National Structure Inventory (NSI)
March 2019
3. Overview of the National Structure Inventory
5. USACE-Developed NSI Base Data
6. 2019 Base Quality Level Data Generation
National Structure Inventory (NSI)
Provide access to point based structure inventories with attribution to support evaluation of consequences from natural and man made hazards.
To support all federal agencies interested in collaborating on structure inventory data
- Provide access to the data to as many people and agencies as possible.
- Improve the quality of the data.
- Improve the ability for the U.S. to respond to disasters.
- Improve the ability for the U.S. to plan for future disasters.
Overview
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.
The value of the NSI points represented through Hexbins at the national scale
The structure inventory databases contained in the NSI are arranged by the quality level of the data in the structure inventory. There are two quality level categories: Base and High. The Base quality level contains the National Structure Inventory Base layer 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 each inventory within the database contains a series of required attributes or fields that describe each point in the inventory. Section 3 provides a detailed list of the fields required for structure inventories to be contained in the NSI repository.
NSI Data Quality Levels
There are potentially two NSI data quality levels for a given geographic location. The Base quality data is available for the contiguous United States as well as Hawaii and Alaska, while High quality data availability will vary subject to where more detailed inventories have been created and uploaded.
| Base | The Base data supports performing portfolio-level decisions across the entire nation. Portfolio-level decisions require the Base data to have internal consistency and universal coverage. The NSI Base level also provides a basis from which more detailed inventories can be created by users. Base data has quality issues, but provides a consistent dataset characterized by nationally appropriate data sources and assumptions. For regional analyses, users should review the Base data for quality assurance prior to use. The USACE created the National Structure Inventory Base data layer, which provides data for the entire contiguous United States, plus Alaska and Hawaii. Section 4 describes the processes used to construct the NSI Base data from numerous nationwide datasets. In addition, Section 4 serves as the metadata for the Base quality data. |
|---|---|
| High | The data in this quality level represent high resolution datasets such as user formatted parcel data and/or ground surveys. The data contained in the High quality level is appropriate in situations where consequences are driving decision-making, and the alternatives under consideration have high costs. Eventually qualified users will be able to upload High quality datasets with metadata documentation of the processes used to generate the datasets. This is a future feature, however, and there is currently no functionality in the NSI databases to upload or edit structure inventory data. |
Disclaimer: It is up to the individual downloading data from the NSI to review the metadata and assess whether the methodologies are consistent with their needs.
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 using the NSI. To successfully upload datasets to the NSI, the datasets must contain the required attributes – with the fields populated whenever applicable. The analyst is responsible for giving approximate values for each attribute, and documenting the assumptions in providing those attributes. The full list of fields for the NSI are:
This section of the document serves as the metadata for the NSI Base data provided by USACE. The development team converted parcel data available through CoreLogic, business location data available through Esri/Infogroup, and HAZUS based previous versions of the NSI where those datasets were unavailable. This Base quality data is not an exact representation of reality, but rather contains many county-level, state-level, or regional assumptions applied to individual structures, often by random assignment. As such while county or other large aggregations of structures will ideally be accurate on average, individual structure characteristics may not be accurate. Although these and other accuracy issues exist, the Base dataset functions as an available common and consistent standard for the United States. Appropriate uses include as a foundation on which to develop more detailed and locally accurate inventories, use with necessary “ground truthing”, 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 inconsistent application or availability of regional assumptions may introduce bias into the analysis.
The following sections describe the processes used to produce the NSI Base data.
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”).
| Source | Database | Dataset | Description |
|---|---|---|---|
| HAZUS | Bndrygbs.mdb | hzMeansCountyLocations | Provides county level price adjustments. |
| hzExposureOccupB | Informs estimated dollar per square foot used in structure valuation. | ||
| hzCensusBlock | Provides the structure building schemes and block type. | ||
| flSchemeCoastal, flSchemeRiverine, flSchemeGLakes | Provides information on foundation type and height. | ||
| MSH.mdb | flGenBldgScheme | Provides the construction type distributions and NFIP entry year for structures. | |
| USACE | NSI 2015 | Base layer | Used in any Census Block that lacks ESRI or CoreLogic data. |
| Homeland Infrastructure Foundation-Level Data | CoreLogic | County Level Data | Parcel polygons and associated data tables; used for initial spatial location and Occupancy Type. |
| Esri | Business Layer | InfoGroup | Provides initial structure location; NAICS code informs occupancy type, number of employee field informs square footage estimate and population weighting. |
| Microsoft | Building Footprints | State level polygons | Paired with parcel polygons to improve structure location and to inform structure aggregation. |
| U. S. Census Bureau | American Community Survey | Population, Demographics | Informs population growth estimates, disability rates, and age distribution. |
| Characteristics of New Housing | Annual, Various | Provide structure characteristic data such as number of stories and square feet. | |
| Longitudinal Employer-Household Dynamic Database | Population Data | Contains worker counts by origin and destination census blocks. | |
| NCES | Schools Database | School Data | Contains the locations of schools, number of teachers and students per school by census block. |
| U. S. Geological Survey | National Elevation Dataset | 10 Meter Dataset (?) | Provides raster ground elevation (in feet) data. |
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.
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.
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.
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 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.
| Damage Category | Occupancy Type Name | Description |
|---|---|---|
| Residential | RES1-1SNB | Single Family Residential, 1 story, no basement |
| Residential | RES1-1SWB | Single Family Residential, 1 story, with basement |
| Residential | RES1-2SNB | Single Family Residential, 2 story, no basement |
| Residential | RES1-2SWB | Single Family Residential, 2 story, with basement |
| Residential | RES1-3SNB | Single Family Residential, 3 story, no basement |
| Residential | RES1-3SWB | Single Family Residential, 3 story, with basement |
| Residential | RES1-SLNB | Single Family Residential, split-level, no basement |
| Residential | RES1-SLWB | Single Family Residential, split-level, with basement |
| Residential | RES2 | Manufactured Home |
| Residential | RES3A | Multi-Family housing 2 units |
| Residential | RES3B | Multi-Family housing 3-4 units |
| Residential | RES3C | Multi-Family housing 5-10 units |
| Residential | RES3D | Multi-Family housing 10-19 units |
| Residential | RES3E | Multi-Family housing 20-50 units |
| Residential | RES3F | Multi-Family housing 50 plus units |
| Residential | RES4 | Average Hotel |
| Residential | RES5 | Nursing Home |
| Residential | RES6 | Nursing Home |
| Commercial | COM1 | Average Retail |
| Commercial | COM2 | Average Wholesale |
| Commercial | COM3 | Average Personal & Repair Services |
| Commercial | COM4 | Average Professional Technical Services |
| Commercial | COM5 | Bank |
| Commercial | COM6 | Hospital |
| Commercial | COM7 | Average Medical Office |
| Commercial | COM8 | Average Entertainment/Recreation |
| Commercial | COM9 | Average Theater |
| Commercial | COM10 | Garage |
| Industrial | IND1 | Average Heavy Industrial |
| Industrial | IND2 | Average light industrial |
| Industrial | IND3 | Average Food/Drug/Chemical |
| Industrial | IND4 | Average Metals/Minerals processing |
| Industrial | IND5 | Average High Technology |
| Industrial | IND6 | Average Construction |
| Commercial | AGR1 | Average Agricultural |
| Commercial | REL1 | Church |
| Public | GOV1 | Average Government Services |
| Public | GOV2 | Average Emergency Response |
| Public | EDU1 | Average School |
| Public | EDU2 | Average College/University |
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.
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.
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 (feet) are determined using the USGS National Elevation Dataset (NED), based on the structure location (website: https://nationalmap.gov/elevation.html).
| API | Application Programming Interface |
|---|---|
| EGM | Economic Guidance Memorandum |
| FEMA | Federal Emergency Management Agency, Department of Homeland Security |
| FIPS | Federal Information Processing Standard |
| FIRM | Flood Insurance Rate Maps |
| GIS | Geospatial Information Systems |
| HAZUS | FEMA’s Hazards of the United States |
| LEHD | U.S. Census Bureau’s Longitudinal Employer-Household Dynamics Database, Department of Commerce |
| NED | National Elevation Dataset |
| NFIP | National Flood Insurance Program |
| NSI | National Structure Inventory |
| USACE | U. S. Army Corps of Engineers, Department of Defense |
| USGS | U. S. Geological Survey, Department of the Interior |