location-diagnostics

name	location-diagnostics
description	Deploy the Location Diagnostics demo: region-agnostic retail site cannibalisation and closure modelling built from existing Overture data (a POI subset becomes the store estate) plus the live ORS drive-time engine. Adds the FLEET_INTELLIGENCE.LOCATION layer, FLEET_APP.LOCATION contract views, SV_LOCATION, and the Site Impact + Closure Impact app views. Use when: setting up store cannibalisation / transfer modelling, closure impact, next-closest-store analysis, retail location diagnostics. Do NOT use for: fleet tracking (fleet-intelligence-car/ebike), route optimization VRP (route-optimization), single-store catchment visualization (retail-catchment). Triggers: location diagnostics, cannibalisation modelling, transfer modelling, store closure impact, next closest store, retail site impact, catchment overlap.
depends_on	["install-fleet-apps"]
metadata	{"author":"Snowflake SIT-IS","version":"1.0.0","category":"demo"}

Deploy Location Diagnostics Demo

Region-agnostic store-location intelligence for retail site decisions, covering the two highest-value workstreams from the customer's "Location Diagnostics" brief:

• Cannibalisation / transfer modelling - for a candidate new site, how much revenue and EBITDA it draws from the existing estate, by drive-time band and by existing store, split by interaction type (Home Visit / Sample / Walk-in).
• Closure modelling - if an existing store closes, which surviving store inherits its households and sales via the next-closest-store (drive-time) approach.

Everything is built from data already present in the accelerator: a deterministic subset of FLEET_INTELLIGENCE.CATCHMENT.POIS (the region's most spatially-spread retail category) becomes the store estate (OWNED existing + CANDIDATE proposed sites) and CATCHMENT.REGIONAL_ADDRESSES is the household proxy (H3 cells). The estate's commercial figures (revenue, EBITDA, HV/Sample/Walk-in mix, sqft, rent) are synthetic, deterministic proxies - a stand-in for a customer's first-party sales, not real data. To plug in richer branded first-party data, see references/data-studio-extension.md.

Live routing (Architecture Tenet 9). Drive-time catchments are NOT precomputed. Each time the user picks a candidate site / band (or a store to close), the app view calls OPENROUTESERVICE_APP.CORE.ISOCHRONES live and attributes households at interaction time. The build only materializes non-ORS reference data (estate, household grid, synthetic facts). This means every interaction shows the actual routing engine at work - and the region's ORS service must be RESUMED or the views return an embedded error until it warms up.

Deferred to a follow-on slice (not built here): gap / penetration analysis, competitor & catchment analytics, property benchmarking, retail-park intelligence. The FLEET_INTELLIGENCE.LOCATION layer and SV_LOCATION are designed to extend to those.

Prerequisites

CRITICAL: Verify these before starting:

• install-fleet-apps has been run (FLEET_SA_APP + FLEET_APP contract + FLEET_INTELLIGENCE.CATCHMENT.* populated + roles exist).
• The active dataset's region has a provisioned, RUNNING ORS graph (driving-car profile). Check with SELECT TO_VARCHAR(OPENROUTESERVICE_APP.CORE.ORS_STATUS('<region>')) -> service_ready:true. If suspended, CALL OPENROUTESERVICE_APP.CORE.RESUME_ALL_SERVICES(). To add a new region, provision it first (see routing-customization).
• FLEET_INTELLIGENCE.CATCHMENT.POIS and REGIONAL_ADDRESSES have rows for the active region (built by install-fleet-apps step 3.5 analytic_layer.sql).

Required Privileges

Privilege	Scope	Reason
USAGE	DATABASE OPENROUTESERVICE_APP	Call ISOCHRONES (drive-time bands)
USAGE	DATABASE FLEET_INTELLIGENCE + CATCHMENT schema	Read POIs / addresses
CREATE SCHEMA	FLEET_INTELLIGENCE, FLEET_APP	Create LOCATION schemas
CREATE TABLE / VIEW / PROCEDURE	FLEET_INTELLIGENCE.LOCATION	Build the diagnostics layer
CREATE VIEW	FLEET_APP.LOCATION	Neutral-contract views
CREATE SEMANTIC VIEW	FLEET_INTELLIGENCE.SEMANTIC	SV_LOCATION for Cortex Analyst
GRANT	FLEET_APP_USER / _OPS / _ADMIN roles	App read access

Note: ACCOUNTADMIN is NOT required. Any role with the above privileges works.

Configuration

Parameter	Default	Description
Region	active dataset (FLEET_INTELLIGENCE.CATCHMENT.CONFIG)	Region whose estate is built; must have an ORS graph
Owned stores	10	Existing estate size (one per H3 res-6 cell of the primary category)
Candidate sites	3	Proposed new sites
Drive-time bands	10,15,20,25,30,45,60 (min)	FLEET_INTELLIGENCE.LOCATION.BANDS
Capture rate	0.5	Share of overlapping demand a candidate captures (cannibalisation)

To change owned/candidate counts or bands, edit BUILD_LOCATION_DIAGNOSTICS / the BANDS seed in scripts/analytic_layer.sql and re-run the build.

Workflow

The FLEET_INTELLIGENCE.LOCATION layer, FLEET_APP.LOCATION views, SV_LOCATION, and the two app views all ship inside install-fleet-apps. This skill's job is to (re)build the layer for the active region and surface the views.

1. Set the query tag for attribution:

   ALTER SESSION SET query_tag = '{"origin":"sf_sit-is-fleet","name":"oss-location-diagnostics","version":{"major":1,"minor":0},"attributes":{"is_quickstart":1,"source":"sql"}}';
   

2. Verify ORS for the active region is service_ready:true (resume if needed).
3. Build the layer (materializes estate, household cells, synthetic facts, band list). NO ORS calls at build time - fast, idempotent per region:

   CALL FLEET_INTELLIGENCE.LOCATION.BUILD_LOCATION_DIAGNOSTICS();
   

   If the objects/proc do not yet exist (older install), run the 5. LOCATION DIAGNOSTICS section of .cortex/skills/install-fleet-apps/scripts/analytic_layer.sql first (it creates the schema, tables, proc, calls it, and creates the FLEET_APP.LOCATION contract views + grants).
4. Verify the reference tables are populated:

   SELECT 'STORES' T, COUNT(*) N FROM FLEET_INTELLIGENCE.LOCATION.STORES
   UNION ALL SELECT 'HH_CELLS', COUNT(*) FROM FLEET_INTELLIGENCE.LOCATION.HH_CELLS
   UNION ALL SELECT 'FACTS', COUNT(*) FROM FLEET_INTELLIGENCE.LOCATION.STORE_FACTS;
   

5. Deploy the app config (the Site Impact + Closure Impact views live in app-views.json):

   SKIP_SERVICE=0 bash .cortex/skills/install-fleet-apps/scripts/deploy_fleet_sa_app.sh <connection>
   

   Or, config-only: upload app-views.json to @FLEET_INTELLIGENCE.SYNAPSE_USER.FLEET_APP_STAGE/config/ and suspend/resume .

How it works

• Store estate (build time) - the region's BASIC_CATEGORY with the most distinct H3 res-6 cells is chosen (data-derived, not hardcoded); one POI per cell is taken, first 10 flagged OWNED, next 3 CANDIDATE.
• Household proxy (build time) - addresses aggregated to H3 res-8 cells (HH_CELLS) with a representative centroid. STORE_FACTS.REFERENCE_HH = each store's nearest-store Voronoi territory over those cells (data-only, no ORS).
• Cannibalisation (LIVE) - the Site Impact view calls ISOCHRONES('driving-car', lon, lat, band_min, region) for the selected candidate at the selected band (coords resolved via scalar subquery from the estate; range in MINUTES). Household cells inside the polygon are assigned to their nearest OWNED store (Voronoi); transfer_pct = captured / that store's REFERENCE_HH (same partition, so pct <= 1); transfer_revenue = revenue x transfer_pct x capture(0.5), split by interaction mix.
• Closure (LIVE) - the Closure Impact view calls ISOCHRONES (20-min) for the selected store; cells inside are reassigned to the nearest surviving owned store; households and revenue aggregated per gaining store.
• Why live - ORS SQL functions only evaluate with literal / scalar-subquery / bind args (not correlated per-row), so the views pass the selection as a scalar subquery. Per Architecture Tenet 9, nothing ORS is cached.

UK / postcode-accurate deployments (optional)

The household layer uses Overture addresses aggregated to H3 cells, which is region-agnostic. For a Great Britain deployment that reports by real postcode (matching the original brief's "postcodes within drive times"), swap the household/boundary layer for these Ordnance Survey Marketplace listings (all verified available, free, provider Ordnance Survey):

Listing	Global name	Use
Postcode units - GB: Code-Point Open	GZ1MOZ2HCBPHF	GB postcode-unit centroids -> postcode-level catchment reporting (replaces H3 cells)
Unique Property Reference Numbers - GB: Open UPRN	GZ1MOZBWYYH	~40M property points -> household proxy
Addresses - UK: OS GB Address and OS Islands Address	GZ1MOZ2HCBPI0	Full UK address detail (sample) -> household proxy
Administrative boundaries - GB: Boundary-Line Open	GZ1MOZBWYYT	Admin boundaries -> region clipping

Install from https://app.snowflake.com/marketplace/listing/<global_name>. Point HH_CELLS (or a new postcode table) at Open UPRN / Code-Point Open instead of CATCHMENT.REGIONAL_ADDRESSES; the live ISOCHRONES logic is unchanged. These are GB-only and do not apply to non-UK regions (e.g. the default SanFrancisco).

US / ZIP-code drive-time drill (enabled by default for US regions)

For US regions the build adds a real ZIP layer used by the Site Impact ZIP-by-drive-time table and the drive-time choropleth. It requires two free Marketplace listings imported into the account (a fresh install must acquire these; the enrichment step is guarded and skips silently if they are absent):

Listing	Global name	Imported DB	Use
U.S. ZIP Code Metadata with Geometry (SFR Analytics)	GZTYZ7P39MI	U_S__ZIP_CODE_METADATA_WITH_GEOMETRY	Real ZCTA polygons + centroid + land area
SafeGraph Open Census (2020 CBG)	(free share)	SAFEGRAPH_OPEN_CENSUS_FREE	Real population / housing / income, rolled up CBG->ZIP

Acquire the SFR listing via SQL: CREATE DATABASE IF NOT EXISTS ZIP_GEOMETRY_SFR FROM LISTING 'GZTYZ7P39MI'; (or Get in Snowsight). SafeGraph Open Census is a free share (Get in Snowsight). BUILD_LOCATION_ZIP_ENRICHMENT() then materializes LOCATION.ZIP_AREAS (region ZIPs = distinct CATCHMENT.REGIONAL_ADDRESSES.POSTCODE, joined to the polygons + a CBG-centroid-in-ZIP demographic rollup). The paid U.S. ZIP Code Demographics listing (GZTYZ7P39MM) is NOT required - SafeGraph provides the same signals for free.

Live objects (isochrones computed on the fly, no precompute):

• FLEET_APP.LOCATION.LIVE_ZIP_BANDS(store_id, region) - each region ZIP assigned to its smallest drive-time band (one multi-range ORS call) with real population/housing.
• FLEET_APP.LOCATION.LIVE_OWNED_CATCHMENTS(band, region) - every owned store's catchment in one multi-location ORS call (group_index->store), for the estate overlap layer.

Output

• Tables (non-ORS reference data): FLEET_INTELLIGENCE.LOCATION.{STORES, BANDS, HH_CELLS, STORE_FACTS, ZIP_AREAS}
• Contract views: FLEET_APP.LOCATION.{VW_STORES, VW_STORE_FACTS, VW_HH_CELLS, VW_BANDS, VW_ZIP_AREAS}
• Live UDTFs: FLEET_APP.LOCATION.{LIVE_ZIP_BANDS, LIVE_OWNED_CATCHMENTS, LIVE_CANNIBALISATION, LIVE_OVERLAPS, LIVE_OVERLAP_ZIPS, LIVE_CLOSURE_OVERLAPS, LIVE_CLOSURE_ZIPS}
• Semantic view: FLEET_INTELLIGENCE.SEMANTIC.SV_LOCATION (agent tool query_location) - estate facts only; cannibalisation/closure/overlaps are live in-app
• App views: Site Impact (live cannibalisation + ZIP drive-time drill + Isochrone Overlap Mode) and Closure Impact (live closure + Isochrone Overlap Mode) under the Location category

Isochrone Overlap Mode (Site Impact + Closure Impact)

Both pages expose a computed-overlap capability (not visual polygon stacking): the candidate/closing store's live drive-time isochrone is intersected (ST_INTERSECTION) with each existing/surviving store's catchment (LIVE_OWNED_CATCHMENTS, 2 ORS calls total), and households (H3 cell-centroid containment) and postcodes (ZIP centroid containment) are attributed to each overlap polygon.

• Overlap choropleth shaded by a Map metric dropdown (Revenue / EBITDA / Transfer probability / Households). The metric is switched in SQL via a CASE :map_metric NTILE(5) impact_bucket, so the map re-shades with no TypeScript change.
• Overlap summary table (ClickableTable, emits selected_overlap) + ZIP-in-overlap table + a slide-over overlap detail drawer (DetailPanel position:drawer, triggerKey: selected_overlap) with the postcodes inside the selected overlap. Selecting a row highlights the overlap polygon on the map (selected-overlap-outline layer).
• Attribution-method label (Markdown) states the method (ST_INTERSECTION + centroid containment) and warns that pairwise overlap rows must not be summed (neighbouring catchments overlap); headline KPIs and the transfer/gainers tables use non-overlapping nearest-store attribution.
• Closure Impact adds a drive-time band selector and overlays the surviving-store catchments; its ZIPs are classified RETAINED vs AT_RISK.
• All overlap objects call ORS live at interaction time (Architecture Tenet 9); the region's ORS service must be RESUMED. Passing a non-OWNED store id to the closure UDTFs returns 0 rows by design.

Data Studio Extension (optional)

The synthetic commercials are a demo proxy. To drive the same views from richer, branded first-party facts (real sales, EBITDA, interaction-type splits, and a Mosaic-style segmentation), see references/data-studio-extension.md for a proposed engine/estate.ts generator and segmentation table.

Cleanup

DROP SCHEMA IF EXISTS FLEET_APP.LOCATION;
DROP SEMANTIC VIEW IF EXISTS FLEET_INTELLIGENCE.SEMANTIC.SV_LOCATION;
DROP SCHEMA IF EXISTS FLEET_INTELLIGENCE.LOCATION;

Then remove the site_impact and closure_impact keys from app-views.json, the query_location tool + resource from agent-spec.json, and SV_LOCATION from semantic_views.sql, and redeploy.