20260430
05505900
1.0
RGDW_Publication_1.4
FALSE
PLN.PARK_NEEDS_INDEX
6145963.489118
6613421.826837
1773541.472915
2129702.168401
1
0.000
002
Projected
GCS_North_American_1983
Linear Unit: Foot_US (0.304801)
<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/3.2.0'><WKT>PROJCS["NAD_1983_StatePlane_California_VI_FIPS_0406_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",6561666.666666666],PARAMETER["False_Northing",1640416.666666667],PARAMETER["Central_Meridian",-116.25],PARAMETER["Standard_Parallel_1",32.78333333333333],PARAMETER["Standard_Parallel_2",33.88333333333333],PARAMETER["Latitude_Of_Origin",32.16666666666666],UNIT["Foot_US",0.3048006096012192],AUTHORITY["EPSG",2230]]</WKT><XOrigin>-450359962737.0495</XOrigin><YOrigin>-450359962737.0495</YOrigin><XYScale>10000</XYScale><ZOrigin>0</ZOrigin><ZScale>1</ZScale><MOrigin>0</MOrigin><MScale>1</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102646</WKID><LatestWKID>2230</LatestWKID></ProjectedCoordinateSystem>
NAD_1983_StatePlane_California_VI_FIPS_0406_Feet
20251105
17414200
20251105
17414200
150000000
5000
FGDC
U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geographic Products Branch
301-763-1128
301-763-4710
4600 Silver Hill Road, Stop 7400
Washington
DC
20233-7400
US
geo.tiger@census.gov
ArcGIS Metadata
1.0
U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geographic Products Branch
301-763-1128
301-763-4710
4600 Silver Hill Road, Stop 7400
Washington
DC
20233-7400
US
geo.tiger@census.gov
The online copy of the TIGER/Line files may be accessed without charge.
To obtain more information about ordering TIGER/Line shapefiles visit http://www.census.gov/geo/www/tiger
TGRSHP (compressed)
PK-ZIP, version 1.93 A or higher
The TIGER/Line shapefiles contain geographic data only and do not include display mapping software or statistical data. For information on how to use the TIGER/Line shapefile data with specific software package users shall contact the company that produced the software.
http://www.census.gov/geo/www/tiger
http://www.census.gov/geo/www/tiger
0.000
SanGIS
(858) 874-7000
(858) 874-7002
5510 Overland Avenue, Suite 230
San Diego
California
92123
US
webmaster@sangis.org
8:00 AM - 4:00 PM Monday thru Friday
Data Librarian
Data Librarian
SanGIS
Refer to SanGIS website (http://www.sangis.org/services/index.html) to obtain
further information on mapping and data extraction services available from SanGIS.
http://www.sangis.org/download/index.html
For Census tracts edited to San Diego County by SanGIS go to http://www.sangis.org/download.index.html. Census tracts also on SanGIS interactive map available at http://sdgis.sandag.org/
Enterprise Geodatabase Feature Class
2010
U.S. Department of Commerce, U.S. Census Bureau, Geography Division
vector digital data
2021-06-11T00:00:00
Park Needs Index (PNI) - Census Block Groups
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This dataset comprises polygons of census block groups for San Diego County. The TIGER/Line tl_2020_06_bg.shp was downloaded from https://www.census.gov/cgi-bin/geo/shapefiles/index.php. Features located within San Diego County (COUNTYPF = 073) were extracted and projected to NAD 83 State Plane. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.Block Groups (BGs) are clusters of blocks within the same census tract. Each census tract contains at least one BG, and BGs are uniquely numbered within census tracts. BGs have a valid code range of 0 through 9. BGs have the same first digit of their 4-digit census block number from the same decennial census. For example, tabulation blocks numbered 3001, 3002, 3003,.., 3999 within census tract 1210.02 are also within BG 3 within that census tract. BGs coded 0 are intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. Block groups generally contain between 600 and 3,000 people. A BG usually covers a contiguous area but never crosses county or census tract boundaries. They may, however, cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. The BG boundaries in this release are those that were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.</SPAN></P></DIV></DIV></DIV>
Polygons of 2020 Census block groups within the City of San Diego, joined to park-access and facility metrics that together form the Park Needs Index (PNI).
U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geographic Products Branch
301-763-1128
301-763-4710
4600 Silver Hill Road, Stop 7400
Washington
DC
20233-7400
US
geo.geography@census.gov
Spatial Data Collection and Products Branch
INCITS.38-200x (R2004),INCITS.31-200x (R2007),INCITS.454-200x,INCITS 455-200x,INCITS 446-2008
California, County of San Diego, Carlsbad, Coronado, Chula Vista, Del Mar, El Cajon, Encinitas, Escondido, Imperial Beach, Lemon Grove, La Mesa, National City, Oceanside, Poway, San Diego, San Marcos, Solana Beach, Santee, Vista
Census Tract, TIGER, demographic boundaries
Park Needs Index
PNI
Recreation
Equity
Block Group
Census 2020
TIGER
San Diego
Planning Department
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>The TIGER/Line Shapefile products are not copyrighted however TIGER/Line and Census TIGER are registered trademarks of the U.S. Census Bureau. These products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source.These products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source.The boundaries in the TIGER/Line Shapefiles are for statistical data collection and tabulation purposes only, their depiction and designation for statistical purposes does not constitute a determination of jurisdictional authority or rights of ownership or entitlement and they are not legal land descriptions. Coordinates in the TIGER/Line shapefiles have six implied decimal places, but the positional accuracy of these coordinates is not as great as the six decimal places suggest.Please refer to SanGIS GIS data end user use agreement and disclaimer which is available at the following: http://www.sangis.org/Legal_Notice.htm.</SPAN></P></DIV></DIV></DIV>
2020 Census data
San Diego County
2020-01-01T00:00:00
SANGIS using data provided by the United States Census Bureau (TIGER).
City of San Diego, City Planning Department – Park Planning; United States Census Bureau (TIGER).
SanGIS
(858) 874-7000
(858) 874-7002
5510 Overland Avenue, Suite 230
San Diego
California
92123
US
webmaster@sangis.org
8:00 AM - 4:00 PM Monday thru Friday
SanGIS
Data Librarian
Data Librarian
Decennial (every 10 years) - last update 2020 (for TIGER data). SanGIS
Esri ArcGIS 13.2.0.49743
1
-117.613729
-116.080206
33.511394
32.525147
The Census Bureau performed automated tests to ensure logical consistency and limits of shapefiles. Segments making up the outer and inner boundaries of a polygon tie end-to-end to completely enclose the area. All polygons are tested for closure. The Census Bureau uses its internally developed geographic update system to enhance and modify spatial and attribute data in the Census MAF/TIGER database. Standard geographic codes, such as FIPS codes for states, counties, municipalities, county subdivisions, places, American Indian/Alaska Native/Native Hawaiian areas, and congressional districts are used when encoding spatial entities. The Census Bureau performed spatial data tests for logical consistency of the codes during the compilation of the original Census MAF/TIGER database files. Most of the codes for geographic entities except states, counties, urban areas, Core Based Statistical Areas (CBSAs), American Indian Areas (AIAs), and congressional districts were provided to the Census Bureau by the USGS, the agency responsible for maintaining FIPS 55. Feature attribute information has been examined but has not been fully tested for consistency. For the TIGER/Line Shapefiles, the Point and Vector Object Count for the G-polygon SDTS Point and Vector Object Type reflects the number of records in the shapefile attribute table. For multi-polygon features, only one attribute record exists for each multi-polygon rather than one attribute record per individual G-polygon component of the multi-polygon feature. TIGER/Line Shapefile multi-polygons are an exception to the G-polygon object type classification. Therefore, when multi-polygons exist in a shapefile, the object count will be less than the actual number of G-polygons.
Data completeness of the TIGER/Line Shapefiles reflects the contents of the Census MAF/TIGER database at the time the TIGER/Line Shapefiles were created.
Accurate against Federal Information Processing Standards (FIPS), FIPS Publication 6-4, and FIPS-55 at the 100% level for the codes and base names. The remaining attribute information has been examined but has not been fully tested for accuracy.
The selected geographic and cartographic information (line segments) are derived from the U.S. Census Bureau's Master Address File Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) database.
Census MAF/TIGER database
MAF/TIGER
U.S. Department of Commerce, U.S. Census Bureau, Geography Division
publication date
2010-01-01
2010-07-01
TIGER/Line Shapefiles are extracted from the Census MAF/TIGER database by nation, state, county, and entity. Census MAF/TIGER data for all of the aforementioned geographic entities are then distributed among the shapefiles each containing attributes for line, polygon, or landmark geographic data.
2010-01-01
Census MAF/TIGER
Federal Information Processing Standards (FIPS), ANSI, and feature names.
0
PARK_NEEDS_INDEX
Feature Class
0
2010 Census Block for San Diego County
U.S. Census Bureau
OBJECTID
OBJECTID
OID
4
10
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Feature geometry.
ESRI
Coordinates defining the features.
Shape
Geometry
8
0
0
GEOID
GEOID
String
12
0
0
Census block identifier; a concatenation of 2010 Census state FIPS code, 2010 Census county FIPS code, 2010 Census tract code, and 2010 Census block number
https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2020/TGRSHP2020_TechDoc.pdf
Join_
Join
String
254
0
0
PNI
PNI
Double
8
38
8
PNI_Rank
PNI_Rank
Integer
4
10
0
ACNI
ACNI
Double
8
38
8
RCNI
RCNI
Double
8
38
8
GlobalID
GlobalID
GlobalID
38
0
0
created_user
Created User
String
255
0
0
created_date
Created Date
Date
8
0
0
last_edited_user
Last Edited User
String
255
0
0
last_edited_date
Last Edited Date
Date
8
0
0
Parks_Point_per_1K_People
Park points per 1000 residents
Park points per 1 000 residents (Fig 2.2)
City of San Diego, Planning Department
Double
8
38
8
Indicator_Score
Indicator Score
Mean of two socio-demographic indicators (Fig 3.2)
City of San Diego, Planning Department
Double
8
38
8
RC_Capacity_Ratio
Rec Center Capacity Ratio
Recreation center capacity to population ratio (Fig 5.2)
City of San Diego, Planning Department
Double
8
38
8
RC_Needs_Score
Rec Center Needs Score
Recreation center need score (Fig 5.3)
City of San Diego, Planning Department
Double
8
38
8
AC_Capacity_Ratio
Aquatic Center Capacity Ratio
Aquatic center capacity to population ratio (Fig 6.2)
City of San Diego, Planning Department
Double
8
38
8
AC_Need_Scores
Aquatic Center Need Scores
Aquatic center need score (Fig 6.3)
City of San Diego, Planning
Double
8
38
8
Shape.STArea()
Shape.STArea()
Double
0
0
0
Shape.STLength()
Shape.STLength()
Double
0
0
0
See field metadata for details of fields
Downloaded tl_2020_06_bg.shp from https://www.census.gov/cgi-bin/geo/shapefiles/index.php
https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2020/TGRSHP2020_TechDoc.pdf
for further documentation on data
dataset
EPSG
5.3(9.0.0)
Simple
FALSE
0
TRUE
FALSE
20251105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