within car beats will
have weights summing to 1. Those partially within car beats sum to less than 1
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Apportionment steps5.) Sum the undereducated population per car
beat
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Join apportionment results The last task is to join
the table containing undereducated population by car beat to the car beats layer, then symbolize the data for map display
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Finished map
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Summary
Proximity buffers
Site suitability example
Basic apportionment
Advanced apportionment
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