The asset locations behind each risk score.
3.2M ISIN-attributed asset locations. Coordinates, operating context and company connections provide the foundation for analysing nature, climate and geopolitical risk where businesses actually operate.
Why it matters
Risk depends on the site.
A factory, a data centre and a store can share a postcode but depend on different things. Water availability, access roads, power networks and surrounding ecosystems all shape what a location can support. The coordinate is where those connections become visible.
Explore the layers
Explore risk around each site.
Each asset location can be viewed alongside multiple layers of environmental, political and infrastructure data. Analysts can examine water stress, land use, heat exposure and the networks supporting a site, then compare those conditions across a company's global operations. This connects local conditions to risks and dependencies across an entire portfolio. The map below illustrates how these layers come together around a group of sites in Saint-Ghislain, Belgium.
What's in the dataset
Asset Location Data supports every AlphaX risk layer. The precision of a risk assessment depends on the coordinate used; financial attribution depends on linking the asset to the right entity. Alongside name, address, latitude and longitude, each record includes information analysts can use to assess its reliability and materiality.
Coordinate precision
Coordinates precision is one of rooftop, street, neighbourhood, city or country, so an analyst can exclude anything coarser than street level before running a basin-level intersection.
Live operating status
Operational, under construction, planned, closed, mothballed, temporarily closed, closing, or under exploration, read alongside the date that status was determined.
Capacity and materiality
Capacity and units capture operational throughput where it is disclosed. Without it, a site count cannot be weighted for its operational importance.
Full provenance on every row
Source, attribution and research date travel with the record, alongside the source's own asset description next to our standardised classification, so any mapping decision can be re-examined.
Six stages of asset discovery
Finding corporate facilities across sectors and countries requires several methods. A central orchestrator coordinates six independent stages. Each reads and writes the same strictly typed asset schema, so mining registries and company site maps produce records in a consistent format.
Profiling
Build the company's corporate family, subsidiaries and ownership structure first, every later stage reads this.
Dataset gathering
Pull candidate assets from structured public registries covering specific sectors, then resolve each row to the company.
Web discovery
An agentic system with search tooling nominates candidate URLs, fetches them, and extracts structured asset rows targeting registry gaps.
Enrichment
Geocode addresses that arrive without coordinates, record the source and precision of every coordinate, then classify the asset type.
Validation
Clean, prioritise, deduplicate, and run a coverage check against the company's expected footprint.
Correction
Run a final verification pass on the coordinates in each asset record.
Check accuracy and find missing assets.
Asset location data always contains some error. AlphaX uses three different checks to find it, each with independent failure modes.
Geospatial plausibility
Ocean boundary layers flag land-based asset types geocoded into the sea, with genuine offshore types such as rigs and wind farms excluded. Urban boundary layers flag large-scale mining sites placed in the middle of a city.
Statistical deduplication
A duplicate is the same company, the same asset type, within a defined proximity. Candidates are confirmed individually, then collapsed to the more authoritative record. Every merge keeps an audit trail.
Coverage QA
Rather than only asking whether each asset found is correct, an automated check asks whether the company's expected footprint has been captured at all. A high-severity gap re-triggers discovery, up to three times.
50 asset types, informed by company revenue data
Raw descriptions arrive as things like "Fab 4", "Assembly Plant" or "Coal Unit 1". We map them to a custom taxonomy of 50 asset type codes in 14 top-level groups, grounded in the industry classification NAICS 2022 but adapted for physical and nature materiality.
Deterministic lookup
A persistent mapping table resolves descriptions seen before without another model call.
Model assignment
Unseen descriptions go to a language model, which assigns a best-fit code and a confidence flag. High-confidence results join the mapping table.
Revenue disambiguation
Ambiguous cases ("Manufacturing Plant" could be food, chemical or machinery) are resolved per company using that company's own NAICS revenue breakdown. Only the remainder reach a model again, with full company context.
Monthly publication, with sector-specific checks
We publish an updated asset database every month. Investigation cycles vary with the pace of change in each sector and region.
Offices and plants that stay put are re-investigated on a slower cycle: the physical reality barely moves year to year.
Data centres are built quickly enough that an annual investigation cycle would leave the map out of date.
Countries or regions seeing heavy infrastructure investment or regulatory change get prioritised the same way: the cadence follows the risk.
How this compares to a conventional asset location database
Asset-level data, in plain terms
The questions we get asked most often about coverage, identifiers and delivery.
What is asset-level data (ALD)?
Asset-level data , often shortened to ALD, describes a company at the level of the individual sites it operates: a mine, a plant, a warehouse, a port terminal, each with coordinates, a type, and a link back to the listed parent. It is the alternative to treating a company as a single point at its registered head office, which is what most company-level datasets do. Risk is physical and local, so a company-level average hides the exposure that matters.
What is an asset location database?
A structured record of where companies physically operate, coordinates, asset type, operator, and the corporate entity each site belongs to. AlphaX's covers 3.2M ISIN-attributed locations within a broader discovery base of 8M+ geolocated facilities. Company coverage spans 12,000+ listed entities, including the MSCI ACWI IMI plus additional companies, across 47 markets. The same term is also used in IT asset management, for tracking laptops and equipment; this is the corporate-facility sense.
Which identifiers link an asset to a listed company?
ISINs, LEIs and exchange tickers help identify companies and their securities, and corporate registries identify subsidiaries, but none of them reaches down to an individual site. Knowing that a plant belongs to a particular subsidiary, and that the subsidiary rolls up to the ISIN in a portfolio, has to be inferred and scored. AlphaX uses Fellegi–Sunter probabilistic record linkage for that step, and drops non-matches rather than guessing.
How does this compare with business directory or firmographic data?
They answer different questions. Directory and firmographic sources are strong on legal entities, ownership hierarchies and registered addresses, and that entity backbone is a useful input. What they generally do not provide is a verified operating footprint: the physical sites, typed and geolocated, with the coverage checks needed to say what is missing as well as what is present. AlphaX builds that layer, and resolves ambiguous asset types against the company's own revenue drivers.
How often is the asset location database refreshed?
Monthly, tiered by how fast each input actually changes, with a continuous verification pipeline running between publications. Corrections feed back into the next run rather than waiting for an annual manual pass.
Is the data available via API?
Yes: asset locations and the resolved risk layers can be delivered into your own models and research stack, alongside flat-file delivery. There is no public developer portal: access is configured per client, so get in touch and we will scope the integration.
Asset locations connect the risk layers
Nature, climate and geopolitical risk are all sampled at the coordinates Asset Location Data provides. Building it required the same financial-data foundation that most competitors sell as separate products, if they sell them at all.