Dan Zinder
Intro to GIS – Assignment 8
1. For my project I am going to look at two states that have been particularly harmed
by the foreclosure crisis and make some determinations about whether the crisis has
been primarily urban, suburban, or rural in those states. I intend to look at this
problem both by comparing impacts within the “Urban Area” regions as defined by
the US Census and using other, yet to be determined, measures of housing density.
The “Urban Areas” are defined by block groups that have a population density over
1000 people/sqmi and the immediately surrounding tracts with over 500
people/sqmi. This is not a good measure of urban vs. suburban because suburban
areas are almost entirely included in these regions. Other methods of density offer a
better tool but there is no cookie cutter mechanism for identifying suburban areas.
I will either choose Florida or Arizona as my state of choice because these are states
that the foreclosure crisis has hit particularly hard and are also states that have
experienced large suburban growth rates over the past several years.
Note: My previous idea of looking at demographic data and including racial and
economic considerations seems to have created a project scope that is too big and
will be confusing on one poster. There is a lot of mileage out of just the urban/nonurban classifications and I think I’ll stick to that for the time being.
2. Data Layers that I will use:
HUD Neighborhood Stabilization Data (i.e. foreclosure data) state last updated in
June, 2008
USPS vacancy data, available at the tract level via HUD
Census data layers: Census tracts (tracts), states (for reference), and Urban Areas.
3. Steps I will go through/layers:
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Download data sets from HUD and import them into ArcMap. Bring up census
layers from M drive.
Create new layers that identify different density population thresholds
(2000/sqmi, 1000/sqmi, 500/sqmi) and compare those areas to the “urban
areas” polygons
Note that housing units/tract may be a better measure. Try those also.
Create separate tract layers for my “case study” states – note: states are defined
by the “FIPS” field on the attribute table. State “FIPS” codes can be identified
on the census website. Other names for the same identification field include
“tractcode” in the foreclosure dataset and “geoid” in the vacancies dataset.
Join the foreclosure data set and the vacancies data set to the case study state
datasets. Use “keep matching” records to keep pesky data from other states
away.
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In “Symbology” normalize both vacancies and foreclosure data by total housing
units and make maps from those. Do the same with total #s of foreclosures.
Each tells a slightly different story about what areas are being impacted.
Run statistical analyses. This would probably include an aggregate of all of the
foreclosure and vacancy data for each of my defined density regions and then
normalized by the total number of housing units to get a percentage. This would
give a general sense of whether different types of developments were getting
particularly impacted.
I would like to work with Mary to set up a test as to whether any differences in
percentages could be considered statistically significant.
Compare the vacancies data to the foreclosures data. Are the housing trends
consistent with each other?
Compare my different density levels with the Urban Areas stats more generally.
I could also make some density maps of foreclosure “hotspots” using special
analyst. I’ll note here that such maps would really benefit from a higher level of
specificity, i.e. state-wide data that was mapped to specific addresses but I do not
believe such data exist. Cities often have that data but not states. But mapping
based on tracts may miss some of the nuances. Perhaps certain neighborhoods
that are particularly hit hard overlap census tracts.
I think the main challenges to this project include the lack of a consistent way to define
different regions. Density thresholds do not seem to be adequate in that there is no way to
factor in land use. Perhaps it would be beneficial to incorporate some land use/zoning maps
of urbanized areas into my analysis to better define my regions. I haven’t checked on Florida
yet but Arizona… particularly Phoenix… presents a challenge and I would want to discuss
how to get the data and approach these questions before proceeding.
The vacancies data is quite interesting to work with as well. There are categories for total
numbers of vacancies as well as new vacancies over various intervals. It may be beneficial to
create new fields that combine numbers from different intervals in order to see trends over
broader spans of time that match up with the foreclosure data. The foreclosure data is over
18 months and ends last June whereas the vacancies data was last recorded in December of
2008 and measures slightly different intervals. I should be able to take new vacancies from
the 12-24 month period and the 6-12 month period and combine them to match the 18
month period give or take a month.