About this dataset

Context

The dataset tabulates the California population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of California across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2024, the population of California was 39.43 million, a 0.59% increase year-by-year from 2023. Previously, in 2023, California population was 39.2 million, an increase of 0.14% compared to a population of 39.14 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of California increased by 5.44 million. In this period, the peak population was 39.52 million in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the California is shown in this column.
• Year on Year Change: This column displays the change in California population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for California Population by Year. You can refer the same here

This is the complete dataset for the 500 Cities project 2019 release. This dataset includes 2017, 2016 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2017, 2016), Census Bureau 2010 census population data, and American Community Survey (ACS) 2013-2017, 2012-2016 estimates. Because some questions are only asked every other year in the BRFSS, there are 7 measures (all teeth lost, dental visits, mammograms, pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) from the 2016 BRFSS that are the same in the 2019 release as the previous 2018 release. More information about the methodology can be found at www.cdc.gov/500cities.

About this dataset

Context

The dataset tabulates the Bay City population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Bay City across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Bay City was 32,082, a 0.39% decrease year-by-year from 2022. Previously, in 2022, Bay City population was 32,208, a decline of 0.73% compared to a population of 32,445 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Bay City decreased by 4,647. In this period, the peak population was 36,729 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Bay City is shown in this column.
• Year on Year Change: This column displays the change in Bay City population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Bay City Population by Year. You can refer the same here

A dataset listing Washington cities by population for 2024.

A dataset listing Florida cities by population for 2024.

Description

This is the 500 Cities Data of a different year (released in 2018) to the ones already present on Kaggle. It was exported and uploaded without modification. The original source states: "This is the complete dataset for the 500 Cities project 2018 release. This dataset includes 2016, 2015 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2016, 2015), Census Bureau 2010 census population data, and American Community Survey (ACS) 2012-2016, 2011-2015 estimates. Because some questions are only asked every other year in the BRFSS, there are 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, cholesterol screening) from the 2015 BRFSS that are the same in the 2018 release as the previous 2017 release. More information about the methodology can be found at www.cdc.gov/500cities."

The original can be found at: https://chronicdata.cdc.gov/500-Cities-Places/500-Cities-Local-Data-for-Better-Health-2018-relea/rja3-32tc

The 500 Cities project ran from 2016 to 2019. In December of 2020, this was expanded into and replaced by the PLACES project.

Content

This dataset contains data for the US, 500 cities within it (the 497 largest cities of the US, then a few that were the largest of their state in order to ensure all states were represented), and the census tracts within those cities. The total population represents about 33.4% of the US population.

Measures include:

• "Current lack of health insurance among adults aged 18–64 Years,"
• "Visits to doctor for routine checkup within the past Year among adults aged >=18 Years,"
• "Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50–75 Years,"
• "Mammography use among women aged 50–74 Years,"
• "Visits to dentist or dental clinic among adults aged >=18 Years,"
• "Cholesterol screening among adults aged >=18 Years,"
• "Older adult men aged >=65 Years who are up to date on a core set of clinical preventive services: Flu shot past Year, PPV shot ever, Colorectal cancer screening,"
• "Older adult women aged >=65 Years who are up to date on a core set of clinical preventive services: Flu shot past Year, PPV shot ever, Colorectal cancer screening,"
• "Papanicolaou smear use among adult women aged 21–65 Years."
• "No leisure-time physical activity among adults aged >=18 Years"
• "Sleeping less than 7 hours among adults aged >=18 Years"
• "Binge drinking among adults aged >=18 Years"
• "Current smoking among adults aged >=18 Years"
• "Physical health not good for >=14 days among adults aged >=18 Years"
• "Mental health not good for >=14 days among adults aged >=18 Years"
• "High blood pressure among adults aged >=18 Years"
• "Arthritis among adults aged >=18 Years"
• "Stroke among adults aged >=18 Years"
• "Obesity among adults aged >=18 Years"
• "All teeth lost among adults aged >=65 Years"
• "Diagnosed diabetes among adults aged >=18 Years"
• "Cancer (excluding skin cancer) among adults aged >=18 Years"
• "Chronic obstructive pulmonary disease among adults aged >=18 Years"
• "Coronary heart disease among adults aged >=18 Years"
• "Current asthma among adults aged >=18 Years"
• "Chronic kidney disease among adults aged >=18 Years"
• "Taking medicine for high blood pressure control among adults aged >=18 Years with high blood pressure"
• "High cholesterol among adults aged >=18 Years who have been screened in the past 5 Years"

Inspiration

Please help this dataset reveal more by investigating anything that captures your attention, but for ideas, consider: * Does the state or region play a role in any of the measures? * Can you build a model to predict any of the measures? * Combining this data with the other years posted on Kaggle to determine how the measures have changed over ...

A dataset listing South Carolina cities by population for 2024.

A dataset listing Mississippi cities by population for 2024.

This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates for the PLACES 2021 release. PLACES is the expansion of the original 500 Cities Project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 29 measures: 4 chronic disease-related health risk behaviors, 13 health outcomes, 3 health status, and 9 on using preventive services. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2010 population data, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

A dataset listing Arkansas cities by population for 2024.

This dataset portrays the boundaries of ‘Settlements’ in Scotland as at 2001 Census. There is widespread interest in statistics for the built-up areas in Scotland as most of the population lives in a built-up environment. When the former two-tier local government structure of regions and districts came into being in May 1975, the small local authorities known as large and small burghs were lost. However, Census users stated that there was a need to know the population (and characteristics) of built-up areas. There are 2 datasets which are designed to show the boundaries of ‘urban areas’ in Scotland: ‘Localities’ and ‘Settlements’. While “Settlements’ can go a long way in defining the towns and cities in Scotland, some are very extensive and have grouped together some very large populations. For example the settlement of ‘Greater Glasgow’ has a large population but no breakdown was given of the settlement into any constituent towns or cities such as Airdrie or Paisley. Accordingly, since 2001, the larger ‘Settlements’ have been divided into ‘Localities’ using as a basis the areas so designated in the 1991 Census report ‘Key statistics for ‘localities’ in Scotland (ISBN 0-11-495736-3)’. For the 2001 Census, NRS had developed a new process to identify ‘Settlements’ which were defined as: ‘A collection of contiguous high population density postcodes whose total population was 500 or more, bounded by low density postcodes (or water).’

This map shows the historical housing unit change in consistent 2010 census tract boundaries from 1940 to 2019. In many cities over that time period—especially in the 1950s and 1960s—federal, state, and local governments demolished thousands of housing units as part of their "urban renewal" programs. These neighborhoods were typically in the older parts of city centers, contained lower income populations, and had higher shares of Black, Hispanic, and immigrant residents than their respective cities. Homes were typically replaced with new interstate highways and thoroughfares, stadiums, civic buildings, parking lots, high rises, rights of way, and other non-residential uses. In a fraction of cases, homes were replaced with public housing. Many of these areas show up as red on this map because they still have not regained the level of housing they had before World War II.Urban renewal is not the only reason for housing loss. Many tracts in places that have been undergoing population decline—especially cities in the North and Midwest and many rural communities—have also lost considerable amounts of housing over this time period.On the other side of things, many suburban and exurban areas—especially in the South and West—have experienced significant population and housing unit growth. These places show up as blue on this map.The data used to make this map comes from the Historical Housing Unit and Urbanization Database 2010, or HHUUD10. To read more on the methodologies used to estimate the housing unit counts, please refer to the methods paper. To download the data in tabular form, please visit the data repository. To download the feature layer used to make this map or read about the attributes, see the feature layer. Please also remember that these data are estimates and are therefore imperfect. They should be treated like all interpolated data: with caution and a healthy dose of skepticism.Citation:Markley, S.N., Holloway, S.R., Hafley, T.J., Hauer, M.E. 2022. Housing unit and urbanization estimates for the continental U.S. in consistent tract boundaries, 1940–2019. Scientific Data 9 (82). https://doi.org/10.1038/s41597-022-01184-x

Abstract (en): The Public Use Microdata Sample (PUMS) 1-Percent Sample contains household and person records for a sample of housing units that received the "long form" of the 1990 Census questionnaire. Data items include the full range of population and housing information collected in the 1990 Census, including 500 occupation categories, age by single years up to 90, and wages in dollars up to $140,000. Each person identified in the sample has an associated household record, containing information on household characteristics such as type of household and family income. All persons and housing units in the United States. A stratified sample, consisting of a subsample of the household units that received the 1990 Census "long-form" questionnaire (approximately 15.9 percent of all housing units). 2006-01-12 All files were removed from dataset 85 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 83 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 82 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 81 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 80 and flagged as study-level files, so that they will accompany all downloads.1998-08-28 The following data files were replaced by the Census Bureau: the state files (Parts 1-56), Puerto Rico (Part 72), Geographic Equivalency File (Part 84), and Public Use Microdata Areas (PUMAS) Crossing State Lines (Part 99). These files now incorporate revised group quarters data. Parts 201-256, which were separate revised group quarters files for each state, have been removed from the collection. The data fields affected by the group quarters data revisions were POWSTATE, POWPUMA, MIGSTATE and MIGPUMA. As a result of the revisions, the Maine file (Part 23) gained 763 records and Part 99 lost 763 records. In addition, the following files have been added to the collection: Ancestry Code List, Place of Birth Code List, Industry Code List, Language Code List, Occupation Code List, and Race Code List (Parts 86-91). Also, the codebook is now available as a PDF file. (1) Although all records are 231 characters in length, each file is hierarchical in structure, containing a housing unit record followed by a variable number of person records. Both record types contain approximately 120 variables. Two improvements over the 1980 PUMS files have been incorporated. First, the housing unit serial number is identified on both the housing unit record and on the person record, allowing the file to be processed as a rectangular file. In addition, each person record is assigned an individual weight, allowing users to more closely approximate published reports. Unlike previous years, the 1990 PUMS 1-Percent and 5-Percent Samples have not been released in separate geographic series (known as "A," "B," etc. records). Instead, each sample has its own set of geographies, known as "Public Use Microdata Areas" (PUMAs), established by the Census Bureau with assistance from each State Data Center. The PUMAs in the 1-Percent Sample are based on a distinction between metropolitan and nonmetropolitan areas. Metropolitan areas encompass whole central cities, Primary Metropolitan Statistical Areas (PMSAs), Metropolitan Statistical Areas (MSAs), or groups thereof, except where the city or metropolitan area contains more than 200,000 inhabitants. In that case, the city or metropolitan area is divided into several PUMAs. Nonmetropolitan PUMAs are based on areas or groups of areas outside the central city, PMSA, or MSA. PUMAs in this 1-Percent Sample may cross state lines. (2) The codebook is provided as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided through the ICPSR Website on the Internet.

2017, 2016. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level. There are 7 measures (all teeth lost, dental visits, mammograms, Pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) in this 2019 release from the 2016 BRFSS that were the same as the 2018 release.

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.

A dataset listing Georgia counties by population for 2024.

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information. This dataset was created by the US Centers for Disease Control and Prevention (CDC) and is hosted and maintained here: https://data.cdc.gov/500-Cities-Places/PLACES-Local-Data-for-Better-Health-Census-Tract-D/em5e-5hvn/about_data If you have questions about underlying source data, contact PLACES at places@cdc.gov. For questions about metadata or this extracted data contact CAFÉ (climatecafe@bu.edu). CDC describes the dataset as follows: "This dataset contains model-based census tract estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 36 measures: 13 for health outcomes, 9 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, and 3 for health status. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2021 or 2020 data, Census Bureau 2010 population data, and American Community Survey 2015–2019 estimates. The 2023 release uses 2021 BRFSS data for 29 measures and 2020 BRFSS data for seven measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places." [Quote from CDC PLACES Data - 2023 Release]

As per the recently released survey list, India is currently at the second position but may soon move to the first position. The survey found that - "24 April 2023 - China will soon lose its long-standing position as the world's most populous country. By the end of this month, India's population is set to reach 1,425,775,850 people." In such a situation, this dataset can be useful in population calculation of different cities.

This dataset contains the details of various major cities of Indian states, such as-

      1. Delhi.
      2. Mumbai.
      3. Bengaluru.
      4. Ahmedabad.
      5. Pune.
      6. Kolkata.
      7. Hyderabad etc.

And also information about the population of its corporation has been given.

This dataset contains model-based place (incorporated and census designated places) level estimates for the PLACES 2021 release in GIS-friendly format. PLACES is the expansion of the original 500 Cities Project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the 2019 Census TIGER/Line place boundary file in a GIS system to produce maps for 29 measures at the place level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=024cf3f6f59e49fe8c70e0e5410fe3cf

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 dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates for the PLACES 2021 release in GIS-friendly format. PLACES is the expansion of the original 500 Cities Project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census 2010 ZCTA boundary file in a GIS system to produce maps for 29 measures at the ZCTA level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software.

https://cdcarcgis.maps.arcgis.com/home/item.html?id=024cf3f6f59e49fe8c70e0e5410fe3cf

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.