This project investigates the demographic and income patterns driving urban displacement across a study population of 9.75 million — exposing the fault lines between who benefits from neighborhood change and who cannot afford to stay.
"Gentrification is not random. It follows the predictable geography of concentrated wealth, moving into neighborhoods where longtime residents cannot compete on income alone."
— Urban Change Lab · 2024↑ Add photo: A street-level view of a neighborhood in transition — older homes alongside new construction. Recommended: wide shot, natural light.
Gentrification reshapes cities neighborhood by neighborhood — changing who can afford to live where, and who gets left behind. This project draws on census data to quantify the demographic and income patterns that drive urban displacement.
By examining the age distribution of residents alongside income brackets across different household types, we can see how structural economic inequality maps directly onto housing pressure — and who, ultimately, pays the price of change.
Use the navigation above to explore the raw data, read the planned interactive narrative, and view our summary visualizations.
"The data doesn't lie: neighborhoods with the highest concentration of young renters and income inequality are the most vulnerable to rapid, irreversible change." — Urban Change Lab · Research Summary · 2024
Two aggregate datasets drawn from census records — who lives here, how old they are, and how much they earn. These are not raw counts; they are structured views that draw conclusions about displacement pressure.
↑ Add photo: Community members, a busy market street, or a local gathering place that represents the neighborhood's existing character.
Population by age cohort. Concentration in the 25–44 band — highlighted — is the primary demographic engine of neighborhood housing demand shifts.
| Age Group | Population | Share of Total | Displacement Signal |
|---|
Nonfamily households — typically single renters and non-married partners — earn a median income of $61,622 per year. In a housing market where married-couple families command $127,806, the same unit is affordable to one group and a burden to the other.
This income disparity is not a footnote in the data. It is the mechanism through which neighborhoods change — not through individual choices, but through structural economic pressure that makes displacement inevitable for lower-income residents.
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Income bracket distribution across four household types. The chasm between married-couple families and nonfamily households reveals where displacement pressure originates.
| Income Bracket | All Households | Families | Married-Couple | Nonfamily HH |
|---|
↑ Add photo: An aerial photograph or map graphic showing the study area. Works well with a subtle overlay highlighting neighborhood boundaries.
A planned scroll-driven, animated walkthrough of the data — guiding the user from broad demographic patterns down to the lived reality of displacement.
↑ Add photo: A portrait or candid of a resident, or a community scene that anchors the human element of the data story.
The interactive narrative is designed as a scroll-triggered sequence: the user moves through scenes one at a time, with charts building, numbers animating, and annotations appearing as they scroll. Every moment is intentional.
The goal is that by the end, the reader understands not just what the numbers say — but what they mean for the people who live inside them.
Planned Feature Full interactive build coming in the final milestone.
A large counter animates from 0 → 9,757,179 over 3 seconds. Three headline stats fade in below. A pulsing scroll arrow at the bottom invites the user downward.
Goal: Create a sense of scale before any analysis.
An age pyramid builds bar by bar. When 25–34 and 35–44 rows appear, they animate red with a tooltip: "These cohorts = 29.8% of residents and drive disproportionate housing demand."
Split-screen: married-couple incomes left, nonfamily right. Both animate simultaneously — but the gap widens as bars grow. A dashed median line appears at $90,845.
Heatmap appears dim. User hovers over cells to illuminate them — a sidebar panel updates with the selected income bracket, household type, percentage, and a displacement risk indicator.
Three key findings appear one at a time in large type. The city graphic from Scene 01 returns — now with red zones over highest-income areas. A final CTA links to full data tables.
Three visualizations synthesizing the core conclusions — age concentration, income distribution by household type, and a direct income comparison.
↑ Add photo: A contextual image representing research or data — could be a map pinboard, a community meeting, or an aerial view of a neighborhood.
Population share by age group. The 25–44 band accounts for nearly 30% of all residents — the primary demographic driver of housing demand and neighborhood change. Highlighted bars mark the highest-pressure cohorts.
Share of each household type at each income bracket. Darker red = higher concentration. The upper rows reveal where wealth clusters most — the structural engine of housing pressure and displacement.
A direct comparison of median and mean incomes across all four household categories. The gap between married-couple families and nonfamily households is the structural driver of who can afford to stay and who cannot.
Married-couple families earn a $66,184 median income premium over nonfamily households — creating entirely separate housing realities within the same city.
The 25–44 cohort is 29.8% of total population — simultaneously the group most likely to be both displacing and displaced.
Over 38% of all households earn $100K+, yet nonfamily households cluster overwhelmingly in brackets below $50K.
↑ Add photo: A wide city skyline or neighborhood panorama that gives visual weight to the conclusion of the analysis.