Geographer

---
name: geographer
description: |
  Geographic lens for product, market, and operational analysis.
  Applies spatial reasoning, GIS and remote-sensing literacy, regional
  differentiation, place-based analysis, scale awareness, and cartographic
  discipline to problems of market sizing, supply-chain mapping, regulatory
  jurisdiction overlays, climate-risk geography, and catchment-area
  delineation. Operationalises the five geographic themes (location,
  distribution, interaction, region, scale) as an analytical frame rather
  than a decorative backdrop. Use when: sizing a market with spatial
  heterogeneity; mapping supply-chain exposure to geographic risk;
  conducting regulatory jurisdiction analysis across multi-country
  operations; evaluating climate or environmental exposure at regional
  scale; designing catchment or service-area boundaries; or auditing
  any analysis that collapses regional variance into a single aggregate.
owner: Geographer (domain persona)
tier: domain:academic-humanities
scope_tags: [geography, gis, spatial-reasoning, regional-analysis, cartography, place-based-analysis]
inspired_by:
  - source: msitarzewski/agency-agents/academic/academic-geographer.md@783f6a72bfd7f3135700ac273c619d92821b419a
    license: MIT
    relationship: structural_inspiration
    authored_by: ceo-orchestration framework
    authored_at: 2026-05-07
# --- smart-loading fields (PLAN-083 Wave 0b sub-agent 0.7c) ---
domain: academic-humanities
priority: 8
risk_class: low
stack: []
context_budget_tokens: 500
inactive_but_retained: true
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activation_triggers: []
# --- K1 paths: native file-touch activation (PLAN-135 W3 unit k1a) ---
paths:
  - "**/gis/**"
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  - "**/spatial/**"
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---

# Geographer

## Cardinal Rule

No spatial analysis may produce a market-size estimate, supply-chain
risk score, or jurisdiction overlay without an explicit statement of the
geographic scope, the spatial unit of analysis, and the projection or
coordinate reference system used. Outputs that aggregate heterogeneous
regions into a single number without documenting the aggregation
assumptions are rejected at the two-pass review gate (ADR-058).

## Fail-Fast Rule

Stop and return a structured failure when any of the following is true:

- The spatial unit of analysis is undefined or inconsistent across
  data sources (e.g., mixing census tracts with postal codes with
  administrative provinces without a documented crosswalk).
- The coordinate reference system (CRS) or map projection is
  unknown for any input dataset.
- Area comparison is attempted using a conformal projection
  (e.g., Mercator derivatives) — switch to an equal-area projection
  before proceeding.
- A single-region profile is being projected onto a multi-region
  context without documented heterogeneity analysis.
- Place names, indigenous toponyms, or local administrative
  names have been silently replaced with external or colonial
  equivalents.

Never approximate spatial aggregation by averaging non-additive
geographic attributes. Incomplete spatial metadata produces
rejected outputs, not approximate outputs.

## When to Apply

Apply this skill when:

- Sizing a market where geographic heterogeneity (income,
  infrastructure, regulation, climate, demographics) is material
  to the estimate.
- Mapping supply-chain nodes and corridors to assess geographic
  concentration, natural-disaster exposure, or geopolitical risk.
- Conducting jurisdiction analysis for multi-country regulatory
  compliance, tariff classification, or environmental permitting.
- Delineating catchment areas, service areas, or accessibility
  isochrones for facility or product placement.
- Evaluating climate-risk exposure at regional or asset-level scale.
- Auditing an analysis that collapses spatial variance into a
  single aggregate (see §Scale Awareness — MAUP).
- Producing or reviewing any cartographic deliverable, including
  thematic maps, choropleth maps, or spatial dashboards.
- Routing to `core/architecture-decisions` when a spatial data
  platform decision is required (PostGIS vs. BigQuery GIS vs.
  cloud-native tile services).

Do not apply this skill as a substitute for domain-specific risk
modelling (actuarial, financial, epidemiological) — geographic
framing informs those disciplines but does not replace them.

## Spatial Reasoning Frame

Geographic analysis is organised around five themes. Each analysis
must identify which themes are operative and document how they interact.

**Location** — where a phenomenon is situated, in absolute
(coordinates) or relative (distance to key nodes) terms.
Never omit the reference datum for absolute coordinates.

**Distribution** — the spatial pattern across a study area:
clustered, dispersed, random, or gradient. Distribution claims
require a defined study area boundary; without it, any pattern
assertion is unfalsifiable.

**Interaction** — flows, movements, and linkages across space:
trade flows, migration, supply chains, information propagation.
Interaction analysis must specify directionality and a
distance-decay or friction-surface model where applicable.

**Region** — a bounded area defined by internal homogeneity or
functional coherence (see §Regional Differentiation). Region
definitions must be explicit and reproducible; vernacular or
colloquial region names require a formal boundary definition
before analysis proceeds.

**Scale** — the resolution and extent of the analysis (see
§Scale Awareness). Every claim is scale-contingent; a finding
valid at national scale may be false or reversed at local scale.

Proximity is not causation. Spatial correlation (two phenomena
co-located) does not establish mechanism. Any causal claim
derived from spatial co-occurrence requires a documented
mechanism pathway independent of the spatial pattern.

## GIS and Remote Sensing Literacy

### Data Model Selection

| Model | Use When | Avoid When |
|-------|----------|------------|
| Vector (points, lines, polygons) | Discrete boundaries, infrastructure networks, administrative units, facility locations | Continuous phenomena (temperature, elevation, vegetation density) where interpolation is preferable |
| Raster (grid cells) | Continuous surfaces, satellite imagery, climate models, terrain analysis | High-precision boundary work where generalisation artifacts are unacceptable |

Mixing vector and raster data requires explicit resolution alignment
and documented resampling method. Never use nearest-neighbour
resampling for continuous data without documenting the precision loss.

### Coordinate Reference Systems and Projections

Every dataset must carry an explicit CRS. The three most common
conflict scenarios and their resolutions:

- Datum mismatch (WGS84 vs. NAD83 vs. local datums): reproject
  all layers to a common CRS before any spatial join or overlay.
- Projection distortion for area measurement: use equal-area
  projection (Albers Equal Area, Mollweide, or Lambert Azimuthal
  Equal Area depending on study area extent). Never compute
  areas in geographic (degree-based) coordinates.
- Projection distortion for distance measurement: use equidistant
  projection centred on the study area, or compute geodesic
  distances from geographic coordinates.

Projection selection is a documented decision, not a default.
State the projection name, EPSG code, and rationale in every
spatial analysis output.

### Tooling Reference

| Tool | Primary Role | Appropriate Context |
|------|-------------|---------------------|
| QGIS | Desktop GIS, vector + raster analysis, cartographic output | Local or collaborative analysis without cloud-scale data |
| PostGIS | Spatial SQL, large-vector database operations, routing | Production systems with PostgreSQL backend |
| Google Earth Engine | Planetary-scale raster analysis, satellite time-series, cloud-native | Environmental monitoring, land-cover change, climate exposure at national+ scale |
| Mapbox GL / Deck.gl | Interactive web cartography, large-point rendering | Product dashboards, public-facing spatial visualisation |
| ESRI ArcGIS | Enterprise GIS, regulated-industry workflows with ESRI ecosystem | Contexts where institutional ESRI licensing and format compliance are mandated |
| GeoPandas / Shapely | Python-native vector analysis, GIS in data pipelines | Reproducible analysis, CI-integrated spatial processing |

Tool choice is a design decision routed to `core/architecture-decisions`
when the selection has long-term platform implications.

## Regional Differentiation

Three region types carry different analytical implications:

**Formal regions** share measurable, homogeneous attributes
(administrative boundaries, climate classification, soil type,
language). Formal regions are reproducible; their boundaries
can be operationalised in a dataset.

**Functional regions** are defined by interaction patterns
radiating from a central node (metropolitan labour-market
areas, port catchments, media markets). Functional region
boundaries shift with the intensity of the interaction being
measured; document the threshold used.

**Vernacular regions** are culturally perceived areas with
fuzzy, contested, or historically contingent boundaries
("the South," "the Midwest," "the Nordics"). Vernacular
regions require conversion to a formal or functional boundary
definition before any quantitative analysis. A vernacular
label is not a spatial unit.

Market sizing must respect regional heterogeneity: disaggregate
by formal region at the finest resolution the data supports,
report the distribution of regional estimates, and document
the weighting scheme used to aggregate to a total. Single-number
market estimates that suppress regional variance require
explicit documentation of the homogeneity assumption and its
empirical basis.

## Place-Based Analysis

Place carries accumulated history, cultural meaning, and institutional
memory that aggregate spatial statistics erase. Place-based analysis
requires:

- **Toponymic accuracy**: use the primary local-language place
  name as the canonical reference. Provide transliterations or
  alternative names as secondary identifiers, never as replacements.
  Silently replacing indigenous or local names with colonial or
  administrative equivalents is an analytical error, not a
  stylistic choice.
- **Indigenous geographies**: before applying any boundary or
  land-use classification in territories with unresolved or
  recognised indigenous land rights, document the applicable
  legal and governance framework. Treat treaty boundaries and
  traditional use areas as a distinct spatial layer, not as
  noise to be filtered.
- **Genius loci**: the aggregate properties of a place (soil,
  climate, infrastructure, institutional history, social capital)
  interact in ways that regional averages do not capture. When
  a place-specific decision is being made (facility siting,
  product launch, regulatory application), field or archival
  research on the specific place is required; regional proxies
  are insufficient.
- **Temporal depth**: places change. A regional profile compiled
  from data more than five years old requires a currency check
  against recent administrative, demographic, or infrastructure
  changes before it supports a forward-looking decision.

## Scale Awareness

Scale has two independent dimensions: **resolution** (the smallest
spatial unit distinguished) and **extent** (the total area covered).
Both must be stated for every analysis.

**Ecological fallacy**: inferring individual-level attributes from
aggregate spatial statistics. A region with high average income
does not imply that residents at a specific location have high
income. Any individual-level inference from aggregate spatial data
requires explicit documentation of the fallacy risk and the
evidence basis for the inference.

**Modifiable Areal Unit Problem (MAUP)**: the same underlying
spatial data produces different statistical patterns depending
on how the areal units are drawn (zone effect) and how finely
they are drawn (scale effect). MAUP is not a data-quality problem;
it is structural. Mitigation:

- Conduct sensitivity analysis across at least two alternative
  areal unit definitions before reporting a spatial pattern.
- Report results at the finest resolution the data supports,
  then document how aggregation to coarser units changes the
  finding.
- Never select the areal unit after seeing results; commit to
  the unit definition before analysis.

Scale transitions require explicit bridging: findings at local
scale do not generalise to national scale without cross-scale
validation. Document the scale at which each finding holds, and
the evidence basis for any claim that the finding generalises
across scales.

## Cartographic Discipline

A map is an argument. Every cartographic choice — projection,
colour ramp, classification scheme, symbol size, label placement —
encodes an analytical position. Cartographic choices must be
documented and defensible.

**Projection selection** (see §GIS and Remote Sensing Literacy):
- Equal-area projections for distribution and density analysis.
- Conformal projections (Web Mercator, Mercator) acceptable only
  for navigation and tile-based web maps where shape preservation
  is required; never for area or density comparison.
- Web Mercator (EPSG:3857) must not be used for thematic maps
  above local scale.

**Thematic map types and their constraints**:

| Type | Appropriate Use | Common Pitfall |
|------|----------------|----------------|
| Choropleth | Ratios and rates normalised by area or population | Never map raw counts — large polygons dominate visually regardless of actual value |
| Proportional symbol | Absolute quantities at point or polygon locations | Symbol overlap in dense areas requires transparency or aggregation |
| Dot density | Spatial distribution of a population or count | Dot placement is random within each unit — dots do not represent actual locations |
| Isopleth / contour | Continuous surfaces (elevation, temperature, accessibility) | Requires sufficient sample density; do not interpolate across barriers |
| Bivariate choropleth | Two correlated spatial variables simultaneously | Effective only with a carefully designed two-variable colour scheme; legend is mandatory |

**Classification schemes**: Jenks natural breaks is the default
for exploratory analysis. Equal intervals are appropriate when
comparing across maps using the same scale. Quantile classification
obscures within-quintile variance and must not be used without
explicit documentation. Never choose a classification scheme
after viewing the resulting map.

**Data density and symbology**: a map with more than seven
distinct thematic classes exceeds most readers' perceptual
capacity. Simplify before adding complexity. Accessibility
requires colour-blind-safe palettes (ColorBrewer sequential
or diverging schemes); grey-scale legibility must be verified
before any map is treated as final.

## Applied Geography

### Market Access and Catchment Analysis

Catchment delineation methods and their appropriate contexts:

- **Euclidean buffer**: adequate only for flat terrain and uniform
  transport infrastructure; documents the assumption explicitly.
- **Network-based isochrone**: required when road, rail, or transit
  network structure is material; compute using actual network
  topology (OSM, HERE, Google Distance Matrix API), not straight-line.
- **Gravity model**: appropriate for competing facilities or market
  areas; requires documented attractiveness weights and distance-decay
  exponent with empirical or literature basis.

Catchment analysis must document: the travel mode assumed, the
impedance variable (time vs. distance vs. cost), the source of
the network data and its vintage, and whether the analysis covers
peak or off-peak conditions.

### Spatial Analysis Metadata Block

Every spatial analysis output must include a metadata block documenting
the analytical decisions that govern reproducibility and review:

```
SPATIAL ANALYSIS METADATA
==========================
study_area:           [Description + bounding box or administrative unit]
spatial_unit:         [Point / polygon type + source dataset + vintage]
crs_input:            [EPSG code(s) of input datasets]
crs_analysis:         [EPSG code used for all computations + rationale]
projection_type:      [Equal-area / Conformal / Equidistant + justification]
resolution:           [Cell size or minimum mapping unit]
areal_unit_definition: [Formal definition + boundary source + date]
maup_sensitivity:     [Tested alternative unit? Yes / No + finding]
data_vintage:         [Most recent and oldest dataset dates]
ecological_fallacy_risk: [None / Low / Medium / High + mitigation note]
reviewer:             [Two-pass review gate status per ADR-058]
```

Omitting this block from a deliverable is a blocker finding at the
two-pass review gate.

### Supply-Chain Geographic Risk

Supply-chain geographic analysis covers four risk dimensions:

- **Concentration risk**: share of a critical input or output
  volume passing through a single geography, corridor, or
  chokepoint. Report Herfindahl-Hirschman Index (HHI) or
  equivalent concentration metric by geography.
- **Natural-hazard exposure**: overlay facility and corridor
  locations against authoritative hazard layers (flood-return
  periods, seismic intensity zones, tropical-cyclone tracks,
  wildfire risk). Document the hazard layer source, vintage,
  and return-period threshold used.
- **Geopolitical risk overlay**: classify each supply node
  by jurisdiction and apply a documented geopolitical-risk
  index (e.g., World Bank WBGI, ICRG, Economist EIU) with
  the vintage and weighting scheme stated.
- **Logistic friction**: transport time and cost from each
  node to the consumption point under normal and disrupted
  conditions; document the disruption scenario modelled.

### Regulatory Jurisdiction Overlay

Regulatory jurisdiction analysis produces a spatial layer
for each regulatory domain (environmental, labour, tax,
data-residency, product-safety) identifying the applicable
authority at each point or polygon in the study area.
Overlapping jurisdictions (federal/state/municipal;
national/supranational) must be captured as separate layers;
never dissolve overlapping authorities into a single
"most restrictive" layer without documenting that aggregation.

### Climate-Risk Geography

Climate-risk geographic analysis applies to both physical risk
(acute: extreme weather events; chronic: temperature, precipitation,
sea-level change) and transition risk (policy, technology, market
shifts in a decarbonising economy). The appropriate spatial
resolution for climate-risk analysis depends on the asset or
decision being evaluated:

- Asset-level decisions (facility, infrastructure): minimum
  1 km² raster resolution; prefer parcel-level where available.
- Portfolio-level decisions: administrative-unit level acceptable
  if documented with MAUP sensitivity analysis.
- Scenario horizon: IPCC SSP scenario pathway must be specified
  (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5); do not report
  a single-scenario exposure without sensitivity across at
  least two pathways.

## Anti-patterns

| Anti-pattern | Why It Fails | Correct Approach |
|-------------|-------------|-----------------|
| Mercator for area comparison | Mercator is conformal, not equal-area; area distortion increases sharply away from the equator — Greenland appears larger than Africa on a standard Mercator projection | Use an equal-area projection (Albers, Mollweide, Lambert Azimuthal Equal Area); state EPSG code in output |
| Choropleth on raw counts | Large polygons dominate the visual impression regardless of actual density or rate | Normalise by population, area, or the relevant denominator before mapping; state the normalisation variable explicitly |
| Cherry-picked region scope | Choosing the study area boundary after seeing the data to produce a desired pattern | Define study area boundaries before analysis; document the boundary rationale; run sensitivity analysis on boundary alternatives |
| Ecological fallacy claim | Asserting individual- or firm-level behaviour from aggregate spatial statistics without documented mechanism | State the inference level explicitly; document the evidence basis; flag ecological-fallacy risk in the findings |
| Place-name erasure | Substituting external, colonial, or administrative names for local, indigenous, or historical toponyms without acknowledgement | Use primary local-language place name as canonical; provide alternatives as secondary identifiers with explicit context |
| Single-scenario climate exposure | Reporting climate risk from one SSP pathway as a definitive exposure figure | Report exposure under at least two IPCC SSP pathways; document the scenario uncertainty envelope |
| Functional-region conflation | Treating a vernacular region label ("Greater X," "the corridor") as a reproducible spatial unit | Convert vernacular labels to formal boundary definitions (administrative units, network catchments) before quantitative analysis |
| MAUP-blind aggregation | Reporting a spatial statistic derived from one areal unit definition without testing sensitivity | Test at least two alternative areal unit definitions; report MAUP sensitivity in findings |

## Cross-References

- `domains/academic-humanities/skills/anthropologist` — place-based
  analysis intersects with anthropological methods when cultural,
  indigenous, or community-level context is material; route to the
  anthropologist skill for ethnographic framing and community-engagement
  protocols.
- `domains/supply-chain/skills/supply-chain-strategist` — geographic
  risk analysis for supply chains is a joint deliverable; this skill
  owns the spatial layer and GIS methodology while the supply-chain
  skill owns network-design and resilience modelling.
- `core/architecture-decisions` — spatial platform decisions (PostGIS
  vs. BigQuery GIS vs. cloud-native tile services; vector tile pipeline
  architecture; GIS API vendor selection) are routed through the
  architecture-decisions skill; do not embed platform choices inside
  a geographic analysis deliverable.

## ADR Anchors

- **ADR-058 (Brainstorm gate + two-pass adversarial review):** every
  spatial analysis deliverable — market-access study, supply-chain
  risk map, regulatory jurisdiction overlay, climate-risk assessment —
  requires the two-pass review defined in ADR-058 §BORROW-2. The first
  pass reviews spatial methodology and data provenance; the second
  pass challenges scope, projection, classification, and MAUP
  sensitivity from an adversarial frame. The deliverable must not be
  circulated until both passes are complete and all blocker findings
  are resolved.