Digital Atlas Hong Kong — Method, Scale & Context of Usage

Digital Atlas Hong Kong — Method, Scale & Context of Usage#

What this is#

A mathematical representation of Hong Kong's urban commercial structure. Every planning unit, every neighborhood, and every commercial place in the territory described by a structured feature vector — capturing what it is, what surrounds it, who needs it, and how it's changing.

Built on atlas-1 at /home/azureuser/digital-atlas-hkg/. Total footprint: 6.2 GB.

Scale#

Territory coverage#

• Area: 1,112 km² (292 Tertiary Planning Units)
• Population: 7.47 million (WorldPop corrected + Census 2021)
• Commercial places: 147,191 (Overture Maps + OpenStreetMap, deduplicated)
• Chain brands: 151 detected, 10,474 outlets
• Data sources: 20 independent layers
• Temporal depth: VIIRS nightlights 2023–2025 (37 months), GHSL built-up 2010/2020/2025

Spatial resolution#

Feature count: 1,371 total across all levels#

Method#

Data acquisition (20 sources)#

Region representation pipeline#

Phase 1: Boundaries + grid - 292 TPU polygons from CSDI - H3 resolution-9 hex grid clipped to land (10,928 cells, 1,124 km²) - H3 resolution-8 parent aggregation (1,896 cells) - Hex → TPU spatial assignment

Phase 2: Structural features - Overture buildings (302K) → per-hex height/area/count - Overture roads (452K segments) → per-hex length/density by class - Overture land use (40K) → per-hex zoning composition (% residential/commercial/park) - WorldCover → per-hex land cover binary flags (tree/built/water) - DEM → elevation, hillside flag, terrain ruggedness - Coastline → coast proximity, waterfront flag

Phase 3: Satellite intelligence - VIIRS nightlights → per-hex radiance mean, per-capita, commercial indicator, growth corridors, decline zones, YoY change - GHSL → built-up surface 2010/2020/2025 (age proxy), population, building height, urbanization class - Hansen → treecover, forest loss/gain - Sentinel-2 → NDVI vegetation index

Phase 4: Place composition - 147K places spatially joined to hex-9 and TPU - Per-region: L1 counts (11 categories), L2 counts (30 categories), top-20 fine categories - Category entropy, cuisine diversity (22 types), chain ratio, brand penetration

Phase 5: Census demographics - 206 features from Census 2021 STPU (166/292 TPUs matched) - Age brackets, sex ratio, income by occupation, dwelling type, commute patterns, ethnicity, language, literacy

Phase 6: Verticality - Estimated floors (GHSL height / 3.5m) - FAR proxy (GFA / hex area) - Stacking intensity (height / built fraction) - Podium-tower signal, skyline prominence - Industrial conversion detection

Phase 7: Proxy features - Daytime population (office×15 + retail×8 + F&B×5 + hotel×20 + school×25) - Tourism intensity (hotels + attractions + landmarks) - Income proxy (building height + nightlight + services) - Footfall proxy (transit + places + nightlight + pop) - Night economy (bars + hospitality + nightlight) - Pedestrian connectivity, mixed-use index, green ratio

Phase 8: Graph features (TPU only) - Queen contiguity adjacency (avg 4.9 neighbors) - Spatial lag + contrast for 12 core features - Ring-1 neighbor sums

Phase 9: Composite scores + indices - Vitality, accessibility, demand, competition, growth potential, saturation - Skyline, redevelopment pressure, verticality, compactness, mixed-height

Phase 10: Archetype clustering - K-means (k=5) on 52 normalized features - Tourist/Entertainment (12 TPUs), Dense Residential (30), Suburban Residential (31), New Development (80), Country/Green Belt (91)

Phase 11: Hex-8 neighborhood layer - Aggregated hex-9 features upward - Broadcast TPU features downward (265 features) - Neighbor influence: boundary/gateway/cluster center flags, interface score, gradient position, net demand flow

Place representation pipeline#

Step 1: Source extraction - Overture Maps 141K places + OSM 47K POIs

Step 2: Conflation + dedup - Spatial join (50m radius) + name similarity - 20K net-new from OSM after deduplication

Step 3: Category taxonomy - L1: 11 groups (food, retail, services, health, education, leisure, hospitality, transit, community, auto, other) - L2: 30 groups (cafe_coffee, grocery, general_dining, bar_nightlife, etc.)

Step 4: Chain detection - 151 known HK chain brands matched by name - 10,474 outlets detected (McDonald's 880, Starbucks 729, 7-Eleven 428, etc.)

Step 5: Spatial assignment - H3 res-9 + res-8, TPU + district via point-in-polygon

Step 6: Competition - cKDTree spatial self-join per L2 category - Same-category count within 200m and 500m - Nearest competitor distance

Step 7: Complementary - Cross-category count within 300m using per-L1 KD-trees - Complementary diversity (unique L1 categories within 300m)

Step 8: Anchor proximity - 16 anchor types from OSM (MTR, schools, hospitals, hotels, malls, supermarkets, parks, bus stops, etc.) - Per-anchor: count within radius + nearest distance + boolean flag - Composite anchor_score (weighted across all types)

Step 9: Demand pull - 6 demand sources: office (nightlight-based), residential (WorldPop 500m), transit (MTR decay), hotel, school, mall - Distance-decay weighted (exponential, halflife 200m) - Composite demand_context_score

Step 10: Co-location synergy - 10 category-pair synergies, each fires ONLY for the target category - cafe×office, grocery×residential, convenience×transit, restaurant×hotel, gym×cafe, pharmacy×clinic, bar×restaurant, school×tutoring, bank×office, bakery×cafe

Step 11: Building context - 12 features from host hex-9: height, floors, FAR, podium, stacking, GHSL height, built fraction, land use, terrain, waterfront, hillside

Step 12: Neighborhood character - 14 features broadcast from parent TPU: archetype, vitality, demand, competition, accessibility, population density, daytime ratio, income proxy, cuisine diversity, nightlight trend, new development

Context of usage#

Site selection — "Where should brand X open next?"#

Query the place table for gaps: high demand_context + low competitors_200m + right archetype. The feature vector tells you not just WHERE but WHY — which demand source (office workers? residents? tourists?) drives the opportunity.

Portfolio analytics — "How diversified is this REIT's portfolio?"#

Map each property to a TPU archetype. Measure concentration risk across the 5 archetypes. Flag properties in decline zones (nl_growth_pct < -20%) or high-redevelopment areas (idx_redevelopment_pressure > 0.7).

Competitive landscape — "Who are my competitors and what's around them?"#

For any place: competitors_200m gives direct competition count, nearest_competitor_m gives breathing room, complementary_diversity shows ecosystem richness. Compare against archetype averages to know if competition is above or below normal for that neighborhood type.

Catchment analysis — "Who is my customer?"#

pull_residential tells you how many residents are nearby. pull_office tells you if office workers drive demand. char_daytime_ratio reveals if the area is office-dominant (>1) or residential-dominant (<1). Census features give age/income/ethnicity breakdown.

Growth corridor detection — "Where is the city growing?"#

nl_growth_corridor flags hexes brightening >20%. proxy_new_development shows GHSL built-surface change. score_growth_potential combines both with low-competition signal. Track these quarterly via VIIRS monthly updates.

Neighborhood scoring — "Is this a walkable 15-minute neighborhood?"#

Hex-8 level: r8_walkability combines transit_score + connectivity + places + anchors. Check osm_*_dist_m for specific amenity distances. r8_residential_quality combines green + parks + schools + low density.

Micrograph construction — "Star diagram for any place"#

Every place has the 4-arm context: T1 (transit_score), competitors (competitors_200m), demand magnets (complementary_*), anchor quality (anchor_score). Plus building context for vertical dimension.

Urban planning — "Where is redevelopment pressure highest?"#

idx_redevelopment_pressure = old buildings (bld_age_index) + low-rise (bld_lowrise_ratio) + high vitality (score_vitality). Cross with census_oq_* (housing tenure) to identify public housing renewal candidates.

Validation summary#

Files on atlas-1#

/home/azureuser/digital-atlas-hkg/

data/outputs/
  tpu_features_final.parquet              292 × 538       1.0 MB
  h3_res8_features.parquet              1,896 × 486       2.3 MB

data/hex_v9/
  hkg_hex_v9_features_v4.parquet       10,928 × 205       6.6 MB

data/places_consolidated/
  hkg_places_final.parquet            147,191 × 142      32.4 MB

data/boundaries/
  hkg_tpu.geojson                     292 TPU polygons    38 MB
  hkg_districts.geojson               19 districts        470 KB
  hkg_sar.geojson                     territory           84 KB
  hkg_hex_v9_land.geojson             10,928 hex-9 polys  6.2 MB

data/serving/                         API-ready JSON      829 MB
  tpu.json, hex8.json, hex9.json, places.json, places_slim.json
  tpu_geo.geojson, hex8_geo.geojson, hex9_geo.geojson
  feature_catalog.json, archetypes.json, places_schema.json
  places_methodology.json, places_examples.json, places_stats.json
  place_representation.json, manifest.json, models/

model/
  v7_gap_model.pkl                    R²=0.923            3.4 MB
  v8_population_model.pkl             R²=0.819            7.8 MB

docs/
  HKG_DATASET_OVERVIEW.html           single-page summary
  HKG_ATLAS_COMBINED.html             7-tab full report
  PLACES_LAYER.md                     places documentation
  + 7 topic-specific HTML reports