Assessing the Foreclosure Crisis From the Ground Up

Newman, Kathe; Schafran, Alex

doi:10.1080/10511482.2013.755318

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…Meanwhile, the bursting of the housing bubble in 2008 briefly suppressed but had no long-term dampening effect on housing costs in cores, particularly San Francisco, while foreclosures devastated eastern Contra Costa County, where the exurban building boom was most pronounced (Soursourian, 2012). Schafran (2013) has correctly referred to this dynamic as an 'urban crisis', refusing the standard narrative of sprawl as a distinctly 'suburban' phenomenon.…”

Section: Mobility and Spatial Restructuring In The San Francisco Bay Areamentioning

confidence: 99%

“…For residents living beyond the catchments of these systems, noncar commuting must be done by infrequent bus or by bike on unsafe roads. Few options therefore exist for the suburban working class other than car dependence, since even planned transit extensions already lag far behind the actual patterns of housing production and migration (Schafran, 2013).…”

Section: Mobility and Spatial Restructuring In The San Francisco Bay Areamentioning

confidence: 99%

“…The spatial contradiction is as follows. A crisis in the logic of extensive exurban development, provoked by the implosion of a housing bubble dependent on the issuance of unsound mortgages (Bardhan and Walker, 2011;Newman, 2009), has stranded working-class populations, many first-time homebuyers among them (Newman and Schafran, 2013), in the "drive 'til you qualify [for a mortgage]" fringes that impose high transportation burdens. Moreover, these areas are politically and organizationally difficult to retrofit for walking and cycling as long as regional governance pursues densification strategies that depend on developer incentives.…”

Section: Mobility and Spatial Restructuring In The San Francisco Bay Areamentioning

confidence: 99%

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Cycles of Investment: Bicycle Infrastructure, Gentrification, and the Restructuring of the San Francisco Bay Area

Stehlin

2015

Environ Plan A

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Bicycling for transportation in American cities has grown dramatically in the past 20 years, symbolizing the return of capital investment and commercial vitality to formerly disinvested urban cores. Pucher et al. (2011) note the cycling "renaissance" taking place to the greatest extent in gentrifying neighborhoods, but the processes relating cycling and gentrification have gone largely unexplored. This article examines the early role that bicycle advocacy organizations in San Francisco played in articulating the specifically economic value of bicycle infrastructure investment. This narrative is now commonplace, and widely applied both to neighborhood revitalization and urban competition for "talent." This alliance of bicycle advocacy with the "livable" turn of gentrification raises serious questions for those who would pursue a more democratic and socially just politics of the bicycle.

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Section: Mobility and Spatial Restructuring In The San Francisco Bay Areamentioning

confidence: 99%

Section: Mobility and Spatial Restructuring In The San Francisco Bay Areamentioning

confidence: 99%

Section: Mobility and Spatial Restructuring In The San Francisco Bay Areamentioning

confidence: 99%

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Cycles of Investment: Bicycle Infrastructure, Gentrification, and the Restructuring of the San Francisco Bay Area

Stehlin

2015

Environ Plan A

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“…Blyth, 2013;Lapavitsas et al, 2012); and in macro-and micro-scale geographical differences in individual and household experiences of the crisis (e.g. Hadjimichalis, 2011;Kitson et al, 2011;Newman and Schafran, 2013).…”

Section: A Highly Geographical Crisismentioning

confidence: 99%

Geographies of finance II

Christophers

2014

Progress in Human Geography

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The global financial and more widely economic crisis which began in 2007–2008 has been a crisis indelibly of political economy. This fact has led scholars of finance ‘back’ to political economy, where geographers’ interest in finance first materialized in the 1980s. In this light, this article reviews recent work on the geographical political economy of the crisis, highlighting such work’s main themes, contributions and lacunae. It shows that this work has been powerfully influenced by developments in geographical political economy, and by the latter’s engagement with alternative intellectual traditions, over the past three decades. But it argues also that the attempt to theorize and document the place of finance within geographical political economy remains very much an ongoing project.

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“…Several studies have characterized the different scales of the crisis as interconnected and mutually constitutive, pointing out the linkages between the global economic downturn and the fate of local urban geographies such as neighborhoods. Given the importance of homeownership in the US economy, US cities constituted ground zero for the crisis (Newman and Schafran 2013 ; Aalbers 2012 ). The recession and the austerity measures taken to address its effects were not equally felt across the North Atlantic economies, affecting some regions and nations more than others (Kitson et al 2011 ; Harvey 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Characterizing the perception of urban spaces from visual analytics of street-level imagery

Freitas

Berreth²,

Chen

et al. 2022

AI & Soc

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This project uses machine learning and computer vision techniques and a novel interactive visualization tool to provide street-level characterization of urban spaces such as safety and maintenance in urban neighborhoods. This is achieved by collecting and annotating street-view images, extracting objective metrics through computer vision techniques, and using crowdsourcing to statistically model the perception of subjective metrics such as safety and maintenance. For modeling human perception and scaling it up with a predictive algorithm, we evaluate perception predictions across two points in time separated by economic changes in the urban core of Raleigh, North Carolina, in the aftermath of the 2008 Great Recession. We hypothesize specific socioeconomic processes can be substantially reflected in the built environment of cities and, thus, render themselves visible at the street level. This paper describes the process of incorporating subjective visual ratings across two datasets of temporally separated street-view images, an algorithm, and a visualization tool. This work serves as a case study for utilizing AI and visualization techniques in a richer characterization of urban spaces that includes both objective metrics such as income (that operates at a broader scale) and subjective metrics such as perception of individuals (that operates at a narrower scale at specific locations). We outline an interdisciplinary methodology to test this hypothesis in streetscape data from Raleigh, NC, from 2008 to 2020. We describe the results of training algorithms that utilized image features with crowdsourced human perception ratings. We provide a comparison of the results with income data. The analysis and interpretation of this comparison provide insight into the challenges and opportunities for using AI technology in characterizing changes in urban environments. One challenge is the ability of human domain experts to interpret the output of algorithms through manipulation and to integrate these results into their workflow. This is addressed with a novel interface designed for interactive analysis and visualization. We conclude with a discussion of some of the benefits and limitations of integrating AI models in the human expert’s decision-making process in the presence of both subjective and objective metrics.

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Assessing the Foreclosure Crisis From the Ground Up

Cited by 5 publications

References 11 publications

Cycles of Investment: Bicycle Infrastructure, Gentrification, and the Restructuring of the San Francisco Bay Area

Cycles of Investment: Bicycle Infrastructure, Gentrification, and the Restructuring of the San Francisco Bay Area

Geographies of finance II

Characterizing the perception of urban spaces from visual analytics of street-level imagery

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