This map shows the life expectancy at birth by census tract in the 3-county region. Life expectancy data originates from Oregon Health Authority, 2018 report.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 2020 County Health Rankings page about Life Expectancy:

Breakdown by race/ethnicity in pop-up:

There are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.

Data from County Health Rankings 2020 (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World

Life expectancy map by census tract calculated based on the County Health Rankings Life Expectancy Calculator. Source data from the California Department of Public Health Vital Records Data

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/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

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 that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. 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 http://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). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. 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. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 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.

Life expectancy at birth data by census tract.

This story map contains details about life expectancy in in King County. It has been developed for the Determinant of Equity - Health and Human Services. It includes information about Life Expectancy equity indicator. This presentation includes charts, maps, and a narrative describing this indicator.

The data for the Life Expectancy dataset was compiled by the Washington State Department of Health (DOH), Center for Health Statistics. Vital Records

For more information about King County's equity efforts, please see:

Equity, Racial & Social Justice Vision Ordinance 16948 describing the determinates of equity Determinants of Equity and Data Tool

Alameda County life expectancy at birth by census tract, 2011-2015

Life Expectancy by Country in 2013. This is a filtered layer based on the "Life Expectancy by country, 1960-2010 time series" layer.Life expectancy values are included for males, females, and total population. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. Data Sources: United Nations Population Division. World Population Prospects, United Nations Statistical Division. Population and Vital Statistics Report, Census reports and other statistical publications from national statistical offices, Eurostat: Demographic Statistics, Secretariat of the Pacific Community: Statistics and Demography Programme, U.S. Census Bureau: International Database via World Bank DataBank; Natural Earth 50M scale data.

Overview:

Living in a rural county, I have often felt the isolation many Tennesseans are forced to face when it comes to accessing medical care. While my family's average drive time ranges from 30 minutes to over an hour to access healthcare, many Tennesseans living in more remote counties are forced drive several times farther.

The story map, "Standing Alone," follows three individuals who have each been differently affected by the disparity in rural Tennessee healthcare. Through their stories, I wanted to peel back the layers of the Tennessee healthcare crisis with geospatial analysis, highlighting underserved counties and advocating for healthcare reform. When it comes to healthcare, no one deserves to be standing alone.

Methods:

Map Showing Rural and Urban Areas: The “USA Urban Areas” and the “USA Counties” layers, both feature layers created by Esri, were added to the map from the Living Atlas. The USA Counties layered was filtered to only counties inside Tennessee. The Derive New Locations analysis tool was then used to find “USA Urban Areas” that intersect the filtered “USA Counties” layer, producing the “Tennessee Urban Areas” layer. Additionally, the Derive New Locations analysis tool was used to find “USA Counties” that do not intersect “USA Urban Areas,” creating the “Tennessee Rural Areas” layer. Custom pop-ups were formatted for the layers. Map Showing Life Expectancy per Tennessee County: The layer, “County Health Rankings 2021” by esri_demographics, was added from the Living Atlas and filtered to show only Tennessee counties. The layer was styled with “Counts and Amounts (Color)” style to show the average life expectancy in years for individuals living in each Tennessee county. The layer “Tennessee Urban Areas”, mentioned above, was also added to the map, and custom pop-ups were created for both layers. Map Showing Percent of Population Living Below the Poverty Level: The layer, “ACS Poverty Status Variables – Boundaries, created by Esri, was added from the Living Atlas and filtered to show only Tennessee counties. This layer was then joined with the Life Expectancy layer created for the Map Showing Life Expectancy per Tennessee county using the Join Features analysis tool, and the resulting layer was styled using “Counts and Amounts (Color)” style to show the percent of population whose income in the past 12 months is below the poverty level. Lastly, the “Tennessee Urban Areas” layer was added to the map, and custom pop-ups were configured for the layers. Map Showing Dr. Copeland’s Office and the Cumberland River Hospital: Addresses and labels for each location were added to an ArcGIS StoryMaps Express Map. Map Showing Rural Counties with Medically Underserved Populations: Using data from the Health Resources Administration’s Find MUA/P (Medically Underserved Area/Population) tool, data showing rural counties with medically underserved populations was inserted in a custom .csv layer and uploaded as a layer. This layer was joined to “USA Counties” using the Join Features analysis tool, and the resulting layer was styled using the “Location (Single symbol)” style. Custom pop-ups were also added to this layer. Maps Showing Ms. Crouch’s Search for Emergency Medical Services: These maps were created by inserting addresses or cities of each location into an ArcGIS StoryMaps Express Map. Map Showing Fentress County Ambulance Station: This map was created by inserting the address of Fentress County Ambulance Service and the location of each city into an ArcGIS StoryMaps Express Map. Map Showing Sum of Ambulance Units per County: Using data from the Tennessee Health Department, a custom .csv layer with the total number of ambulances per EMS station was created and uploaded as a layer. This layer was joined to the “USA Counties Layer” using the Join Features analysis tool, and the resulting layer was styled using the “Counts and Amounts (Size)” style to show the sum of ambulances in each county. Custom pop-ups were added for this layer. Map Showing Hospitals That Have Closed Since 2010: A custom .csv file was created using data from a Tennessee Healthcare Campaign report, and this data was uploaded as a layer showing the location of each hospital that has closed since 2010. The “Tennessee Urban Areas” layer and the “Tennessee Rural Areas” layer were also added to this map. Lastly, custom pop-ups were configured for these layers. Map Showing Drive Time Areas to Trauma Hospitals: Using data from the Tennessee Health Department, a custom .csv file was uploaded as a layer showing the locations of Tennessee trauma hospitals. A drive time buffer was created using the Create Drive-Time Areas analysis tool to map locations 15, 30, 45, and 60 minutes away from a trauma hospital. The “USA Counties” layer was added from the Living Atlas, and the Derive New Locations analysis tool was used to find locations over 60 minutes away from a trauma hospital. Finally, custom pop-ups were added to the layers. Map Showing COVID-19 Case Rate per Hundred Thousand for Each State: Using data from the Centers for Disease Control, a custom .csv file was created and uploaded as a layer, which was joined to “USA Counties” using the Join Features analysis tool. The resulting layer was styled using the “Counts and Amounts (Color)” style to display the case rate per hundred thousand, and customized pop-ups were made for the layer. Map Showing COVID-19 Death Rate per Hundred Thousand for Each State: Using the same layer created in for the Map Showing COVID-19 Case Rate per Hundred Thousand for Each State, the layer was changed to show the death rate per hundred. Customized pop-ups were also added. Map Showing Percent of Deadly COVID-19 Cases in Tennessee: Using data from the Tennessee Health Department, a custom .csv was created, and the percentage of deadly COVID-19 was calculated. This file was uploaded as a layer, which was joined to “USA Counties” using the Join Features analysis tool and styled using “Counts and Amounts (Color)”. Finally, customized pop-ups were added to the map. Map Showing Percent Difference Between National Vaccination Average and County Rates: Using the same data as the Map Showing Percent of Deadly COVID-19 Cases in Tennessee, a custom attribute was created to show the percent difference between county vaccination rates and the national average. The map was styled using the “Counts and Amounts (Color)”, and customized pop-ups were created for the map.

The following methods were used to create the graphics in this story map.

Thumbnail of Clay County: This thumbnail was created using the "Blank White Vector Basemap" by j_nelson. Two copies of the "USA Counties" layer by Esri were added to the map, with one layer outlining all the counties in Tennessee and the other layer highlighting Clay County. A screen shot of this map was uploaded to the story map as an image.Thumbnail of Fentress County: This thumbnail was also created using the "Blank White Vector Basemap" by j_nelson. Two copies of the "USA Counties" layer by Esri were added to the map, with one layer outlining all the counties in Tennessee and the other layer highlighting Fentress County. Finally, a screen shot of this map was uploaded to the story map as an image.

All remaining graphics were custom images created in Microsoft PowerPoint.

Sources and Acknowledgements:

This map was created for the 2022 ArcGIS Online Competition for US High Schools.

I would like to give special thanks to my geomentor and my parents, whose help and guidance were invaluable during the creation of this story map.All sources for information, data, and photographs are included as links throughout the story map.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 2020 County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Click on the map to see a breakdown by race/ethnicity in the pop-up: Full details about this measureThere are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Data from County Health Rankings 2020 (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World

This dataset contains the geographic data used to create maps for the San Diego County Regional Equity Indicators Report led by the Office of Equity and Racial Justice (OERJ). The full report can be found here: https://data.sandiegocounty.gov/stories/s/7its-kgpt

Demographic data from the report can be found here: https://data.sandiegocounty.gov/dataset/Equity-Report-Data-Demographics/q9ix-kfws

Filter by the Indicator column to select data for a particular indicator map.

Export notes: Dataset may not automatically open correctly in Excel due to geospatial data. To export the data for geospatial analysis, select Shapefile or GEOJSON as the file type. To view the data in Excel, export as a CSV but do not open the file. Then, open a blank Excel workbook, go to the Data tab, select “From Text/CSV,” and follow the prompts to import the CSV file into Excel. Alternatively, use the exploration options in "View Data" to hide the geographic column prior to exporting the data.

USER NOTES: 4/7/2025 - The maps and data have been removed for the Health Professional Shortage Areas indicator due to inconsistencies with the data source leading to some missing health professional shortage areas. We are working to fix this issue, including exploring possible alternative data sources.

5/21/2025 - The following changes were made to the 2023 report data (Equity Report Year = 2023). Self-Sufficiency Wage - a typo in the indicator name was fixed (changed sufficienct to sufficient) and the percent for one PUMA corrected from 56.9 to 59.9 (PUMA = San Diego County (Northwest)--Oceanside City & Camp Pendleton). Notes were made consistent for all rows where geography = ZCTA. A note was added to all rows where geography = PUMA. Voter registration - label "92054, 92051" was renamed to be in numerical order and is now "92051, 92054". Removed data from the percentile column because the categories are not true percentiles. Employment - Data was corrected to show the percent of the labor force that are employed (ages 16 and older). Previously, the data was the percent of the population 16 years and older that are in the labor force. 3- and 4-Year-Olds Enrolled in School - percents are now rounded to one decimal place. Poverty - the last two categories/percentiles changed because the 80th percentile cutoff was corrected by 0.01 and one ZCTA was reassigned to a different percentile as a result. Low Birthweight - the 33th percentile label was corrected to be written as the 33rd percentile. Life Expectancy - Corrected the category and percentile assignment for SRA CENTRAL SAN DIEGO. Parks and Community Spaces - corrected the category assignment for six SRAs.

5/21/2025 - Data was uploaded for Equity Report Year 2025. The following changes were made relative to the 2023 report year. Adverse Childhood Experiences - added geographic data for 2025 report. No calculation of bins nor corresponding percentiles due to small number of geographic areas. Low Birthweight - no calculation of bins nor corresponding percentiles due to small number of geographic areas.

Prepared by: Office of Evaluation, Performance, and Analytics and the Office of Equity and Racial Justice, County of San Diego, in collaboration with the San Diego Regional Policy & Innovation Center (https://www.sdrpic.org).

Human Development Index by country for 2013. This is a filtered layer based on the "Human Development Index by country, 1980-2010 time-series" layer.The Human Development Index measures achievement in 3 areas of human development: long life, good education and income. Specifically, the index is computed using life expectancy at birth, Mean years of schooling, expected years of schooling, and gross national income (GNI) per capita (PPP $).The United Nations categorizes the HDI values into 4 groups. In 2013 these groups were defined by the following HDI values:

Very High Human Development: 0.736 and higher High Human Development: 0.615 to 0.735 Medium Human Development: 0.494 to 0.614 Low Human Development: 0.493 and lower

Country shapes from Natural Earth 50M scale data. Human Development Index attributes are from The World Bank: HDRO calculations based on data from UNDESA (2013a), Barro and Lee (2013), UNESCO Institute for Statistics (2013), UN Statistics Division (2014), World Bank (2014) and IMF (2014).

In December of 1829, nine Lucases of Loudoun County left Northern Virginia for Hampton Roads, where they boarded a brig bound for the western coast of Africa. Two of the men, Mars and Jesse Lucas, had recently been emancipated by Albert and Townsend Heaton of Loudoun County. Over the next decade, the two sets of brothers corresponded about family back home in Loudoun and the challenges of life in Liberia. Seven of those letters are in the Loudoun Museum in Leesburg, Virginia.

This Story Map includes excerpts (and links to copies of) the letters, the locations mentioned within them, and a little about the events that led the Lucas family and other emancipated people to move across the Atlantic for a new life. It was developed as a collaboration among Museum staff, volunteers, and staff from the Loudoun County Office of Mapping and Information.

If you have questions, please contact the Loudoun Museum. For a shortcut to this Story Map, please visit loudounmuseum.org/lucas-heaton....

This MSOA atlas provides a summary of demographic and related data for each Middle Super Output Area in Greater London. The average population of an MSOA in London in 2010 was 8,346, compared with 1,722 for an LSOA and 13,078 for a ward. The profiles are designed to provide an overview of the population in these small areas by combining a range of data on the population, births, deaths, health, housing, crime, commercial property/floorspace, income, poverty, benefits, land use, environment, deprivation, schools, and employment. If you need to find an MSOA and you know the postcode of the area, the ONS NESS search page has a tool for this. The MSOA Atlas is available as an XLS as well as being presented using InstantAtlas mapping software. This is a useful tool for displaying a large amount of data for numerous geographies, in one place (requires HTML 5). CURRENT MSOA BOUNDARIES (2011) PREVIOUS MSOA BOUNDARIES (2001) NB. It is currently not possible to export the map as a picture due to a software issue with the Google Maps background. We advise you to print screen to copy an image to the clipboard. Tips: - Select a new indicator from the Data box on the left. Select the theme, then indicator and then year to show the data. - To view data just for one borough*, use the filter tool. - The legend settings can be altered by clicking on the pencil icon next to the MSOA tick box within the map legend. - The areas can be ranked in order by clicking at the top of the indicator column of the data table. Themes included here are Census 2011 Population, Mid-year Estimates, Population by Broad Age, Households, Household composition, Ethnic Group, Country of Birth, Language, Religion, Tenure, Dwelling type, Land Area, Population Density, Births, General Fertility Rate, Deaths, Standardised Mortality Ratio (SMR), Population Turnover Rates (per 1000), Crime (numbers), Crime (rates), House Prices, Commercial property (number), Rateable Value (£ per m2), Floorspace; ('000s m2), Household Income, Household Poverty, County Court Judgements (2005), Qualifications, Economic Activity, Employees, Employment, Claimant Count, Pupil Absence, Early Years Foundation Stage, Key Stage 1, GCSE and Equivalent, Health, Air Emissions, Car or Van availability, Income Deprivation, Central Heating, Incidence of Cancer, Life Expectancy, and Road Casualties. The London boroughs are: City of London, Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster. These profiles were created using the most up to date information available at the time of collection (Spring 2014). You may also be interested in LSOA Atlas and Ward Atlas.

Over the period 2007-2011, life expectancy at birth was 78.5 years for the total population in New Mexico, 75.8 years for males, and 81.3 years for females.For comparison, in 2011, life expectancy at birth was 78.7 years for the total U.S. population, 76.3 years for males, and 81.1 years for females. (http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6335a8.htm?s_cid=mm6335a8_e )PLEASE NOTE: The data in this map corrects, updates and replaces life expectancy data included in the 2012 Bernalillo County Place Matters 'Community Health Equity Report'. Compare life expectancy in Europe and the USA - Map ImageNOTE: Changes in life expectancy (Increase, Decrease, No Change) over the periods 1999-2003 to 2007-2011 are tested for statistical significance using a rule of one standard deviation.

Life Expectancy at Birth, Small Areas, by Sex, 1999-2003 and 2007-2011 - LEBSASEX

Summary: Life Expectancy at Birth, Small Areas, by Sex, 1999-2003 and 2007-2011

Prepared by: NEW MEXICO COMMUNITY DATA COLLABORATIVE, http://nmcdc.maps.arcgis.com/home/index.html ; T Scharmen, thomas.scharmen@state.nm.us, 505-897-5700 x126,

Data Sources: New Mexico Death Certificate Database, Office of Vital Records and Statistics, New Mexico Department of Health; Population Estimates: University of New Mexico, Geospatial and Population Studies (GPS) Program, http://bber.unm.edu/bber_research_demPop.html. Retrieved Mon, 21 June 2014 from New Mexico Department of Health, Indicator-Based Information System for Public Health Web site: http://ibis.health.state.nm.us

Shapefile: http://nmcdc.maps.arcgis.com/home/item.html?id=1e97d2715d8640ab9023fa35fc7b2634

Feature: http://nmcdc.maps.arcgis.com/home/item.html?id=3104749c2c094044914abf9ba6953eab

Master File:

NM DATA VARIABLE DEFINITION

999 SANO Small Area Number

NEW MEXICO SANAME Small Area Name

9250534 PB9903 Population at Risk, Both Sexes, 1999-2003

77.7 LEB9903 Life Expectancy at Birth, Both Sexes, 1999-2003

77.7 CILB9903 Lower Confidence Interval for Life Expectancy at Birth, Both Sexes, 1999-2003

77.7 CIUB9903 Upper Confidence Interval for Life Expectancy at Birth, Both Sexes, 1999-2003

10188104 PB0711 Population at Risk, Both Sexes, 2007-2011

78.5 LEB0711 Life Expectancy at Birth, Both Sexes, 2007-2011

78.5 CILB0711 Lower Confidence Interval for Life Expectancy at Birth, Both Sexes, 2007-2011

78.5 CIUB0711 Upper Confidence Interval for Life Expectancy at Birth, Both Sexes, 2007-2011

0.8 LEBDIFF Difference in Life Expectancy, Both Sexes, 2007-2011 MINUS 1999-2003

INCREASE LEBSIG Trend of the Difference in Life Expectancy, Both Sexes, (1 standard deviation = 68.2% confidence interval)

4683013 PF9903 Population at Risk, Females, 1999-2003

80.6 LEF9903 Life Expectancy at Birth, Females, 1999-2003

80.6 CILF9903 Lower Confidence Interval for Life Expectancy at Birth, Females, 1999-2003

80.6 CIUF9903 Upper Confidence Interval for Life Expectancy at Birth, Females, 1999-2003

5155192 PF0711 Population at Risk, Females, 2007-2011

81.3 LEF0711 Life Expectancy at Birth, Females, 2007-2011

81.3 CILF0711 Lower Confidence Interval for Life Expectancy at Birth, Females, 2007-2011

81.3 CIUF0711 Upper Confidence Interval for Life Expectancy at Birth, Females, 2007-2011

0.7 LEFDIFF Difference in Life Expectancy, Females, 2007-2011 MINUS 1999-2003

INCREASE LEFSIG Trend of the Difference in Life Expectancy, Females, (1 standard deviation = 68.2% confidence interval)

4567521 PM9903 Population at Risk, Males, 1999-2003

74.8 LEM9903 Life Expectancy at Birth, Males, 1999-2003

74.8 CILM9903 Lower Confidence Interval for Life Expectancy at Birth, Males, 1999-2003

74.8 CIUM9903 Upper Confidence Interval for Life Expectancy at Birth, Males, 1999-2003

5032911 PM0711 Population at Risk, Males, 2007-2011

75.8 LEM0711 Life Expectancy at Birth, Males, 2007-2011

75.7 CILM0711 Lower Confidence Interval for Life Expectancy at Birth, Males, 2007-2011

75.8 CIUM0711 Upper Confidence Interval for Life Expectancy at Birth, Males, 2007-2011

1 LEMDIFF Difference in Life Expectancy, Males, 2007-2011 MINUS 1999-2003

INCREASE LEMSIG Trend of the Difference in Life Expectancy, Males, (1 standard deviation = 68.2% confidence interval)

1.077540107 FMRT9903 Female to Male Ratio of Life Expectancy, 1999-2003

1.072559367 FMRT0711 Female to Male Ratio of Life Expectancy, 2007-2011

5.8 FMDT9903 Female Life Expectancy MINUS Male Life Expectancy, 1999-2003

5.5 FMDT0711 Female Life Expectancy MINUS Male Life Expectancy, 2007-2011

-0.3 FMDTDIFF Difference in Female Life Expectancy MINUS Male Life Expectancy, over both time periods, in Years

Canada is an aging society. In 2006, 13.7% of the total population of Canada was 65 years and over. This proportion was 9.7% only twenty five years ago in 1981. During the same period, the proportion of the population that was very old increased at a more rapid pace. For example, between 1981 and 2006 the proportion of the population that was 80 years and over rose from 1.7% to 3.7%. The number of people in this age group topped the 1 million mark (at 1.2 million) for the first time in 2006. In 2006, the population of Saskatchewan was the oldest in the country with 15.4% of the population 65 years and over. It also had the largest proportion of the oldest old, where one out of every 20 Saskatchewan residents was 80 years of age and over. The national average was one in 27. Saskatchewan's situation is unique, in that it has both the largest proportion of seniors and one of the largest proportions of children among the provinces. This is attributable to several factors: higher fertility compared to any other Canadian province due to a large Aboriginal population; a life expectancy that was, until quite recently, one of the highest in the country; and substantial losses of young adults migrating to Alberta to find employment. In general, Atlantic Canada (Newfoundland, and Labrador, Prince Edward Island, Nova Scotia, and New Brunswick) and British Columbia had an older age structure population (14-15% in the age group 65 and over) compared with the national average, once again a reflection of their lower fertility rates.

This data is based on the human living environment assessment framework proposed by the sub topic. Through expert discussions and suggestions from various parties, a human living environment assessment system for the Qinghai Tibet Plateau was constructed, and the quality of the human living environment on the Qinghai Tibet Plateau was obtained. The data type is tabular data. We provided the county-level vector data (2019 China Map Review No. GS (2019) 1822) used for research and visualization by linking it with tables in ArcGIS software. Through domestic and international research on human living environment and multiple expert discussions, we have selected a total of 20 evaluation indicators from four subsystems: health environment, natural environment, economic environment, and social environment, and jointly constructed an evaluation index system for human quality of life on the Qinghai Tibet Plateau (Table 2). The life expectancy is obtained by calculating the age-specific mortality rate and age-specific population. After standardization of social statistical data, the data is linked to county units based on county codes using Arc GIS software, and the raster data is adapted to county units through Arc GIS software zoning statistical mean adaptation. For missing data in a certain year, linear interpolation is used to obtain data from other years. For missing data in a certain county unit, ArcGIS software is used to calculate and fill in the mean of adjacent units.

How did the City create the Equity IndexWorking with Ohio State University's Kirwan Institute of Race and Social Justice, the City complied the Equity/Opportunity Index to help facilitate data-driven decision-making processes and enable leaders to distribute resources better and plan to fund programs and services, minimize inequities and maximize opportunities.The indicators displayed in the Equity/Opportunity Index have been shown to have a direct correlation to equity. For more information, please reference the additional document on the evidence-based research determinant categories. The data is measured granularly by census block group.The list below comprise the Indicators per index: Accessibility Parks & Open SpaceVoter ParticipationHealthy Food Access IndexAverage Road QualityHome Internet AccessTransit Options & AccessVehicle AccessLivabilityTacoma Crime IndexESRI Crime IndexCost-Burdened HouseholdsAverage Life ExpectancyUrban Tree CanopyTacoma Nuisance IndexMedian Home ValueEducationAverage Student Test RateAverage Student Mobility4-Year High School Graduation RatePercent of 25+-Year-Olds with Bachelor's Degree or MoreEconomyPierce County Jobs IndexMedian Household Income200% of the Poverty line or LessUnemployment RateEnvironmental HealthEnvironmental ExposuresNOx- Diesel Emissions (Annual Tons/Km2)Ozone ConcentrationPM2.5 ConcentrationPopulations Near Heavy Traffic RoadwaysToxic Releases from Facilities (RSEI Model)Environmental EffectsLead Risk from Housing (%)Proximity to Hazardous Waste Treatment Storage and Disposal Facilities (TSDFs)Proximity to National Priorities List Facilities (Superfund Sites)Proximity to Risk Management Plan (RMP) FacilitiesWastewater DischargeWhat does Very High or Very Low Equity/Opportunity mean?Very High Equity/Opportunity represents locations that have access to better opportunities to succeed and excel in life. The data indicators would include high-performing schools, a safe environment, access to adequate transportation, safe neighborhoods, and sustainable employment. In contrast, Low Equity/Opportunty areas have more obstacles and barriers within the area. These communities have limited access to institutional or societal investments with limits their quality of life.Why is the North and West End labeled Red?When looking at data related to equity and social justice, we want to be mindful not to reinforce historical representations of low-income or communities of color as bad or negative. To help visualize the areas of high opportunity and call out the need for more equity, we chose to use red. We flipped the gradient to highlight disparities within the community. Besides, we refrained from using green or positive colors with referring to dominant communities (white communities).Can I add more data and indicators to the Equity Index?Yes, by downloading the file and uploading it to ArcGIS, you can add data and indicators to the Index, and you can import the shapefiles into your database. The indicators and standard deviations are available on ArcGIS online.Can I see additional or multiple map layers?Within the left navigation panel, you can aggregate the index layers by determinate social categories; Accessibility, Education, Economy, Livability

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. Countywide Statistical Areas (CSA) are current as of October 2023.

Fields ending in _yr1 were calculated for the original 2021-2022 SBLA report, while fields ending in _yr2 or without a year suffix were calculated for the 2023-2025 version. Eviction Filings per 100 (eviction_filings_per100) and Life Expectancy (life_expectancy) did not have updated data and are the same data shown in the Year 1 report.

Population and demographic data are from US Census American Community Survey (ACS) 5-year estimates, aggregated up from census tract or block group to CSA. Year 1 data are from 2020, year 2 data are from 2022.

Poverty Data (200% FPL) are from LA County ISD-eGIS Demographics. Year 1 data are from 2021, Year 2 are from 2022.

The 2023-2025 report includes several new indicators that are calculated as the percent of countywide population by race that resides in a geographic area of interest. Population for these indicators is estimated based on intersection with census block group centroids. These indicators are:

Indicator

Fields

Source

Health Professional Shortage Areas (HPSA) for Primary Care

hpsa_primary_pct hpsa_primary_black_pct

LA County DPH https://data.lacounty.gov/datasets/lacounty::health-professional-shortage-area-primary-care/about

Health Professional Shortage Areas (HPSA) for Mental Health

hpsa_mental_pct hpsa_mental_black_pct

LA County DPH https://data.lacounty.gov/datasets/lacounty::health-professional-shortage-area-mental-health/about

Concentrated Disadvantage

cd_pct cd_black_pct

LA County ISD-Enterprise GIS https://egis-lacounty.hub.arcgis.com/datasets/lacounty::concentrated-disadvantage-index-2022/explore

Firearm Dealers

firearm_dl_count (count of dealers in CSA) firearm_dl_per10000 (rate of dealers per 10,000)

LA County DPH Office of Violence Prevention (OVP)

High and Very High Park Need Areas

parks_need_pct parks_need_black_pct

LA County Parks Needs Assessment Plus (PNA+) https://lacounty.maps.arcgis.com/apps/instant/media/index.html?appid=3d0ef36720b447dcade1ab87a2cc80b9

High Quality Transit Areas

hqta_pct hqta_black_pct

SCAG https://lacounty.maps.arcgis.com/home/item.html?id=43e6fef395d041c09deaeb369a513ca1

High Walkability Areas

walk_total_pct walk_black_pct

EPA Walkability Index https://www.epa.gov/smartgrowth/smart-location-mapping#walkability

High Poverty and High Segregation Areas

highpovseg_total_pct highpovseg_black_pct

CTCAC/HCD Opportunity Area Maps https://www.treasurer.ca.gov/ctcac/opportunity.asp

LA County Arts Investments

arts_dollars (total $$ for CSA) arts_dollars_percap (investment dollars per capita)

LA County Department of Arts and Culture https://lacountyartsdata.org/#maps

Strong Start (areas with at least 9 Strong Start indicators)

strongstart_total_pct strongstart_black_pct

CA Strong Start Index https://strongstartindex.org/map

For more information about the purpose of this data, please contact CEO-ARDI.

For more information about the configuration of this data, please contact ISD-Enterprise GIS.