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Appraisal · deed countsLandowners · developers · investors
Compare buildings, prices and nearby constraints to find the places worth checking before you invest.
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Start with real views that reveal what is interesting about a place, then open the sources, scope and details when you are ready to decide.
HQ · branches · new activityTell the 50,000-branch network story through headquarters, sectors, and newly registered branches
Road types · dead ends · intersectionsPair coloured archetype areas on the satellite map with dead-end and intersection metrics
GLA · tenants · segmentCompare retail structure through supply, leasable area, tenant scale, and market segment
GFA · height · floorsPair the province comparison map with GFA, height, and floor metrics to explain development intensity
14 years · recurrence · worst yearUse the 14-year timeline, recurrence count, and worst year as the historical risk baseline
Age · sex · densityUse age-sex structure and density as baseline context for market reading and service planning
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38 DATA VIEWS · 3 LENSES
Land is the city’s base. Living is people, services and everyday life. Together they show how each Location differs—and how some patterns change over time.
Land · buildings · infrastructure
People · services · everyday life
Business · mobility · access
Organises 38 views so people can find, compare and inspect evidence in one place.
Shows what to check next and how to move a place decision forward.
Explore 3D building intensity, GFA, height and floor counts in Suan Plu
See where buildings concentrate and compare gross floor area, height and floor counts to choose places for closer study.
Google Research — Open Buildings — CityMETER names Open Buildings directly, so this is an external source rather than GD Catalog lineage.
Use it to compare building intensity, gross floor area, height and floors at summary level—not as a building register or verified footprint for every structure.
Bring project counts, plot counts, and developer ranking into one frame
What you can do with this dataFind permitted housing estates and compare their plot counts, developers and distribution across an area.
Department of Lands — Project, permit, developer, title-document and plot fields match the official land-allotment permit dataset.
Monthly nationwide coverage; this card uses the land-allotment part of the package.
Use it for permit history and project distribution. A permit does not prove completion, current availability or occupancy.
This badge confirms same-dataset lineage through an official owner channel. It does not mean every file was downloaded directly from the central GD Catalog.
Compare IEE, EIA, and EHIA project counts alongside their location context
What you can do with this dataFind projects with EIA records, compare project type, owner and approval date, and open the source documents for follow-up.
ONEP — Project, owner, report type, document, approval date, category and location fields match Smart EIA.
Nationwide projects since 1992; the live API changes, so the card avoids a frozen total.
CityMETER is a curated, geocoded snapshot. Approval does not prove construction, operation or current compliance.
This badge confirms same-dataset lineage through an official owner channel. It does not mean every file was downloaded directly from the central GD Catalog.
See monthly cultivated area and output in Wiang Thong TAO, separated by crop and time period
See which crops are grown, compare cultivated area and monthly output, and identify places that need closer field or service planning.
Department of Agricultural Extension production registry family (candidate) — The field family resembles DOAE production registries, but CityMETER's 2025–2026 monthly grain and taxonomy have not been reconciled to one exact package.
Use as provisional crop-area and output context until the exact resource, taxonomy and coverage are reconciled.
See the 3D land-appraisal pattern across Mueang Chonburi with deed counts and the price distribution
What you can do with this dataCompare parcel location, area and official appraisal values to screen sites and prepare questions before parcel-level due diligence.
Department of Lands / Treasury Department — Combines parcel location and area from the Department of Lands with official Treasury appraisal values.
Versioned, transformed parcel layer; appraisal update dates vary by province.
Useful for screening and reference checks before parcel due diligence; it does not prove ownership, encumbrances or transaction price.
This badge confirms same-dataset lineage through an official owner channel. It does not mean every file was downloaded directly from the central GD Catalog.
Show listing supply and price-per-square-wah distribution as an asking-market signal
Compare asking-price ranges and listing concentrations to shortlist areas for further market checks.
Private listing layer — public lineage not yet verified — No exact government-dataset lineage was verified for the land asking-price layer.
This is an asking-price signal, not a closing price. Verify source, collection date, duplicate handling and outlier treatment before comparison.
Use average rent and price ranges to compare rental-market signals between areas
Compare sampled apartment rent ranges across areas before checking current offers in the market.
Public source lineage not yet verified — No public package or manifest currently reconciles apartment-rent records to a repeatable source.
Treat it as a sampled rent-range signal, not the whole market or current rent for every building.
Use P25, P50, and P75 as a value baseline before comparing listing prices
What you can do with this dataFind official condominium appraisal values and use a consistent baseline when comparing projects or areas.
Treasury Department — Project/building schema and appraisal values align with the official condominium valuation dataset; CityMETER is a transformed snapshot.
The source states a four-year cycle; the inspected resource was updated 14 Aug 2024.
Use it as an official value baseline before market comparison; appraisal is neither listing nor latest transaction price.
This badge confirms same-dataset lineage through an official owner channel. It does not mean every file was downloaded directly from the central GD Catalog.
Pair asking prices with appraisal values to reveal the gap before project-level analysis
Compare asking prices with appraisal benchmarks to spot unusually high or low gaps for further review.
Private asking-price layer + candidate Treasury benchmark — The appraisal benchmark is official, but condo/building IDs and the ETL join to private listings have not been fully evidenced.
Use the gap as an exploratory signal; project-level use requires duplicate, unit and join-method checks.
Compare price ranges with land size so the detached-house story is not reduced to one number
Compare detached-house asking prices, land sizes and listing counts to understand market ranges and shortlist areas.
Private listing layer — public lineage not yet verified — No public source manifest yet states coverage, vintage or deduplication rules for detached-house listings.
Price and land size help screen the market, but these are asking prices and sampling may vary by area.
Use listing counts and percentiles to show both supply and the price range
Compare townhouse listing counts and asking-price ranges to understand indicative supply across areas.
Private listing layer — public lineage not yet verified — No public manifest yet documents townhouse listing coverage, date, duplicates or outlier treatment.
Listing counts and percentiles show supply and asking ranges, not sales volume or closing prices.
Lead with rent, estimated yield, and investment-score cards rather than a wall of listings
Compare estimated condominium rents and yields across areas to shortlist options for a fuller income-and-cost review.
LivingInsider / private listings — CityMETER names LivingInsider directly, so this is not GD Catalog lineage.
Rent, yield and investment scores are listing-derived; verify assumptions, costs, vacancy and duplicates before decisions.
Use company status and capital distribution to explain the business base for the stated period
What you can do with this dataSee where company registrations and dissolutions concentrate, and compare business types and registered capital across areas.
Department of Business Development — Uses the legal-entity register and new/dissolution events; CityMETER is a curated, geocoded subset.
CityMETER snapshot: Apr 2024–Apr 2025; verify each resource title rather than inferring event type from package ID alone.
Legal registration status is not actual opening or closure, and registered capital is not investment, revenue or employment.
This badge confirms same-dataset lineage through an official owner channel. It does not mean every file was downloaded directly from the central GD Catalog.
Tell the 50,000-branch network story through headquarters, sectors, and newly registered branches
What you can do with this dataMap VAT-registered branches by business type to shortlist areas for deeper business-base and competitor research.
Revenue Department — Records were reconciled by legal-entity number, branch code, name and address against the VAT operator register.
Monthly source; CityMETER is a transformed snapshot.
CityMETER uses an enriched, geocoded 50,000-branch subset. Registered address is not proof of operation at that point and does not cover every business.
This badge confirms same-dataset lineage through an official owner channel. It does not mean every file was downloaded directly from the central GD Catalog.
Combine factory, worker, capital, and machinery metrics into one industrial-base story
Compare factories, workers, capital and machinery capacity to understand the industrial base and choose areas for follow-up.
Industrial factory register (high-confidence candidate) — The field fingerprint matches the factory register, but factory-ID reconciliation or an ETL source manifest is still missing.
Use the factory, worker, capital and machinery measures exploratorily until definitions, units and registry coverage are confirmed.
Use building supply, area, age, floors, and asking rent to frame Bangkok's office market
Compare Bangkok office inventory and asking rents to see where supply concentrates and how price ranges differ.
Commercial inventory — public lineage not yet verified — The office-building and asking-rent inventory did not match the official building packages reviewed.
The evidenced scope is Bangkok. Prices are asking rents, not contracted rent or occupancy.
Use density, reviews, and price bands to explain the local food-competition context
Compare restaurant density, price bands and ratings to explore competitive context and areas with more or fewer dining options.
Private place/review source — public lineage not yet verified — Ratings, price bands and density did not align with the government restaurant registers reviewed.
Use as competitive context, not demand, customer count or independently verified quality; collection date and coverage are required for comparison.
Compare retail structure through supply, leasable area, tenant scale, and market segment
Compare shopping-centre size, type, market segment and tenant counts to understand retail structure and local competitors.
Commercial inventory — no matching government package verified — GLA, GFA, tenant count and market segment did not match the government datasets reviewed.
Use to compare retail structure in the sample; unequal field coverage means it should not be presented as market totals.
Use hotel supply, rooms, ADR, and the seasonal curve to frame the market and an initial comparison set
Explore the displayed accommodation, room, asking-rate and seasonality measures to choose areas for further hotel-market checks.
Tourism Authority of Thailand — Accommodation (candidate) — The field signature closely resembles TAT Accommodation, but hotel and room totals have not been reconciled to the same snapshot.
Use supply, asking-rate and seasonality as an initial comparison—not as occupancy, bookings or hotel revenue.
Use visitors, spending, spend per visitor, change, and province ranking to tell the demand story
What you can do with this dataCompare visitor volume, spending, seasonality, activities and attractions to choose provinces or periods for further service planning.
Ministry of Tourism and Sports / Tourism Authority of Thailand — Demand reconciles to the official MOTS workbook after unit conversion; activities and attractions use official TAT channels.
Year–month–province grain; exact values reconcile to the official workbook, not every byte of the frozen GD JSON.
Compare visitors, receipts, seasonality, activities and attractions. Visitor counts are not unique people and figures may be revised.
This badge confirms same-dataset lineage through an official owner channel. It does not mean every file was downloaded directly from the central GD Catalog.
Use station counts, density, and fuel mix to explain automotive-service availability
Explore the displayed mix of EV, LPG, NGV and conventional-fuel services to choose areas for checking actual stations and availability.
No official package yet matches the displayed fuel mix — The DOEB package reviewed did not contain the same EV, LPG, NGV and fuel-availability mix shown by CityMETER.
Use as provisional automotive-service context until station definition, effective date and multi-fuel counting are verified.
Pair the province map with brand, model, category, and period to explain the registered-vehicle base
What you can do with this dataTrack first registrations by vehicle type, make and model to see how registration patterns change month by month.
Department of Land Transport — February 2026 values were reconciled at year, month, vehicle type, make, model and count level.
Monthly national statistics; CityMETER shows Jan 2020–Feb 2026.
This is first-registration statistics, not the active fleet, travel behaviour or where vehicles operate.
This badge confirms same-dataset lineage through an official owner channel. It does not mean every file was downloaded directly from the central GD Catalog.
Explore Pathum Wan road-network archetypes with dead-end ratio, intersection density and Road DNA
Compare street patterns, dead ends and intersection density to understand network structure and frame questions for field checks.
CityMETER derived model — This is a derived road-network model rather than an atomic GD Catalog dataset.
Dead-end and intersection measures are diagnostic signals—not location quality, accessibility, traffic or a good/bad verdict.
Use speed, congestion, and the seven-day trend when the feed status is available
Read displayed traffic speed and trends to identify routes or time periods that need closer travel-condition checks.
Current/7-day traffic feed — public source contract not yet verified — The current and seven-day feed did not match the static OTP studies reviewed, and no public SLA was found.
Use only with timestamp and feed status visible; delayed or unavailable data does not mean free-flow traffic.
Frame what should be validated in the field rather than presenting behaviour as fact
Read a concise place profile and suggested questions to prioritise areas and prepare field validation.
CityMETER derived / LLM-assisted contextual layer — This is a contextual synthesis across several signals, not one exact GD Catalog dataset.
Use it to frame questions, prioritise places and plan field validation; do not treat behavioural descriptions as facts without evidence and a boundary crosswalk.
Use age-sex structure and density as baseline context for market reading and service planning
What you can do with this dataCompare registered population by age, sex and area to plan services for children, working-age groups and older people.
Bureau of Registration Administration, Department of Provincial Administration — Monthly age, sex and area dimensions align with DOPA statistics for people listed in house registration.
The official download covers 2013–Jul 2026 and can reach village level; always show the reference month.
Use for age-structure planning, but call it registered population—not daytime, resident or live population.
This badge confirms same-dataset lineage through an official owner channel. It does not mean every file was downloaded directly from the central GD Catalog.
Use school, student, teacher, and ratio metrics to explain education-service context
Compare schools, students, teachers and ratios to identify areas where education-service capacity needs closer review.
Ministry of Education (candidate) — Owner, grain and schema align with the school dataset, but school-code/name/year records have not been reconciled.
School, student, teacher and ratio measures describe service context—not quality, access or spare capacity.
Use revenue per person, per area, and revenue mix to compare local-finance context
What you can do with this dataCompare local own-source revenue, state allocations, grants and total revenue to understand each local authority's funding structure.
Department of Local Administration — The backend owner confirmed DLA localincome; fields and local-authority grain align. No exact central GD Catalog package was found.
Verified CityMETER lineage covers 2017–2024; a 2025 resource was added later to the catalogue.
Compare own-source, allocated, grant and total revenue. Per-capita/area metrics are CityMETER calculations and are not service quality.
This badge confirms same-dataset lineage through an official owner channel. It does not mean every file was downloaded directly from the central GD Catalog.
Use agency, workforce, density, and contact metrics to explain structural service context
Review the agencies, contact points and workforce categories shown by CityMETER to plan service access or follow-up coordination.
Composite layer — no single matching package verified — CityMETER combines agency and workforce categories, but no public manifest separates the owner and release of each component.
Counts, contacts and workforce describe service structure—not availability, quality or eligibility.
See annual flood extent and recurrence in Phak Hai with a 14-year comparison chart
See where flooding recurred, which years had greater impact and which subdistricts need closer site or preparedness review.
GISTDA disaster layers (candidate) — CityMETER uses annual observed extents for 2011–2024, while the located recurrence/risk package covers 2011–2023 and produces a different output.
Use for recurrent history—not current conditions, a forecast or a statutory risk determination.
Make the observation date more prominent than the word ‘latest’ when showing detected flood areas
View the areas shown as flooded on the stated date and possible exposed locations to choose places for current-condition checks.
GISTDA flood boundary (candidate) — Geometry may come from GISTDA, but endpoint, resource, vintage and exposure-overlay transformation remain unverified.
Read the observation date before the word ‘latest’; uncoloured areas are not automatically safe and a snapshot is not live conditions.
Use flooded area, maximum and average depth, and run time to prioritise follow-up
Review forecast area and depth by model run to choose places for monitoring and current checks with responsible agencies.
Department of Water Resources forecast layer; exact public package not yet verified — Forecast runs and depth output did not match the historical-risk packages reviewed in GD Catalog.
Show run time, method, model and limits. A forecast is not an observation, guarantee or emergency instruction.
See 24-hour risk levels across Thailand with province ranking and forecast run time
See provinces with a 24-hour flash-flood signal and its run time to prioritise areas for official-alert monitoring.
Google Flood Forecasting — CityMETER identifies the Google provider/model, so this is not GD Catalog lineage.
Use as a 24-hour monitoring signal with experimental status and run time—not confirmation of an event or travel advice.
Use station locations, MMI, acceleration, and update time to explain the sensing network
Read station locations, update times, shaking measures and related events to identify areas needing closer earthquake monitoring.
TMD event feed with RUGON / USGS — Located GD records are station inventories or annual aggregates at a different grain from CityMETER's event/sensor view, so lineage is not counted.
Read station location, MMI, acceleration and update time together; freshness must always remain visible.
Use the time-window control as the visual and clearly mark the data object as awaiting confirmation
View the distribution of signals used for fire monitoring to choose areas for checking reports and current conditions.
NASA VIIRS / FIRMS — The field fingerprint points to NASA VIIRS/FIRMS rather than a GD Catalog dataset.
CityMETER's unit still needs confirmation as hotspot, burned area or incident, so counts should not be turned into risk conclusions.
Use hazard type, year, and impacts on people, households, businesses, and assets to tell the historical story
What you can do with this dataCompare disaster types, frequency and reported impacts across places to prioritise preparedness and follow-up checks.
Department of Disaster Prevention and Mitigation — Event–area/village grain, 2014–2024 period and impact fields match the official village disaster-event statistics.
Annual CSVs for 2014–2024; latest verified data year is 2024.
Use for historical impact comparison and follow-up; one event may span several rows and the data is neither forecast nor current conditions.
This badge confirms same-dataset lineage through an official owner channel. It does not mean every file was downloaded directly from the central GD Catalog.
Pair the 24–27 November timeline with people and building context in the event area
Review the sequence and reported locations of the 24–27 Nov 2025 Hat Yai flood to discuss response lessons.
Thai PBS / ThaiHelp case reports — Core case reports come from event-reporting sources rather than a GD Catalog dataset.
Use as a 24–27 Nov 2025 event archive for sequence and context—not as a reusable flood layer or complete impact assessment.
Use inspection-status charts and comments to explain post-event follow-up
See the post-earthquake building-inspection workflow and statuses to understand follow-up steps and responsible contacts.
BMA Building Inspection Dashboard — exact record lineage not yet verified — Attribution points to the BMA dashboard, but no repeatable package or record reconciliation was found.
Use to understand post-event follow-up workflow—not as structural certification, a sensor network or a current inventory of every building.
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