Mission

Provide access to point based structure inventories with attribution to support evaluation of consequences from natural and man made hazards.

Vision

To support all federal agencies interested in collaborating on structure inventory data,

Goals

1. Provide access to the data to as many people and agencies as possible.
2. Improve the quality of the data.
3. Improve the ability for the U.S. to respond to disasters.
4. Improve the ability for the U.S. to plan for future disasters.

Purpose

The National Structure Inventory (NSI) is a system of databases containing structure inventories of varying quality and spatial coverage. The purpose of the NSI databases is to facilitate storage and sharing of point based structure inventories used in the assessment and analysis of natural hazards. Flood damage analysis is the primary usage, but sufficient data exists on each structure to compute damages due to other hazard types. The purpose of this document is to describe the NSI data structure and to document the processes utilized to produce the 2019 NSI base data.

Overview of the National Structure Inventory

The National Structure Inventory Base layer is created by the U. S. Army Corps of Engineers (USACE). The USACE Base data layer was created to simplify the GIS pre-processing workflow for the USACE Modeling Mapping and Consequence center. The NSI is a repository of point structure inventories with a structured RESTful API service, and the inventory contains a series of required attributes or fields that describe each point in the inventory. 

NSI Required Fields

The NSI application programming interface (API) requires structure inventory attributes to be consistent across all datasets in the NSI databases. These required attributes exist to meet the computational constraints of the software consuming the NSI. To successfully upload datasets to the NSI, the datasets must contain the required attributes with the fields populated. The analyst is responsible for giving approximate values for each attribute, and documenting the assumptions in providing those attributes. The required attributes for the NSI are:

USACE-Developed NSI Base Data

This section of the document serves as the metadata for the NSI Base data provided by USACE. The document assumes a familiarity with GIS, and FEMA's HAZUS database. The HAZUS (2010) database provided the bulk of the base data included in the NSI Base layer (website: https://msc.fema.gov/portal/resources/hazus). The team converted Census Block aggregated HAZUS data into disaggregated, discrete points, and modified and improved the attributes of the points to preserve the aggregate values. This Base quality data is not an exact representation of reality, but rather a pseudo inventory with homogeneous assumptions across the United States. Although there are some accuracy issues, the Base dataset functions as a minimum standard for the United States. Appropriate uses include situations where more accurate data is too costly to produce and cannot be created, or when limited by time constraints. Another general use of the NSI Base dataset is for assessments on a national level, where regional assumptions may introduce bias into the analysis.
The current NSI Base data represents the 2010 HAZUS dataset. The following sections describe the processes used to produce the NSI Base data.

2019 Base Quality Level Data Generation

In 2018 and 2019 the NSI team created the data using the following inputs from numerous input data sources. The two main sources of data are CoreLogic parcel files for residential structures and ESRI business layer for non-residential structures. Each data file used contains data on the type of development that exists at a given location. For example, the parcel data often stated whether a structure was Single Family Residential or a multi-family structure; ESRI data reported the NAICS code for each structure. These source data categories were converted to a format consistent with one of 40 different HAZUS Occupancy Type classification. Residential Occupancy types are further revised later in the process based on other structure characteristic assignment, with single family residences’ “RES1” classification being appended with the number of stories and basement status (e.g. “RES1-2SNB”).

Main Data Sources

Structure Placement Refinement

The XY location for each structure is initially provided by the source data, such as the centroid of the parcel or the geo-reference of a business’s address. However, the NSI Generator modifies these initial locations by matching the structures to Microsoft buildings footprints within the same parcel polygon. If there are multiple footprints within a parcel polygon, structures are placed in the largest footprints first. If there are multiple structures types within a parcel polygon, then structures are paired with footprints in the following order: schools first, then commercial structures, and finally residential structures. Structures are placed in unpaired footprints until all footprints are paired with structures, at which point multiple structures of the same type may be stacked within the same footprint

Structure Aggregation

If structures are stacked within the same location, then the structures may be partially or completely merged together. Residential units stacked at the same location are assumed to be multi-family structures; the number of units will be used later to update the occupancy type of the structure (for instance, more than 50 units would mean that a residential structure would be identified as a RES3F). However, commercial structures are not completely merged; instead, the NSI generator links the stacked structures so that they share certain characteristics such as number of stories and construction material. Each commercial business within the stack will receive a weighted portion of the square footage which informs the valuation of each structure.

Population Growth and Assignment to Structures

County level population estimates were available for 2017, however the most recent block level residential population estimates are from the 2010 Decennial Census. To account for this difference, the NSI Generator was provided a table that recorded the number of increased persons residing in a county above 2010 population levels (counties that lost population received no adjustment). The NSI estimates block level population growth in an iterative process until the total increased population for the county is depleted. Population is first added to structures that had no housing units in 2010 but now have housing units in the newly generated inventory. Next population is distributed to blocks whose number of housing units is greater in the NSI than it was in the 2010 census. Finally, population is randomly assigned to census blocks until the population growth is fully distributed.

Commercial worker population was derived from the U. S. Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) database (website: https://lehd.ces.census.gov/). This database contains counts for the number of residents leaving a census block to work and the number of workers arriving in a census block. Departing workers are subtracted from the residential population; as are enrolled students.

Once block level population estimates are made, population is assigned to particular structures within the block. Population is assigned from 8 separate pools, reflecting combinations of Day and Night, Over and Under 65 years of age, and Workers and Residents. Population is assigned from commercial population pools to commercial businesses weighted by number of employees, and from residential population pools to residences weighted by number of housing units. The assignment process also accounts for the relative likelihood of those over 65 years of age to work or stay at home. Schools based on NCES data had student estimates added directly to those structures in addition to the teachers added through the worker assignment process

Structure Valuation

The HAZUS dataset contains dollars per square foot for each Occupancy Type; these values are taken from 2014 RS Means estimates, except for RES1 structures which are taken from 2006 estimates. These values are indexed to 2018 prices levels using the ENR Construction Cost Index. Dollars per square foot estimates are then multiplied by the square footage estimate for each structure to obtain the structure value.

These replacement values for structures are then depreciated in order to obtain depreciated replacement value; each structure is depreciated by 1% per year for the first 20 years, after which it is assumed that routine maintenance would keep structure values at 80% of their replacement values.

Content values are obtained by multiplying structure values against an occupancy type specific structure to content value ratio. It is important to note that RES1 structures assumed content values are equal to structure values; this is because USACE Economic Guidance Memorandum (EGM) depth damage functions implicitly assume such a relationship. If NSI users are not relying on the USACE EGM curves, they should instead assume a 50% relationship unless better data is available to suggest otherwise.

Occupancy Type

Occupancy types are used to help determine structure valuation, population, and to define structure damage criteria (for flooding). The occupancy types are based on the FEMA occupancy type definitions with further classification to meet the criteria for USACE economic guidance memorandums. The table of occupancy type names and their descriptions are below. These are utilized to support the base level data and are not required for other datasets.

Construction Type

The hzCensusBlock table contains an attribute for building scheme, and this attribute is related to the flGenBldgScheme tables from the MSH.mdb database. The building scheme attribute is used to define structures as Wood, Masonry, Concrete Block, Manufactured, and Steel using random assignment based on the probabilities indicated in the HAZUS table. Structures that were estimated to be more than 5 stories are assumed to be of steel construction.

Foundation Type and Height

Based on the information in the hzCensusBlock table for building scheme and the tables in the MSH.mdb database that also contain the building scheme attribute, structures are classified into Slab, Pier, Unattached, and Basement using random assignment.

Foundation height (in feet) are calculated and provided based on the foundation type and whether the structures are in blocks that were dated pre- or post-NFIP.

Vehicles

Vehicle values for each structure are based on the number of housing units for residential structures or the number of employees for commercial structures.

Ground Elevations

Ground elevations (feet) are determined using the USGS National Elevation Dataset (NED), based on the structure location (website: https://nationalmap.gov/elevation.html).

Acronyms