Construction Materials Data Provider · Head-to-head
Monthly Global Cement Production Data (CICERO) vs BMAT — Global Building Facade Material Dataset
Which construction materials data provider data fits your job: Monthly Global Cement Production Data, or BMAT — Global Building Facade Material Dataset. API, files, or your warehouse. Daily, weekly, or hourly.
Monthly Global Cement Production Data (CICERO)
BMAT — Global Building Facade Material Dataset
Coverage, side by side
| Monthly Global Cement Production Data | BMAT — Global Building Facade Material Dataset | |
|---|---|---|
| Granularity | Country-level series columns — 24 countries live, roughly 57 geographies total including Chinese provinces | Individual buildings — 22.09 million footprints across 73 cities |
What each contains
They tie on 1 attribute. Pick by fit, not by loyalty.
| Monthly Global Cement Production Data | BMAT — Global Building Facade Material Dataset | |
|---|---|---|
| Documented fields | 9 | 8 |
| Field types | Month stamp (YYYYMM) plus one numeric kilotonne column per country-series, with flow and material encoded in the code suffix; a metadata JSON adds title, organisation, URI, geography and units per series | One row per building: enum material, temporal materials text, Overture subtype, class, names and sources, plus polygon geometry |
| Signature fields | `MEX.CEM.PRD.KTN` (Mexico monthly cement production); `JPN.CLK.PRD.KTN` (clinker); `ESP.CEM.EXP.KTN`; `KOR.CLK.STK.KTN`; `JPN.QLM.PRD.KTN` (quicklime) | `material` (concrete); `materials` (brick -> concrete); `names` (Beijing Railway Station); `geometry` (POLYGON(...)) |
| Shared concepts | Geography key, time dimension, declared units | Geography key, time dimension, declared units |
| Entity granularity | Country-level series columns — 24 countries live, roughly 57 geographies total including Chinese provinces | Individual buildings — 22.09 million footprints across 73 cities |
| Overlap verdict | Structural overlap only: place, time, unit. Monthly national production tonnage versus per-building material labels — no field joins without your own mapping. | — |
What each does better
Monthly Global Cement Production Data
Monthly cadence on physical production. Cement output is among the fastest-reading industrial signals there is — construction activity shows up in kilotonnes within weeks of being reported. The current version reaches August 2025 for seven major producers including China and India, and new series keep appearing: Egypt, Sri Lanka and Viet Nam gained monthly lines in the latest revision, and Japan extended back another 16 years.
Depth of history. Taiwan starts in 1982, Thailand 1987, Japan 1989 — four decades of monthly observations long before most industrial series begin. The companion clinker-trade file separates imports and exports so domestic production can be reconciled against apparent consumption (see clinker).
Traceable provenance per column. The metadata file names the publishing organisation, source document URI, geography and unit for each of the 175 series, so any single number can be traced to whoever printed it. And because no interpolation or estimation is applied, gaps mean gaps — never invented values.
Emissions-ready scope. Quicklime and slaked lime series ride alongside clinker precisely because lime calcination emits CO2 like clinker does, making the file a natural input to cement CO2 work — the curator publishes it as the monthly companion to his global cement emissions record.
BMAT — Global Building Facade Material Dataset
Resolution of a million-fold smaller grain. CICERO stops at the country border; BMAT describes single structures. Each of the 22.09 million rows is a polygon with a name — Beijing Railway Station is in there — a subtype and class, and a material verdict. No production statistic anywhere in this catalog reaches that level of detail on the built environment.
Nine-category classification, not two. Brick, concrete, glass, metal, stone, stucco, tile, wood, other — enough resolution to separate a glass curtain wall from a stone-clad tower in the same block. A production table cannot tell a skyscraper from a warehouse.
Time lives inside the rows. Because imagery spans 2007–2025, the materials field records what a facade used to be: brick -> concrete is urban renewal written as data. That makes renovation and rebuilding monitoring possible from a static snapshot — stated applications include energy modelling, embodied carbon accounting, urban heat simulation and disaster vulnerability assessment.
City coverage weighted where buildings actually are. About 37 of the 73 cities are Chinese, alongside Tokyo, Seoul, Mumbai, New York, London, Paris, Sao Paulo, Lagos, Nairobi, Sydney and more — a deliberately dense sample of the world's tallest, fastest-changing building stock.
Where they're equivalent
Both are academic public-interest compilations, not vendor products. CICERO is maintained by a researcher at the Center for International Climate Research in Oslo; BMAT was authored at Peking University's Institute of Remote Sensing and GIS with National Natural Science Foundation of China funding (grant 42371468). Both were released as scholarly contributions meant for reuse.
Both carry verified field dictionaries and documented units. CICERO declares kilotonnes throughout; BMAT's enum categories are fixed in the GeoPackage schema. Neither leaves you guessing what a value means.
Neither observes anything itself.
The verdict
Verdict: sample both, pick by fit — different questions, different machines.
Take Monthly Global Cement Production Data (CICERO) if your unit of analysis is a country-month. Demand forecasting for cement and construction inputs, emissions accounting from clinker volumes, trade-balance studies via the clinker files, nowcasting construction activity ahead of quarterly statistics, long-run industrial history back to 1982. Accept that a country is as fine as it gets. The workflow patterns suit the data scientists use cases.
Take BMAT — Global Building Facade Material Dataset if your unit of analysis is a building. Urban heat and energy modelling needing facade type per structure, embodied-carbon estimates at city scale, disaster exposure screening by construction material, renovation monitoring through the temporal materials history, site selection against named buildings. Accept 73 cities and no volumes. That shape fits the journalists academics use cases.
Rule of thumb: if the question begins "how much did China produce", take CICERO; if it begins "what is this district built of", take BMAT. Both live in the construction materials data hub.
Sample both, pick by fit. See Monthly Global Cement Production Data · See BMAT — Global Building Facade Material Dataset
Or take both in one feed
Yes — their axes barely intersect, which is why they stack cleanly. A defensible loop: read national cement demand pressure off CICERO's monthly kilotonnes (see building materials), then use BMAT to characterize the receiving stock — which of the 73 cities holds concrete-heavy towers versus brick low-rise, and which facades flipped since 2007. Production tells you how fast material flows into the system; facades tell you what it became when it got there.
Two cautions straight from the records. First, there is no join key — CICERO rows are YYYYMM plus country codes, BMAT rows are Overture polygon IDs — so the bridge is a city-to-country allocation you own. Second, keep the semantics apart: CICERO measures flows per period while BMAT labels states per building; aggregating one to the other's grain throws away exactly the detail you paid for.
Datadory ships either record alone or both merged onto one calendar, delivered daily, weekly, or hourly — your call. Or take both in one feed.
API, files, or your warehouse. Daily, weekly, or hourly.
Fair questions
Is Monthly Global Cement Production Data (CICERO) better than BMAT — Global Building Facade Material Dataset?
Better at different jobs, tied on substance rather than score — CICERO runs an 8/10 rubric against BMAT's 7/10. CICERO wins when the question is how much: monthly cement and clinker output per country in kilotonnes, 175 series reaching back to 1982. BMAT wins when the question is what of: nine-category facade material labels on individual building footprints across 73 cities.
Do the two datasets cover the same ground anywhere?
Only at the edges. Both are Construction Materials datasets with verified field dictionaries, both carry geographic identity as a first-class field, and both reach back to imagery or records from the early 1980s. Beyond that they share nothing measurable: CICERO keys to country-month observations, BMAT keys to building polygons — no cement tonnage appears in BMAT, no footprint appears in CICERO.
Which dataset covers more of the world?
Depends on depth versus spread. CICERO's live-updated core spans 24 countries while its full file stretches to roughly 57 geographies including Chinese provinces through historical UN series; national coverage is broad but shallow per observation. BMAT concentrates 22.09 million buildings inside just 73 major cities — about 37 of them Chinese — trading breadth for per-building detail neither production dataset can offer.
Which one changes over time?
BMAT is a snapshot — imagery from 2007 to 2025 frozen into its second release — though it preserves per-building material history like brick becoming concrete, so time lives inside the rows rather than in future deliveries.
Can Datadory deliver both datasets together?
Yes. Either record arrives alone or merged onto one delivery calendar, delivered daily, weekly, or hourly — your call — with field definitions attached. Name the countries, months, cities and material categories when you request the sample and it lands pre-cut; the join between them is a geography mapping you own, not a shared key.