Introduction
The geographical concentration of the urban poor is viewed as both a cause and a
consequence of a range of social and economic issues. Some researches has generally
focused on the entire metropolitan area and assumed that most high-poverty neighborhoods
were within the central city in the US, just like I first thought that each city has the same
situation in the US. More recent research hints that the geographical distribution of
high-poverty neighborhoods may have been slowly changing from the past 20 years from
central-city to suburban areas, and they are not the same in each city.
This research explores suburbanization of poverty in four different metropolitan regions in the
US, find out how key variables for socio-economic classes result different in gentrification in
those metropolitan regions.
Methodology
The model is based on 2010 census tract level data and includes four variables of
homeownership, poverty status, and two different races data from 2007-2011 American
Community Survey 5-Year Estimate, use the quantile classification method to show the trend
and level of suburbanization. The four indicators are Asian alone, Black population, the very
poor population, and Nonfamily households.
Literature Review
1. Cooke, T., & Marchant, S. (2006). The changing intrametropolitan location of high-poverty
neighbourhoods in the US, 1990-2000. Urban Studies, 43(11), 1971-1989
Some people assumed that most high-poverty neighbourhoods were within the
central city in the US. Like myself first thought that each city has the same situation in the
US. More recent research hints that the geographical distribution of high-poverty
neighbourhoods may have been slowly changing from the past 20 years from central-city
to suburban areas. And they are not same in every city. This research regarding the extent
of high-poverty neighbourhoods has generally focused on the entire metropolitan area, to
explore the changing geographical distribution of high-poverty neighbourhoods both
between and within American metropolitan areas between 1990 and 2000. Specifically
focus on the shift in the number of high-poverty neighbourhoods between central-city,
inner-ring and outer-ring suburbs. This is a basic reference and it is very close to what I
want to do for my project.
2. Amy L. Holliday, Rachel E. Dwyer. (2009). Suburban Neighborhood Poverty in U.S.
Metropolitan Areas in 2000.City & Community,8(2),155–176
This research use Census 2000 summary data to examine the characteristics and
determinants of suburban poverty at the neighborhood and metropolitan levels across the
entire country, and the results they turn out identify a particularly distinctive racial profile for
suburban poverty, associated especially with Hispanic residential location, with
implications for trends in racial segregation as well. This article makes me decide to use
Hispanic Race specifically as a indicator of my project.
3. Jed Kolko. (2007).The determinants of gentrification. Public Policy Institute of California
This article examines the reasons for gentrification and outcomes of gentrification vary in
cities. It also analyzes changes in statistics at census tract level, for example household
income, migration pattern, vacancy rate and demographic characteristics. The results
show that gentrification is accompanied by increases in the number of households and a
growing housing stock, as well as changes in residential demographic composition. This
article is a good example using census data combine with GIS, and provides information
about indicators of gentrification, which is helpful when I select variables revealing
gentrification.
4. Je Moua. (2006). Poverty distribution in Twin Cities using GIS. Journal of Undergraduate
Research, Minnesota State University.
This is a very good example of using GIS to examine the spatial distribution of poverty
within the Twin Cities Metro Area. It uses poverty distribution to compare with selected
variables such as education level, family size, family compositions, race, and immigration
status. I think the format and the content construction is the most similar one to my
thought.
Data Layers
Layer
Data Set
Description
Year
Data
Source
Agency
URL
M:\Country\U
Cities
point
Boston, Portland,
Atlanta, Phoenix
SA\ESRIData
City boundaries
2010
ESRI
Map10\usa\ce
nsus
M:\Country\U
Airport
point
Boston, Portland,
Atlanta, Phoenix
U.S.
National
Airports
SA\ESRIData
2010
ESRI
Map10\usa\tr
ansairports
Census
Tracts
Boston, Portland,
Atlanta, Phoenix
Census tract
2010
Census
M:\Country\U
SA\Census20
10_NHGIS_tr
acts_US
Populatio
Total Population
Total population
ACS
Social
Social
n
20072011
explore
Census
Bureau
explore
Family
Status
T17 Household
Nonfamily
Households
ACS
20072011
Social
explore
Census
Bureau
Social
explore
Poverty
Level
T118
Ratio Of
Income
Population
for
whom
poverty
status is Under
1.00
(Doing
Poorly)
ACS
20072011
Social
explore
Census
Bureau
Social
explore
Race
T13.Total
Population
Race
Asian Alone
ACS
20072011
Social
explore
Census
Bureau
Social
explore
Black or African
American Alone
ACS
20072011
Social
explore
Census
Bureau
Social
explore
Race
T13.Total
Population
Race
by
by
Data Layer Processing
1.
2.
3.
4.
5.
Select four cities from the Cities points layer.
Export those to a new shape file (four_city).
Select by Location to select tracts within 20 miles of the 4 city points
export those to a new shape file(four_within20)
Circle the data sets of each indicator with all census tracts within the selected 20 mile
area of each city in social explore and download with selected census tracts
6. Extract the data to excel file and join with census tract
Limitations and Difficulties
The main problem and difficulty is, I can’t join tables at the same time to four cities, and the
table data’s boundary when I selected in social explore are different from the boundary I
selected in census tract at former steps. So I should circle the area first and then report the
data set. The next difficulty I faced was the Arizona State has FIPS codes that start with a
zero, and because Excel removes that leading zero, the table won't join properly, I never got
into this situation in the past, so I had to put it back on and then keep going. The project I
actually did was some extent different from what I expected before, because I have some
difficulties in join tables with areas that I selected, so I can’t test some indicators that I
thought before. But the general idea is the same, and I think this project was primarily
targeted at gaining experience using Arc GIS rather than constructing an accurate spatial
analysis. Indeed, more variables would be better to expect sufficient accuracy in the model’s
predictions. Education level would also be a particularly interesting and important one for
predict a relationship between poverty status with suburbanization. And also, a further
study may focus on the increasing rate or the change rate in a certain time period is also
valuable. All in all, thanks for the great experience with GIS study.