Residential_Segregation_and_Factory_Closures_in_Milwaukee_A_GIS_Analysis

Residential Segregation and Factory Closures in Milwaukee: A GIS
Analysis
By Marcus Van Grinsven
Geography 600: Perspectives in Geography
Wednesday, December 18, 2013

1 VanGrinsven
Milwaukee has a reputation as being America's most segregated city. 2010 census
figures lend support to this idea. African Americans are concentrated on the northwest side of
the city, Latinos are concentrated on the near south side, and whites are dispersed throughout the
suburbs. Many older, industrialized cities have social structures in place that make those cities
prone to segregation (Farley & Frey 1994). Milwaukee has a historically “Fordist” industry
based economy that fits this model (Sharma and Brown 2012).
This paper will use census data from the US Census Bureau to analyze the spatial
distribution of different races throughout the city. This paper will analyze how four of the five
dimensions of segregation: evenness, exposure, Concentration, and Centralization have changed
in Milwaukee County since 1950. How has de-industrialization in the 1970’s and 1980’s effected
segregation in Milwaukee?
Literature Review
In the early 20th century, there was institutional segregation in housing. The United
States Government passed The Housing Act of 1937, “which mandated slum clearance for the
construction of public housing projects.” (Avila & Rose pp. 338). When the first public housing
was created, blacks were kept out by means of legal discrimination (Bickford & Massey pp.
1012). In 1954, another housing act was passed, which allowed the federal funding to be used for
non-residential projects. (Avila & Rose pp. 338). This act allowed funds to be diverted from
housing to cover other activities. This led to problems. For instance, in the early 1960’s, Atlanta
Mayor Ivan Allen made a deal with local African American leaders to win their support of a
local ballpark and civic center. In exchange that the city would build new housing for the people
whose homes were raised to make room for these projects (Avila & Rose pp. 341-342). It took

2 VanGrinsven
the passage of several years and riots to get the mayor to make good on that promise (Avila &
Rose pp. 342).
During the civil rights movement of the late 1960’s and early 1970, a civil rights act was
passed that banned discrimination in housing (Massey and Denton (1993) pp. 60). After the civil
rights movement, minorities gained access to public housing, but these housing developments
were built in poor black neighborhoods, which only increased the concentration of poverty in the
area (Bickford & Massey pp. 1012). Housing projects for the elderly are made up mostly of
white residents, while projects for families are made up mostly of black residents (Bickford &
Massey pp. 1034). Publicly owned housing projects tend to be mostly black, while privately
owned projects tend to be mostly white (Bickford & Massey pp. 1034). During the 1970’s many
blacks also moved out of the inner city to the suburbs, and back to the south (Massey and Denton
(1993) pp. 60). Many of these blacks migrated back to the south, due to the decline of industry
in the north (F. Wilson pp. 16)
Most of the suburbs that saw an increase in black residents were older, more blue collar
suburbs, that were much like the center city itself (Massey and Denton (1993) pp. 69).
Nonetheless, blacks were still heavily concentrated in the central city; while the suburbs were
mostly white (Massey and Denton (1993) pp. 61). Black suburbanization is often merely an
expansion of the ghetto across the city limit, into the suburbs (Massey and Denton (1993) pp.
70). There can also be expansion of the ghetto within the city limits. When whites move out of
an area that is on the fringes of the ghetto, they have abandoned it, never to return (Rose pp. 5).
This can lead to an expansion of the inner city. Once an area is defined as belonging to a group,
it is established, and attempts to change this are met with conflict (Rose pp.4).

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Milwaukee is an older city located in the industrial Midwestern region, known as the rust
belt, which was once a powerhouse of industry and manufacturing. The decline of
manufacturing of the Midwest has led to unemployment other problems, and Milwaukee has
seen its share of this decline, “Milwaukee had a heavily Fordist economy, steeped in the type of
economic activity (smokestack industry) that was impacted by this transition” (Sharma & Brown
pp. 318). In urban areas with economies that focused on heavy industry, people usually got hired
in a factory because they had connections, such as relatives who worked there (Helms &
Cumbers pp. 73). These practices “meant that labor and skills were socialized over generations”
(Helms & Cumbers pp. 73). With fewer people working in manufacturing, there are fewer
people who have the connections to get manufacturing jobs, which are scarce.
Another factor that indicates likelihood of segregation is the age of a metropolitan area.
This is measured by the decade when the city reached a population of 50,000 (Farley & Frey pp.
32). Farley and Frey’s figures show that “In 1990, the average segregation score is 76 in
metropolitan areas whose central cities reached 50,000 before 1890, whereas in the newest
metropolitan areas, the average score is 58” (Farley & Frey pp. 32). Cities built before World
War II are more prone to segregation, in part because they are so densely populated, and because
of the established structure of their neighborhoods (Massey & Denton (1987) pp. 818).
Milwaukee’s population is today about what it was at the end of World War II. Since the end of
the Second World War, Milwaukee’s population has risen and fallen.
High unemployment, especially among blacks is a strong contributing factor to
segregation. Blacks who live in hyper segregated cities tend to be highly isolated from the rest
of the population unless they have a job in another party of the city, but many do not (Massey
and Denton (1993) pp. 81). The spatial mismatch theory theorizes that jobs in urban centers are

4 VanGrinsven
disappearing, and the people who live in those areas are the victims (Kaplan 155). There is also
a spatial mismatch between inner city workers and suburban jobs, due to an inadequate public
transportation system, and the fact that so few poor people own their own car (W. J. Wilson pp.
583).
Milwaukee has been hit hard by deindustrialization, especially in its inner city. 41
percent of workers from Milwaukee’s inner city were employed in manufacturing in 1970, but
that number dropped to 19 percent by 2000 (University of Wisconsin-Milwaukee Center for
Economic Development pp. 3). Since the 1970’s the poverty rate in the city has been driven up
by increased joblessness (University of Wisconsin-Milwaukee Center for Economic
Development pp.14).
In addition to a loss of jobs, the inner city has also seen a loss of people. During the
1990’s many poor residents of the inner city, moved to other areas of the city, including the
northwest side (University of Wisconsin-Milwaukee Center for Economic Development pp. 3).
Overall, the inner city has seen a significant decline in population in the last 3 decades
(University of Wisconsin-Milwaukee Center for Economic Development pp.11)
Over the course of the twentieth century, institutional rules that once drove segregation
have gone by the wayside. Milwaukee never had Jim Crow laws, but in its past, people were
highly segregated in where they lived. Today, poverty seems to be driving the segregation, since
most people who live in the inner city are trapped there by their financial limitations. Many of
these people are not highly mobile due to a lack of automobile ownership and adequate public
transportation, and are thus not able to travel to jobs in the suburbs or other cities. This paper
will look at how the decline of manufacturing jobs has affected Milwaukee spatially, and how
the dimensions of residential segregation have changed over time.

5 VanGrinsven
Data
I downloaded decennial census data and shapefiles for census tracts for every decade
from 1950 to 2010 from the Minnesota Population Center - National Historic Geographic
Information System (NHGIS) (https://www.nhgis.org/). In all, I downloaded 7 spreadsheets, and
7 shapefiles, 1 of each for each decade. Since the spreadsheets had data for many and in later
cases all counties in the United States, I had to go through them and delete all of the non-
Milwaukee County information. The shapefiles, covered the same area as the spreadsheets, so I
had to obtain a Milwaukee County Boundary shapefile (Wisconsin Department of
Administration) from the AGS Library at the University of Wisconsin Milwaukee to perform a
clip, to just clip out Milwaukee County’s census tracts.
The spreadsheets of census data were arranged into rows for each census tract, and
columns for each statistic. The statistics in each column included the total population of that
tract, as well as the number of persons living in each tract by race. Upon inspection of the data, I
noted that the number and classification of racial categories varied from census to census. For
instance, in the 1950 data, only 3 racial categories were given: “White,” “Negro,” and “Other
non-white”; while the 2010 data included “White alone,” “Black or African American alone,”
“American Indian or Alaska Native alone,” “Asian alone,” “Native Hawaiian or Pacific Islander
alone,” as well as other and mixed racial categories. To make the research easier, I combined all
races other than white into a new column called “nonwhite,” where I added the total of all
nonwhite people in each tract.
I found labor statistics for Milwaukee County in ten year intervals from 1960 to 2010
(1950 was not available) from the National Historic Geographic Information System (NHGIS).

6 VanGrinsven
Each dataset contained the number of persons employed in different sectors of the economy in
every county in the United States, so I deleted all of the counties except Milwaukee County,
Wisconsin. Each dataset was aggregated a little differently, for instance, one decade had a
category called “manufacturing”, while others had it broken down into manufacturing of durable
versus non-durable goods, and one decade had it broken down even further. I aggregated that
data into four categories: “Service Jobs,” “Skilled Professional Jobs,” and “Unskilled-Semi-
Skilled Jobs,” which was further broken down into two categories: “Manufacturing” and
“Other.” The “Service Jobs” category includes bars restaurants and various types of wholesale
and retail sales. The “Skilled Professional Jobs” category includes: banking/finance, medical,
education, public administration, and various business services. The “Semi-Skilled/Unskilled
Jobs Other” subcategory includes: agriculture, forestry, fishing, mining, construction,
transportation, and utilities. I charted my aggregated numbers into line charts to show the
change in the numbers over time.
Methods
I analyzed two phenomena as they changed over time. First, I analyzed the change in the
number of persons employed in manufacturing and where they lived, and how that changed over
time from 1960 to 2010, during the time of deindustrialization. Second, I analyzed the five
dimensions of segregation: evenness, exposure, concentration, centralization, and clustering; to
see how they changed over time from 1950 to 2010. My aim is to determine if the decline of
manufacturing jobs was disproportional in predominantly non-white census tracts.
To analyze how the number of manufacturing jobs changed, and how the spatial
distribution of manufacturing workers changed over time, I downloaded census data on the
sectors of employment in which people in Milwaukee County were employed by census tract for

7 VanGrinsven
every decennial census from 1960 to 2010. Each census was aggregated a little differently. For
instance, one census had a category called “manufacturing”, while another had it broken down
into “durable goods” and “non-durable goods”, and yet another had it broken down even further.
I aggregated each table into four categories: manufacturing jobs, other skilled/semi-skilled jobs
(which included utilities, transportation, construction, etc.), service jobs (including retail,
bar/restaurant, etc.), and skilled/professional jobs (including health care, education, banking and
finance, etc.). I summed the totals for all census tracts for each decade for each of the four
categories, and used to resulting number to make a line graph. I then joined the spreadsheet for
each decade to its respective shapefile and created a quantity map for each decade showing the
number of persons in each census tract employed in manufacturing.
The first dimension of segregation I calculated is evenness. Evenness measures the
spatial distribution of two groups across the area, to see how evenly distributed they are (Massey
and Denton (1988) pp. 283). In this case, the two groups are white and nonwhite persons, the
study area is Milwaukee County, and the areal units are the census tracts of Milwaukee County.
Evenness is measured in dissimilarity. The formula I used to find dissimilarity is =
0.5 ∑ |
𝑥 𝑖
𝑋
−
𝑦𝑖
𝑌
|𝑛
𝑖=1 , where xi is the population of nonwhite persons in a census tract, X is the
population of nonwhite persons in Milwaukee County, yi is the population of white persons in
the census tract, and Y is the population of white persons in Milwaukee County (What-When-
How (http://what-when-how.com/sociology/segregation-indices/
)). I labeled the columns “WHITE” and “NONWHITE” for the white and nonwhite populations
of each census tract. For each of the seven decennial censuses, I summed the white and
nonwhite populations to get their totals for the counties, and put them in new columns called
“TOTALWHITE” and “TOTALNONWHITE” respectively. I created a new column called

8 VanGrinsven
“DIFFERENCE”, and input the formula “=(NONWHITE / TOTALNONWITE) – (WHITE /
TOTALWHITE) and dragged it down the entire column to get each census tract value. I then
created a column called “DISSIMILARITY” and input the formula “SQRT (DIFFERENCE *
DIFFERENCE)” and dragged it down the column. This made all the values positive. I then
summed the values in the “DISSIMILARITY” column and divided the answer by two, to come
up with the dissimilarity index for that decade. I then joined the spreadsheet to the shapefile in
ArcMap, and mapped each tract’s individual dissimilarity value. I repeated the process for the
other decades. Finally, I put the seven maps into ArcMap as individual data frames, and
classified their ranges so that they would be the same. That way, I could use one legend for all
seven data frames.
The second dimension I calculated is Exposure. Exposure measures the amount of
possible interaction or isolation between two groups (Massey and Denton (1988) pp. 287). There
are two formulas, 1 for interaction, and 1 for isolation. I used the formula for isolation: xP*x =
∑ [ 𝑥 𝑖/𝑋]𝑛
𝑖=1 [ 𝑥 𝑖/𝑡𝑖], where xi is the population of nonwhite persons in the census tract, X is the
nonwhite population of Milwaukee County, and ti is the total population of the census tract
(Massey and Denton (1988) pp. 288). I labeled the column that had the total population of the
census tract as “TOTALPOP”. I created a new column called “ISOLATION” and entered the
formula “=(NONWHITE / TOTALNONWHITE) * (NONWHITE / TOTALPOP)” and dragged
the formula down the entire column to get the isolation values for each census tract. I then
summed the values from this column to find the isolation index for the county as a whole, and
repeated for the other decades. I then mapped the individual tract values for each decade and put
them into seven data frames in ArcMap. I classified their ranges to all be equal, so I could use
legend.

9 VanGrinsven
The third dimension I calculated is Concentration. Concentration is the relative amount
of physical space a group occupies in the total area, and is measured in a value called “Delta”
(Massey and Denton (1988) pp. 289). The formula I used is 𝐷𝐸𝐿 = 1/2∑ |[ 𝑥 𝑖/𝑋 − 𝑎𝑖/𝐴]|𝑛
𝑖=1 ,
where xi is the nonwhite population of the census tract, X is the total nonwhite population of the
County, ai is the area of the census tract, and A is the total area of the county (Massey and
Denton (1988) pp. 289). Since this calculation calls for area, which is not contained in the
spreadsheet, I had to join the spreadsheet to the shapefile to get the area. I then opened the
attribute table of the shapefile as a spreadsheet, and it contained the area for the census tracts in a
column called “SHAPE_AREA. I summed this column to get the total area, and then copied it
into a new column called “TOTAL_AREA” and copied it down the entire column. I then
created a new column called “PRE_DELTA” and entered the formula “(NONWHITE /
TOTALNONWHITE) – (SHAPE_AREA / TOTAL_AREA)” and dragged it down the entire
column to get the census tract values. Since some of the values were negative, I created a new
column called “DELTA” and entered the formula “SQRT (PRE_DELTA * PRE_DELTA)” to
make them all positive, and then dragged formula down the entire column to get the Delta values
for each tract. I then summed them and divided the answer by 2 to get the index. I joined the
spreadsheet to the shapefile and mapped the individual tracts. I repeated the process for the other
decades, and put the maps into data frames, and set their legends to be the same so I could use
one key for all data frames.
The fourth dimension I calculated is Centralization. Centralization is a measure of how
many people from a group (in this case nonwhites) within a study area (in this case Milwaukee
County) live in the central city (Milwaukee) (Massey and Denton (1988) pp. 291).
Centralization is measured in Percent in Central City (PCC), the formula is PCC = XCC/X, where

10 VanGrinsven
Xcc is the number of nonwhites in the central city, and X is the number of nonwhites in the study
area (Massey and Denton (1988) pp. 292). To calculate the Pcc, first, created a new column
called “CC.” I looked at which census tracts were part of the city of Milwaukee, and put a 1 in
the “CC” column, and put zeroes in the “CC” column for the suburban tracts. I then created a
new column called “XCC” where I entered the formula “NONWHITE * XCC” and summed the
column. I then divided the XCC total by the total nonwhite population of the county to get the
PCC index. I repeated the process for the other decades. I joined the spreadsheet to the
shapefile, and mapped the nonwhite population for each census tract (rather than the PCC) to see
how many nonwhites were outside the city as well. I also created a city boundary file from this
table using the “CC” column.
The last dimension I calculated is Clustering. Clustering is the measure of whether or not
areal units are adjacent to one another in area (Massey and Denton (1988) pp. 293). To calculate
clustering, I used ArcMap, and ran Global Moran’s I to calculate the countywide index, and
Local Moran’s I to map the clusters. The Global Moran’s I formula is 𝐼 =
𝑛
∑ (𝑦−𝑦̅)2𝑛
𝑖=1
∑ ∑ 𝑤 𝑖𝑗 ( 𝑦𝑖 −𝑦̅̅)(𝑦𝑗− 𝑦̅)𝑛
𝑗=1
𝑛
𝑖=1
∑ ∑ 𝑤 𝑖𝑗
𝑛
𝑗=1
𝑛
𝑖=1
, where n is the number of census tracts, yi is the number of
nonwhites in a census tract, yj is the number of nonwhites in an adjacent census tract, ȳ is the
mean population of nonwhites per census tract, and wij is a weighted matrix that equals 1 if two
census tracts are adjacent, and 0 if they are not. The Local Moran’s I formula is 𝐼𝑖 =
𝑧𝑖 ∑ 𝑤𝑖𝑗𝑗≠𝑖 𝑧𝑗, where zj is equal to yj-ȳ.
Findings and Analysis

11 VanGrinsven
The graph shown above shows the trends in the number of persons employed in
manufacturing, other skilled and semi-skilled jobs (such as construction, transportation, and
0
50000
100000
150000
200000
250000
1960 1970 1980 1990 2000 2010
Manufacturing
Other Unskilled/Semi-
Skilled
Service Jobs
Professional/Skilled

12 VanGrinsven
utilities), service sector jobs, and professional/skilled jobs that require education beyond high
school. While manufacturing have been steadily declining for decades, the only sector that has
been increasing substantially is professional and skilled jobs. This leaves uneducated people in
the community to vie with one another for lower paying service jobs. The map shows how the
residents of individual census tracts have been affected by the loss of manufacturing jobs. While
the entire county has been hit, the area hit the hardest is the central city around the north and
west of downtown, which saw a decline in residents employed in manufacturing as early as the
1970’s. Today, the entire north and northwest side of Milwaukee have very few people
employed in manufacturing, compared to the rest of the area.
Year 1950 1960 1970 1980 1990 2000 2010
Evenness(Dissimilarity) 0.857243 0.860439 0.835579 0.754123 0.697456 0.655374 0.614743
Exposure(Isolation) 0.519660 0.632519 0.702936 0.668421 0.666396 0.664039 0.663825
Concentration 0.928932 0.914629 0.865917 0.778158 0.730749 0.646074 0.579235
Centralization 0.978529 0.984646 0.979426 0.965838 0.957151 0.923800 0.881518
Clustering(Moran’s I) 0.187054 0.558124 0.622258 0.609185 0.564505 0.427572 0.479762
Changes in segregation indices for Milwaukee County 1950-2010
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1950196019701980199020002010
Evenness
(Dissimilarity)
Exposure (Isolation)
Concentration
Centralization
Clustering (Global
Moran's I)

13 VanGrinsven
The index of dissimilarity has gone down from 0.85 to 0.61 from 1950 to 2010. The
maps show dissimilarity being concentrated in the inner city on the north side in 1950, but over
time, it spread in area, and diminished in intensity, to the point where it barely registers on the
map at all anymore. This means that the nonwhite population has gotten more evenly distributed
across the area. But, with an index of 0.61, it is still segregated, with most of the dissimilarity
still in the same general area. These tracts still have an unusually high population of nonwhites,
which makes the population distribution slightly uneven, although it has gotten more even over
time.

14 VanGrinsven
The index of isolation went up from 0.51 in 1950 to a high of 0.70 in 1970, and has been
hovering at 0.66 since 1980. The maps show isolation starting in a small area of the inner city
in1950 and spreading. Since 1990 it has been spreading into the northwest side of the city, and
diminishing in intensity. The isolation is lower, but the isolation is spread out over a wider area.
The area that it has spread into is a newer part of the city, having been annexed in the 1950’s, as
shown by the city limit border on the maps. The reason isolation did not show up in that area in
earlier decades was due to a lack of nonwhite people in those tracts. The nonwhite people in
those tracts are more likely to have contact with white people than in the past, but an isolation
factor of 0.66 is still high, and is higher than it was in 1950.

15 VanGrinsven
The concentration index (Delta) went from 0.92 in 1950 to 0.57 in 2010. I believe that
most of the red, orange, and yellow tracts in 1950 are due to the areal size of the tract. We see a
high concentration of nonwhites in the inner city in 1950 that weakens in intensity over time and
disappears by 2010. The index has also dropped below 0.60, so it is no longer a serious concern.
Once again, we see from the map, that the nonwhite population is not as concentrated in one area
as it was in decades past.

16 VanGrinsven
The centralization index went from 0.97 in 1950 to 0.88 in 2010. These high numbers
indicate that the overwhelming majority of the nonwhite population is concentrated inside the
city limits. The maps show the number of nonwhites per census tract, and as we can see, they
have dispersed from the inner city to the northwest side, which is the new part of the city. There
is also some moderate dispersion to the near southwest side. The older parts of the city are still
mostly white. This kind of supports Farley and Frey’s argument about older cities being more
segregated. In this case, it is the older parts of the city. It also reflects the report of the
University of Wisconsin-Milwaukee Center for Economic Development that inner city blacks
moved from the inner city to the northwest side of the city in droves in the 1990’s.

17 VanGrinsven
I used Moran’s I to measure clustering, because the other indices I found for clustering
would have required me to make extensive contiguity matrices, and time did not allow. The
Global Moran’s I index has been falling since its high of 0.62 in 1970 to its current 0.47. The
dip in 2010 is probably the result of the way the census data was aggregated that year. The maps
show that the amount of nonwhite clustering is spreading in area, but this may be misleading,
since the nonwhite clustering is shrinking in area. The area that the clustering is spreading to is
the same as the other dimensions. The drop in the global index shows that overall clustering is
dropping, but, as the maps show, it is covering more area. The nonwhites are clustering into a
bigger area.

18 VanGrinsven
While Milwaukee has higher rates of segregation than most cities, overall
segregation in Milwaukee is DECLINING. In spite of the exacerbation of social
problems such as poverty, unemployment, and a lack of decent paying unskilled and
semi-skilled jobs, segregation in Milwaukee continued to decline through the period of
deindustrialization. The one dimension that is not declining is isolation, which is getting
weaker but spreading into the Northwest side of the city. Minorities are spreading out,
especially on the northwest side, but are not spreading into older parts of the city. The
city, county, and metro area are still very segregated, with non-whites concentrated on the
northwest side of the city.
While doing this project, I was met with a few challenges. The most challenging
part of this project was finding the data I needed. I was unable to find data on the dates
and locations of factory closures in the area. I find this disappointing, since I am sure
that someone out there probably HAS that data. I also found it difficult to find
government records on census information and employment information going back more
than 2 decades on the government’s own website. I was unable to get the data from the
Census Bureau or the Bureau of Labor Statistics. Luckily, I found the data at the
Minnesota Population Center’s National Historic Geographic Information System. Once
I got the data, the challenge was working with census and labor data that was aggregated
differently from decade to decade. The number of racial and occupational categories
varied in aggregation and number.

19 VanGrinsven
Reference List
Avila, E., & Rose, M. H. (2009). Race, Culture, Politics, and Urban Renewal. Journal Of Urban
History, 35(3), 335-347.
Bickford, A., & Massey, D. S. (1991). Segregation in the Second Ghetto: Racial and Ethnic
Segregation in American Public Housing, 1977. Social Forces, 69(4), 1011-1036.
Farley, R., & Frey, W. H. (1994). Changes in the segregation of whites from blacks during the
1980s: Small steps toward a more integrated society. American Sociological Review, 23-45.
Helms, G., & Cumbers, A. (2006). Regulating the new urban poor: Local labour market control
in an old industrial city. Space & Polity, 10(1), 67-86.
Kaplan, D. (2004). Ethnic Segregation: Measurements, Causes and Consequences. In D.G.
Janelle, B. Wharf, & K. Hansen (Eds.), World MindsL Geographical Perspectives on 100
Problems (pp. 151-156). Boston: Kluwer Academic Publishers.
Massey, D. S. & N. A. Denton (1993). American apartheid : Segregation and the Making of the
Underclass. Cambridge, MA: Harvard University Press.
Massey, D. S., & Denton, N. A. (1988). The Dimensions of Residential Segregation. Social
Forces, 67(2), 281-315.
Massey, D. S., & Denton, N. A. (1987). Trends In The Residential Segregation Of Blacks,
Hispanics, And Asians: 1970-1980. American Sociological Review, 52(6), 802-825.
Rose, H. M. (1970). The Development Of An Urban Subsystem: The Case Of The Negro Ghetto.
Annals Of The Association Of American Geographers, 60(1), 1-17.
Sharma, M., & Brown, L. A. (2012). Racial/Ethnic Intermixing in Intra-Urban Space and
Socioeconomic Context: Columbus, Ohio and Milwaukee, Wisconsin. Urban Geography, 33(3),
317-347.
University of Wisconsin-Milwaukee Center for Economic Development. (2002). The Economic
State of Milwaukee’s Inner City 1970-2000. Milwaukee, WI: University of Wisconsin-
Milwaukee Center for Economic Development. <retrieved online from:
http://www4.uwm.edu/ced/publications/innercity2002.pdf>
What-Where-How (n.d.) Segregation Indices. Retrieved from: http://what-when-
how.com/sociology/segregation-indices/
Wilson, F. (2005). Recent Changes in the African American Population within the United States
and the Question of the Color Line at the Beginning of the 21st Century. Journal Of African
American Studies, 9(3), 15-32
Wilson, W. J. (1997). When work disappears. Political Science Quarterly, 111(4), 567-595.

20 VanGrinsven
DATA USED:
Minnesota Population Center. National Historical Geographic Information System (Census Tract
Dataset 1950): Version 2.0. Minneapolis, MN: University of Minnesota 2011 (retrieved from:
https://www.nhgis.org/)
Shapefile 1950): Version 2.0. Minneapolis, MN: University of Minnesota 2011. (retrieved from:

21 VanGrinsven
Wisconsin Department of Administration. Milwaukee County Boundary Shapefile, 2000:
Milwaukee County, Wisconsin [e-mail attachment]. Madison, WI.: Wisconsin Department of
Administration, 2000. (e-mailed to me by the American Geographical Society Library at the
University of Wisconsin-Milwaukee).

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