Context

The United States Census is a decennial census mandated by Article I, Section 2 of the United States Constitution, which states: "Representatives and direct Taxes shall be apportioned among the several States ... according to their respective Numbers."
Source: https://en.wikipedia.org/wiki/United_States_Census

Content

The United States census count (also known as the Decennial Census of Population and Housing) is a count of every resident of the US. The census occurs every 10 years and is conducted by the United States Census Bureau. Census data is publicly available through the census website, but much of the data is available in summarized data and graphs. The raw data is often difficult to obtain, is typically divided by region, and it must be processed and combined to provide information about the nation as a whole.

The United States census dataset includes nationwide population counts from the 2000 and 2010 censuses. Data is broken out by gender, age and location using zip code tabular areas (ZCTAs) and GEOIDs. ZCTAs are generalized representations of zip codes, and often, though not always, are the same as the zip code for an area. GEOIDs are numeric codes that uniquely identify all administrative, legal, and statistical geographic areas for which the Census Bureau tabulates data. GEOIDs are useful for correlating census data with other censuses and surveys.

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Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:census_bureau_usa

https://cloud.google.com/bigquery/public-data/us-census

Dataset Source: United States Census Bureau

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Steve Richey from Unsplash.

Inspiration

What are the ten most populous zip codes in the US in the 2010 census?

What are the top 10 zip codes that experienced the greatest change in population between the 2000 and 2010 censuses?

https://cloud.google.com/bigquery/images/census-population-map.png" alt="https://cloud.google.com/bigquery/images/census-population-map.png"> https://cloud.google.com/bigquery/images/census-population-map.png

Census Congressional District Shape file 2021

Census Congressional Districts Shapefile 2020 (20M)

These datasets contains statements about demographic factors and outstanding members from Wiki-based knowledge (i.e., Wikipedia and Wikidata).

Group-centric dataset (sample of what is it about):

• Demographic factors of winners of Nobel Prize in Physics include: male, physicist, american, university teacher, and researcher. Outstanding members in this group include Maria Curie (who isn't male but female) and Wilhelm Röntgen (who isn't a citizen of the U.S. but Germany).

Subject-centric dataset (sample of what is it about):

• Fun trivia about Max Planck include: unlike 93% of winners of Liebig Medal (an award by Society of German Chemists), Planck was not a chemist, but a physicist.

This data can be also browsed at: https://wikiknowledge.onrender.com/demographics/

A binary dasymetric analysis, using National Land Cover Databases (NLCD) as an auxiliary, is applied to US Census Tracts. American Community Survey (ACS) Demographic data is often joined and used to generate impacted population with a hazard/AOI.

Boundary Shapes for the US Census 'Places' 2021

The BJS Census of State and Local Law Enforcement Agencies (CSLLEA) is conducted every 4 years to provide a complete enumeration of agencies and their employees. Employment data are reported by agencies for sworn and nonsworn (civilian) personnel and, within these categories, by full-time or part-time status. The pay period that included September 30, 2008, was the reference date for all personnel data. Agencies also complete a checklist of functions they regularly perform, or have primary responsibility for, within the following areas: patrol and response, criminal investigation, traffic and vehicle-related functions, detention-related functions, court-related functions, special public safety functions (e.g., animal control), task force participation, and specialized functions (e.g., search and rescue). The CSLLEA provides national data on the number of state and local law enforcement agencies and employees for local police departments, sheriffs' offices, state law enforcement agencies, and special jurisdiction agencies. It also serves as the sampling frame for BJS surveys of law enforcement agencies.

The Current Population Survey Civic Engagement and Volunteering (CEV) Supplement is the most robust longitudinal survey about volunteerism and other forms of civic engagement in the United States. Produced by AmeriCorps in partnership with the U.S. Census Bureau, the CEV takes the pulse of our nation’s civic health every two years. The CEV can support evidence-based decision making and efforts to understand how people make a difference in communities across the country.

The findings on this page are based on data collected in September of 2017, 2019, 2021, and 2023. All figures are weighted to account for the random selection of eligible respondents and missing data due to nonresponse. They reflect national rates of 17 measures of civic engagement for key demographic subgroups. Please see column descriptions for details.

A spreadsheet with all of these figures is provided as an attachment along with additional resources about the CEV data. Click on "Show More" to view and download.

To explore CEV findings in an interactive dashboard, please see https://data.americorps.gov/stories/s/AmeriCorps-Civic-Engagement-and-Volunteering-CEV-D/62w6-z7xa

For the full CEV datasets, please see https://data.americorps.gov/browse?q=cev&sortBy=last_modified&utf8=%E2%9C%93

Housing Assessment Resource Tools (HART) This dataset contains 2 tables and 5 files which draw upon data from the 2021 Census of Canada. The tables are a custom order and contain data pertaining to older adults and housing need. The 2 tables have 6 dimensions in common and 1 dimension that is unique to each table. Table 1's unique dimension is the "Ethnicity / Indigeneity status" dimension which contains data fields related to visible minority and Indigenous identity within the population in private households. Table 2's unique dimension is "Structural type of dwelling and Period of Construction" which contains data fields relating to the structural type and period of construction of the dwelling. Each of the two tables is then split into multiple files based on geography. Table 1 has two files: Table 1.1 includes Canada, Provinces and Territories (14 geographies), CDs of NWT (6), CDs of Yukon (1) and CDs of Nunavut (3); and Table 1.2 includes Canada and the CMAs of Canada (44). Table 2 has three files: Table 2.1 includes Canada, Provinces and Territories (14), CDs of NWT (6), CDs of Yukon (1) and CDs of Nunavut (3); Table 2.2 includes Canada and the CMAs of Canada excluding Ontario and Quebec (20 geographies); and Table 2.3 includes Canada and the CMAs of Canada that are in Ontario and Quebec (25 geographies). The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and data fields: Geography: - Country of Canada as a whole - All 10 Provinces (Newfoundland, Prince Edward Island (PEI), Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia) as a whole - All 3 Territories (Nunavut, Northwest Territories, Yukon), as a whole as well as all census divisions (CDs) within the 3 territories - All 43 census metropolitan areas (CMAs) in Canada Data Quality and Suppression: - The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. - Area suppression is used to replace all income characteristic data with an 'x' for geographic areas with populations and/or number of households below a specific threshold. If a tabulation contains quantitative income data (e.g., total income, wages), qualitative data based on income concepts (e.g., low income before tax status) or derived data based on quantitative income variables (e.g., indexes) for individuals, families or households, then the following rule applies: income characteristic data are replaced with an 'x' for areas where the population is less than 250 or where the number of private households is less than 40. Source: Statistics Canada - When showing count data, Statistics Canada employs random rounding in order to reduce the possibility of identifying individuals within the tabulations. Random rounding transforms all raw counts to random rounded counts. Reducing the possibility of identifying individuals within the tabulations becomes pertinent for very small (sub)populations. All counts are rounded to a base of 5, meaning they will end in either 0 or 5. The random rounding algorithm controls the results and rounds the unit value of the count according to a predetermined frequency. Counts ending in 0 or 5 are not changed. Universe: Full Universe: Population aged 55 years and over in owner and tenant households with household total income greater than zero in non-reserve non-farm private dwellings. Definition of Households examined for Core Housing Need: Private, non-farm, non-reserve, owner- or renter-households with incomes greater than zero and shelter-cost-to-income ratios less than 100% are assessed for 'Core Housing Need.' Non-family Households with at least one household maintainer aged 15 to 29 attending school are considered not to be in Core Housing Need, regardless of their housing circumstances. Data Fields: Table 1: Age / Gender (12) 1. Total – Population 55 years and over 2. Men+ 3. Women+ 4. 55 to 64 years 5. Men+ 6. Women+ 7. 65+ years 8. Men+ 9. Women+ 10. 85+ 11. Men+ 12. Women+ Housing indicators (13) 1. Total – Private Households by core housing need status 2. Households below one standard only...

National Risk Index Version: March 2023 (1.19.0)Coastal Flooding is when water inundates or covers normally dry coastal land as a result of high or rising tides or storm surges. Annualized frequency values for Coastal Flooding are in units of events per year.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

This is a filtered View to show the total number of claims grouped by assigned Census Tracts in the dataset

As of year-end 2006 a total of 648 state and local law enforcement academies were providing basic training to entry-level recruits in the United States. State agencies approved 98 percent of these academies. This data collection describes the academies in terms of their personnel, expenditures, facilities, curricula, and trainees using data from the 2006 Census of State and Local Law Enforcement Training Academies (CLETA) sponsored by the Bureau of Justice Statistics (BJS). The 2006 CLETA, like the initial 2002 study, collected data from all state and local academies that provided basic law enforcement training. Academies that provided only in-service training, corrections and detention training, or other special types of training were excluded. Federal training academies were also excluded.

The Juvenile Residential Facility Census (JRFC), which is conducted biennially, collects basic information on juvenile residential facility characteristics, including security, capacity and crowding, injuries and deaths in custody, and facility ownership and operation. The JRFC also includes questions about facility type (such as detention center, training school, ranch, or group home) and residential services provided by the facility (such as independent living, foster care, or other arrangements), and detailed questions about mental health, substance abuse, and educational services provided to young persons.

In 2016, the JRFC was divided into seven sections:

1. General facility information
2. Mental health services
3. Educational services
4. Substance abuse services
5. Events in the 30 days prior to the census reference date
6. Deaths in the year prior to the census reference date
7. Space shared with other facilities

Congress requires the Office of Juvenile Justice and Delinquency Prevention (OJJDP) to report annually on the number of deaths of juveniles in custody; the JRFC gathers this information and offers a portrait of the nation's juvenile facilities. The census reference date was the fourth Wednesday in October.

This dataset provides comparisons of demographic group prevalence in AmeriCorps Member/Volunteers populations to that of the greater U.S. population. The odds ratio analysis was completed by the Office of the Chief Data Officer. Population estimates were obtained from U.S. Census Bureau data reported in American Community Survey 5-Year tables DP05 (total U.S. populations) and S1701 (U.S. populations below poverty line), and socioeconomic status-related microdata maintained by IPUMS USA. See Attached Document 'AmeriCorps Demographic Analysis Procedure.pdf' for a full technical documentation of the analysis.

Overview: FEMA and Argonne National Laboratory completed the first analysis of community resilience indicators in 2018 and repeated the process in 2022. The analysis process begins with a literature review and cataloguing of published peer-reviewed assessment methodologies on social vulnerability and community resilience. The literature review findings are then filtered by inclusion criteria established by the research team to ensure the methodologies are:

Quantitative, Data and methodology are publicly available, Calculated at the county level or lower, Examine generalized hazard risk (rather than a singular hazard), and Focused on pre-disaster community conditions.

After this, the research team identifies the commonly used indicators across these methodologies and selects the best data source for each indicator. Finally, the research team bins the data for visualization, conducts a correlation analysis, and creates a composite index called the "FEMA Community Resilience Challenges Index (CRCI)".

In 2022, the FEMA and Argonne research team updated the 2018 literature review and examined 14 methodologies published between 2003 and 2021. Examining the indicators used in these methodologies, the research team identified 22 indicators as commonly used (indicators used in five or more of the 14 methodologies). The research team produced the FEMA CRCI at the county and the census tract levels. More details on these indicators and the research process can be found in the FEMA CRCI Storymap. Data last updated on May 13, 2023. This is the latest available version of the CRCI. Questions or comments about this layer? Email the RAPT team at FEMA-TARequest@fema.dhs.gov

From 2011 to 2013, a total of 664 state and local law enforcement academies provided basic training to entry-level officer recruits in the United States. During this period, more than 135,000 recruits (45,000 per year) entered a basic training program, and 86 percent completed the program successfully. This completion rate was the same as was observed for the 57,000 recruits who entered training programs in 2005. This data collection describes basic training programs for new recruits based on their content, instructors, and teaching methods. It also describes the recruits' demographics, completion rates, and reasons for failure. The data describing recruits cover those entering basic training programs from 2011 to 2013. The data describing academies are based on 2013, the latest year referenced in the survey. Like prior BJS studies conducted in 2002 and 2006, the 2013 CLETA collected data from all state and local academies that provided basic law enforcement training. Academies that provided only in-service, corrections and detention, or other specialized training were excluded. Federal training academies were also excluded. Any on-the-job training received by recruits subsequent to their academy training is not covered.

National Risk Index Version: March 2023 (1.19.0)The National Risk Index Census Tracts feature layer contains Census tract-level data for the Risk Index, Expected Annual Loss, Social Vulnerability, and Community Resilience.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

Note: The data release is complete as of August 14th, 2023. 1. (Added April 4th) Canada and Census Divisions = Early April 2023 2. (Added May 1st) Ontario, British Columbia, and Alberta Census Subdivisions (CSDs) = Late April 2023 3a. (Added June 8th) Manitoba and Saskatchewan CSDs 3b. (Added June 12th) Quebec CSDs = June 12th 2023 4. (Added June 30th) Newfoundland and Labrador, Prince Edward Island, New Brunswick, and Nova Scotia CSDs = Early July 2023 5. (Added August 14th) Yukon, Northwest Territories, and Nunavut CSDs = Early August 2023. For more information, please visit HART.ubc.ca. Housing Assessment Resource Tools (HART) This dataset contains 18 tables which draw upon data from the 2021 Census of Canada. The tables are a custom order and contains data pertaining to core housing need and characteristics of households. 17 of the tables each cover a different geography in Canada: one for Canada as a whole, one for all Canadian census divisions (CD), and 15 for all census subdivisions (CSD) across Canada. The last table contains the median income for all geographies. Statistics Canada used these median incomes as the "area median household income (AMHI)," from which they derived some of the data fields within the Shelter Costs/Household Income dimension. Included alongside the data tables is a guide to HART's housing need assessment methodology. This guide is intended to support independent use of HART's custom data both to allow for transparent verification of our analysis, as well as supporting efforts to utilize the data for analysis beyond what HART did. There are many data fields in the data order that we did not use that may be of value for others. The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and data fields: Geography: - Country of Canada, all CDs & Country as a whole - All 10 Provinces (Newfoundland, Prince Edward Island (PEI), Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia), all CSDs & each Province as a whole - All 3 Territories (Nunavut, Northwest Territories, Yukon), all CSDs & each Territory as a whole Data Quality and Suppression: - The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. - Area suppression is used to replace all income characteristic data with an 'x' for geographic areas with populations and/or number of households below a specific threshold. If a tabulation contains quantitative income data (e.g., total income, wages), qualitative data based on income concepts (e.g., low income before tax status) or derived data based on quantitative income variables (e.g., indexes) for individuals, families or households, then the following rule applies: income characteristic data are replaced with an 'x' for areas where the population is less than 250 or where the number of private households is less than 40. Source: Statistics Canada - When showing count data, Statistics Canada employs random rounding in order to reduce the possibility of identifying individuals within the tabulations. Random rounding transforms all raw counts to random rounded counts. Reducing the possibility of identifying individuals within the tabulations becomes pertinent for very small (sub)populations. All counts are rounded to a base of 5, meaning they will end in either 0 or 5. The random rounding algorithm controls the results and rounds the unit value of the count according to a predetermined frequency. Counts ending in 0 or 5 are not changed. Universe: Full Universe: Private Households in Non-farm Non-band Off-reserve Occupied Private Dwellings with Income Greater than zero. Households examined for Core Housing Need: Private, non-farm, non-reserve, owner- or renter-households with incomes greater than zero and shelter-cost-to-income ratios less than 100% are assessed for 'Core Housing Need.' Non-family Households with at least one household maintainer aged 15 to 29 attending school are considered not to be in Core Housing Need, regardless of their housing circumstances. Data Fields: Note 1: Certain data fields from the original .ivt...

First, a caveat: the NFIP data does NOT provide information specific to individual homes or parcels. This information is protected under federal law. All personal identifying information about policy holders has been redacted, and data has been anonymized to census tract, reported ZIP code, and one decimal point digit of latitute and longitude. If mapped, flood insurance policies and claims may appear to be clustered at a particular location due to this anonymization. What all that means: you cannot search for an address to see whether it has flooded. However, among many things, this data shows flooding trends in Norfolk over the last 40+ years. It shows the census tracts that flood most frequently. And it shows where the largest number and highest value of claims occur.

FEMA believes this historic release of NFIP data promotes transparency, reduces complexity related to public data requests, and improves how stakeholders interact with and understand the program. This is the largest, most comprehensive release of NFIP data coordinated by FEMA to date. This dataset allows for customizable searches to create reports, analyze and visualize present and historical NFIP data faster and easier than before. This data will help FEMA build a national culture of preparedness by providing claims and policy information people need to make better choices about their flood risk and the insurance they need to protect the life they've built. Norfolk's Open Data team extracted city-specific information from the FEMA dataset. The dataset included here represents almost 6,000 claims on record from 1977 through 2019, totaling 67 million dollars in damage in the City of Norfolk.

2010 Amerika Birleşik Devletleri Nüfus Sayımı ingilizce 2010 United States Census veya bilinen adıyla Census 2010 A