20220629
15484100
1.0
FALSE
URBAN_HEAT_INDEX
002
0.000
6244555.141672
6361005.685673
1775507.545426
1986027.660803
1
Projected
GCS_NAD_1983_2011
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/2.8.0'><WKT>PROJCS["NAD_1983_2011_StatePlane_California_VI_FIPS_0406_Ft_US",GEOGCS["GCS_NAD_1983_2011",DATUM["D_NAD_1983_2011",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",6426]]</WKT><XOrigin>-118608900</XOrigin><YOrigin>-91259500</YOrigin><XYScale>3048.0060960121928</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.0032808333333333331</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>103008</WKID><LatestWKID>6426</LatestWKID></ProjectedCoordinateSystem>
NAD_1983_2011_StatePlane_California_VI_FIPS_0406_Ft_US
20240206
08202900
20240207
13044200
150000000
5000
ItemDescription
ISO 19139 Metadata Implementation Specification
Esri ArcGIS 12.8.3.29751
URBAN_HEAT_INDEX / Heat Vulnerability
Spring 2021 San Diego Urban Development NASA DEVELOP Team
San Diego
NASA DEVELOP
Urban Heat
Heat Vulnerability
Heat Risk
Heat Exposure
Urban Planning
Climate Change
Remote Sensing
Landsat 8
ECOSTRESS
Health Indicators
Socioeconomic Indicators
Extreme Heat
Climate Adaptation
Urban Heat Island Effect
Land Surface Temperature (LST)
Daytime Temperature
Nighttime Temperature
American Community Survey
CDC 500 Cities
SanGIS
SANDAG
The Heat Vulnerability layer metadata summarizes the social vulnerability across San Diego regions to heat, incorporating socio-demographic indicators. This layer, pivotal in urban heat analysis, is derived from principal component analysis on census data, offering insights crucial for targeted interventions and planning.
<DIV STYLE="text-align:Left;font-size:12pt"><DIV><DIV><P><SPAN>The Heat Vulnerability layer in the Urban Heat Index assesses regions' susceptibility to heat within San Diego, using indicators like age, income, and health from census data. This layer aids in identifying communities at higher risk during heat events, guiding targeted mitigation and planning efforts.</SPAN></P><P><SPAN /></P><P><SPAN>The Heat Vulnerability layer, part of the Urban Heat Index, utilizes the "Heat_Vulnerability" field to categorize areas based on their susceptibility to heat. It employs quantile breaks to visualize the distribution of vulnerability across San Diego, effectively highlighting regions with varying levels of risk and aiding in targeted urban planning and public health strategies.</SPAN></P><P><SPAN /></P><P><SPAN>Urban Heat Index Fields:</SPAN></P><P><SPAN>- GEOID: Census block group identifier.</SPAN></P><P><SPAN>- Heat_Vulnerability: Score indicating social vulnerability to heat.</SPAN></P><P><SPAN>- Heat_Exposure: Score representing heat magnitude based on surface temperatures.</SPAN></P><P><SPAN>- Heat_Risk: Combined score of heat exposure and vulnerability.</SPAN></P><P><SPAN>- Census_Tract: The census tract number.</SPAN></P><P><SPAN /></P><P><SPAN>Geographical Coverage: City of San Diego, California</SPAN></P><P><SPAN>Temporal Coverage: 2015-2020</SPAN></P><P /><P><SPAN><SPAN>Contact:</SPAN></SPAN></P><P><SPAN>City Planning Department </SPAN></P><P><SPAN>619-235-5200</SPAN></P><P><SPAN>planning@sandiego.gov</SPAN></P><P><SPAN>202 C Street, M.S. 413</SPAN></P><P><SPAN>San Diego, CA 92101</SPAN></P><P><SPAN /></P></DIV></DIV></DIV>
1
-117.285639
-116.901049
33.114800
32.533640
File Geodatabase Feature Class
0.000
dataset
EPSG
8.2.10(10.3.1)
0
Simple
FALSE
0
TRUE
FALSE
URBAN_HEAT_INDEX
Feature Class
0
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
GEOID
GEOID
String
11
0
0
HVI_SCORE
Heat_Vulnerability
Double
8
0
0
exp
Heat_Exposure
Double
8
0
0
HVI_NORM
Heat_Vulnerability_Normalized
Double
8
0
0
Risk_NORM
Heat_Risk
Double
8
0
0
Exp_Norm
Heat_Exposure_Normalized
Double
8
0
0
TRACT
Census_Tract
Double
8
0
0
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
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.
20240207
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