About

Acknowledgments

We would like to thank Dr. Scott Caddy, Rebecca Baugh, and Sabrina Jaszi for giving their invaluable advice, comments, and critique to advance our project. Without their help, we would be lost.

We would also like to acknowledge the following sources of data:

Opportunity Atlas - Module 1 - Neighborhood Mobility Outcomes
Opportunity Atlas - Module 2 - Neighborhood Mobility Trends
Mapping Inequality - HOLC Residential Security Maps (1935–40)
TIGER/Line Shapefiles - 2020 Census tracts & 2024 Wayne-County roads

Full scholarly citations appear on the “Reference List” page.

Technical and Methodological Decisions

Detroit Map

Stack. Python 3.11, GeoPandas 0.14, Folium 0.16, MapClassify, Shapely, Pandas, NumPy.
Projection. All spatial joins executed in EPSG 32616 (UTM zone 16 N); map served in EPSG 4326 (Web Mercator).
Density calculation. Opportunity Atlas child counts ÷ tract land-area (km²). We opted not to rescale by the total 2010 population because the research question targets the same specific population for their income in 2014-2015.
Jenks natural breaks (k = 5) used for choropleth classes to emphasize within-layer variation.
Data cleaning. Removed tracts with zero land area; clipped HOLC polygons to Detroit boundary for clarity; normalized income-rank variables from 0% to 100% for cross-race comparison.
Color palette. Used colors that pass WCAG AA for color-blind safety when denoting the races.

New Orleans Map

Stack. Python 3.11, GeoPandas 0.14, Folium 0.16, MapClassify, Shapely, Pandas, NumPy.
Projection. All spatial joins executed in EPSG 32616 (UTM zone 16 N); map served in EPSG 4326 (Web Mercator).
Jenks natural breaks (k = 5) used for choropleth classes to emphasize within-layer variation.
Data extracted from Opportunity Atlas demographic data (layers are named the same as data categories in CSV data)

Oakland Map

ArcGIS. We used the ArcGIS software to combine information from Mapping Inequality with other sources.
Popups design. Legend includes popup icons and names and includes full interactivity.
Data extracted from the Mobility Trends from Opportunity Atlas demographic data and used in the popup information. GeoJSON data from Mapping Inequality was downloaded and laid on top of the default map along with a georectified HOLC map.

Redlining Timeline

TimelineJS. The Google Sheets version was used to format and create the timeline. The program has both color limitations and backgrounds, so for readability, everything is white background and black text. Also, there are picture limitations as only pictures that are publicly available through their own link can be used, which left some slides without a photo.
Data was taken from research about the timelines for redlining and each respective city from the specific outcome studied. These are not the only outcomes and main events, but due to the time limitation, not every major event could be included.

Contributors

Rohit Vasantha

Economics & Data Science, Class of 2027

Contributions:

• Organized and assembled team data critique and citations

• Conducted research on redlining in New Orleans, synthesizing information about the city's environmental history

• Sourced Opportunity Insights dataset and relevant economic indicators

• Described and identified the topic and research question in the written narrative, suggesting preliminary conclusions

• Primarily contributed to the data critique and research question development

Danielle Reshef

Sociology & Science, Technology, & Society, Class of 2025

Contributions:

• Wrote the historical context section, created the submitted group research question, and edited the narrative

• Researched historical context for all three cities and archival photos, complied all materials to create the redlining timeline using the TimelineJS Google Sheet option

• Sourced Mapping Inequality project for the group project

• Wrote the description of Detroit's map visualization goal

• Contributed to the "About" page, the final data critique, and the annotated bibliography

Joseph (Joey) Park

Chemical Engineering & Data Science, Class of 2026

Contributions:

• Acquired and cleaned Detroit-specific tables from the Opportunity Atlas

• Downloaded and re-projected 2020 Census tracts and 2024 TIGER/Line roads

• Clipped HOLC polygons to city limits (Mapping Inequality)

• Wrote the Python notebook detroit_mapping.ipynb (GeoPandas, Folium)

• Contributed to the “About” page, the data critique for Detroit, and annotated bibliography

Amith Vasantha

Electrical and Computer Engineering & Data Science, Class of 2027

Contributions:

• Acquired and visualized HOLC redlining maps for New Orleans

• Acquired and visualized Opportunity Atlas demographic data for New Orleans

• Conducted research on New Orleans-specific effects of redlining

• Wrote description of New Orleans' map visualization goal

• Contributed to the "About" page, data critique, and annotated bibliography

Dalin Try

Economics & Data Science, Class of 2026

Contributions:

• Contributed to popup information for the different zones, infrastructure and included in relevant economic indicators

• Helped designed the labels on the popup and sourced images for the narrative

• Contributed to the writing of the project narrative and helped updated the timeline for the Oakland section

• Researched the history of freeways and redlining in Oakland, building context into the narrative

Larry Hui

Mechanical Engineering & EECS, Class of 2026

Contributions:

• Acquired and visualized redlining maps in West Oakland on ArcGIS

• Contributed to popup information for different zones, infrastructural changes including current economic indicators

• Contributed to the final data critique, the conclusion, and visualization technology and accessibility in the narrative

• Included and punctuated narrative with images

• Created Google Form for feedback, layout for the website including HTML embedding for the West Oakland map

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