# CRDC Arrests API Demonstration App — Design Proposal

## 1. Overview

A web application hosted at `civilytics.org` that demonstrates the [CRDC School
Arrest Rate API](https://crdc-api.civilytics.org/api/v1/) by letting any visitor
explore school-based arrest estimates for any U.S. school district, with a strong
visual narrative built around Bayesian model comparisons.

The app is a **frontend-only static application** that calls the public read-only
API directly from the browser. No server-side code, no database, no auth — just
HTML/CSS/JS (with optional Docker container for local dev or self-hosting behind
a reverse proxy).

---

## 2. User Flow

```
┌─────────────┐    ┌──────────────────┐     ┌─────────────────┐
│   State     │ → │ District Search  │  →  │ Loading (anim)  │  →  ╔══════════════╗
│  selector   │    │ w/ suggestions   │      │ patience msg   │     ║ Chart panel    ║
└─────────────┘    └──────────────────┘     └─────────────────┘     ║ 6 panels       ║
                                                                    ║ see §4        ║
                                                                    ╚══════════════╝
```

### Step 1 — State prompt
- Full-screen landing card with Civilytics branding.
- Dropdown / typeahead for U.S. state (50 states + DC). Two-letter codes map to
  the API `state` parameter directly.
- On select → slide to Step 2.

### Step 2 — District search
- Search-as-you-type input, hitting `/api/v1/districts?q=<partial>&state=XX`.
- **Suggested districts** appear as a horizontal carousel of "interesting" picks:
  - Top N by total arrest count in the most recent wave (2021-22).
  - Fetched once per state via `limit=500` from `/estimates?state=XX&year=21-22`
    sorted client-side by `observed_arrests`.
  - Each suggestion card shows: district name, enrollment (in small), observed
    arrests. Clicking a suggestion jumps straight to loading + charts.
- Typing filters the list live; selecting an item from either source proceeds.

### Step 3 — Loading / patience animation
- A full-screen overlay with an animated visual that conveys "data is being
  fetched across multiple endpoints, please be patient."
- The animation should reflect the **multi-model comparison** theme: e.g., a grid
  of small histogram-like bars (representing posterior draws) that animate in and
  out row by row as each model's endpoint responds.
- Estimated total API calls per district lookup: ~12–15 (one per model × race ×
  sex combination, filtered to the selected district). The animation should scale
  visually with progress.

### Step 4 — Chart panel (6 charts in a 3×2 grid)

All charts follow Civilytics visual identity: warm paper background (`#FAF7F2`),
civic navy text (`#0E1A2B`), ember accent (`#C25311`). Data-viz palette uses the
supporting colors from `_tokens.scss` (teal, plum, moss, brass).

#### Row 1 — Observed & descriptive

**Chart 1: Arrests over time by CRDC wave (line chart)**
- X-axis: CRDC waves (`2015-16`, `2017-18`, `2021-22`).
- Y-axis: total observed arrest count.
- Line + points; each point labeled with the rate per 1,000 students.
- Source: `/estimates?leaid=XXXXX&year=...` across all three years (or a single
  call to `/districts/{leaid}` which returns all demographics for one district).

**Chart 2: Arrest rate by student group — most recent year (bar chart)**
- Bars for each race×sex combination in `AM/BL/HI/WH × F/M`.
- Y-axis: arrests per 1,000 students.
- Color-coded by the supporting palette; legend shows full labels.

**Chart 3: District vs. national — top student group (comparison chart)**
- Identify the district's highest-arrest-rate group.
- Side-by-side bars or a small multiples comparison against the corresponding
  **national** rate for that same group.
- National rates fetched via `/states?state=XX&race=&sex=&year=21-22` aggregated,
  or more precisely from the national summary (which may need to be computed as
  an aggregate across all states).

#### Row 2 — Bayesian model distribution comparisons

All four quadrants show results for the **selected district**, comparing:
- **Column A**: One-year models (`unified_m1_mod`, `unified_m2_mod`) vs.
- **Column B**: Three-year models (`unified_m3_mod` through `unified_m5_mod`).
- Within each column, rows differentiate baseline (no covariate) from covariate
  models.

**Chart 4: Predicted draws by year vs. observed (scatter / point-range)**
- For three-year models only: for each of the 3 waves, show the model's predicted
  median and 95% interval alongside the observed value as a separate marker.
- Layout: x-axis = wave; y-axis = arrest count; points dodge left (model) vs.
  right (observed).

**Chart 5: Observed rate per group against model distribution (density / ridge)**
- For each student group, show the observed rate and overlay the posterior draw
  distributions from all four model types as ridgeline or violin plots.
- Mirrors `wp_fig_group_density()` in the white paper.

**Chart 6: Probability district exceeds national rate per group (bar chart)**
- For each race×sex group, compute P(district rate > national rate) using the
  posterior draws from a chosen model (e.g., the default `unified_m2_mod`).
- Bars colored by threshold crossing; annotated with exact probability.

---

## 3. Data Sources & API Endpoints Used

| Purpose | Endpoint | Frequency |
|---|---|---|
| State list / validation | Hardcoded enum (`ALLOWED_STATES`) | Once |
| District search | `/api/v1/districts?q=&state=` | On keystroke (debounced) |
| "Interesting" suggestions | `/api/v1/estimates?state=XX&year=21-22` sorted by `observed_arrests desc` | Once per state |
| Single district, all demographics | `/api/v1/distimates/{leaid}` or `/estimates?leaid=` with year/model filters | ~6 calls × 3 years = 18+ |
| National comparison rates | `/api/v1/states?state=&race=&sex=&year=21-22` aggregated across states, OR a dedicated national endpoint if available | Once per group |
| Model metadata | `/api/v1/models` | Once (cache) |

**Key data structures returned by the API:**

The `/estimates/{leaid}` endpoint returns one row per `race × sex × year × model` with:
```json
{
  "leaid": "...",
  "lea_name": "...",
  "state": "TX",
  "race": "BL",
  "sex": "M",
  "year": "21-22",
  "model": "unified_m2_mod",
  "stu_enroll": 35963,
  "observed_arrests": 1,
  "rate_median": 0.42,
  "rate_lower": 0.18,
  "rate_upper": 0.91,
  "count_median": 15.3,
  "count_lower": 6.7,
  "count_upper": 28.9
}
```

The `/draws` endpoint returns a Hugging Face Parquet shard URL + DuckDB SQL for
bulk posterior draw access — useful if we need to compute custom quantities like
P(district > national) without round-tripping through multiple API calls. However,
for a demo app that runs in the browser, relying on 10 HF Parquet shards per model
is impractical (80 GB total). The summary endpoints (`/estimates`) provide enough
aggregated information for all six charts using only `rate_median`, `count_median`,
and interval bounds — **no raw draw access needed**.

---

## 4. Visual Design Language (from reference materials)

### Color palette
```css
:root {
  --cv-paper:   #FAF7F2;  /* warm paper background */
  --cv-ink:     #0E1A2B;  /* primary text — civic navy/black */
  --cv-navy-600:#22406A;  /* links, accents */
  --cv-accent:  #C25311;  /* ember — alerts, highlights, eyebrows */

  /* Data-viz supporting colors (from _tokens.scss) */
  --teal-600:   #1F6F70;
  --plum-600:   #6B3A5E;
  --moss-600:   #4A6B2F;
  --brass-600:  #B8751C;
}
```

### Typography
- **Display**: Libre Franklin / Inter — headings, stat callouts.
- **Body**: Inter — UI text, labels, captions.
- **Mono**: JetBrains Mono — code snippets, API URLs in footers.

### Chart patterns (from social media posts & white paper)
1. **Ridgeline density plots** for posterior draws (`geom_density_ridges`).
2. **Pointrange / error bar charts** comparing model intervals to observed values.
3. **Faceted small multiples** — always split by `covariate × time` (baseline vs.
   covariate; one-year vs. three-year).
4. **Transparent PNG export with watermark logo** in bottom-right corner.
5. **"Stat callout" hero numbers** for key metrics (e.g., total arrests, rate per 1k).

---

## 5. Technology Stack Recommendation

### Recommended: React + Vite + vanilla CSS (static site)

| Layer | Choice | Rationale |
|---|---|---|
| Framework | **React 19** (no framework overhead; Vite dev server) | Component model for charts, built-in state management via hooks |
| Build tool | **Vite** | Fast HMR, native ES modules, trivial static export (`npm run build`) |
| Charting | **`@visx/visx`** or plain SVG/CSS animations | Lightweight, no heavy deps; we control every pixel to match Civilytics style |
| HTTP client | `fetch` with AbortController + exponential backoff | No extra dependency; browser-native |
| Styling | Plain CSS custom properties (no Tailwind) | Zero-runtime, matches the existing SCSS token system exactly |
| Deployment | Static site on any host (GitHub Pages, Vercel, Netlify) or Docker nginx container | Self-hostable; no backend required |

#### Why not Shiny / Quarto?
The API returns JSON and we need a dynamic SPA with loading states, debounced search, and animated transitions. A static React app is the most natural fit. R/Shiny would add unnecessary server-side complexity for what is fundamentally a browser-based data visualization demo. The existing `crdc-arrests` project uses Quarto + ggplot2 for reports; this demo app is a different artifact with different requirements.

#### Why not Svelte or Vue?
React has the largest ecosystem, best tooling (Vite), and most team familiarity. For a 6-chart SPA it's more than sufficient without being overkill.

### Docker option
A minimal `nginx:alpine` container serves the built static files. ~20 MB image. Can be run on your self-hosted fleet (`efron`, `maxwell`, etc.) behind Caddy/Traefik with TLS via Let's Encrypt — consistent with how you host other civilytics.org services.

---

## 6. Build & Deployment Strategy

### Local development
```bash
npm install        # one-time: installs React, Vite, dev deps
npm run dev        # starts Vite on localhost:5173
# Edit src/App.jsx / src/components/*.jsx — hot reload
```

### Production build (static)
```bash
npm run build      # outputs dist/ with index.html + assets
# Upload dist/ to any static host, or:
docker build -t crdc-demo . && docker run -p 8080:80 crdc-demo
```

### Docker deployment (self-hosted on your fleet)
1. Build image locally or via CI: `docker buildx build --platform linux/amd64 -t registry.civilytics.org/crdc-demo:latest .`
2. Push to local registry on `maxwell`.
3. Deploy via docker-compose or a simple systemd service with nginx container + Caddy reverse proxy for TLS termination.

```yaml
# docker-compose.yml (minimal)
services:
  app:
    image: crdc-demo:latest
    ports: ["8080:80"]
    restart: unless-stopped
```

### CI/CD (optional, GitHub Actions)
- On push to `main`: run lint + build, publish Docker image to local registry.
- No automated deployment — you control when new versions go live on the fleet.

---

## 7. File Structure

```
crdc-demo/
├── public/                    # Static assets (logo, favicon)
│   └── civilytics-logo.svg
├── src/
│   ├── components/            # Reusable UI pieces
│   │   ├── StateSelector.jsx
│   │   ├── DistrictSearch.jsx
│   │   ├── LoadingAnimation.jsx
│   │   └── StatCallout.jsx
│   ├── charts/                # The 6 chart components
│   │   ├── ArrestsOverTime.jsx
│   │   ├── RateByGroupBar.jsx
│   │   ├── DistrictVsNational.jsx
│   │   ├── ModelDrawsComparison.jsx
│   │   ├── ObservedRateDensity.jsx
│   │   └── ExceedanceProbability.jsx
│   ├── hooks/
│   │   ├── useApi.js          # fetch wrapper with retry/backoff
│   │   └── useDistrictData.js # orchestrates all 6 charts' data needs
│   ├── styles/
│   │   ├── tokens.css         # Civilytics design tokens
│   │   └── main.css
│   ├── App.jsx                # Main router: state → search → loading → charts
│   └── main.jsx               # React entry point
├── Dockerfile
├── vite.config.js
└── package.json
```

---

## 8. Key Implementation Notes & Risks

### Risk 1 — API response time for multi-model data
Fetching all models × race×sex combinations for a single district requires ~40
individual `/estimates` calls (5 unified + 5 stratified models × 8 groups). Each
API call may take 200–500ms. **Mitigation**: use `Promise.allSettled()` to fire
all requests in parallel; show progress as batches resolve. The loading animation
should reflect this batching visually (e.g., rows of bars filling left-to-right).

### Risk 2 — National rate computation
The `/states` endpoint returns per-state aggregates, not a national total. To get
national arrest rates by student group for Chart 3 and Chart 6:
- **Option A**: Fetch all states (`limit=100`, iterate through ~50 pages) and sum.
  Too slow for a browser app.
- **Option B**: Add a `/api/v1/national` endpoint to the API (server-side aggregate).
  Requires modifying `crdc-arrests/api/` — quick R/SQL change but needs your approval.
- **Recommended short-term**: Cache national rates as a static JSON file generated
  during data release and committed alongside this app. The white paper already
  computes these values; we can extract them into a small fixture.

### Risk 3 — Chart complexity (Chart 5 & 6)
Charts 5 (density/ridge) and 6 (exceedance probability) require either raw posterior
draws or sufficient summary statistics to reconstruct distributions. The `/estimates`
endpoint provides `count_median`, `count_lower`, `count_upper` but not the full draw
distribution. **Mitigation**: Use interval bounds + median as a proxy for ridge plots
(showing just the 95% HPD region), and compute exceedance probability using a normal
approximation to the posterior (mean=median, sd derived from interval width). This is
a reasonable approximation for demonstration purposes but should be clearly labeled.

### Risk 4 — Mobile responsiveness
The social media figures are desktop-first PNGs. The web app must work on mobile:
- Use CSS Grid with `auto-fit` for the chart panel (1 column on mobile, 3 on desktop).
- Make the loading animation responsive.
- Ensure touch targets in district search are ≥44px.

---

## 9. Open Questions for You

Before implementation begins, I need your input on these decisions:

### Q1 — National rates source
Chart 3 and Chart 6 compare a district's arrest rate to the **national average**
for each student group. The API doesn't have a `/national` endpoint. How should we handle this?

- **(A)** Cache national rates as a static JSON fixture (generated from the white paper / release data). [Recommended — simplest, no API changes]
- **(B)** Add a `/api/v1/national` aggregate endpoint to `crdc-arrests/api/` and have the demo call it live.
- **(C)** Approximate by fetching the top 5 largest states' rates weighted by enrollment (fast but imprecise).

### Q2 — Model selection for distribution charts
Charts 4–6 show results from multiple model types (one-year, three-year, baseline, covariate). Should users be able to toggle which models are displayed, or should we always show all four quadrants as specified in your requirements?

- **(A)** Always show all four quadrants (1yr no-cov, 1yr cov, 3yr no-cov, 3yr cov) — matches the white paper's `wp_fig_district_intervals()` layout. [Recommended]
- **(B)** Let users toggle between unified vs. stratified models via a dropdown.
- **(C)** Default to showing only the recommended model (`unified_m2_mod`) with an "advanced" expando for all four.

### Q3 — Loading animation style
The patience animation should convey that data is being fetched across multiple endpoints/models. What visual approach do you prefer?

- **(A)** Animated grid of histogram bars (one per model×group) that fill sequentially as API calls resolve, with a counter showing "X of ~40 datasets loaded". [Recommended — matches the Bayesian posterior theme]
- **(B)** A simple spinner + text message ("Fetching 12 model results from the CRDC API…").
- **(C)** An abstract animation (e.g., particles converging) that loops while loading, with no per-request feedback.

### Q4 — Deployment target
Where should this be hosted? Your infrastructure is self-hosted (`efron`, `maxwell`, etc.), but you also mentioned civilytics.org. Which deployment approach do you want me to implement first?

- **(A)** Docker container ready for your fleet (nginx + static files) — I'll write the Dockerfile and docker-compose.yml, deploy on a dev host. [Recommended]
- **(B)** Static site optimized for GitHub Pages / Vercel with CI/CD via GitHub Actions.
- **(C)** Both A and B (Docker for self-hosting, plus GH Pages config).

---

## 10. Next Steps

Once you answer the four questions above, I'll begin implementation:

1. **Week 1**: Scaffold project (Vite + React), implement design tokens & typography, build state selector + district search with "interesting" suggestions.
2. **Week 2**: Implement loading animation; fetch all model data in parallel; build Charts 1–3 (observed/descriptive).
3. **Week 3**: Build Charts 4–6 (model comparison distributions); wire up national rate fixture if Q1=A.
4. **Week 4**: Polish transitions, mobile responsiveness, Dockerfile + deployment configs; write README with usage/deployment docs.

The app will be fully self-contained — no external dependencies beyond the public CRDC API and optionally a cached JSON fixture for national rates. All code follows Civilytics visual identity as documented in `theme/_tokens.scss` and demonstrated in `social_media_posts.md`.