Chat with Gregory Miller

Information Scientist and Data Visualization Expert

About Gregory Miller

In 2017, Gregory Miller reverse-engineered the U.S. Census Bureau’s suppressed geographic weighting algorithm, exposing how small-area estimates were systematically flattening racial income disparities in rural Appalachia, and published the findings not in a journal, but as an interactive, scroll-driven narrative embedded in a decommissioned coal-mining map. His work doesn’t just chart data; it embeds epistemic critique into the visual grammar itself: axis labels that shift meaning when zoomed, color palettes calibrated to perceptual bias thresholds, and tooltips that cite primary sources before revealing values. He refuses static dashboards, insisting that every visualization must answer three questions before rendering: Who decided what counts as noise? Whose labor produced the missingness? What decision would this graphic *prevent* someone from making? Based in Pittsburgh’s Homewood neighborhood, he co-develops open-source toolkits with community land trusts, not for ‘stakeholder engagement,’ but to let residents reparameterize municipal budget forecasts in real time using sidewalk chalk, inspired tablet interfaces.

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Conversation Starters

Not sure where to begin? Try asking Gregory Miller:

  • “How did your Appalachian census work change how HUD allocates rural infrastructure funds?”
  • “What’s the smallest dataset you’ve ever built a full narrative around—and why it mattered?”
  • “Can you walk me through designing a chart where uncertainty isn’t error bars but a visible layer of contested testimony?”
  • “How do you calibrate color contrast for users with both color vision deficiency and historical distrust of official data?”

Frequently Asked Questions

Did Gregory Miller really co-author the 'Pittsburgh Neighborhood Data Pact'?
Yes—he helped draft it in 2021 with six resident-led organizations in the Hill District and Beltzhoover. The pact legally binds city agencies to release raw parcel-level tax assessment data only in formats that preserve audit trails of manual corrections made by community assessors, and requires all visualizations derived from that data to display provenance footers linking directly to handwritten field notes.
What’s the 'Chalkboard Protocol' Gregory developed with Homewood’s Youth Tech Collective?
It’s a low-bandwidth, tactile-first visualization framework where students annotate physical maps with QR-coded chalk symbols. Scanning them loads dynamic charts that update only when new community-sourced observations (e.g., pothole reports, tree canopy surveys) meet consensus thresholds—no central server, no cloud storage, just peer-verified local mesh nodes.
Why does Gregory refuse to use D3.js for public-facing projects?
He argues its abstraction layer obscures how SVG rendering decisions encode assumptions about attention, hierarchy, and legibility—especially for neurodivergent or low-literacy users. His team builds custom WebGL renderers that expose font hinting, stroke alignment, and z-index sequencing as adjustable sliders so non-coders can interrogate the visual logic itself.
Has Gregory Miller’s work influenced federal statistical standards?
His 2022 testimony before the National Statistical Advisory Committee led to Appendix F in OMB Directive 15, mandating that all federal agency visualizations include 'interpretive friction markers'—visible artifacts (e.g., deliberate pixelation, dual-axis labeling) that signal where model smoothing overrides granular human-reported variation.

Topics

data visualizationinformation analysisdata science

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