intent driven development
Week 2: Data Analysis
With the literature synthesized, Sarah moves to data analysis. This phase processes 30 years of temperature data to generate statistical findings.
The Analysis Pattern
Data analysis uses the analysis pattern — processing source data to extract findings:
┌─────────────────────────────────────────────────────────────┐
│ Origin Data Files │
├─────────────┬─────────────┬─────────────┬─────────────────────┤
│ station-01 │ station-02 │ station-03 │ ... │
│ .csv │ .csv │ .csv │ (12 stations) │
└──────┬──────┴──────┬──────┴──────┬──────┴──────────┬──────────┘
│ │ │ │
└─────────────┴─────────────┴─────────────────┘
│
▼
┌─────────────────────────┐
│ Research Spec │
│ origin: │
│ - data/temp/*.csv │
└────────────┬────────────┘
│
▼
┌─────────────────────────┐
│ Statistical Analysis │
│ (Findings & Figures) │
└─────────────────────────┘
The origin: field tells the agent what data to analyze. Changes to origin files trigger drift detection.
Data Overview
Sarah’s temperature data spans 12 Arctic monitoring stations:
data/temperature/
├── barrow-alaska.csv # 1994-2024, 10,957 daily readings
├── resolute-canada.csv # 1994-2024, 10,957 daily readings
├── longyearbyen-svalbard.csv # 1994-2024, 10,957 daily readings
├── tiksi-russia.csv # 1994-2024, 10,957 daily readings
├── alert-canada.csv # 1994-2024, 10,957 daily readings
├── thule-greenland.csv # 1994-2024, 10,957 daily readings
├── nord-greenland.csv # 1994-2024, 10,957 daily readings
├── dikson-russia.csv # 1994-2024, 10,957 daily readings
├── eureka-canada.csv # 1994-2024, 10,957 daily readings
├── jan-mayen-norway.csv # 1994-2024, 10,957 daily readings
├── bear-island-norway.csv # 1994-2024, 10,957 daily readings
└── ny-alesund-svalbard.csv # 1994-2024, 10,957 daily readings
Each CSV contains:
date,temp_c,temp_anomaly,quality_flag
1994-01-01,-28.3,-2.1,A
1994-01-02,-29.1,-2.9,A
...
Creating the Analysis Spec
Sarah creates a research spec for statistical analysis:
chant add "Analyze Arctic temperature acceleration" --type research
She edits the spec with detailed methodology:
File: .chant/specs/2026-01-16-001-ana.md
---
type: research
status: pending
prompt: research-analysis
depends_on:
- 2026-01-15-001-lit
origin:
- data/temperature/*.csv
informed_by:
- analysis/literature-review.md
- analysis/research-gaps.md
target_files:
- analysis/temperature-findings.md
- analysis/figures/trend-acceleration.png
- analysis/figures/seasonal-patterns.png
- analysis/figures/station-comparison.png
---
# Analyze Arctic temperature acceleration
## Problem
30 years of temperature data from 12 Arctic stations needs statistical analysis
to answer the thesis question: Have Arctic temperature increases accelerated
since 1995?
This analysis builds on the literature review findings that identified gaps in:
- Post-2015 acceleration quantification
- Station-level variability analysis
- Seasonal acceleration patterns
## Research Questions
- [ ] What is the overall warming trend (1994-2024)?
- [ ] Is there evidence of acceleration (nonlinear trend)?
- [ ] Do stations show consistent or divergent patterns?
- [ ] How do seasonal patterns differ?
- [ ] When did acceleration begin (if present)?
## Methodology
### Data Preparation
1. Load all CSV files from `data/temperature/`
2. Handle quality flags (exclude 'X' flagged observations)
3. Convert to annual and seasonal means
4. Calculate anomalies from 1994-2000 baseline
### Trend Analysis
1. Linear regression for overall trend
2. Quadratic regression to test for acceleration
3. Piecewise regression to find breakpoints
4. Mann-Kendall test for monotonic trends
### Station Comparison
1. Calculate trends for each station
2. Test for homogeneity across stations
3. Identify outlier stations
4. Map spatial patterns
### Seasonal Decomposition
1. Separate annual means into DJF, MAM, JJA, SON
2. Calculate trends by season
3. Test for acceleration by season
4. Identify which seasons drive overall acceleration
## Acceptance Criteria
- [ ] All 12 stations analyzed with consistent methodology
- [ ] Quality-flagged data excluded
- [ ] Linear and quadratic trend estimates with confidence intervals
- [ ] Breakpoint analysis with statistical tests
- [ ] Station-level trends with heterogeneity test
- [ ] Seasonal trends with acceleration testing
- [ ] Three figures generated (trend, seasonal, station comparison)
- [ ] temperature-findings.md with all results and statistics
Running the Analysis
Sarah executes the spec:
chant work 001-ana
The agent:
- Reads all CSV files via
origin: - Reads the literature review via
informed_by:for context - Runs the statistical methodology
- Generates findings and figures
Agent Output
After execution, Sarah finds new analysis files:
File: analysis/temperature-findings.md (generated)
# Arctic Temperature Acceleration Analysis
## Executive Summary
Analysis of 12 Arctic monitoring stations (1994-2024) confirms acceleration:
- **Overall warming:** +0.67°C per decade (p < 0.001)
- **Acceleration detected:** Quadratic term significant (p = 0.003)
- **Breakpoint:** Acceleration begins ~2005 (95% CI: 2003-2008)
- **Seasonal pattern:** Winter (DJF) acceleration 2x stronger than summer
## 1. Data Summary
| Metric | Value |
|--------|-------|
| Stations analyzed | 12 |
| Total observations | 131,484 |
| Quality-excluded | 1,247 (0.95%) |
| Period | 1994-01-01 to 2024-12-31 |
## 2. Overall Trend
### Linear Model
Temperature = -0.89 + 0.067 × Year
| Parameter | Estimate | SE | 95% CI | p-value |
|-----------|----------|-----|--------|---------|
| Intercept | -0.89°C | 0.12 | [-1.12, -0.66] | < 0.001 |
| Trend | +0.067°C/yr | 0.008 | [0.051, 0.083] | < 0.001 |
Interpretation: +0.67°C warming per decade.
### Quadratic Model (Acceleration Test)
Temperature = -0.72 + 0.042 × Year + 0.0018 × Year²
| Parameter | Estimate | SE | p-value |
|-----------|----------|-----|---------|
| Quadratic term | +0.0018 | 0.0006 | 0.003 |
Interpretation: Statistically significant acceleration. Rate of warming is increasing.
### Breakpoint Analysis
Piecewise regression identified a breakpoint at **2005** (95% CI: 2003-2008):
| Period | Trend (°C/decade) | SE |
|--------|-------------------|-----|
| 1994-2005 | +0.42 | 0.11 |
| 2005-2024 | +0.89 | 0.09 |
Warming rate doubled after 2005.
## 3. Station Comparison
| Station | Trend (°C/decade) | Rank |
|---------|-------------------|------|
| Tiksi, Russia | +0.92 | 1 |
| Longyearbyen | +0.84 | 2 |
| Barrow, Alaska | +0.78 | 3 |
| Resolute, Canada | +0.72 | 4 |
| Ny-Alesund | +0.68 | 5 |
| Thule, Greenland | +0.65 | 6 |
| Nord, Greenland | +0.61 | 7 |
| Dikson, Russia | +0.59 | 8 |
| Alert, Canada | +0.58 | 9 |
| Eureka, Canada | +0.55 | 10 |
| Jan Mayen, Norway | +0.51 | 11 |
| Bear Island, Norway | +0.44 | 12 |
Heterogeneity test: Q = 45.2, p < 0.001 (significant station variation)
## 4. Seasonal Patterns
| Season | Trend (°C/decade) | Acceleration (°C/decade²) | p-value |
|--------|-------------------|---------------------------|---------|
| Winter (DJF) | +1.12 | +0.048 | 0.001 |
| Spring (MAM) | +0.71 | +0.022 | 0.018 |
| Summer (JJA) | +0.34 | +0.008 | 0.241 |
| Autumn (SON) | +0.52 | +0.015 | 0.067 |
Winter acceleration is 2x the overall average. Summer shows no significant acceleration.
## 5. Figures
- `figures/trend-acceleration.png` — Time series with linear and quadratic fits
- `figures/seasonal-patterns.png` — Seasonal decomposition with trend lines
- `figures/station-comparison.png` — Map of station-level trends
## 6. Conclusions
1. Arctic warming is accelerating (quadratic trend significant)
2. Acceleration began around 2005 (±3 years)
3. Post-2005 warming rate (0.89°C/decade) is 2x pre-2005 rate
4. Winter drives acceleration; summer shows no acceleration
5. Stations vary significantly, with Siberian stations warming fastest
## 7. Statistical Notes
- All tests use α = 0.05
- Confidence intervals are 95%
- Autocorrelation addressed via Newey-West standard errors
- Missing data handled via pairwise deletion
Verifying the Analysis
Sarah checks the completed spec:
chant show 001-ana
Spec: 2026-01-16-001-ana
Type: research
Status: completed
Depends on: 2026-01-15-001-lit (completed)
Origin: data/temperature/*.csv (12 files)
Informed by: analysis/literature-review.md, analysis/research-gaps.md
Research Questions:
[x] What is the overall warming trend (1994-2024)?
[x] Is there evidence of acceleration (nonlinear trend)?
[x] Do stations show consistent or divergent patterns?
[x] How do seasonal patterns differ?
[x] When did acceleration begin (if present)?
Acceptance Criteria:
[x] All 12 stations analyzed with consistent methodology
[x] Quality-flagged data excluded
[x] Linear and quadratic trend estimates with confidence intervals
[x] Breakpoint analysis with statistical tests
[x] Station-level trends with heterogeneity test
[x] Seasonal trends with acceleration testing
[x] Three figures generated (trend, seasonal, station comparison)
[x] temperature-findings.md with all results and statistics
Origin Tracking
The spec records exactly which data files were analyzed:
origin:
- data/temperature/barrow-alaska.csv (sha256: a1b2c3...)
- data/temperature/resolute-canada.csv (sha256: d4e5f6...)
# ... 10 more files
If any of these files change, drift detection will flag the analysis as stale.
Connecting Literature and Data
The informed_by: field ensured the analysis addressed the gaps identified in the literature review:
| Gap Identified | How Analysis Addressed |
|---|---|
| Post-2015 acceleration | Extended analysis to 2024 |
| Station-level variability | Analyzed 12 stations individually |
| Seasonal patterns | Decomposed by DJF, MAM, JJA, SON |
The depends_on: field ensured the literature review completed before analysis began.
What’s Next
With individual analyses complete, Sarah coordinates a multi-step pipeline:
Pipeline Coordination — Using driver specs to coordinate data cleaning, analysis, and visualization