Suzanne Lis
Final Paper
due 12.13.12
Project Description:
How has the racial makeup of Hartford and its suburbs changed over time (since 1950)?
This project aimed to map and visually demonstrate the “white flight” phenomenon that many
American cities experienced beginning in the 1950s. I decided to focus on Hartford County, as I
had often experienced the strict demographic divide between the city of Hartford and its suburbs.
Further research (and mapping) revealed that Hartford was a powerful case-study of “white
flight” and its deleterious effects on racial integration and economic strength.
What are factors that limit “white flight”, or this drastic change in the racial makeup?
What are factors that aggravate it? My research has shown me that it is impossible to posit just
one cause for “white flight.” Rather, there are many diverse causes, many of which converged
unfortunately in the case of Hartford. On my poster, I highlight some of these factors and how
they may have affected the geographic extent and distribution of the racial change.
GIS is a fitting tool to look at racial change over time, as one can easily join racial data to
shapefiles, and “click through” layers to see how racial demographics change. The dot density
also effectively represents multiple races at once, while allowing the viewer to see which areas
are denser in terms of population and certain races. The geographic extent and directional flow of
racial change is also visually clear through GIS.
Data:
Data layer
Source
Format
Year that data
represents
1950 census tracts
NHGIS
Shapefile
(https://www.nhgis.or
g/)
1950
1960 census tracts
shapefile
NHGIS
Shapefile
1960
1970 census tracts
shapefile
NHGIS
Shapefile
1970
1980 census tracts
shapefile
NHGIS
Shapefile
1980
1990 census tracts
shapefile
NHGIS
Shapefile
1990
2000 census tracts
shapefile
NHGIS
Shapefile
2000
2010 census tracts
NHGIS
Shapefile
2010
Suzanne Lis
Final Paper
due 12.13.12
shapefile
1950 race data
NHGIS
Feature/Vector
1950
1960 race data
NHGIS
Feature/Vector
1960
1970 race data
NHGIS
Feature/Vector
1970
1980 race data
NHGIS
Feature/Vector
1980
1990 race data
NHGIS
Feature/Vector
1990
2000 race data
NHGIS
Feature/Vector
2000
2010 race data
NHGIS
Feature/Vector
2010
2010 demographic
profile data
UCONN’s CT State
Data Center
Feature/Vector
2010
Major Highways
MAGIC
Shapefile
(http://magic.lib.ucon
n.edu/connecticut_dat
a.html)
2010
Interstate Highways
M drive
Shapefile
Current
Hartford County town divisions
MAGIC
Shapefile
2010 - While MAGIC
did have 1990 and
2000 town divisions
available, I compared
the years, and there
was no significant
difference.
CT Hydrography
(polygon features)
MAGIC
Shapefile
2010
Schools in Hartford
County
Reference USA
Excel (then converted Current
to shapefile)
Major steps:
1 I downloaded the data:
a NHGIS had data on race and accompanying GIS boundary files (see specific data
layers below). I selected for the years 1970,1980, 1990, 2000, and 2010.
b Barbara helped me immensely with the 1950 and 1960 data (thank you!!!). The
1950 data was obtained through NHGIS. Barbara added “total non-white” and
Suzanne Lis
Final Paper
due 12.13.12
2
3
4
5
6
7
“total” columns and projected the data to NAD 1983 CT State Plane. The 1960s
data was compiled with the census tract shapefile provided by NHGIS to race data
from Social Explorer (which conveniently provides one with a “GISJOIN” field).
Barbara selected for Hartford, after which the shapefile and data were joined.
I joined each data layer to its relevant shapefile where necessary (i.e. 1990 race data to
1990 census tract shapefile).
I downloaded the shapefiles for highways, hydrography, etc.
I clipped all the data layers (where necessary) to Hartford County.
I exported these clipped layers so they would be their own data layers and only contain
Hartford County data.
Using “dot density” under Symbology properties for each layer, I represented the races as
such:
a Red/pink = white; blue = black; gray = other; yellow = Hispanic; green = Asian;
purple = multi-racial.
For my smaller maps on the side:
a 1990 schools: I downloaded school addresses in Hartford, CT from Reference
USA and geocoded them.
b 1950 schools: I did a different symbology in order to show % non-white of total
population. I used a different highways shapefile at the last minute (from the M
drive) in order to label the two interstates differently.
c 1970 highways: I added the highways shapefile, and decided not to clip it to
Hartford County so that one can see how the roads extend even further into the
suburbs.
Difficulties/future advice:
● When downloading data from NHGIS, go one year at a time. I made the initial mistake of
downloading many years at once, overwhelming the file, and getting frustrated with the
computer crashing. If you begin one year a time, clip to your desired area, then the
ArcGIS will not be reading as much data at once.
● Export these “clipped” areas to their own data layer so your program is not reading
through all the other data unnecessarily.
● (Barbara learned this, but I will put it here for posterity’s sake:) If NHGIS does not have
the data you want, try getting data from Social Explorer and joining it (through
“GISJOIN” field) to the shapefiles that NHGIS provides.
Annotated citations:
1 Gordon, C. (2008). Mapping Decline: St. Louis and the American City. Mapping
Decline. Retrieved September 10, 2012 from http://mappingdecline.lib.uiowa.edu/
Suzanne Lis
Final Paper
due 12.13.12
a
While this was technically not a source for me, this website inspired me to
propose this topic in the first place. This is a website created by a University of
Iowa professor; it accompanied his book of the same name which came out in
2008. The website is easy to navigate but not overly simplistic. I also like that the
viewer gets to control how they view the data through the time, which is
something that neither a Powerpoint nor a poster can fully accomplish.
2 Crowder, Kyle, and Scott South. 2008. Spatial Dynamics of White Flight: The Effects of
Local and Extralocal Racial Conditions on Neighborhood Out-Migration. American
Sociological Review 73.5 (2008): 792-812.
a This paper attempts to explain the motives behind “white flight.” The authors
found that the existence of “large and diverse minority populations in extralocal
neighborhoods tend to reduce the likelihood that white residents will leave their
neighborhood” (because they feel there is less availability for where to migrate).
The paper is notable in the sense that it looks beyond individual and family-level
motives to “extralocal” motives, which are here represented by census data and
tracking data. Interestingly enough, the paper centers around the idea that
neighborhoods are part of a larger “urban mosaic”; the neighborhood was the unit
of interest. However, my mapping focused more on towns within a “county
mosaic.” Nevertheless, this paper’s discussion of contributing factors to “white
flight” was helpful.
3 Diamond E. and Bodenhamer D. (2001). Investigating white-flight in Indianapolis: A
GIS approach. History and Computing, 13, pp. 25-44.
a This article begins by exalting the merits of joining history and geography
through GIS. The main goal of the article is to investigate the relationship
between “white flight” in Indianapolis to the disappearance of urban Protestant
churches in the second half of the 1950s. The article argues that “[the story of
white Protestant abandonment of the central city] offers an argument accepted by
most scholars, yet it has one important flaw: it is not entirely clear how accurate it
is.” They found that only a small number of churches actually moved, but those
that did generally moved from inner-city areas with large African American
populations to white suburbs. Thus, the churches were not “following” their
congregations to the suburbs, as previously posited.
4 Gregory, I. and Healey, R.G. (2007). Historical GIS: structuring, mapping and analysing
geographies of the past. Progress in Human Geography, 35(5), pp. 638-653. Retrieved
from http://phg.sagepub.com/content/31/5/638.short#cited-by.
a This article presents an overview of the theory and goals of historical GIS, with
discussion of several case studies (including the one above!). I read the section
devoted to “change over space and time.” The other case studies that they
described were varied and interesting, such as fertility in China between the 1960s
and 1990s. One particularly interesting case study dealt with mapping poverty
Suzanne Lis
Final Paper
due 12.13.12
levels and mortality data since Victorian England to present; the authors found
that these areas were still suffering from the same problems.
5 Lauria, M. (1998). A new model of neighborhood change: Reconsidering the role of
white flight. Housing Policy Debate, 9(2), pp. 395-424. Retrieved 11 Dec 2012.
a Much like Diamond and Bodenhamer’s study above, this study went against the
conventional wisdom that “urban neighborhood transformation is driven largely
by white flight.” Instead, the author finds that a depressed market in New Orleans
in 1985-1990 made housing (that had been previously occupied by white people)
affordable to middle-class blacks and made economic opportunities available to
them. Specifically, the article was relevant to my project in its discussion of
foreclosures and low home ownership rates (which are low in Hartford, as is often
the case in lower-income areas).
6 Sohoni, D. and Saporito, S. (2009). Mapping school segregation: using GIS to explore
racial segregation between schools and their corresponding attendance areas. American
Journal of Education, 115 (4), pp. 569-600. Retrieved 11 Dec 2012.
a This article linked maps of school attendance boundaries with 2000 census data
and other school data. The authors found that if children attended their most local
school, there would be less racial segregation. They also found that “private,
magnet, and charter schools contribute to overall racial segregation within most
school districts.” I found this interesting, as I have heard previously that magnet
schools are a solution to racial segregation. This article was relevant to my project
because Hartford continues to struggle with racial segregation in its schools.
When racial segregation was at its peak and they had not yet divided up school
districts, people often said that the Board of Education was running two entirely
different school systems.
7 Wang, Y. (2011). White flight in Los Angeles county, 1960-1990: a model of fuzzy
tipping. The Annals of Regional Science, 47(1), pp. 111-129. Retrieved 11 Dec 2012.
a This article tests Schelling’s tipping point model for racial residential segregation
against census data and mapping for Los Angeles county. Schelling proposes that
there is a critical point of minority immigration into an area after which white
natives of the area begin to leave. The author found that “the tipping point has
shifted from around 0.36 between 1960 and 1970 to 0.78 between 1980 and
1990,” as well as a decreasing pattern of white flight and thus a “steady
progression toward racially integrated urban residential pattern in Los Angeles
county from 1960 to 1990.” This is one of the few optimistic articles I read, which
was refreshing. The idea of a tipping point may be applicable in the case of
Hartford as well. As I discuss below, this article also answers one of my questions
that could be pursued further: is the pattern of change in racial demographics
steadily increasing or decreasing?
Suzanne Lis
Final Paper
due 12.13.12
Concluding thoughts/discussion:
As I mentioned in my project description, I think GIS is an effective tool for visualizing the
geographic extent and distribution of racial change, especially in the dramatically clear case of
Hartford. However, I feel that a Powerpoint is even more effective, in that one can animate the
change for the viewer, rather than having to lead the viewer (visually) between maps.
Another major flaw in my maps and data is the Hispanic demographic. I realized too late
in my process that my data did not include this distinction, and I came across many articles
discussing the growth of the Hispanic demographic in Hartford. Unfortunately, when I managed
to add Hispanic data to certain years (1960 and 1980), it somehow skewed my race data for other
census tracts. Furthermore, I did not feel that it was fair to present a series of maps where one
huge factor would inconsistently show up. If I were to do this project again, I would figure this
out from the beginning. It should also be noted that the Hispanic demographic is an excellent
case-study of how the census has evolved to meet the needs of the population. In 1950, the
census counted Hispanics as “people of Spanish surname”; by 1980, they were already dividing
the “Hispanic” column into places of origin and how people identified (Mexican, Puerto Rican,
etc).
I proposed some further work in my original assignment proposal that I did not end up
tackling in this project. While it is visually clear in my maps as to how far the “white flight”
extends, what determines just how far it extends? Time-wise, is the pattern of change in the
racial makeup regular or steadily increasing? If so, why? And finally, how does the racial change
in Hartford County compare to others studied in American cities? I was originally inspired by
Gordon’s website that maps racial change in St. Louis (http://mappingdecline.lib.uiowa.edu/),
and many other American cities have experienced this phenomenon. It would be fascinating to
map several cities and compare the racial change.