Wireframes/storyboards

cancer_alley_NEW cancer_alley_map picture THIS ONE

Second image: The Inspired map above is from “ArcGIS Dashboards.” Arcgis.com, 2025, ft.maps.arcgis.com/apps/dashboards/6a14d723c6f14f2ebaa5d919296e07a5. Accessed 2 Oct. 2025.

Drafted Visualizations

I have developed several draft visualizations that anchor my storyboard. The socioeconomic comparison table shows that Cancer Alley has a much higher percentage of Black residents, lower income, and greater vulnerability compared to the state and national averages. The ArcGIS petrochemical facility map provides detail on specific plants, like Addis Carbon Black and BASF Geismar, and makes visible how these facilities cluster along the river. A second Cancer Alley distribution map uses color coding to highlight density across the tract. My next step is to build a forecasting visualization that carries lessons from Cancer Alley into present-day siting decisions, paired with demographic overlays to spotlight inequities. I’ll close with a policy panel that translates the visuals into actions. I will also focus on major cities that have been affected or will be affected like Virginia, Louisiana, and California.

Target audience

This story is directed at policymakers who hold influence over land-use decisions that allow datacenters, refineries, and petrochemical facilities to be sited near urban neighborhoods, particularly the ones in Cancer Alley. These decisions have disproportionately impacted low-income, predominantly Black communities, concentrating environmental risks where residents already face structural inequities. Policymakers such as Senator Eddie Lambert, who chairs Louisiana’s Senate Environmental Quality Committee, and Mayor-President Sharon Weston Broome of Baton Rouge have the power to change this trajectory. They can strengthen zoning and permitting standards, require robust environmental justice screenings, and invest in community-driven planning processes that safeguard public health. By shifting from reactive regulation to proactive prevention, leaders like Lambert and Broome can ensure that economic development does not come at the expense of the very communities most in need of protection.

Interview script

Goal	Questions to Ask
Test clarity of the story and visualizations	When you look at this chart/map, what message do you take away?
Is the concept easy to understand, or is anything confusing?
Identify the most relevant cancer outcomes to highlight.	Which types of health impacts (e.g., lung cancer, reproductive cancers, asthma) feel most relevant to you in this context?
Do you think focusing on one cancer type is more powerful than showing overall cancer risk?
Getting emotional and policy resonance	Does this visualization make the issue feel urgent?

Interview findings

Questions	Interview 1 (briefly describe)	Interview 2	Interview 3
Does the data make sense to you at first glance?	Talked about grounding the work	Said the story could stand out more if it compared future projections with past petrochemical impacts.	Felt the visualization was impactful, but recommended adding more local data to ground the story.
Which health outcomes feel most relevant?	Suggested focusing on cancers linked to air quality (lung, reproductive)	Agreed with lung cancer, but also mentioned asthma and other respiratory issues.	Thought focusing only on cancer might be limiting, but still supported lung cancer as the main one
Would this be useful for advocacy or policy?	Yes, if it shows what policymakers can actually do about it.	Said it could be powerful for public awareness campaigns.	Said it could influence policy if I pair it with specific recommendations.

From my interviews, I learned that the story needs to be more focused and forward-looking. Multiple people said I should center the narrative on data centers as the main issue, while still connecting it back to Cancer Alley as an example of what happens when polluting facilities are allowed to cluster in vulnerable communities. Another big takeaway was the idea of adding a forecasting visualization that shows the potential health and environmental impacts of future data centers in urban areas. Interviewees felt this would make the story more urgent and relevant for policymakers.

Identified changes for Part III

Research synthesis	Anticipated changes for Part III
Need to focus more clearly on data centers as the main story, while still tying in Cancer Alley as a historical example	Shift my visualizations to highlight forecasting around data centers, and use Cancer Alley as a comparison point instead of the main focus
Feedback asked for forecasting elements that show projected health/environmental impacts	Build out a projection chart that estimates cancer risk or pollution exposure if more data centers are built in certain areas
Some concern that focusing only on “cancer” might be limiting	Keep lung cancer as the primary health outcome, but consider adding respiratory outcomes like asthma to show broader impacts
Policy usefulness depends on clear recommendation	End the story with a set of concrete options (zoning rules, buffer zones, stronger permitting, etc.) tied directly to the data

What I’m changing (Part III plan)

I’m narrowing the core from “reproductive disorders in Cancer Alley” to a forward looking forecast of where petrochemical adjacent build out, including data centers, could create “next Cancer Alleys.” I’ll add a projection map and chart that estimate added exposure under plausible siting scenarios and elevate lung cancer as the primary health lens while considering respiratory outcomes to broaden relevance. The final section will translate evidence into action through concrete options, including zoning and buffers, EJ screening and cumulative impact review, targeted monitoring, and community benefits, so the work is immediately usable.

How I got here:

I began by exploring reproductive disorders in Cancer Alley, including preterm birth, low birth weight, fertility, and miscarriage, to link outcomes to proximity. After a data reality check, I found that public, small-area datasets for reproductive disorders were not accessible in a way that would support this project. I tried dashboards and ArcGIS layers, but many were non-exportable or too aggregated for the analysis I needed. I considered alternatives like preterm and low birth weight, but these still did not cleanly connect to what I was trying to connect. That led me to pivot and use Cancer Alley as a case study to create a forecast of what happens when new facilities, especially data centers and related plants, are sited with the same patterns.

New focus

My new core question focuses on which neighborhoods become the next “Cancer Alley” in practice? I’ll take this approach, then extend it statewide or to other metros using baseline layers such as TRI and GHGRP facilities.

For my visuals, I will present a baseline map of current facilities over a tract level risk surface with the corridor outlined, a scenario map showing added sites or expansion with a delta choropleth from “before” to “after,” a distribution chart of added population within one mile by race and income, a climate panel that summarizes added CO2 emissions under each scenario with low, medium, and high bands, and a ranked table of the top ten tracts with the largest air pollution.

References

ArcGIS Dashboards. (2025). Arcgis.com. https://ft.maps.arcgis.com/apps/dashboards/6a14d723c6f14f2ebaa5d919296e07a5

Beckler, H., Ho, R., Narimes Parakul, Campbell, D., & Thomas, E. (2025, June 17). How Business Insider investigated the true cost of data centers. Business Insider. https://www.businessinsider.com/how-calculate-data-center-cost-environmental-impact-methodology-2025-6

Delaney Dryfoos. (2024, October 6). The majority-Black districts that became Cancer Alley, The Lens. The Lens. https://thelensnola.org/2024/10/06/the-majority-black-districts-that-became-cancer-alley/

FracTracker Petrochem Data Portal. (2025). Fractracker.org. https://petrochem.fractracker.org/

James, W., Jia, C., & Kedia, S. (2012). Uneven Magnitude of Disparities in Cancer Risks from Air Toxics. International Journal of Environmental Research and Public Health, 9(12), 4365–4385. https://doi.org/10.3390/ijerph9124365

Kofman, A. (2021, November 2). The Most Detailed Map of Cancer-Causing Industrial Air Pollution in the U.S. ProPublica. https://projects.propublica.org/toxmap/#location/-90.6329/30.2081

Louisiana Cancer Maps. (n.d.). Public Health. https://sph.lsuhsc.edu/louisiana-tumor-registry/data-usestatistics/louisiana-data-interactive-statistics/louisiana-cancer-maps/

Martin-Manjaly, 04/20/2023, A. (2023, April 20). The Toxic Toll of Cancer Alley. ArcGIS StoryMaps. https://storymaps.arcgis.com/stories/941607e3f5b4475dbb4b0ab3147a4b3d

Maternal and Child Health Data Indicators, La Dept. of Health. (2017). La.gov. https://ldh.la.gov/page/maternal-and-child-health-data-indicators

Meaders, J. S. (2021). Health Impacts of Petrochemical Expansion in Louisiana and Realistic Options for Affected Communities. Tulane Environmental Law Journal, 34(1), 113–147. JSTOR. https://doi.org/10.2307/27089955

Petrochemical Air Pollution Map. (2025, April 7). Clear Collaborative. https://www.clearcollab.org/pollutionmap/

Terrell, K. A., & Julien, G. St. (2023). Discriminatory outcomes of industrial air permitting in Louisiana, United States. Environmental Challenges, 10(2667-0100), 100672. https://doi.org/10.1016/j.envc.2022.100672

The Shocking Hazards of Louisiana’s Cancer Alley, Johns Hopkins Bloomberg School of Public Health. (2025, August 4). Johns Hopkins Bloomberg School of Public Health; Public Health on Call. https://publichealth.jhu.edu/2025/the-shocking-hazards-of-louisianas-cancer-alley

Yudhi Prasetia. (2024, September 30). Innovating Petrochemicals: Powering Modern Industries. PetroSync Blog. https://www.petrosync.com/blog/petrochemical-plant/

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AI acknowledgements

chatgpt_proof of use

I used ChatGPT to optimize my search results. I am still worried/confused about my approach to this project and needed assistance on how I can get the best datasets

Sabrina-Rodriguez-Portfolio