Geisler, Kathrin Zippel, Ph.D., and Linda Blum, Ph.D. Student Contributors Nivi Biju Branson Fox Jack Norys Institute for Health Equity and Social Justice Research Especially Alisa K. Lincoln, MPH, Ph.D.
“Definitely Declining” D - “Hazardous” “‘homogeneous,’ and were in demand in ‘good times or bad’” (Hillier 2005:216) Hillier, Amy. 2005. “Residential Security Maps and Neighborhood Appraisals.” Social Science History 29(2):207-233.
“Definitely Declining” D - “Hazardous” “In St. Louis, the white middle class suburb of Ladue was colored green because…it had ’not a single foreigner or negro.’” (Rothstein 2017) Rothstein, Richard. 2017. The Color of Law. New York, NY: W.W. Norton & Co.
“Hazardous” B - “Still Desirable” “‘They are like a 1935 automobile—still good, but not what the people are buying today who can afford a new one.’” (Hillier 2005:217) Hillier, Amy. 2005. “Residential Security Maps and Neighborhood Appraisals.” Social Science History 29(2):207-233.
“Hazardous” C - “Definitely Declining” “‘infiltration of a lower grade population’” (Hillier 2005:217) Hillier, Amy. 2005. “Residential Security Maps and Neighborhood Appraisals.” Social Science History 29(2):207-233.
“Definitely Declining” D - “Hazardous” Hillier, Amy. 2005. “Residential Security Maps and Neighborhood Appraisals.” Social Science History 29(2):207-233. “‘detrimental influences in a pronounced degree,’ and ‘undesirable population or an infiltration of it’” (Hillier 2005:217)
“Definitely Declining” D - “Hazardous” Rothstein, Richard. 2017. The Color of Law. New York, NY: W.W. Norton & Co. “Lincoln Terrace was colored red because ‘it had little or no value today…due to the colored element now controlling the district’” (Rothstein 2017)
▸ Growth in recent interest in dynamics related to redlining ▸ Hillier (2003; 2005a; 2005b; 2005c), Rothstein (2017), and Woods (2012) have all recently focused heavily on the history of the Home Owners’ Loan Corporation ▸ Most sociological focus (~30 recent studies) has been on redlining as a piece of history related to the development of segregated metros and cities in the mid-20th century ▸ National Community Reinvestment Coalition report (2018) ▸ Little direct use of redlining data, particularly across cities ▸ No direct study of how health has been affected by redlining
Have long recognized the segregation may impact communities’ health • Yankauer (1950) is one of the first examples of research in this vein ▸ Hypothesis has been that segregation impacts individuals through concentrated poverty and disadvantage (Collins and Williams 1999) ▸ Studies treat segregation primarily as a compositional factor in neighborhoods ▸ Literature has focused primarily on African American communities (Anderson 2016)
of Richmond) GIS data for HOLC redlining boundaries in ~150 cities; Redlining data 500 Cities Project (Centers for Disease Control) Census tract estimates for 28 health behaviors, outcomes, and prevention; CDC data
of Richmond) GIS data for HOLC redlining boundaries in ~150 cities; Redlining data 500 Cities Project (Centers for Disease Control) Census tract estimates for 28 health behaviors, outcomes, and prevention; CDC data 2012-2016 American Community Survey (U.S. Census Bureau) Census tract estimates for demographic measures (race, poverty, etc.); ACS data Tiger/Line Shapefiles (U.S. Census Bureau) GIS data for census tract boundaries and other features
Data Census Tract Boundary GIS Data Area (m2) of “C” and “D” Grade per tract CDC Data ACS Data Tract-level data set with redlining, health, and demographic measures
Data Census Tract Boundary GIS Data Area (m2) of “C” and “D” Grade per tract CDC Data ACS Data Tract-level data set with redlining, health, and demographic measures Code for project specific calculations generalized in an R package redHealth Open science approach
Data Census Tract Boundary GIS Data Area (m2) of “C” and “D” Grade per tract CDC Data ACS Data Tract-level data set with redlining, health, and demographic measures Reproducible API calls for downloading data instead of “point-and-click” Open science approach
Data Census Tract Boundary GIS Data Area (m2) of “C” and “D” Grade per tract CDC Data ACS Data Tract-level data set with redlining, health, and demographic measures Repackaged CDC data in “tidy” format available in an R package cityHealth Open science approach
Data Census Tract Boundary GIS Data Area (m2) of “C” and “D” Grade per tract CDC Data ACS Data Tract-level data set with redlining, health, and demographic measures Full process implemented with open source tools (principally R) Open science approach
Data Census Tract Boundary GIS Data Area (m2) of “C” and “D” Grade per tract CDC Data ACS Data Tract-level data set with redlining, health, and demographic measures Full process documented publicly on GitHub and the Open Science Framework Open science approach
Data Census Tract Boundary GIS Data Area (m2) of “C” and “D” Grade per tract CDC Data ACS Data Tract-level data set with redlining, health, and demographic measures Analytic data set available for download via GitHub and citable via Figshare Open science approach
segregation in St. Louis? OLS and spatial regression: 2. What is the relationship between redlining and contemporary health outcomes? 3. DATA & PILOT METHODS DATA ANALYSIS Tract-level data set with redlining, health, and demographic measures
Open science approach Segregation calculations and mapping: 1. What is the geography of segregation in St. Louis? OLS and spatial regression: 2. What is the relationship between redlining and contemporary health outcomes? 3. DATA & PILOT METHODS DATA ANALYSES Tract-level data set with redlining, health, and demographic measures
segregation in St. Louis? OLS and spatial regression: 2. What is the relationship between redlining and contemporary health outcomes? 3. DATA & PILOT METHODS DATA ANALYSES Tract-level data set with redlining, health, and demographic measures Models fit using the PySAL spatial analysis library for Python Open science approach
of redlining explained away by segregation and poverty ▸ Robust spatial findings that neighborhood-level asthma rates impacted significantly by segregation, poverty, and un-insurance rates ▸ Limitations • These models are not ideal for proportional outcomes • Models would benefit from regular geographical features (such as square kilometer grids) • Measurement error in both ACS and CDC data
segregation 2. Development of tidyseg 3. Effect sizes for spatial regression models • Not a part of the PySAL library 4. Measuring effects across sparse data • Meta analysis is one possibility Funding 1. Initial grants applications a. National Institutions of Health LRP b. Internal funding mechanism 2. Longterm funding a. National Science Foundation? b. National Institutes of Health? 1. What are the geographies of segregation across the 150 cities in the Mapping Inequality data set? 2. What are the contemporary relationships between redlining and segregation? 3. What health outcomes are most salient for measuring the effect of redlining on contemporary population health? 4. What is the contemporary relationship between these health outcomes and historical redlining boundaries?
github.com/prenerlab/redHealth github.com/slu-openGIS/tidyseg Slides available via SpeakerDeck speakerdeck.com/chrisprener/nu18 Raw data, code available via GitHub github.com/prenerlab/redliningPilot [email protected] chris-prener.github.io LEARN MORE THANKS FOR COMING! Analysis data is archived on Figshare 10.6084/m9.figshare.7223621 , : @chrisprener Project software github.com/slu-openGIS/cityHealth
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