Enterprise and Domain GIS

Use When

• Use when administering ArcGIS Enterprise or building real-estate-specific GIS features — ArcGIS components, publishing services, security/roles, backup/DR, plus property search, neighbourhood analysis, catchment/isochrones, market heatmaps, and real-estate-SaaS integration.
• The task needs reusable judgment, domain constraints, or a proven workflow rather than ad hoc advice.

Do Not Use When

• The task is unrelated to gis-enterprise-domain or would be better handled by a more specific companion skill.
• The request only needs a trivial answer and none of this skill's constraints or references materially help.

Required Inputs

• Gather relevant project context, constraints, and the concrete problem to solve; load references only as needed.
• Confirm the desired deliverable: design, code, review, migration plan, audit, or documentation.

Workflow

• Read this SKILL.md first, then load only the referenced deep-dive files that are necessary for the task.
• Apply the ordered guidance, checklists, and decision rules in this skill instead of cherry-picking isolated snippets.
• Produce the deliverable with assumptions, risks, and follow-up work made explicit when they matter.

Quality Standards

• Keep outputs execution-oriented, concise, and aligned with the repository's baseline engineering standards.
• Preserve compatibility with existing project conventions unless the skill explicitly requires a stronger standard.
• Prefer deterministic, reviewable steps over vague advice or tool-specific magic.

Anti-Patterns

• Treating examples as copy-paste truth without checking fit, constraints, or failure modes.
• Loading every reference file by default instead of using progressive disclosure.

Outputs

• A concrete result that fits the task: implementation guidance, review findings, architecture decisions, templates, or generated artifacts.
• Clear assumptions, tradeoffs, or unresolved gaps when the task cannot be completed from available context alone.
• References used, companion skills, or follow-up actions when they materially improve execution.

Evidence Produced

Category	Artifact	Format	Example
Operability	ArcGIS Enterprise admin runbook	Markdown doc per skill-composition-standards/references/runbook-template.md covering portal, Server, and Data Store ops	docs/gis/arcgis-runbook.md
Data safety	Real-estate domain data model	Markdown doc per skill-composition-standards/references/entity-model-template.md covering parcel, ownership, and valuation entities	docs/gis/real-estate-model.md

References

• Use the references/ directory for deep detail after reading the core workflow below.

ArcGIS Enterprise administration for regulated / established GIS environments, plus real-estate-specific GIS patterns that map cleanly to PostGIS + map clients.

Prerequisites: Load gis-postgis-backend for the spatial backend, gis-mapping or gis-maps-integration for the map client, and multi-tenant-saas-architecture for tenant isolation.

When this skill applies

• Operating or integrating with ArcGIS Enterprise (Portal, Server, Data Store, Web Adaptor).
• Publishing authoritative GIS services (map, feature, image, geocoding).
• Designing GIS features for real estate: property search, neighbourhood analysis, catchment, market heatmaps.
• Building a real-estate SaaS that needs spatial features at a high bar.

When ArcGIS Enterprise is right

Government / regulated sector with ArcGIS mandated               -> ArcGIS Enterprise
Team already trained on ArcGIS Pro                               -> ArcGIS Enterprise
Need authoritative imagery/demographics datasets under licence    -> ArcGIS Online/Enterprise
Small startup, modern stack, cost-sensitive                       -> PostGIS + Mapbox/MapLibre
You need ESRI-specific analytical tools (Network Analyst)        -> ArcGIS Enterprise

Most SaaS startups pick the PostGIS + OSS map stack. ArcGIS Enterprise earns its cost in sectors where it's already the standard (government, utilities, defence) or when licenced datasets are critical.

See references/when-arcgis-enterprise.md.

ArcGIS Enterprise components

• Portal for ArcGIS — identity, sharing, groups, content catalogue.
• ArcGIS Server — publishes services (map, feature, image, geoprocessing, geocoding).
• Data Store — relational, tile, and spatiotemporal data backends.
• Web Adaptor — IIS/Java web server integration for HTTPS, custom URLs.

Typical base deployment: one of each, highly available variant adds replication for each.

See references/arcgis-components.md.

Publishing services

• Feature services — CRUD + query on features, the workhorse for interactive apps.
• Map services — pre-rendered tiles for fast display at fixed scales.
• Image services — raster data (imagery, DEM).
• Geocoding services — address to coordinate.
• GeoProcessing services — spatial analysis functions as endpoints.

Versioning: branch versioning for concurrent editing without conflicts; default version for most read workloads.

See references/publishing-services.md.

Security + roles

Portal users assigned roles: Viewer, Data Editor, Publisher, Administrator. Groups control sharing.

Server-side Role-Based Access Control on published services. Integrate with enterprise IdP (SAML / OIDC).

Audit logs — enable and ship to the SIEM.

See references/arcgis-security-roles.md.

Backup + DR

1. Portal content — webgisdr utility (export + import).
2. Data Store — automated backups + geo-replicate for HA.
3. Server config — backup the machine's `config-store` directory.
4. Web Adaptor — stateless, reinstall from config.
5. Restore drill quarterly.

webgisdr is authoritative for Portal/Server/Data Store coordinated backup.

See references/arcgis-backup-dr.md.

Real estate GIS recipes

Property search with spatial filters

Filter by district/neighbourhood + within walking distance to transit + inside a school zone:

WITH search_area AS (
  SELECT ST_Union(geom) AS geom FROM districts WHERE code IN ('KAM01', 'KAM02')
), school_zones AS (
  SELECT ST_Union(geom) AS geom FROM zones WHERE type = 'school' AND rating >= 4
), transit AS (
  SELECT ST_Buffer(geom::geography, 800)::geometry AS geom FROM transit_stops  -- 800m walking
)
SELECT l.*
FROM listings l
JOIN search_area s ON ST_Contains(s.geom, l.geom)
JOIN school_zones sz ON ST_Contains(sz.geom, l.geom)
JOIN LATERAL (SELECT 1 FROM transit t WHERE ST_Intersects(t.geom, l.geom) LIMIT 1) tr ON TRUE
WHERE l.tenant_id = :tenant_id AND l.status = 'active';

See references/property-search-spatial.md.

Neighbourhood analysis

For a listing, compute:

• Walk score — count of amenities within 800m / 1600m weighted by category.
• Transit access — nearest N transit stops with travel time.
• Comparables — similar listings within 2 km in last 180 days.
• Demographics — join to census or tenant-provided block statistics.

Cache per-listing summaries; refresh nightly.

See references/neighbourhood-analysis.md.

Catchment / drive-time isochrones

• Simple: buffer by straight-line distance.
• Better: drive-time isochrones from OSRM, Mapbox Isochrone API, or Google Distance Matrix.
• Cache isochrone polygons in PostGIS; key by (point, mode, minutes).
• Use in "find listings I can reach in 20 minutes" features.

CREATE TABLE isochrones (
  id serial PRIMARY KEY,
  point geometry(Point, 4326) NOT NULL,
  mode text NOT NULL,
  minutes int NOT NULL,
  geom geometry(Polygon, 4326) NOT NULL,
  computed_at timestamptz NOT NULL DEFAULT now(),
  UNIQUE (point, mode, minutes)
);

See references/catchment-isochrones.md.

Market heatmaps

Heatmap of listing price per square metre by small area (H3 hex index, grid, or admin boundary):

SELECT h.cell, AVG(l.price_per_sqm) AS avg_price, COUNT(*) AS listings
FROM listings l
JOIN h3_cells_resolution_8 h ON h3_indexes_contain(h.cell, l.geom)
WHERE l.tenant_id = :tenant_id AND l.sold_date > now() - interval '180 days'
GROUP BY h.cell
HAVING COUNT(*) >= 5;

Render client-side as a choropleth on Mapbox/MapLibre. Suppress cells with low N to avoid privacy + noise.

See references/market-heatmaps.md.

Real-estate-SaaS integration

Architecture:

Web app (Next.js/React)
  -> API (Fastify/PHP) reads MySQL listings table
  -> API joins to PostGIS for spatial queries
  -> Client renders map (Mapbox GL with MVT tiles from PostGIS)
  -> Geocoding via Google Places (cached)
  -> Isochrones via Mapbox Isochrone (cached in PostGIS)

Keep transactional listing data in MySQL; keep spatial in PostGIS; keep them linked by listing_id.

See references/real-estate-saas-integration.md.

Anti-patterns

• Double-entry of property location in MySQL and PostGIS without a canonical source.
• Heatmaps rendered per-request without caching (expensive queries every pan/zoom).
• Isochrones computed live instead of cached.
• Exposing raw ArcGIS service URLs on the public internet without auth and rate limits.
• ArcGIS Enterprise on a single node for production (no HA).
• No webgisdr backup schedule.
• Publishing feature services without field-level visibility rules for multi-tenant contexts.

Read next

• gis-postgis-backend — spatial backbone.
• gis-maps-integration — client mapping.
• multi-tenant-saas-architecture — tenant isolation end-to-end.

References

• references/when-arcgis-enterprise.md
• references/arcgis-components.md
• references/publishing-services.md
• references/arcgis-security-roles.md
• references/arcgis-backup-dr.md
• references/property-search-spatial.md
• references/neighbourhood-analysis.md
• references/catchment-isochrones.md
• references/market-heatmaps.md
• references/real-estate-saas-integration.md
• references/arcgis-pro-workflows.md — geodatabase invariants, topology rules for parcels, branch vs traditional versioning, georeferencing with RMS targets, service pre-flight checklist