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GIS Tutorial 1 Lecture 9 Spatial Analysis

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GIS Tutorial 1. Lecture 9 Spatial Analysis. Outline. Proximity buffers Site suitability example Basic apportionment Advanced apportionment. 2. Lecture 9. Proximity buffers. Proximity buffers. Points Circular buffers with user supplied radius Lines - PowerPoint PPT Presentation

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Page 1: GIS Tutorial 1

GIS Tutorial 1

Lecture 9Spatial Analysis

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Outline

Proximity buffers

Site suitability example

Basic apportionment

Advanced apportionment

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PROXIMITY BUFFERSLecture 9

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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

Parcels within 150′ of selected property

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Select features in buffer

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Polygon buffer example River buffer to analyze environmental

conditions, flooding, etc.

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SITE SUITABILITYLecture 9

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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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BASIC APPORTIONMENTLecture 9

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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 9

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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

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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 9

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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 9

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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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