Gentrification and Displacement in the San Francisco Bay Area: A Comparison of Measurement Approaches

Abstract

Gentrification may play an important role in influencing health outcomes, but few studies have examined these associations. One major barrier to producing empirical evidence to establish this link is that there is little consensus on how to measure gentrification. To address this barrier, we compared three gentrification classification methodologies in relation to their ability to identify neighborhood gentrification in nine San Francisco Bay Area counties: the Freeman method, the Landis method, and the Urban Displacement Project (UDP) Regional Early Warning System. In the 1580 census tracts, 43% of the population had a bachelor’s degree or higher. The average median household income was $79,671 in 2013. A comparison of gentrification methodologies revealed that the Landis and Freeman methodologies characterized the vast majority of census tracts as stable, and only 5.2% and 6.1% of tracts as gentrifying. UDP characterized 46.7% of tracts at risk, undergoing, or experiencing advanced stages of gentrification and displacement. There was substantial variation in the geographic location of tracts identified as gentrifying across methods. Given the variation in characterizations of gentrification across measures, studies evaluating associations between gentrification and health should consider using multiple measures of gentrification to examine the robustness of the study findings across measures.

Application of Spatiotemporal Hybrid Model of Deformation in Safety Monitoring of High Arch Dams: A Case Study

“…According to the relationship between the displacement calculated by the finite element method and the normalized water level , the fitting curve is fitted, which is shown in Figure 16. The expression is as follows: = −11.588 − 67.402 − 128.580 2 − 59.787 3 + 83.438 4 (26) According to the Equation 26, the regression fitting displacement can be obtained and used as the calculation basis of the hydraulic component of PL13-2 in the hybrid model.…”

“…The monitoring data of dam environmental quantity, such as deformation and temperature, is acquired regularly, so as to build the dam monitoring system. As an important feature, deformation data analysis is particularly important [1][2][3][4][5]. In fact, the deformation data of arch dam comes from the monitoring points with different spatial coordinates.…”

Mapping gentrification and displacement pressure: An exploration of four distinct methodologies

“…While early studies of gentrification were often qualitative analyses of specific neighborhoods (Hammel and Wyly, 1996), recent quantitative measures of gentrification have relied primarily on census data to measure changes in neighborhood composition by income, race, education, housing value and other factors (Clark, 2005;Davidson and Lees, 2005;Ding et al, 2016;Ellen and O'Regan, 2011;Freeman, 2005). Academic debate continues over the appropriate measures of gentrification (Barton, 2016;Bousquet, 2017;Ding et al, 2016;Freeman, 2009), with studies demonstrating the sensitivity of mapping measures to the variables included (Galster and Peacock, 1986;Mujahid et al, 2019). Previous gentrification mapping efforts have included various population and housing measures, including income, education, race, housing costs and housing tenure (Bostic and Martin, 2003;Ding et al, 2016;Ellen and O'Regan, 2011;Freeman, 2005;McKinnish et al, 2008).…”

Case-comparison study protocol for gauging effects of neighbourhood trends and sickness: examining the perceptions of transit-Induced gentrification in Prince George’s County

“…Grounded in the previously discussed research and adapted from a study examining gentrification in the San Francisco, CA area, the GENTS Study conceptual model of perceived gentrification and health theorises that TOD, such as a new light rail line, can lead to both TIG and perceived TIG ( figure 1 ). 78 Instigated by any actual or perceived changes in the economic (eg, increased taxes), social (eg, perceived crime increase), built (eg, new sidewalks) or natural (eg, new parks) environments, as a result of the TOD, perceived TIG, by way of TIG or not, may be capable of influencing positive (eg, walking) or negative (eg, smoking) health behaviours. Ultimately, these health behaviours can bring about positive or negative health outcomes ( figure 1 —orange arrows).…”

“…The GENTS Study (Gauging Effects of Neighborhood Trends and Sickness: Examining the Perceptions of Transit-Induced Gentrification in Prince George's County) will address these limitations by using a longitudinal research framework at the neighbourhood level in order to examine health impacts related to TIG perception. Grounded in the previously discussed research and adapted from a study examining gentrification in the San Francisco, CA area, the GENTS Study conceptual model of perceived gentrification and health theorises that TOD, such as a new light rail line, can lead to both TIG and perceived TIG (figure 1) 78. Instigated by any actual or perceived changes in the economic (eg, increased taxes), social (eg, perceived crime increase), built (eg, new sidewalks) or natural (eg, new parks) environments, as a result of the TOD, perceived TIG, by way of TIG or not, may be capable of influencing positive (eg, walking) or negative (eg, smoking) health behaviours.…”