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

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

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

The U.S. Small-area Life Expectancy Estimates Project (USALEEP) is a partnership of NCHS, the Robert Wood Johnson Foundation (RWJF)external icon, and the National Association for Public Health Statistics and Information Systems (NAPHSIS)external icon to produce a new measure of health for where you live. The USALEEP project produced estimates of life expectancy at birth—the average number of years a person can expect to live—for most of the census tracts in the United States for the period 2010-2015.MethodsThe abridged period life tables calculated to estimate census-tract life expectancy at birth for the period 2010-2015 are based on a methodology developed for this project and described in the report:Arias E, Escobedo LA, Kennedy J, Fu C, Cisewski J. U.S. Small-area Life Expectancy Estimates Project: Methodology and Results Summary pdf icon[PDF – 8 MB]. National Center for Health Statistics. Vital Health Stat 2(181). 2018.Data and Documentation FilesLife Expectancy Files contain geographic identifiers, life expectancy at birth for 2010-2015, and flags noting whether the estimates were based exclusively on observed data, a combination of observed and predicted values, or exclusively predicted values.Abridged Period Life Table Files contain geographic identifiers and the abridged, period life tables for 2010-2015 that were calculated to generate the life expectancy estimates for each census tract.Record layout pdf icon[PDF – 69 KB] excel icon[XLS – 18 KB]Copyright informationAll material appearing on this page is in the public domain and may be reproduced or copied without permission; citation as to source, however, is appreciated.Suggested citationFor data files: National Center for Health Statistics. U.S. Small-Area Life Expectancy Estimates Project (USALEEP): Life Expectancy Estimates File for {Jurisdiction}, 2010-2015]. National Center for Health Statistics. 2018. Available from: https://www.cdc.gov/nchs/nvss/usaleep/usaleep.html.For methodology: Arias E, Escobedo LA, Kennedy J, Fu C, Cisewski J. U.S. Small-area Life Expectancy Estimates Project: Methodology and Results Summary pdf icon[PDF – 8 MB]. National Center for Health Statistics. Vital Health Stat 2(181). 2018.

Algeria had the highest life expectancy at birth in Africa as of 2023. A newborn infant was expected to live over 77 years in the country. Cabo Verde, Tunisia, and Mauritius followed, with a life expectancy between 77 and 75 years. On the other hand, Chad registered the lowest average, at nearly 54 years. Overall, the life expectancy in Africa was almost 63 years in the same year.

Area Deprivation Index state score in 2020. The Area Deprivation Index (ADI) ranks neighborhoods on the basis of socioeconomic disadvantage in the areas of income, education, employment, and housing quality. Areas with greater disadvantage are ranked higher. National scores are normalized to the whole country, and state scores are normalized to a particular state. Higher Area Deprivation Index scores have been shown to correlate with worse health outcomes in measures such as life expectancy. This index was created by researchers at the University of Wisconsin-Madison based on a methodology originally developed by the Health Resources and Services Administration. Areas on this map are ranked against other areas within the state. State scores represent deciles. In other words, they are divided into 10 groups of the same size, where 1 is the lowest rate of disadvantage and 10 is the highest.

This dataset contains polygons that represent the boundaries of statistical neighborhoods as defined by the DC Department of Health (DC Health). DC Health delineates statistical neighborhoods to facilitate small-area analyses and visualization of health, economic, social, and other indicators to display and uncover disparate outcomes among populations across the city. The neighborhoods are also used to determine eligibility for some health services programs and support research by various entities within and outside of government. DC Health Planning Neighborhood boundaries follow census tract 2010 lines defined by the US Census Bureau. Each neighborhood is a group of between one and seven different, contiguous census tracts. This allows for easier comparison to Census data and calculation of rates per population (including estimates from the American Community Survey and Annual Population Estimates). These do not reflect precise neighborhood locations and do not necessarily include all commonly-used neighborhood designations. There is no formal set of standards that describes which neighborhoods are included in this dataset. Note that the District of Columbia does not have official neighborhood boundaries. Origin of boundaries: each neighborhood is a group of between one and seven different, contiguous census tracts. They were originally determined in 2015 as part of an analytical research project with technical assistance from the Centers for Disease Control and Prevention (CDC) and the Council for State and Territorial Epidemiologists (CSTE) to define small area estimates of life expectancy. Census tracts were grouped roughly following the Office of Planning Neighborhood Cluster boundaries, where possible, and were made just large enough to achieve standard errors of less than 2 for each neighborhood's calculation of life expectancy. The resulting neighborhoods were used in the DC Health Equity Report (2018) with updated names. HPNs were modified slightly in 2019, incorporating one census tract that was consistently suppressed due to low numbers into a neighboring HPN (Lincoln Park incorporated into Capitol Hill). Demographic information were analyzed to identify the bordering group with the most similarities to the single census tract. A second change split a neighborhood (GWU/National Mall) into two to facilitate separate analysis.

These data contain the Estimated Life Expectancy at Birth for residents of census tracts across the State of Colorado based on vital records data from 2010-2015. The Colorado Statewide Life Expectancy (2010-2015) is 80.5 years. The U.S. Small-area Life Expectancy Estimates Project (USALEEP) is a partnership of NCHS, the Robert Wood Johnson Foundation (RWJF), and the National Association for Public Health Statistics and Information Systems (NAPHSIS) to produce a new measure of health for where you live. The life expectancy estimates are based on data collected through Colorado's vital statistics system for deaths among residents of these census tracts between 2010-2015. Life expectancy estimates developed by the National Center for Health Statistics, Centers for Disease Control and Prevention. https://www.cdc.gov/nchs/nvss/usaleep/usaleep.html Data used in creating these estimates was provided by the Vital Statistics Program, Center for Health and Environmental Data, Colorado Department of Public Health and Environment.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 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."Breakdown by race/ethnicity in pop-up: (This map has been updated with new data, so figures may vary from those in this image.)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.Proven strategies to improve life expectancy and health in general A database of dozens of strategies can be found at County Health Rankings' What Works for Health site, sorted by Health Behaviors, Clinical Care, Social & Economic Factors, and Physical Environment. Policies and Programs listed here have been evaluated as to their effectiveness. For example, consumer-directed health plans received an evidence rating of "mixed evidence" whereas cultural competence training for health care professionals received a rating of "scientifically supported." Data from County Health Rankings (layer referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World.

ABSTRACT CONTEXT AND OBJECTIVE: Traffic accidents have gained prominence as one of the modern epidemics that plague the world. The objective of this study was to identify the spatial distribution of potential years of life lost (PYLL) due to accidents involving motorcycles in the state of São Paulo, Brazil. DESIGN AND SETTING: Ecological and exploratory study conducted in São Paulo. METHODS: Data on deaths among individuals aged 20-39 years due to motorcycle accidents (V20-V29 in the International Classification of Diseases, 10th revision) in the state of São Paulo in the years 2007-2011 were obtained from DATASUS. These data were stratified into a database for the 63 microregions of this state, according to where the motorcyclist lived. PYLL rates per 100,000 inhabitants were calculated. Spatial autocorrelations were estimated using the Global Moran index (IM). Thematic, Moran and Kernel maps were constructed using PYLL rates for the age groups of 20-29 and 30-39 years. The Terraview 4.2.2 software was used for the analysis. RESULTS: The PYLL rates were 486.9 for the ages of 20-29 years and 199.5 for 30-39 years. Seventeen microregions with high PYLL rates for the age group of 20-29 years were identified. There was higher density of these rates on the Kernel map of the southeastern region (covering the metropolitan region of São Paulo). There were no spatial autocorrelations between rates. CONCLUSIONS: The data presented in this study identified microregions with high accident rates involving motorcycles and microregions that deserve special attention from regional managers and traffic experts.

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

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.

DescriptionClick a census tract on the map to view the details. Click "Legend" above to explore other demographic layers.The Racial and Social Equity Index combines information on race, ethnicity, and related demographics with data on socioeconomic and health disadvantages to identify where priority populations make up relatively large proportions of neighborhood residents. The Composite Index includes sub-indices of: Race, English Language Learners, and Origins Index ranks census tracts by an index of three measures weighted as follows: Persons of color (weight: 1.0) English language learner (weight: 0.5) Foreign born (weight: 0.5)Socioeconomic Disadvantage Index ranks census tracts by an index of two equally weighted measures: Income below 200% of poverty level grad Educational attainment less than a bachelor’s degreeHealth Disadvantage Index ranks census tracts by an index of seven equally weighted measures: No leisure-time physical activity Diagnosed diabetes Obesity Mental health not good AsthmaLow life expectancy at birth DisabilityThe index does not reflect population densities, nor does it show variation within census tracts which can be important considerations at a local level.Produced by City of Seattle Office of Planning & Community Development. For more information on the indices, including guidance for use, contact Diana Canzoneri (diana.canzoneri@seattle.gov).Get the data for this map from SeattleGeoDataSources: 2011-2015 Five-Year American Community SurveyEstimates, U.S. Census Bureau; estimates from the Centers for Disease Control’ Behavioral Risk Factor Surveillance System (BRFSS) published in the “The 500 Cities Project,” Washington State Department of Health’s Washington Tracking Network (WTN), and estimates form the Public Health – Seattle & King County (based on the Community Health Assessment Tool).Language is for population age 5 and older. Educational attainment is for the population age 25 and over.Life expectancy is life expectancy at birth. Other health measures based on percentages of the adult population.

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

The California Healthy Places Index (HPI) is a powerful new tool, developed by the Public Health Alliance of Southern California, to assist you in exploring local factors that predict life expectancy and comparing community conditions across the state. The HPI provides overall scores and more detailed data on specific policy action areas that shape health, like housing, transportation, education and more. This website offers other resources everyone will find useful, including an interactive map, graphs, data tables, and policy guide with practical solutions for improving community conditions and health.The purpose of the HPI is to prioritize public and private investments, resources and programs. It contains user-friendly mapping and data resources at the census tract level across California. The HPI also provides scores based on community conditions to allow for comparisons between areas, as well as deeper dives on conditions in any given area. The tool includes detailed policy guides to support specific policy interventions that improve community conditions and health.Source: https://healthyplacesindex.org/More information: https://healthyplacesindex.org/faq/