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Urbanization Perceptions Small Area Index, 2025

Identification Information
Citation
Publication Date
20230731
Title
Urbanization Perceptions Small Area Index, 2025
Geospatial Data Presentation Form
vector digital data
Collection Title
U.S. Department of Housing and Urban Development Maps and GIS Data
Publication Information
Publication Place
Publisher
United States. Department of Housing and Urban Development
Abstract
Definitions of “urban” and “rural” are abundant in government, academic literature, and data-driven journalism. Equally abundant are debates about what is urban or rural and which factors should be used to define these terms. Absent from most of this discussion is evidence about how people perceive or describe their neighborhood. Moreover, as several housing and demographic researchers have noted, the lack of an official or unofficial definition of suburban obscures the stylized fact that a majority of Americans live in a suburban setting. In 2017, the U.S. Department of Housing and Urban Development added a simple question to the 2017 American Housing Survey (AHS) asking respondents to describe their neighborhood as urban, suburban, or rural. This service provides a tract-level dataset illustrating the outcome of analysis techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural. To create this data, analysts first applied machine learning techniques to the AHS neighborhood description question to build a model that predicts how out-of-sample households would describe their neighborhood (urban, suburban, or rural), given regional and neighborhood characteristics. Analysts then applied the model to the American Community Survey (ACS) aggregate tract-level regional and neighborhood measures, thereby creating a predicted likelihood the average household in a census tract would describe their neighborhood as urban, suburban, and rural. This last step is commonly referred to as small area estimation. The approach is an example of the use of existing federal data to create innovative new data products of substantial interest to researchers and policy makers alike. If aggregating tract-level probabilities to larger areas, users are strongly encouraged to use occupied household counts as weights. We recommend users read Section 7 of the working paper before using the raw probabilities. Likewise, we recognize that some users may: prefer to use an uncontrolled classification, or prefer to create more than three categories. To accommodate these uses, our final tract-level output dataset includes the ";raw" probability an average household would describe their neighborhood as urban, suburban, and rural. These probability values can be used to create an uncontrolled classification or additional categories. The final classification is controlled to AHS national estimates (26.9% urban; 52.1% suburban, 21.0% rural). For more information about the 2017 AHS Neighborhood Description Study click on the following visit: https://www.hud.gov/program_offices/comm_planning/communitydevelopment/programs/ Data Dictionary: DD_Urbanization Perceptions Small Area Index.
Purpose
This service provides a tract-level dataset illustrating the outcome of machine learning techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural.
Bounding Box
West
-178.227822
East
-65.244234
North
71.390482
South
17.881242
Theme Keyword
Cities and towns
Theme Keyword Thesaurus
lcsh
Theme Keyword
boundaries
economy
location
society
Theme Keyword Thesaurus
ISO 19115 Topic Categories
Place Keyword
United States
Place Keyword Thesaurus
geonames
Temporal Keyword
Access Restrictions
Other Constraints
Use Restrictions
HUD and the dataset and metadata authors assume no responsibility for the use or misuse of the dataset. No warranty, expressed or implied is made with regard to the accuracy of the spatial accuracy, and no liability is assumed by the U.S. Government in general, the dataset creators or the U.S. Department of Housing and Urban Development specifically, as to the spatial or attribute accuracy of the data.
Status
Complete
Maintenance and Update Frequency
As needed
Point of Contact
Contact Organization
U.S. Department of Housing and Urban Development
Delivery Point
451 7th Street SW rm. 8126
City
Washington
State
D.C.
Postal Code
20410-0001
Country
US
Contact Telephone
202-402-4153
Contact Electronic Mail Address
GIShelpdesk@hud.gov
Credit
U.S. Department of Housing and Urban Development
Native Data Set Environment
Esri ArcGIS 12.9.3.32739
Collection
Title
U.S. Department of Housing and Urban Development Maps and GIS Data
Spatial Data Organization Information
Direct Spatial Reference Method
Vector
Point and Vector Object Information
SDTS Terms Description
SDTS Point and Vector Object Type
GT-polygon composed of chains
Point and Vector Object Count
73752
Entity and Attribute Information
Entity Type
Entity Type Label
Urbanization Perceptions Small Area Index
Entity Type Definition
This service provides a tract-level dataset illustrating the outcome of machine learning techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural.
Entity Type Definition Source
U.S. Department of Housing and Urban Development
Attributes
OBJECTID
In ArcGIS, a system-managed value that uniquely identifies a record or feature. (Sequential unique whole numbers that are automatically generated.)
Definition Source
HUD Authors
GEOID
Geographic Identifier - fully concatenated geographic code (State FIPS and district number)
Definition Source
HUD Authors
UPSAI_URBAN
"Raw" probability an average household would describe their neighborhood as as "urban."
Definition Source
HUD Authors
ACS17_Occupied_Housing_Units_Es
UPSAI_SUBURBAN
"Raw" probability an average household would describe their neighborhood as as "suburban."
Definition Source
HUD Authors
UPSAI_RURAL
"Raw" probability an average household would describe their neighborhood as as "rural."
Definition Source
HUD Authors
UPSAI_CAT_CONTROLLED
The author's final UPSAI neighborhood description category. This is based on controlling the share of households predicted to describe their neighborhood as "urban", "suburban", and "rural" to national totals from the American Housing Survey.
Definition Source
HUD Authors
SHAPE
The characteristic appearance or visible form of a geographic object as represented on a map (Coordinates defining the features.)
Definition Source
HUD Authors
Shape_Length
Length of feature in internal units. (Positive real numbers that are automatically generated.)
Definition Source
Esri
Shape_Area
Area of feature in internal units squared. (Positive real numbers that are automatically generated.)
Definition Source
Esri
Distribution Information
Distributor
Stanford Geospatial Center
Metadata Reference Information
Metadata Date
20230808
Metadata Contact
Contact Information
Contact Organization Primary
Contact Organization
Stanford Geospatial Center
Contact Address
Address
Mitchell Bldg. 2nd floor
Address
397 Panama Mall
City
Stanford
State or Province
California
Postal Code
94305
Country
US
Contact Voice Telephone
650-723-2746
Contact Electronic Mail Address
brannerlibrary@stanford.edu
Metadata Standard Name
FGDC Content Standard for Digital Geospatial Metadata
Metadata Standard Version
FGDC-STD-001-1998

Urbanization Perceptions Small Area Index, 2025

Identification Information

Citation
Title
Urbanization Perceptions Small Area Index, 2025
Publisher
United States. Department of Housing and Urban Development
Publication Date
2023-07-31
Identifier
https://purl.stanford.edu/yk823ct8656
Geospatial Data Presentation Form
mapDigital
Collection Title
U.S. Department of Housing and Urban Development Maps and GIS Data
Abstract
Definitions of “urban” and “rural” are abundant in government, academic literature, and data-driven journalism. Equally abundant are debates about what is urban or rural and which factors should be used to define these terms. Absent from most of this discussion is evidence about how people perceive or describe their neighborhood. Moreover, as several housing and demographic researchers have noted, the lack of an official or unofficial definition of suburban obscures the stylized fact that a majority of Americans live in a suburban setting. In 2017, the U.S. Department of Housing and Urban Development added a simple question to the 2017 American Housing Survey (AHS) asking respondents to describe their neighborhood as urban, suburban, or rural. This service provides a tract-level dataset illustrating the outcome of analysis techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural. To create this data, analysts first applied machine learning techniques to the AHS neighborhood description question to build a model that predicts how out-of-sample households would describe their neighborhood (urban, suburban, or rural), given regional and neighborhood characteristics. Analysts then applied the model to the American Community Survey (ACS) aggregate tract-level regional and neighborhood measures, thereby creating a predicted likelihood the average household in a census tract would describe their neighborhood as urban, suburban, and rural. This last step is commonly referred to as small area estimation. The approach is an example of the use of existing federal data to create innovative new data products of substantial interest to researchers and policy makers alike. If aggregating tract-level probabilities to larger areas, users are strongly encouraged to use occupied household counts as weights. We recommend users read Section 7 of the working paper before using the raw probabilities. Likewise, we recognize that some users may: prefer to use an uncontrolled classification, or prefer to create more than three categories. To accommodate these uses, our final tract-level output dataset includes the ";raw" probability an average household would describe their neighborhood as urban, suburban, and rural. These probability values can be used to create an uncontrolled classification or additional categories. The final classification is controlled to AHS national estimates (26.9% urban; 52.1% suburban, 21.0% rural). For more information about the 2017 AHS Neighborhood Description Study click on the following visit: https://www.hud.gov/program_offices/comm_planning/communitydevelopment/programs/ Data Dictionary: DD_Urbanization Perceptions Small Area Index.
Purpose
This service provides a tract-level dataset illustrating the outcome of machine learning techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural.
Bounding Box
West
-179.147339
East
179.778467
North
71.390482
South
-14.548699
Bounding Box
West
-178.227822
East
-65.244234
North
71.390482
South
17.881242
ISO Topic Category
boundaries
economy
location
society
Place Keyword
United States
Place Keyword Thesaurus
geonames
Theme Keyword
Cities and towns
Theme Keyword Thesaurus
lcsh
Resource Constraints
Use Limitation
HUD and the dataset and metadata authors assume no responsibility for the use or misuse of the dataset. No warranty, expressed or implied is made with regard to the accuracy of the spatial accuracy, and no liability is assumed by the U.S. Government in general, the dataset creators or the U.S. Department of Housing and Urban Development specifically, as to the spatial or attribute accuracy of the data.
Legal Constraints
Access Restrictions
otherRestrictions
Other Restrictions
Other Constraints
Legal Constraints
Use Restrictions
otherRestrictions
Other Restrictions
This work is in the Public Domain, meaning that it is not subject to copyright.
Security Constraints
Status
completed
Maintenance and Update Frequency
asNeeded
Collection
Collection Title
U.S. Department of Housing and Urban Development Maps and GIS Data
URL
https://purl.stanford.edu/yk823ct8656
Language
eng
Credit
U.S. Department of Housing and Urban Development
Point of Contact
Contact
HUD eGIS Team
Delivery Point
451 7th Street SW rm. 8126
City
Washington
Administrative Area
D.C.
Postal Code
20410-0001
Country
US
Email
GIShelpdesk@hud.gov
Phone
202-402-4153

Spatial Reference Information

Reference System Identifier
Code
3857
Code Space
EPSG
Version
6.18.3(9.3.1.2)

Distribution Information

Format Name
Shapefile
Distributor
Stanford Geospatial Center
Online Access
Protocol
Name

Content Information

Feature Catalog Description
Compliance Code
false
Language
eng
Included With Dataset
true
Feature Catalog Citation
Title
Entity and Attribute Information
Feature Catalog Identifier
1bb92c1c-4757-4be3-9161-5ccbcc2488c1UUID

Spatial Representation Information

Vector
Topology Level
geometryOnly
Vector Object Type
composite
Vector Object Count
73752

Metadata Reference Information

Hierarchy Level
dataset
Metadata File Identifier
https://purl.stanford.edu/yk823ct8656
Parent Identifier
https://purl.stanford.edu/wc590wy7539.mods
Dataset URI
https://purl.stanford.edu/yk823ct8656
Metadata Date Stamp
2023-08-08
Metadata Standard Name
ISO 19139 Geographic Information - Metadata - Implementation Specification
Metadata Standard Version
2007
Character Set
utf8
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