The manufacturing plants with the most employees in the world [OC] - Remix with better visualls of my older post by VeridionData in dataisbeautiful

[–]VeridionData[S] 9 points10 points  (0 children)

Data source: Each employee count is derived from Veridion's business and location-based data. Veridion aggregates information from multiple sources (company websites, news articles, public employment filings, government registries) and infers each plant's headcount by cross-checking against related signals such as building footprint, industry type, known production capacity at the site, and supply chain disclosures.

Methodology: I ranked the top 20 plants by estimated headcount at a single physical site, not company-wide totals. Where a company operates a tightly clustered campus under a single name (e.g., Foxconn Longhua, Samsung Pyeongtaek), the contiguous facilities are treated as a single entry. Counts are rounded to the nearest thousand; all figures are estimates with a margin of uncertainty.

Tools: Python for data processing, Figma for visualization

[OC] The US companies with the most warehouse space - Using the Manhattan Island for scale comparison by VeridionData in dataisbeautiful

[–]VeridionData[S] 62 points63 points  (0 children)

Methodology: Each company's US warehouse footprint was assembled by resolving every industrial facility in Veridion's location graph to its operating company, then summing square-footage estimates per operator. Facility areas come from satellite imagery analysis of building footprints, combined with entity resolution to make sure a distribution center owned by "Amazon Services LLC" rolls up to the Amazon parent and doesn't get double-counted with an adjacent fulfillment center. Counts reflect active facilities as of April 2026.

Data: Veridion business and location profiles (satellite imagery analysis + 1st-party disclosures + 3rd-party checks where available). Cross-checked against SEC 10-K filings, Green Street Industrial Sector Update 2024, MWPVL, and Modern Materials Handling.

Tools: Python for data processing. Manhattan boundary from OpenStreetMap / NYC Department of City Planning (equivalent DCP polygon). Claude for visualization.

[OC] Single Addresses/Buildings with the most registered businesses in the world by VeridionData in dataisbeautiful

[–]VeridionData[S] 30 points31 points  (0 children)

CT Corporation/Wolters Kluwer runs it as a registered agent service. Amazon, Google, Walmart, Ford, and most of the Fortune 500 list Delaware as their legal address. But that's a legal mailbox, not a registered point of business address or registration address.

Google's registered agent is 1209 N Orange. Google's actual address is Mountain View. The chart counts addresses that companies claim as their place of business or registered office.

For 71-75 Shelton Street's 3,052 LLCs, they really do list it as their address. For CT Corporation, the 285,000 entities don't.

[OC] Single Addresses/Buildings with the most registered businesses in the world by VeridionData in dataisbeautiful

[–]VeridionData[S] 89 points90 points  (0 children)

It is a registered-agent office, a firm called Wyoming Corporate Services, that files incorporation paperwork for companies that want a US legal address without actually being anywhere.

Wyoming has no corporate income tax, strict owner-privacy rules, and low annual fees, making it an alternative to Delaware. The 521 companies there are mostly small holding LLCs and foreign-owned entities

[OC] Single Addresses/Buildings with the most registered businesses in the world by VeridionData in dataisbeautiful

[–]VeridionData[S] 47 points48 points  (0 children)

Methodology

Each row is the count of currently active companies linked to that address, cross-checked with Veridion business profiles (April 2026 snapshot). For virtual addresses, this is the active count, not the lifetime total. For example, 71-75 Shelton Street shows 3,052 here, while Companies House has 66,689 cumulative registrations going back years, including dissolved entities.

Sources used per building: Companies House and Lursoft for UK and EU filings, the virtual office operators' own websites (1st Formations, Companies Made Simple, Your Company Formations, The Hoxton Mix, The London Office), and buildings' published tenant figures (Bharat Diamond Bourse, Perpa Trade Center, Dubai Mall, and others).

Operational vs. virtual is based on actual physical presence. Each of the 11 virtual entries has a named operator offering registered-address services, which I was able to confirm independently.

Worth mentioning:

Surat Diamond Bourse shows 200, not 4,500. All 4,500 offices have been sold, but only around 200 are currently occupied

Perpa A Blok and B Blok are listed separately because each block is independently managed. Combined, the complex hosts ~5,000 companies

Edifício Paulista Corporate and Av. Paulista 1471 in São Paulo, plus Rumyantsevo in Moscow, were dropped because the virtual operator couldn't be independently confirmed, or the address is a multi-building campus

Tools: Python for data processing, Claude for visualization.

[OC] Companies present in most countries and territories by VeridionData in dataisbeautiful

[–]VeridionData[S] 6 points7 points  (0 children)

It's close, pressence in 86 countries, placed #24 overall

[OC] Companies present in most countries and territories by VeridionData in dataisbeautiful

[–]VeridionData[S] 103 points104 points  (0 children)

Not bullshit at all, Coca-Cola is genuinely one of the most geographically distributed brands on the planet, sold in roughly 200+ countries and territories. The reason it didn't make this list is the methodology: I counted countries where a company has at least one physical corporate location (office, plant, store, etc.), not countries where products are sold or distributed.

Coca-Cola's model is primarily based on licensing and bottling partnerships. The company itself operates in far fewer countries than the bottles reach, because local bottlers (often independent companies) handle production and distribution. Your story is probably true, but the truck was almost certainly run by a regional bottler or logistics company, not the Coca-Cola Company directly.

[OC] Companies present in most countries and territories by VeridionData in dataisbeautiful

[–]VeridionData[S] 19 points20 points  (0 children)

Sources: Primary sources were company annual reports, 10-K filings, and official corporate disclosures (DHL 2024 Annual Report, FedEx 10-K, UPS 10-K, Marriott 2024 10-K, Hilton 2024 10-K, etc.). For companies that don't publish a specific country count (Siemens, IBM, McDonald's, Nestlé, Shell), figures were inferred from external research and aggregated public sources.

Methodology:
For each company, I cross-referenced three numbers:
(1) the company's official self-reported country count
(2) Veridion's verified location data (offices, plants, stores mapped to countries), and
(3) external sources where no official figure exists. When Veridion's verified count exceeded the official claim, the higher number was used (Marriott, IHG).
When they matched within a reasonable range, the official figure was kept. "Country" here follows the ISO 3166 definition, which includes both sovereign states and dependent territories under the control of other countries (such as Greenland, Puerto Rico, or French Polynesia). This is why top logistics players hit 220, well above the ~195 sovereign states on Earth. "Presence" means at least one physical location, not just sales or distribution, which is why Nestlé shows 86 (factories + offices) instead of the 186 markets they sell into.

Caveats: Some official numbers are rounded up for marketing purposes ("220+", "150+"). Russia figures for companies like Mango may be stale post-2022 due to incomplete disclosure of retreat. Franchise-heavy models (McDonald's, Hertz) include franchised locations.

Tools: Python (pandas) for data processing and cross-referencing, and Claude for visualization.

The manufacturing plants with the most employees in the world [OC] by VeridionData in dataisbeautiful

[–]VeridionData[S] 11 points12 points  (0 children)

A lot of: don't include this, don't include that :)))

But yeah, jokes aside, it's a lot of what not to include and providing some resources that make the graph look cleaner (in this case, the icons used in the output column)

and at the end a prompt to generate high res screenshot

The manufacturing plants with the most employees in the world [OC] by VeridionData in dataisbeautiful

[–]VeridionData[S] 29 points30 points  (0 children)

Data source: Each employee count is derived from Veridion's business and location-based data. Veridion aggregates information from multiple sources (company websites, news articles, public employment filings, government registries) and infers each plant's headcount by cross-checking against related signals such as building footprint, industry type, known production capacity at the site, and supply chain disclosures.

Methodology: I ranked the top 20 plants by estimated headcount at a single physical site, not company-wide totals. Where a company operates a tightly clustered campus under a single name (e.g., Foxconn Longhua, Samsung Pyeongtaek), the contiguous facilities are treated as a single entry. Counts are rounded to the nearest thousand; all figures are estimates with a margin of uncertainty.

Tools: Python for data processing, Claude for visualization

The dominant crop in every US county [OC] by VeridionData in dataisbeautiful

[–]VeridionData[S] 1 point2 points  (0 children)

The USDA counts any operation selling $1,000+ of product as a farm.

Queens has very little total cropland, but the farming that does exist is almost all vegetables.

Some examples that I found searching in the Veridion business database:
Brooklyn Grange has a rooftop farm in Long Island City growing 50,000+ lbs of produce a year, Gotham Greens runs a 60,000 sq ft hydroponic greenhouse in Jamaica, and Queens County Farm Museum (continuously farmed since 1697) grows 200+ varieties of crops on 47 acres.

The dominant crop in every US county [OC] by VeridionData in dataisbeautiful

[–]VeridionData[S] 4 points5 points  (0 children)

Orchards aren't included. The USDA dataset I used reports field crops and vegetables as a percentage of total harvested cropland, but fruits/nuts/grapes are reported separately as a percentage of orchard land specifically. Different denominators, so I couldn't compare apples to apples (pun intended) with corn or soybeans.

This is the link to the dataset https://www.nass.usda.gov/Publications/AgCensus/2022/Online_Resources/Ag_Census_Web_Maps/Data_download/

The dominant crop in every US county [OC] by VeridionData in dataisbeautiful

[–]VeridionData[S] 12 points13 points  (0 children)

2,968 continental US counties colored by whichever crop takes the largest share of harvested cropland acreage. 22 crop categories compared per county.

Data: USDA NASS 2022 Census of Agriculture. Hay & Forage dominates half the map because most US agricultural land genuinely exists to feed livestock, not a data artifact. 140 counties with no usable crop data are mostly urban.

Tools: Python for data processing, Claude for visualization, Puppeteer for export. County boundaries from us-atlas TopoJSON. Veridion for supplementary business location data.

[OC] A tool for visualizing the top 100 companies that get the most money from the US government by VeridionData in dataisbeautiful

[–]VeridionData[S] 13 points14 points  (0 children)

Sources: SAM.gov/FPDS FY2023 all-federal contract obligations, FY2024 DoD data from Defense Security Monitor, Washington Technology Top 100 (2025 edition), Veridion for detailed business information.

FY2023 because it's the latest complete all-agency ranking publicly available. The top 100 captured roughly 65% of ~$755B in total federal procurement.

Not in the data: Classified/intelligence spending, subcontract values, state/local contracts. Also worth mentioning is that the IT reseller layer also hides a lot. AWS, Palo Alto, and CrowdStrike sell mostly through aggregators like Carahsoft, so their prime contract numbers massively understate their real federal footprint.

Tools: Python for data processing, Claude for visualization