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Introduction to Geographic Information Systems Spring 2013 (INF 385T-28437) Dr. David Arctur Lecturer, Research Fellow PowerPoint Presentation
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Introduction to Geographic Information Systems Spring 2013 (INF 385T-28437) Dr. David Arctur Lecturer, Research Fellow University of Texas at Austin Lectures 8 & 9 Feb 28, 2013 8 - Spatial Analysis 9 - Geocoding. Review. ArcInfo coverages (from Lecture 5).

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Introduction to Geographic Information Systems Spring 2013 (INF 385T-28437)

Dr. David Arctur

Lecturer, Research Fellow

University of Texas at Austin

Lectures 8 & 9

Feb 28, 2013

8 - Spatial Analysis

9 - Geocoding

arcinfo coverages from lecture 5

Review

ArcInfocoverages(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

INF385T(28437) – Spring 2013 – Lecture 5

inside a coverage
Inside a coverage…

View from the operating system:

INF385T(28437) – Spring 2013 – Lecture 8

coverage attribute table
Coverage attribute table
  • Area and perimeter
    • Coverage_ and Coverage_ID

INF385T(28437) – Spring 2013 – Lecture 5

labels vs annotation
Labels vs. Annotation
  • Labels are based on one or more attributes of features.
  • Annotationis 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

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)

INF385T(28437) – Spring 2013 – Lecture 8

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proximity buffers
Lecture 8Proximity buffers

INF385T(28437) – Spring 2013 – Lecture 8

proximity buffers1
Proximity buffers

Points

  • Circular buffers with user supplied radius

Lines

  • Looks like worm based on line feature

INF385T(28437) – Spring 2013 – Lecture 8

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proximity buffers2
Proximity buffers

Polygons

  • Extends polygons outward and rounds off corners
  • Created by assigning a buffer distance around polygon

INF385T(28437) – Spring 2013 – Lecture 8

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point buffer example
Point buffer example

Polluting company buffers

  • Added schools
  • Added population

INF385T(28437) – Spring 2013 – Lecture 8

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point buffer example1
Point buffer example

Crimes near schools

INF385T(28437) – Spring 2013 – Lecture 8

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line buffer example
Line buffer example

Businesses within .25 miles of a selected street

INF385T(28437) – Spring 2013 – Lecture 8

select features in buffer
Select features in buffer

INF385T(28437) – Spring 2013 – Lecture 8

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spatial join to count
Spatial join to count

Join business points to buffer polygon

INF385T(28437) – Spring 2013 – Lecture 8

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polygon buffer example
Polygon buffer example

River buffer to analyze environmental conditions, flooding, etc.

INF385T(28437) – Spring 2013 – Lecture 8

polygon buffer example1
Polygon buffer example

Parcels within 150′ of selected property

INF385T(28437) – Spring 2013 – Lecture 8

select features in buffer1
Select features in buffer

INF385T(28437) – Spring 2013 – Lecture 8

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site suitability
Lecture 8SITE Suitability

INF385T(28437) – Spring 2013 – Lecture 8

locate new police station
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

INF385T(28437) – Spring 2013 – Lecture 8

starting map
Starting map

Lake Precinct of the Rochester, New York, Police Department

  • Police car beats
  • Retail business points
  • Street centerlines

INF385T(28437) – Spring 2013 – Lecture 8

create car beat centroids
Create car beat centroids

XY centroids for police beats

INF385T(28437) – Spring 2013 – Lecture 8

buffer car beat centroids
Buffer car beat centroids

.33 mile buffer

INF385T(28437) – Spring 2013 – Lecture 8

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buffer retail businesses
Buffer retail businesses

0.1 mile buffer

INF385T(28437) – Spring 2013 – Lecture 8

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select major streets
Select major streets

Select by attribute

INF385T(28437) – Spring 2013 – Lecture 8

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buffer major streets
Buffer major streets

0.05 mile buffer

INF385T(28437) – Spring 2013 – Lecture 8

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intersect buffers
Intersect buffers

Can only intersect two at a time

  • Car beat and businesses
  • Streets

INF385T(28437) – Spring 2013 – Lecture 8

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site suitability result
Site suitability result

Map showing possible sites for police station

INF385T(28437) – Spring 2013 – Lecture 8

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spatial analysis summary
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 - Geocoding

INF385T(28437) – Spring 2013 – Lecture 8

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basic apportionment
Lecture 8Basic apportionment

INF385T(28437) – Spring 2013 – Lecture 8

apportionment example
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

INF385T(28437) – Spring 2013 – Lecture 8

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population by voting district
Population by voting district

Start with census tracts

INF385T(28437) – Spring 2013 – Lecture 8

population by voting district1
Population by voting district

Overlay voting districts (not contiguous with tracts)

INF385T(28437) – Spring 2013 – Lecture 8

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population by voting district2
Population by voting district

Better to use block centroids for population

  • Smaller than tracts

INF385T(28437) – Spring 2013 – Lecture 8

spatially join centriods
Spatially join centriods

Join centroids to voting districts

INF385T(28437) – Spring 2013 – Lecture 8

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other simple apportionments
Other simple apportionments

Population by

  • Neighborhoods
  • Zip Codes
  • Historic sites
  • Others?

INF385T(28437) – Spring 2013 – Lecture 8

census data to apportion
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?

INF385T(28437) – Spring 2013 – Lecture 8

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advanced apportionment
Lecture 8Advanced apportionment

INF385T(28437) – Spring 2013 – Lecture 8

advanced apportionment1
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)

INF385T(28437) – Spring 2013 – Lecture 8

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data to apportion
Data to apportion

Car beats

Census tracts

Beats and tracts

  • Not contiguous

INF385T(28437) – Spring 2013 – Lecture 8

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beats and tracts zoomed
Beats and tracts zoomed

Tracts clearly cut across beats

INF385T(28437) – Spring 2013 – Lecture 8

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tract attribute table
Tract attribute table

Tracts contain undereducated data

  • No high school degree

INF385T(28437) – Spring 2013 – Lecture 8

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math of apportionment
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

INF385T(28437) – Spring 2013 – Lecture 8

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math of apportionment1
Math of apportionment

Tract 360550002100

Car beats 261 and 251

INF385T(28437) – Spring 2013 – Lecture 8

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math of apportionment2
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

INF385T(28437) – Spring 2013 – Lecture 8

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math of apportionment3
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 approximation

INF385T(28437) – Spring 2013 – Lecture 8

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math of apportionment4
Math of apportionment

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.

Tract 360550002100 has 39 block centroids that span 2 beats

INF385T(28437) – Spring 2013 – Lecture 8

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math of apportionment5
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

INF385T(28437) – Spring 2013 – Lecture 8

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math of apportionment6
Math of apportionment

Eventually, by apportioning all tracts, we can sum up the total undereducated population for car beats 261 and 251

INF385T(28437) – Spring 2013 – Lecture 8

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background steps
Lecture 8Background steps

INF385T(28437) – Spring 2013 – Lecture 8

background steps1
Background steps

1.) 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

INF385T(28437) – Spring 2013 – Lecture 8

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background steps2
Background steps

2.) 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

INF385T(28437) – Spring 2013 – Lecture 8

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background steps3
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

INF385T(28437) – Spring 2013 – Lecture 8

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background steps4
Background steps

4.) 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

INF385T(28437) – Spring 2013 – Lecture 8

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background steps5
Background steps

5.) 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

INF385T(28437) – Spring 2013 – Lecture 8

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apportionment steps
Lecture 8Apportionment steps

INF385T(28437) – Spring 2013 – Lecture 8

apportionment steps1
Apportionment steps

1.)Intersect tracts and car beats to create new polygons that each have a tract ID and car beat number

INF385T(28437) – Spring 2013 – Lecture 8

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apportionment steps2
Apportionment steps

2.) 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 steps3
Apportionment steps

2.)

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apportionment steps4
Apportionment steps

3.) 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 steps5
Apportionment steps

3.) Export the join as a precaution

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apportionment steps6
Apportionment steps

4.) 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 steps7
Apportionment steps

4.) Calculate values

INF385T(28437) – Spring 2013 – Lecture 8

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apportionment steps8
Apportionment steps

5.) Sum the apportionment weights by tract as a check for accuracy (they should sum to 1.0 for each tract)

INF385T(28437) – Spring 2013 – Lecture 8

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apportionment steps9
Apportionment steps

5.) Each tract that is totally within car beats will have weights summing to 1. Those partially within car beats sum to less than 1

INF385T(28437) – Spring 2013 – Lecture 8

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apportionment steps10
Apportionment steps

5.) Sum the undereducated population per car beat

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join apportionment results
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

INF385T(28437) – Spring 2013 – Lecture 8

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finished map
Finished map

INF385T(28437) – Spring 2013 – Lecture 8

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summary
Summary

Proximity buffers

Site suitability example

Basic apportionment

Advanced apportionment

INF385T(28437) – Spring 2013 – Lecture 8

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