This layer originated from ZCTAs and has been modified in places by Grant County GIS staff based on input from local US Postal Service staff and landowners.ZIP Code Tabulation Areas (ZCTAs™) are a statistical geographic entity produced by the U.S. Census Bureau for tabulating summary statistics from the 2010 Census, first developed for Census 2000. This entity was developed to overcome the difficulties in precisely defining the land area covered by each ZIP Code™, which is necessary in order to accurately tabulate census data for that area.ZCTAs are generalized area representations of U.S. Postal Service (USPS) ZIP Code service areas. They represent the most frequently occurring five-digit ZIP Code found in a given area. Simply put, each ZCTA is built by aggregating 2010 Census blocks, whose addresses use a given ZIP Code. Each resulting ZCTA is then assigned the most frequently occurring ZIP Code as its ZCTA code. For more information, please refer to the ZCTA Frequently Asked Questions (FAQ).

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

One of the many challenges that social science researchers and practitioners face is the difficulty of relating United States Postal Service (USPS) ZIP codes to Census Bureau geographies. There are valuable data available only at the ZIP code level that, when combined with demographic data tabulated at various Census geography levels, could open up new avenues of exploration.While some acceptable methods of combining ZIP codes and Census geography exist, they have limitations. To provide additional avenues for merging these data, PD&R has released the HUD-USPS Crosswalk Files. These unique files are derived from data in the quarterly USPS Vacancy Data. They originate directly from the USPS; are updated quarterly, making them highly responsive to changes in ZIP code configurations; and reflect the locations of both business and residential addresses. The latter feature is of particular interest to housing researchers because many of the phenomena that they study are based on housing unit or address. By using an allocation method based on residential addresses rather than by area or by population, analysts can take into account not only the spatial distribution of population, but also the spatial distribution of residences. This enables a slightly more nuanced approach to allocating data between disparate geographies. Please note that the USPS Vacancy Data is constructed from ZIP+4 data that contains records of addresses, it does not contain ZIP+4 data that are associated with ZIP codes that exclusively serve Postal Office Boxes (PO Boxes). As a result, ZIP codes that only serve PO Boxes will not appear in the files.In addition to the crosswalk files, this dataset also includes screenshots of HUDs documentation and FAQ pages.Understanding ZIP Code Crosswalk FilesThough often used for mapping, spatial analysis, and data aggregation careful attention is required when interpreting ZIP Code data relative to other administrative geographies. The following article demonstrates how to more effectively use the U.S. Department of Housing and Urban Development (HUD) United States Postal Service ZIP Code Crosswalk Files when working with disparate geographies.Wilson, Ron and Din, Alexander, 2018. “Understanding and Enhancing the U.S. Department of Housing and Urban Development’s ZIP Code Crosswalk Files,” Cityscape: A Journal of Policy Development and Research, Volume 20 Number 2, 277 – 294. https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdfUsing a GIS to Geoprocess ZIP Code Crosswalk FilesThis article demonstrates how to use a GIS to process ZIP Code Crosswalk Files. In this article, calls for service from New York City's Open Data Portal are estimated at the county-level and census tract-level. This article also includes an accuracy analysis.Din, Alexander and Wilson, Ron, 2020. "Crosswalking ZIP Codes to Census Geographies: Geoprocessing the U.S. Department of Housing & Urban Development’s ZIP Code Crosswalk Files," Cityscape: A Journal of Policy Development and Research, Volume 22, Number 1, https://www.huduser.gov/portal/periodicals/cityscpe/vol22num1/ch12.pdf

A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by Census ZIP Code Tabulation Areas and normalized by 2018 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents.

Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset.

Dataset is cumulative and covers cases going back to March 2nd, 2020 when testing began. It is updated daily.

B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2018 ACS estimates for population provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset each day.

D. HOW TO USE THIS DATASET Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Cases dropped altogether for areas where acs_population < 1000

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are polygonal representations of USPS ZIP Code service area routes. Read how the Census develops ZCTAs on their website.

This dataset is a filtered view of another dataset You can find a full dataset of cases and deaths summarized by this and other geographic areas.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases and deaths summarized by ZIP code tabulation area are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

The property’s Flood Factor as well as data on economic loss.

The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

Field Name

Type

Description

fsid

int

First Street ID (FSID) is a unique identifier assigned to each location

long

float

Longitude

lat

float

Latitude

zcta

int

ZIP code tabulation area as provided by the US Census Bureau

blkgrp_fips

int

US Census Block Group FIPS Code

tract_fips

int

US Census Tract FIPS Code

county_fips

int

County FIPS Code

cd_fips

int

Congressional District FIPS Code for the 116th Congress

state_fips

int

State FIPS Code

floodfactor

int

The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)

CS_depth_RP_YY

int

Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00

CS_chance_flood_YY

float

Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00

aal_YY_CS

int

The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low

hist1_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist1_event

string

Short name of the modeled historic event

hist1_year

int

Year the modeled historic event occurred

hist1_depth

int

Depth (in cm) of flooding to the building from this historic event

hist2_id

int

A unique First Street identifier assigned to a historic storm event modeled by First Street

hist2_event

string

Short name of the modeled historic event

hist2_year

int

Year the modeled historic event occurred

hist2_depth

int

Depth (in cm) of flooding to the building from this historic event

adapt_id

int

A unique First Street identifier assigned to each adaptation project

adapt_name

string

Name of adaptation project

adapt_rp

int

Return period of flood event structure provides protection for when applicable

adapt_type

string

Specific flood adaptation structure type (can be one of many structures associated with a project)

fema_zone

string

Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders

footprint_flag

int

Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)

These datasets provide aggregated community risk scores for exposure to flooding using the First Street Foundation Flood Model (Version 1.3) at the county and zip code level. county_flood_score and zcta_flood_score provide the overall community risk score. county_flood_category_score and zcta_flood_category_score provide the risk score to specific categories of infrastructure. Each category; critical infrastructure, social infrastructure, residential properties, roads, and commercial properties, is a component of the overall community risk.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The following fields are in the overall risk datasets:

Attribute

Description

county_id

The county FIPS code

count

The count (#) of infrastructure facilities

flood_score

A score of 1, 2, 3, 4, or 5 is shown. Community risk rankings represent risk as Minimal, Minor (1), Moderate (2), Major (3), Severe (4) and Extreme (5). Minimal risk is a case where no facilities within a category have flood risk. County level risks are ranked based on how their total depths compare to counties across the country.

The following fields are in the category risk datasets:

Attribute

Description

FIPS

County FIPS code

ZIP_CODE

ZIP code

count

The approximate length of roads (miles) within the geography of aggregation (i.e. ZIP Code, County)

flood_score

A score (Community Risk level) of 0, 1, 2, 3, 4, or 5 is shown. Community risk levels represent risk as Minimal (0), Minor (1), Moderate (2), Major (3), Severe (4) and Extreme (5). Minimal risk is a case where no facilities within a category have flood risk. ZIP Code and County level risks are assessed based on how their total depths compare to ZIP Codes and Counties across the country.

risk_direction

A score of 1, -1, or 0 is shown. These note if flood risk is expected to increase (1), decrease (-1), or remain constant (0) over the next 30 years.

infrastructure_category_id

1= critical infrastructure, 4 = social infrastructure , 6 = residential properties, 8 - roads, 9 = commercial properties

This dataset contains measures of land cover (e.g., low-, medium-, or high-density development, forest, wetland, open water) derived from the National Land Cover Database (NLCD) and aggregated by United States ZIP code tabulation area (ZCTA). Land cover is measured both in total square meters and as a proportion of all land within the ZCTA.A curated version of this data is available through ICPSR at https://doi.org/10.3886/E110663V1

This feature service displays the zip code distribution rate of incident location for all of the 2015-2018 Stark County unintentional overdose deaths per 10,000 population. This layer calculates the total overdoses within Stark County based on a 4 year range. The grand total was then divided by the population per zip code (apportioned for zip codes that cross the county jurisdiction) and normalized per 10k. This data was aggregated by Stark County Health Department.It is important to note this layer does not include the residence of the overdose fatality and that information is within another feature service. The purpose of keeping these layers separate was to allow map swiping functionality to compare the aggregated data by zip code for all overdoses by residence of the decedent and the location of the overdose incident. This map only shows data for the incidents and residents of Stark County and may not include zip codes of individuals who overdosed within county and lived outside of the county boundary.

This map image layer represents the U.S. Department of Health and Human Services (HHS) emPOWER Program, a partnership between ASPR and the Centers for Medicare and Medicaid Services, provides dynamic data and mapping tools to help communities protect the health of more than 4.1 million Medicare beneficiaries who live independently and rely on electricity-dependent medical equipment and health care servicesASPR, in partnership with the Centers for Medicare and Medicaid Services (CMS), provide de-identified and aggregated Medicare beneficiary claims data at the state/territory, county, and ZIP code levels in the HHS emPOWER Map and this public HHS emPOWER REST Service. The REST Service includes aggregated data from the Medicare Fee-For-Service (Parts A&B) and Medicare Advantage (Part C) Programs for beneficiaries who rely on electricity-dependent durable medical equipment (DME) and cardiac implantable devices. Data includes the following DME and devices: cardiac devices (left, right, and bi-ventricular assistive devices (LVAD, RVAD, BIVAD) and total artificial hearts (TAH)), ventilators (invasive, non-invasive and oscillating vests), bi-level positive airway pressure device (BiPAP), oxygen concentrator, enteral feeding tube, intravenous (IV) infusion pump, suction pump, end-stage renal disease (ESRD) at-home dialysis, motorized wheelchair or scooter, and electric bed. Purpose: Over 2.5 million Medicare beneficiaries rely on electricity-dependent medical equipment, such as ventilators, to live independently in their homes. Severe weather and other emergencies, especially those with long power outages, can be life-threatening for these individuals. The HHS emPOWER Map and public REST Service give every public health official, emergency manager, hospital, first responder, electric company, and community member the power to discover the electricity-dependent Medicare population in their state/territory, county, and ZIP Code. Data Source: The REST Service’s data is developed from Medicare Fee-For-Service (Part A & B) (>33M 65+, blind, ESRD [dialysis], dual-eligible, disabled [adults and children]) and Medicare Advantage (Part C) (>21M 65+, blind, ESRD [dialysis], dual-eligible, disabled [adults and children]) beneficiary administrative claims data. This data does not include individuals that are only enrolled in a State Medicaid Program. Note that Medicare DME are subject to insurance claim reimbursement caps (e.g. rental caps) that differ by type, so the DME may have different “look-back” periods (e.g. ventilators are 13 months and oxygen concentrators are 36 months). ZIP Code Aggregation: Some ZIP Codes do not have specific geospatial boundary data (e.g., P.O. Box ZIP Codes). To capture the complete population data, the HHS emPOWER Program identified the larger boundary ZIP Code (Parent) within which the non-boundary ZIP Code (Child) resides. The totals are added together and displayed under the parent ZIP Code. Approved Data Uses: The public HHS emPOWER REST Service is approved for use by all partners and is intended to be used to help inform and support emergency preparedness, response, recovery, and mitigation activities in all communities. Privacy Protections: Protecting the privacy of Medicare beneficiaries is an essential priority for the HHS emPOWER Program. Therefore, all personally identifiable information are removed from the data and numerous de-identification methods are applied to significantly minimize, if not completely mitigate, any potential for deduction of small cells or re-identification risk. For example, any cell size found between the range of 1 and 10 is masked and shown as 11.HHS emPOWER Program Executive SummaryHHS emPOWER Program Informational Power Point.

The COVID-19 Vulnerability and Recovery Index uses Tract and ZIP Code-level data* to identify California communities most in need of immediate and long-term pandemic and economic relief. Specifically, the Index is comprised of three components — Risk, Severity, and Recovery Need with the last scoring the ability to recover from the health, economic, and social costs of the pandemic. Communities with higher Index scores face a higher risk of COVID-19 infection and death and a longer uphill economic recovery. Conversely, those with lower scores are less vulnerable.

The Index includes one overarching Index score as well as a score for each of the individual components. Each component includes a set of indicators we found to be associated with COVID-19 risk, severity, or recovery in our review of existing indices and independent analysis. The Risk component includes indicators related to the risk of COVID-19 infection. The Severity component includes indicators designed to measure the risk of severe illness or death from COVID-19. The Recovery Need component includes indicators that measure community needs related to economic and social recovery. The overarching Index score is designed to show level of need from Highest to Lowest with ZIP Codes in the Highest or High need categories, or top 20th or 40th percentiles of the Index, having the greatest need for support.

The Index was originally developed as a statewide tool but has been adapted to LA County for the purposes of the Board motion. To distinguish between the LA County Index and the original Statewide Index, we refer to the revised Index for LA County as the LA County ARPA Index.

*Zip Code data has been crosswalked to Census Tract using HUD methodology

Indicators within each component of the LA County ARPA Index are:Risk: Individuals without U.S. citizenship; Population Below 200% of the Federal Poverty Level (FPL); Overcrowded Housing Units; Essential Workers Severity: Asthma Hospitalizations (per 10,000); Population Below 200% FPL; Seniors 75 and over in Poverty; Uninsured Population; Heart Disease Hospitalizations (per 10,000); Diabetes Hospitalizations (per 10,000)Recovery Need: Single-Parent Households; Gun Injuries (per 10,000); Population Below 200% FPL; Essential Workers; Unemployment; Uninsured PopulationData are sourced from US Census American Communities Survey (ACS) and the OSHPD Patient Discharge Database. For ACS indicators, the tables and variables used are as follows:

Indicator

ACS Table/Years

Numerator

Denominator

Non-US Citizen

B05001, 2019-2023

b05001_006e

b05001_001e

Below 200% FPL

S1701, 2019-2023

s1701_c01_042e

s1701_c01_001e

Overcrowded Housing Units

B25014, 2019-2023

b25014_006e + b25014_007e + b25014_012e + b25014_013e

b25014_001e

Essential Workers

S2401, 2019-2023

s2401_c01_005e + s2401_c01_011e + s2401_c01_013e + s2401_c01_015e + s2401_c01_019e + s2401_c01_020e + s2401_c01_023e + s2401_c01_024e + s2401_c01_029e + s2401_c01_033e

s2401_c01_001

Seniors 75+ in Poverty

B17020, 2019-2023

b17020_008e + b17020_009e

b17020_008e + b17020_009e + b17020_016e + b17020_017e

Uninsured

S2701, 2019-2023

s2701_c05_001e

NA, rate published in source table

Single-Parent Households

S1101, 2019-2023

s1101_c03_005e + s1101_c04_005e

s1101_c01_001e

Unemployment

S2301, 2019-2023

s2301_c04_001e

NA, rate published in source table

The remaining indicators are based data requested and received by Advancement Project CA from the OSHPD Patient Discharge database. Data are based on records aggregated at the ZIP Code level:

Indicator

Years

Definition

Denominator

Asthma Hospitalizations

2017-2019

All ICD 10 codes under J45 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Gun Injuries

2017-2019

Principal/Other External Cause Code "Gun Injury" with a Disposition not "Died/Expired". ICD 10 Code Y38.4 and all codes under X94, W32, W33, W34, X72, X73, X74, X93, X95, Y22, Y23, Y35 [All listed codes with 7th digit "A" for initial encounter]

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Heart Disease Hospitalizations

2017-2019

ICD 10 Code I46.2 and all ICD 10 codes under I21, I22, I24, I25, I42, I50 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Diabetes (Type 2) Hospitalizations

2017-2019

All ICD 10 codes under E11 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

For more information about this dataset, please contact egis@isd.lacounty.gov.

Weekly updates have finished with the June 28th update.

Some information may be found here: https://covid.cdc.gov/covid-data-tracker/#maps_new-admissions-rate-state

This dataset contains aggregate COVID-19 case counts and rates by date of first report for all counties in Pennsylvania and for the state as a whole. Counts include both confirmed and probable cases as defined by the Council of State and Territorial Epidemiologists (CSTE). At present, a person is counted as a case only once. Note that case counts by date of report are influenced by a variety of factors, including but not limited to testing availability, test ordering patterns (such as day of week patterns), labs reporting backlogged test results, and mass screenings in nursing homes, workplaces, schools, etc. Case reports received without a patient address are assigned to the county of the ordering provider or facility based on provider zip code. Cases reported with a residential address that does not match to a known postal address per the commonwealth geocoding service are assigned to a county based on the zip code of residence. Many zip codes cross county boundaries so there is some degree of misclassification of county. All counts may change on a daily basis due to reassignment of jurisdiction, removal of duplicate case reports, correction of errors, and other daily data cleaning activities. Downloaded data represents the best information available as of the previous day.

Data will be updated between 11:30 am to 1:30pm each Wednesday.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Weekly updates have finished with the June 28th update. This dataset contains aggregate data of COVID vaccines administered to citizens based on zip code of residence. Data includes counts of individuals who received a vaccine dose that provides partial coverage against the disease and counts of individuals that received a vaccine dose that provides full coverage against the disease. Suppression applies for quantities less than 5. Data only includes information reported to PA-SIIS, the Pennsylvania Statewide Immunization Information System.
Effective 7/9/2021, the COVID-19 Vaccine Dashboard is updated to more accurately reflect the number of people who are partially and fully vaccinated in each county outside of Philadelphia, along with the demographics of those receiving vaccine. For state-to-state comparisons refer to the CDC vaccine data tracker located here: https://covid.cdc.gov/covid-data-tracker/#county-view

Data Overview: ASPR, in partnership with the Centers for Medicare and Medicaid Services (CMS), provide de-identified and aggregated Medicare beneficiary claims data at the state/territory, county, and ZIP code levels in the HHS emPOWER Map and this public HHS emPOWER REST Service. The REST Service includes aggregated data from the Medicare Fee-For-Service (Parts A&B) and Medicare Advantage (Part C) Programs for beneficiaries who rely on electricity-dependent durable medical equipment (DME) and cardiac implantable devices.

Data includes the following DME and devices: Cardiac devices (left, right, and bi-ventricular assistive devices
  (LVAD, RVAD, BIVAD) and total artificial hearts (TAH)), ventilators
  (invasive, non-invasive and oscillating vests), bi-level positive airway
  pressure device (BiPAP), oxygen concentrator, enteral feeding tube,
  intravenous (IV) infusion pump, suction pump, end-stage renal disease
  (ESRD) at-home dialysis, motorized wheelchair or scooter, and electric
  bed.



Purpose: Over 3 million Medicare beneficiaries rely on electricity-dependent
 medical equipment, such as ventilators, to live independently in their
 homes. Severe weather and other emergencies, especially those with long
 power outages, can be life-threatening for these individuals. The HHS
 emPOWER Map and public REST Service give every public health official,
 emergency manager, hospital, first responder, electric company, and
 community member the power to discover the electricity-dependent Medicare
 population in their state/territory, county, and ZIP Code.



Data Source: The REST Service’s data is developed from Medicare Fee-For-Service
  (Part A & B) (>33M 65+, blind, ESRD [dialysis], dual-eligible,
  disabled [adults and children]) and Medicare Advantage (Part C) (>21M
  65+, blind, ESRD [dialysis], dual-eligible, disabled [adults and
  children]) beneficiary administrative claims data. This data does not
  include individuals that are only enrolled in a State Medicaid Program.
  Note that Medicare DME are subject to insurance claim reimbursement caps
  (e.g. rental caps) that differ by type, so the DME may have different
  “look-back” periods (e.g. ventilators are 13 months and oxygen
  concentrators are 36 months).



ZIP Code Aggregation: Some ZIP Codes do not have specific geospatial boundary data (e.g.,
  P.O. Box ZIP Codes). To capture the complete population data, the HHS
  emPOWER Program identified the larger boundary ZIP Code (Parent) within
  which the non-boundary ZIP Code (Child) resides. The totals are added
  together and displayed under the parent ZIP Code.




Approved Data Uses: The public HHS emPOWER REST Service is approved for use by all partners
  and is intended to be used to help inform and support emergency
  preparedness, response, recovery, and mitigation activities in all
  communities.






Privacy Protections: Protecting the privacy of Medicare beneficiaries is an essential
  priority for the HHS emPOWER Program. Therefore, all personally
  identifiable information are removed from the data and numerous
  de-identification methods are applied to significantly minimize, if not
  completely mitigate, any potential for deduction of small cells or
  re-identification risk. For example, any cell size found between the
  range of 1 and 10 is masked and shown as 11.

Dataset Description This dataset contains aggregated meteorological variables for U.S. counties and ZIP Code Tabulation Areas (ZCTAs) derived from the gridMET dataset. The gridMET product combines high-resolution spatial climate data (e.g., temperature, precipitation, humidity) from the PRISM Climate Group with daily temporal attributes and additional meteorological variables from the NLDAS-2 regional reanalysis dataset. The final product includes daily meteorological data at approximately 4km x 4km spatial resolution across the contiguous United States. This dataset has been processed to provide spatial (ZCTA, County) and temporal (daily, yearly) aggregations for broader climate analysis. This dataset was created to support climate and public health research by providing ready-to-use, high-resolution meteorological data aggregated at county and ZCTA levels. This allows for efficient linking with health and socio-demographic data to explore the impacts of climate on public health. Contributors: Harvard T.H. Chan School of Public Health, NSAPH (National Studies on Air Pollution and Health) The data is organized by geographic unit (County and ZCTA) and temporal scale (daily, yearly). The dataset is structured to facilitate the computation of climate exposure variables for health impact studies. A data processing pipeline was used to generate this dataset.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and Zip codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential Zip code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality. For the Zip code-level life expectancy calculation, it is assumed that postal Zip codes share the same boundaries as Zip Code Census Tabulation Areas (ZCTAs). More information on the relationship between Zip codes and ZCTAs can be found at https://www.census.gov/geo/reference/zctas.html. Zip code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 Zip code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for Zip codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest Zip code with population. Zip code population for 2000 estimates comes from the Decennial Census. Zip code population for 2013 estimates are from the American Community Survey (5-Year Average). The ACS provides Zip code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to Zip codes based on majority land-area.

Zip codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, Zip codes with populations of less than 5,000 were aggregated with neighboring Zip codes until the merged areas had a population of more than 5,000. In this way, the original 305 Bay Area Zip codes were reduced to 218 Zip code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

Data Overview: ASPR, in partnership with the Centers for Medicare and Medicaid Services (CMS), provide de-identified and aggregated Medicare beneficiary claims data at the state/territory, county, and ZIP code levels in the HHS emPOWER Map and this public HHS emPOWER REST Service. The REST Service includes aggregated data from the Medicare Fee-For-Service (Parts A&B) and Medicare Advantage (Part C) Programs for beneficiaries who rely on electricity-dependent durable medical equipment (DME) and cardiac implantable devices.

Data includes the following DME and devices: Cardiac devices (left, right, and bi-ventricular assistive devices
  (LVAD, RVAD, BIVAD) and total artificial hearts (TAH)), ventilators
  (invasive, non-invasive and oscillating vests), bi-level positive airway
  pressure device (BiPAP), oxygen concentrator, enteral feeding tube,
  intravenous (IV) infusion pump, suction pump, end-stage renal disease
  (ESRD) at-home dialysis, motorized wheelchair or scooter, and electric
  bed.



Purpose: Over 3 million Medicare beneficiaries rely on electricity-dependent
 medical equipment, such as ventilators, to live independently in their
 homes. Severe weather and other emergencies, especially those with long
 power outages, can be life-threatening for these individuals. The HHS
 emPOWER Map and public REST Service give every public health official,
 emergency manager, hospital, first responder, electric company, and
 community member the power to discover the electricity-dependent Medicare
 population in their state/territory, county, and ZIP Code.



Data Source: The REST Service’s data is developed from Medicare Fee-For-Service
  (Part A & B) (>33M 65+, blind, ESRD [dialysis], dual-eligible,
  disabled [adults and children]) and Medicare Advantage (Part C) (>21M
  65+, blind, ESRD [dialysis], dual-eligible, disabled [adults and
  children]) beneficiary administrative claims data. This data does not
  include individuals that are only enrolled in a State Medicaid Program.
  Note that Medicare DME are subject to insurance claim reimbursement caps
  (e.g. rental caps) that differ by type, so the DME may have different
  “look-back” periods (e.g. ventilators are 13 months and oxygen
  concentrators are 36 months).



ZIP Code Aggregation: Some ZIP Codes do not have specific geospatial boundary data (e.g.,
  P.O. Box ZIP Codes). To capture the complete population data, the HHS
  emPOWER Program identified the larger boundary ZIP Code (Parent) within
  which the non-boundary ZIP Code (Child) resides. The totals are added
  together and displayed under the parent ZIP Code.




Approved Data Uses: The public HHS emPOWER REST Service is approved for use by all partners
  and is intended to be used to help inform and support emergency
  preparedness, response, recovery, and mitigation activities in all
  communities.






Privacy Protections: Protecting the privacy of Medicare beneficiaries is an essential
  priority for the HHS emPOWER Program. Therefore, all personally
  identifiable information are removed from the data and numerous
  de-identification methods are applied to significantly minimize, if not
  completely mitigate, any potential for deduction of small cells or
  re-identification risk. For example, any cell size found between the
  range of 1 and 10 is masked and shown as 11.

Data Overview: ASPR, in partnership with the Centers for Medicare and Medicaid Services (CMS), provide de-identified and aggregated Medicare beneficiary claims data at the state/territory, county, and ZIP code levels in the HHS emPOWER Map and this public HHS emPOWER REST Service. The REST Service includes aggregated data from the Medicare Fee-For-Service (Parts A&B) and Medicare Advantage (Part C) Programs for beneficiaries who rely on electricity-dependent durable medical equipment (DME) and cardiac implantable devices.

Data includes the following DME and devices: Cardiac devices (left, right, and bi-ventricular assistive devices
  (LVAD, RVAD, BIVAD) and total artificial hearts (TAH)), ventilators
  (invasive, non-invasive and oscillating vests), bi-level positive airway
  pressure device (BiPAP), oxygen concentrator, enteral feeding tube,
  intravenous (IV) infusion pump, suction pump, end-stage renal disease
  (ESRD) at-home dialysis, motorized wheelchair or scooter, and electric
  bed.



Purpose: Over 3 million Medicare beneficiaries rely on electricity-dependent
 medical equipment, such as ventilators, to live independently in their
 homes. Severe weather and other emergencies, especially those with long
 power outages, can be life-threatening for these individuals. The HHS
 emPOWER Map and public REST Service give every public health official,
 emergency manager, hospital, first responder, electric company, and
 community member the power to discover the electricity-dependent Medicare
 population in their state/territory, county, and ZIP Code.



Data Source: The REST Service’s data is developed from Medicare Fee-For-Service
  (Part A & B) (>33M 65+, blind, ESRD [dialysis], dual-eligible,
  disabled [adults and children]) and Medicare Advantage (Part C) (>21M
  65+, blind, ESRD [dialysis], dual-eligible, disabled [adults and
  children]) beneficiary administrative claims data. This data does not
  include individuals that are only enrolled in a State Medicaid Program.
  Note that Medicare DME are subject to insurance claim reimbursement caps
  (e.g. rental caps) that differ by type, so the DME may have different
  “look-back” periods (e.g. ventilators are 13 months and oxygen
  concentrators are 36 months).



ZIP Code Aggregation: Some ZIP Codes do not have specific geospatial boundary data (e.g.,
  P.O. Box ZIP Codes). To capture the complete population data, the HHS
  emPOWER Program identified the larger boundary ZIP Code (Parent) within
  which the non-boundary ZIP Code (Child) resides. The totals are added
  together and displayed under the parent ZIP Code.




Approved Data Uses: The public HHS emPOWER REST Service is approved for use by all partners
  and is intended to be used to help inform and support emergency
  preparedness, response, recovery, and mitigation activities in all
  communities.






Privacy Protections: Protecting the privacy of Medicare beneficiaries is an essential
  priority for the HHS emPOWER Program. Therefore, all personally
  identifiable information are removed from the data and numerous
  de-identification methods are applied to significantly minimize, if not
  completely mitigate, any potential for deduction of small cells or
  re-identification risk. For example, any cell size found between the
  range of 1 and 10 is masked and shown as 11.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and Zip codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential Zip code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality. For the Zip code-level life expectancy calculation, it is assumed that postal Zip codes share the same boundaries as Zip Code Census Tabulation Areas (ZCTAs). More information on the relationship between Zip codes and ZCTAs can be found at https://www.census.gov/geo/reference/zctas.html. Zip code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 Zip code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for Zip codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest Zip code with population. Zip code population for 2000 estimates comes from the Decennial Census. Zip code population for 2013 estimates are from the American Community Survey (5-Year Average). The ACS provides Zip code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to Zip codes based on majority land-area.

Zip codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, Zip codes with populations of less than 5,000 were aggregated with neighboring Zip codes until the merged areas had a population of more than 5,000. In this way, the original 305 Bay Area Zip codes were reduced to 218 Zip code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

Weekly updates have finished with the June 28th update.

This dataset contains aggregate data by zip code of residence and by gender for individuals that received a COVID vaccination. Data includes counts of individuals who received a vaccine dose that provides partial coverage against the disease and counts of individuals that received a vaccine dose that provides full coverage against the disease. Suppression applies for quantities less than 5.

Effective 7/9/2021, the COVID-19 Vaccine Dashboard is updated to more accurately reflect the number of people who are partially and fully vaccinated in each county outside of Philadelphia, along with the demographics of those receiving vaccine. For state-to-state comparisons refer to the CDC vaccine data tracker located here: https://covid.cdc.gov/covid-data-tracker/#county-view

Data only includes information reported to PA-SIIS, the Pennsylvania Statewide Immunization Information System.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This map shows the commuter concentration, based on the place of residence of employees (per ZIP code), into Indianola, IA for employment.

Data is based on the 2014 business survey, conducted by IWD, of employers in the Laborshed area with five or more employees. Businesses were asked to report the residential ZIP codes of their employees to IWD. The aggregated data was then used to create this map which shows the concentration levels of the surrounding communities that have residents that commute into Indianola, IA for employment.