FEMA NFIP Geospatial Hub (ArcGIS)

It is FEMA's mapped regulatory hazard record for the National Flood Insurance Program: the National Flood Hazard Layer, holding the current effective flood hazard picture for over 90 percent of the U.S. population across 33 layers. Where a claim table tells you what happened at an address, this tells you which rules were governing the water there - the distinction that separates quoting a rating code from knowing where the boundary runs.

The layer inventory reads like the whole apparatus of American flood regulation rendered as geometry: Flood Hazard Zones polygons with zone codes, Special Flood Hazard Area flags and static base flood elevations; FIRM Panels carrying identifiers, effective dates and printed scales; LOMR and LOMA layers tracking every Letter of Map Revision and Amendment since adoption; Base Flood Elevations, Levees, Coastal Transects, Cross-Sections, Hydrologic Reaches, Gages and High Water Marks; plus the Limit of Moderate Wave Action along the coasts. Around the flagship service sit Community Rating System layers, state and local DFIRM services and NFIP community participation datasets.

Within Datadory's Property & Casualty Insurance slice this record owns the where-it-is-governed half of the flood problem; see the rest of the shelf at the property casualty insurance data hub.

One feature per mapped hazard area, typed identically whether it is a Gulf-coast V zone or a remapped X. The block above shows the working core of the Flood Hazard Zones row: FLD_ZONE names the zone family, SFHA_TF answers the regulatory question in one character, STATIC_BFE carries the elevation the area is held to, and V_DATUM says which vertical reference that elevation speaks in - the detail that quietly decides whether your join to survey data is correct or silently offset.

Then the provenance columns, which are what make the layer citable: SOURCE_CIT names the study that drew the boundary, DFIRM_ID ties it to a specific digital map, and STUDY_TYP qualifies how the underlying analysis was performed. Read them together and every polygon arrives with its paper trail attached.

An honesty note: no sample records were transcribed during research, so the rows above describe the documented structure rather than pretending to be captured output. Your sample returns real features scoped to the counties and panels you name - and the schema in the sample is the schema that ships.

Twenty-nine documented fields on the Flood Hazard Zones layer, and unlike most of the catalog they are marked verified - checked against the published schema, not inferred from documentation about the documentation. Only about 86 percent of cataloged datasets clear that bar, which makes this the best-documented hazard layer in the Property & Casualty slice.

Nine fields do the analytical work. FLD_ZONE and ZONE_SUBTY classify the area, SFHA_TF flags the regulatory trigger, STATIC_BFE with V_DATUM fixes the elevation, DEPTH and VELOCITY add physical severity where studies support them, DUAL_ZONE handles areas carrying two classifications, and SOURCE_CIT preserves provenance. The other twenty carry panel identity (FIRM_ID, FIRM_PAN, EFF_DATE, PRE_DATE, SCALE, DFIRM_ID), study qualification (STUDY_TYP, STUDY_ID) and geography keys (ST_FIPS, PCOMM).

Two fields deserve special mention because they bridge to the transactional side: PCOMM carries the participating-community identifier that also rides on NFIP policy and claim extracts, and AR_REVERT through DEP_REVERT document what happens when restored flood-control areas revert - the rare case where a zone tells you its own future. Definitions confirmed against the live schema travel with your sample.

Geography - the United States and its territories, with NFHL digital data reaching over 90 percent of the U.S. population. Coverage is effectively national wherever modern mapping has been adopted; the residual gaps sit where older paper-era maps have yet to be digitized, which is precisely why the panel metadata travels on every feature.

Temporal - the layers hold the current effective picture, revised continuously as new studies and map changes take effect. There is no historical archive here; instead each feature carries its own EFF_DATE, and the LOMR and LOMA layers record every revision since the panel was adopted. Depth comes from the panel dates themselves, not from stacked snapshots.

Granularity - one vector feature per mapped hazard area, at polygon, line or point depending on layer. The zone layer documents 29 attributes per feature, and the geometry resolves to exact containment - the difference between "the ZIP code looks risky" and "this parcel sits inside the AE polygon." Set against the wider catalog - average quality score 7.81 across 1,744 datasets - this record scores 8/10, carried by the verified dictionary and national geometry, held back by the absence of captured sample rows during research.

API, files, or your warehouse. Daily, weekly, or hourly.

Cadence matters more here than on most shelves, because the map moves. When a LOMR takes effect, a boundary that screened clean yesterday can be governed differently today - so treat this as a feed with a refresh loop, not a shapefile snapshot frozen the week someone exported it.

Geometry ships in whatever form your geoprocessing speaks, normalized to the field dictionary above with citations and panel identifiers intact, so every zone assertion you make traces to the study that drew it. Pair it with the transactional side - claims and policies - and the underwriting loop closes: mapped hazard as the frame, paid losses as the outcome. Delivered daily, weekly, or hourly, alone or merged onto one calendar.

• Parcel-level flood screening - resolve addresses and portfolios against current zone polygons, SFHA flags and static base flood elevations in one lookup.
• Underwriting and rating alignment - compare the zone mix around insured points to the rating codes in the book, and surface drift when a revision moves a boundary.
• Map-change monitoring - diff panel effective dates between pulls to catch newly effective LOMRs before renewal season does.
• Cat-model validation - test modeled losses against the governing regulatory hazard by zone family, with source citations keeping every comparison attributable.
• Coverage-gap research - test whether properties inside the Special Flood Hazard Area actually hold coverage, joined against the transactional claims shelf.
• Regulatory citation - quote the effective panel, its date and its scale rather than inferring hazard from a rating field.

Data scientists get the strongest fit: zones and base flood elevations spatially joined to parcel points become a first-class model feature with a 29-field verified dictionary behind it. Developers and builders build in-app flood-zone lookups on the fixed schema without reverse-engineering anyone's API quirks. Investors and quants screen property-backed exposure against mapped floodplain before the thesis gets underwritten. Market researchers and consultants quantify the share of housing stock inside the Special Flood Hazard Area for regional reports. Competitive intelligence teams cite effective panels when rival risk maps disagree. Across all five, the constant holds: geometry with its paperwork attached.

This record owns the mapped hazard. Its nearest neighbour owns the ledger: OpenFEMA Data Sets Hub catalogs 49 datasets holding roughly 73.6 million NFIP policy transactions, 2.72 million claims reaching back to 1970 and the federal disaster-aid ledger. The two join on geography rather than sharing a schema - map and ledger, not one table - and the pairing is worked through properly in OpenFEMA Data Sets Hub vs FEMA NFIP Geospatial Hub.

Around the pair: First Street Foundation Climate Risk Data models forward-looking hazard per building, where this record states the current regulatory zones. NOAA NCEI Billion-Dollar Disasters tabulates catastrophic events as time series. NAIC Research and Insurance Data supplies regulator-grade market analysis. Only this slice answers which governing zone applies to this parcel, under which panel, as of when - because only the regulator publishes the map itself.

Four things worth settling upfront.

First, this is the present tense. The layers hold today's effective picture, not an archive - historical analysis inherits today's boundaries, so date-stamped questions need the panel dates handled explicitly rather than assumed.

Second, no sample rows were captured during research. Field evidence rests on the verified published schema rather than extracted features; your sample closes that gap with real polygons scoped to named counties and panels.

Third, zone semantics need reconciling if you join to the transactional shelf. Rating codes such as A07 fold into zone-family codes such as A, and deciding that mapping rule belongs to your pipeline - not to chance - is part of scoping.

Fourth, terms travel with the data. FEMA publishes its usage conditions alongside the layers and is explicit that no warranty of accuracy accompanies them; quote the map as regulator-issued fact rather than treating it as a warranted risk rating. Deliveries carry the conditions so redistribution decisions are made against the record, not folklore.

Because the raw artifact is a 33-layer geospatial service built for cartographers, and most questions want one table keyed to places. Datadory normalizes the zone polygons to the dictionary above, resolves point-in-polygon against the address or parcel list you supply, aligns the companion layers - levees, transects, cross-sections, high-water marks - onto the same geography, and keeps the feed current so a newly effective revision diffs into your warehouse instead of waiting for someone to re-export a file.

Browse the rest of the industry on the property casualty insurance data hub, the best property-casualty-insurance datasets ranking, or the background reading in the National Flood Hazard Layer, Flood Hazard Zones and National Flood Insurance Program glossary entries.