20200514
13164800
1.0
FGDC
002
Urbanization_Perceptions_Small_Area_Index
-19840230.390300
-7262954.906100
2023652.798600
11537127.319200
1
https://www.arcgis.com/sharing/rest/content/items/9b13dc7e75474eab9a4a643d91c34f58/info/thumbnail/thumbnail1753813658238.png
Projected
GCS_WGS_1984
Linear Unit: Meter (1.000000)
WGS_1984_Web_Mercator_Auxiliary_Sphere
<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/2.9.0'><WKT>PROJCS["WGS_1984_Web_Mercator_Auxiliary_Sphere",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Mercator_Auxiliary_Sphere"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",0.0],PARAMETER["Standard_Parallel_1",0.0],PARAMETER["Auxiliary_Sphere_Type",0.0],UNIT["Meter",1.0],AUTHORITY["EPSG",3857]]</WKT><XOrigin>-20037700</XOrigin><YOrigin>-30241100</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102100</WKID><LatestWKID>3857</LatestWKID></ProjectedCoordinateSystem>
150000000
5000
20230808
13562500
FALSE
20230807
16360700
<p>
<font size='3'>
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.
</font>
</p>
<p><font size='3'>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.</font></p>
<p><font size='3'>If aggregating tract-level probabilities to larger areas, users are strongly
encouraged to use occupied household counts as weights.</font></p>
<p><font size='3'>We recommend users read Section 7 of the working paper before using the raw probabilities. Likewise, we recognize that some users may:
</font>
<font size='3'>
</font></p><ol><font size='3'>
<li>
prefer to use an uncontrolled classification, or
</li>
<li>
prefer to create more than three categories.
</li>
<br />
</font></ol><font size='3'>
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.</font><p></p>
<p><font size='3'>The final classification is controlled to AHS national estimates (26.9%
urban; 52.1% suburban, 21.0% rural).</font></p>
<p>
<font size='3'>
For more information about the 2017 AHS Neighborhood Description Study click on the following visit: <a href='https://www.hud.gov/program_offices/comm_planning/communitydevelopment/programs/' target='_blank' rel='nofollow ugc noopener noreferrer'><b>https://www.hud.gov/program_offices/comm_planning/communitydevelopment/programs/</b></a></font><span style='font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'>, for questions about the spatial attribution of this dataset, please reach out to us at </span><a href='mailto:GISHelpdesk@hud.gov' style='color:rgb(0, 97, 155); text-decoration-line:none; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;' target='_blank' rel='nofollow ugc noopener noreferrer'><b>GISHelpdesk@hud.gov</b></a><span style='font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'>. </span><font size='3'><br />
</font></p>
<p><font size='3'>Data Dictionary: <b><a href='https://hud.maps.arcgis.com/sharing/rest/content/items/a90c8fec7d774eeaa2e423fa6bac5f5a/data' target='_blank' rel='nofollow ugc noopener noreferrer'>DD_Urbanization Perceptions Small Area Index</a>.</b></font></p>
Urbanization Perceptions Small Area Index
2023-07-31
vector digital data
-179.147339
179.778467
71.390482
-14.548699
true
-178.227822
-65.244234
71.390482
17.881242
1
HUD eGIS Team
U.S. Department of Housing and Urban Development
GIShelpdesk@hud.gov
451 7th Street SW rm. 8126
Washington
D.C.
20410-0001
US
202-708-1112
HUDOffice of Policy Development and ResearchPD&ROffice of Economic AffairsOEAUrbanization Perceptions Small Area IndexUPSAITRACThud.official.contentHousing & Demographic Analysis DivisionU.S. Department of Housing and Urban Development
U.S. Department of Housing and Urband Development
HUD
Office of Policy Development and Research
PD&R
Office of Economic Affairs
OEA
Urbanization Perceptions Small Area Index
UPSAI
TRACT
hud.official.content
Housing & Demographic Analysis Division
ISO 19115 Topic Category
Boundaries
Economy
Location
Society
NGDA Portfolio Themes
Governmental Units
and Administrative and Statistical Boundaries
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. 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.
U.S. Department of Housing and Urban Development, U.S. Census Bureau
<span style='font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'>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.</span>
Other Constraints
United States Federal Classification System
At will.
Esri ArcGIS 12.9.3.32739
https://hud.maps.arcgis.com/sharing/rest/content/items/9b13dc7e75474eab9a4a643d91c34f58/info/thumbnail/thumbnail.png?
Marginal and subscript text featuring image of geographic feature content. Department of Housing and Urban Development and eGIS logo in bottom right.
PNG
ISO 3166 — Codes for the representation of names of countries and their subdivisions
UNITED STATES OF AMERICA
UNITED STATES MINOR OUTLYING ISLANDS
dataset
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
20230808
HUD eGIS Team
U.S. Department of Housing and Urban Development
GIShelpdesk@hud.gov
451 7th Street SW rm. 8126
Washington
D.C.
20410-0001
US
202-708-1112
File Geodatabase Feature Class
EPSG
6.18.3(9.3.1.2)
Urbanization Perceptions Small Area Index
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.
U.S. Department of Housing and Urban Development
Feature Class
73752
OBJECTID
In ArcGIS, a system-managed value that uniquely identifies a record or feature.
HUD Authors
OBJECTID
OID
4
0
0
Sequential unique whole numbers that are automatically generated.
GEOID
Geographic Identifier - fully concatenated geographic code (State FIPS and district number)
HUD Authors
GEOID
String
11
0
0
UPSAI_URBAN
"Raw" probability an average household would describe their neighborhood as as "urban."
HUD Authors
UPSAI_urban
Double
8
0
0
ACS17_Occupied_Housing_Units_Es
ACS17_Occupied_Housing_Units_Est
Integer
4
0
0
UPSAI_SUBURBAN
"Raw" probability an average household would describe their neighborhood as as "suburban."
HUD Authors
UPSAI_suburban
Double
8
0
0
UPSAI_RURAL
"Raw" probability an average household would describe their neighborhood as as "rural."
HUD Authors
UPSAI_rural
Double
8
0
0
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.
HUD Authors
UPSAI_Cat_Controlled
Integer
4
0
0
SHAPE
The characteristic appearance or visible form of a geographic object as represented on a map
HUD Authors
Shape
Geometry
0
0
0
Coordinates defining the features.
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
Shape_Area
Shape_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
United States Federal Classification System
At will.
73752
ArcGIS Metadata
1.0
Simple
FALSE
73752
TRUE
FALSE