Residential segregation is an inherently spatial phenomenon as it measures the separation of different types of people within a region. The spatial nature of segregation results in three distinct yet interrelated challenges that should be accounted for by the empirical scientist. First, most researchers utilize data provided by public sources, and with this comes the concern that administrative boundaries are not necessarily optimized for segregation research. Second, "neighborhoods" are generally not walled enclaves. This results in residential interaction across neighborhood boundaries in shops, schools and other shared locations. Third, residential segregation captures the socio-spatial interaction of individuals meaning that some spatial interaction function must be defined to allow for segregation to be measured. The classic segregation measures assumed administrative areas to be equivalent to neighborhoods, ignored the potential for interaction across neighborhoods and therefore implicitly defined a spatial interaction function that ended at the administrative boundary. The evolution of segregation measurement has seen a number of advances that directly address these challenges. This research will further advance the methods of segregation measurement in the face of these challenges. As such it first uses a Monte Carlo approach to look at how underling spatial properties could manifest themselves in the magnitude of measured segregation. It then uses data on 359 Metropolitan Statistical Areas to delve deeper into the impact of administrative boundaries on measured segregation. An innovative approach is proposed to build random census tracts to test whether actual census tracts deviate from a random pattern. This approach also offers a new framework of segregation measurement that lessens the effect of administrative boundaries on measured segregation. Finally, a new class of segregation measures is proposed that marries two aspects of segregation: relationships of people within neighborhoods and those between neighborhoods. By treating these distinct concepts as equals for the first time in the segregation literature, this project will shed new light on segregation measurement in general, and on the U.S. urban system in particular.
While the legal infrastructure of segregation was pulled down decades ago, its vestiges remain. Since segregation remains a part of society, it is important that researchers and policy makers have the best tools available for its measurement. This research takes a critical eye to measurement challenges and existing approaches, and proposes innovative new methods that address missing and underrepresented aspects of this critical metric on the health of communities. The focus on methods will contribute to both segregation theory as well as empirical research. Finally, this work will both utilize and contribute to the open source software community. New approaches and measures developed as a result of this work will be delivered to the wider research community through the open source project PySAL (Python Spatial Analysis Library).
Decades ago in the U.S., clear lines delineated which neighborhoods were acceptable for certain people and which were not. Techniques such as steering and biased mortgage practices continue to perpetuate a segregated outcome for many residents. In contrast, ethnic enclaves and age restricted communities are viewed as voluntary segregation based on cultural and social amenities. This diversity surrounding the causes of segregation are not just regional characteristics, but can vary within a region. Local segregation analysis aims to uncover this local variation, and hence open the door to policy solutions not visible at the global scale. The centralization index, originally introduced as a global measure of segregation focused on spatial concentration of two population groups relative a region's urban center, has lost relevancy in recent decades as regions have become polycentric, and the index's magnitude is sensitive to the particular point chosen as the center. These attributes, which make it a poor global measure, are leveraged here to repurpose the index as a local measure. The index's ability to differentiate minority from majority segregation, and its focus on a particular location within a region make it an ideal local segregation index. Based on the local centralization index for two groups, a local multi-group variation is defined, and a local space-time redistribution index is presented capturing change in concentration of a single population group over two time periods. Permutation based inference approaches are used to test the statistical significance of measured index values. Applications to the Phoenix, Arizona metropolitan area show persistent cores of black and white segregation over the years 1990, 2000 and 2010, and a trend of white segregated neighborhoods increasing at a faster rate than black. An analysis of the Phoenix area's recently opened light rail system shows that its 28 stations are located in areas of significant white, black and Hispanic segregation, and there is a clear concentration of renters over owners around most stations. There is little indication of statistically significant change in segregation or population concentration around the stations, indicating a lack of near term impact of light rail on the region's overall demographics.
