This layer shows Health Insurance Coverage. This is shown by state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.
This layer is symbolized to show percent uninsured. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2015-2019ACS Table(s): B27010, DP03Data downloaded from: Census Bureau's API for American Community Survey Date of API call: February 10, 2021National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS): About the SurveyGeography & ACSTechnical Documentation News & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes.
All of these are rendered in this dataset as null (blank) values.

ISO is an independent advisory organization that collects information on a community's building-code adoption and enforcement services in order to provide a ranking for insurance companies. ISO assigns a Building Code Effectiveness Classification from 1 to 10 based on the data collected. Class 1 represents exemplary commitment to building-code enforcement.Municipalities with better rankings are lower risk, and their residents' insurance rates can reflect that. The prospect of minimizing catastrophe-related damage and ultimately lowering insurance costs gives communities an incentive to enforce their building codes rigorously.This page provides data for the Insurance Services Organization (ISO) performance measure. This data includes residential and commercial building code enforcement ratings for the City of Tempe.The performance measure dashboard is available at 1.15 Insurance Services Organization (ISO) RatingAdditional InformationSource: Insurance Service Organization RatingContact: Chris ThompsonContact E-Mail: Christopher_Thompson@tempe.govData Source Type: ExcelPreparation Method: Information added to Excel spreadsheet from rating reportPublish Frequency: Every 5 YearsPublish Method: ManualData Dictionary

Table contains county residents without health insurance. Data are summarized as people of all ages and those 19 to 64 years old. Data are presented at county, city, zip code and census tract level. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B27001; data accessed on June 30, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographypop (Numeric): Population for whom health insurance coverage was assessedt_uninsured (Numeric): Number of people (all ages) who were without health insurancep_uninsured (Numeric): Percent of people (all ages) who were without health insurancet_19_64 (Numeric): Population ages 19 to 64 years for whom health insurance coverage was assessedt_unins_19_64 (Numeric): Number of people ages 19 to 64 years who were without health insurancep_unins_19_64 (Numeric): Percent of people ages 19 to 64 years who were without health insurance

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

This project focuses on developing a machine learning-driven system to classify hospital claims and treatment outcomes, offering a second opinion on healthcare costs and decision-making for insurance claims and treatment efficacy.Key Features and Tools:Machine Learning Algorithms: Leveraging Python (pandas, numpy, scikit-learn) for predictive modeling to assess claim validity and treatment outcomes.APIs Integration: Used Google Maps API to retrieve and map the locations of private hospitals in Malaysia.GIS Mapping Dashboard: Created a GIS-enabled dashboard in Microsoft Power BI to visualize private hospital distribution across Malaysia, aiding healthcare planning and analysis.Advanced Analytics Tools: Integrated Microsoft Excel, Python, and Google Collab for data processing and automation workflows.

Table from the American Community Survey (ACS) 5-year series on disabilities and health insurance related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes C21007 Age by Veteran Status by Poverty Status in the Past 12 Months by Disability Status, B27010 Types of Health Insurance Coverage by Age, B22010 Receipt of Food Stamps/SNAP by Disability Status for Households. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

Historical ownership data of GIS by CINCINNATI INSURANCE CO

Historical ownership data of GIS by Cincinnati Specialty Underwriters Insurance CO

This dataset contains data from the National Flood Hazard Layer, a GIS database of flood risks and regulatory flood determination data. Flood hazard and supporting data are developed using specifications for horizontal control consistent with 1:12,000–scale mapping. If you plan to display maps from the National Flood Hazard Layer with other map data for official purposes, ensure that the other information meets FEMA’s standards for map accuracy. The minimum horizontal positional accuracy for base map hydrographic and transportation features used with the NFHL is the NSSDA radial accuracy of 38 feet. USGS imagery and map services that meet this standard can be found by visiting the Knowledge Sharing Site (KSS) for Base Map Standards (420). Other base map standards can be found at http://pm.riskmapcds.com/kss/MapChanges/default.aspx. You will need a username and password to access this information. The NFHL data are from FEMA’s Flood Insurance Rate Map (FIRM) databases. New data are added continually. The NFHL also contains map changes to FIRM data made by Letters of Map Revision (LOMRs). The NFHL is stored in North American Datum of 1983, Geodetic Reference System 80 coordinate system, though many of the NFHL GIS web services support the Web Mercator Sphere projection commonly used in web mapping applications. This dataset displays FEMA's Flood Insurance Rate Map (FIRM) panels. The spatial entities representing FIRM panels are polygons. The polygon for the FIRM panel corresponds to the panel neatlines. Panel boundaries are generally derived from USGS DOQQ boundaries. As a result, the panels are generally rectangular. FIRM panels must not overlap or have gaps within a study. In situations where a portion of a panel lies outside the jurisdiction being mapped, the user must refer to the S_Pol_Ar table to determine the portion of the panel area where the FIRM Database shows the effective flood hazard data for the mapped jurisdiction. This information is needed for the FIRM Panel Index and the following tables in the FIS report: Listing of NFIP Jurisdictions, Levees, Incorporated Letters of Map Change, and Coastal Barrier Resources System Information.View Dataset on the Gateway

Historical holdings data showing quarterly positions, market values, shares held, and portfolio percentages for GIS held by CINCINNATI INSURANCE CO from Q3 2013 to Q4 2017

This dataset provides locations and related information for Insurance Navigators and Application Organizations as of 08/29/2014 based on information provided by the Indiana Department of Insurance. Navigators are individuals who help Hoosier insurance consumers complete health coverage applications on the federall-facilitated marketplace or state-based insurance affordabillty program applications. An Application Organization is an organization that has employees and/or volunteers helping Hoosier insurance consumers complete state or federal applications for health coverage. Visit http://www.in.gov/idoi/2823.htm for more information about this resource.

This layer shows the percentage of the civilian noninstitutionalized population who do not have insurance. This is shown by county centroids. The data values are from the 2012-2016 American Community Survey 5-year estimate in the B27001 Table for health insurance coverage status broken down by by age and sex characteristics.This map helps to answer a few questions:How many people in the United States don't have health insurance?Where are the concentrations of uninsured population?This map helps to tell a regional pattern about insurance in the United States. The data can be stratified by different age and sex characteristics in order to create additional maps. By default, the pop-up provides a breakdown of total male and female uninsured population. This data was downloaded from the United States Census Bureau American Fact Finder on March 1, 2018. It was then joined with 2016 vintage centroid points and hosted to ArcGIS Online and into the Living Atlas. The data contains additional attributes that can be used for mapping and analysis. Nationally, the breakdown of insurance for the civilian noninstitutionalized population in the US is:Total:313,576,137+/-10,365Male:153,162,940+/-12,077Under 6 years:12,227,441+/-11,224With health insurance coverage11,643,526+/-12,783No health insurance coverage583,915+/-6,4386 to 17 years:25,282,489+/-12,396With health insurance coverage23,659,835+/-16,339No health insurance coverage1,622,654+/-14,50018 to 24 years:15,350,990+/-8,369With health insurance coverage12,112,729+/-19,586No health insurance coverage3,238,261+/-24,08125 to 34 years:20,901,264+/-8,155With health insurance coverage15,669,472+/-36,401No health insurance coverage5,231,792+/-38,88735 to 44 years:19,499,072+/-6,321With health insurance coverage15,722,620+/-41,969No health insurance coverage3,776,452+/-41,91645 to 54 years:20,965,500+/-5,283With health insurance coverage17,819,431+/-33,014No health insurance coverage3,146,069+/-31,18155 to 64 years:19,068,251+/-3,959With health insurance coverage17,076,497+/-20,830No health insurance coverage1,991,754+/-19,81365 to 74 years:12,168,198+/-3,453With health insurance coverage12,041,594+/-4,736No health insurance coverage126,604+/-3,20775 years and over:7,699,735+/-3,458With health insurance coverage7,657,815+/-3,794No health insurance coverage41,920+/-1,719Female:160,413,197+/-8,724Under 6 years:11,684,980+/-10,395With health insurance coverage11,115,775+/-13,062No health insurance coverage569,205+/-7,1326 to 17 years:24,280,468+/-11,445With health insurance coverage22,723,174+/-14,642No health insurance coverage1,557,294+/-13,46818 to 24 years:15,151,707+/-5,432With health insurance coverage12,591,379+/-16,744No health insurance coverage2,560,328+/-18,82625 to 34 years:21,367,510+/-4,829With health insurance coverage17,505,087+/-32,122No health insurance coverage3,862,423+/-31,65135 to 44 years:20,279,901+/-4,751With health insurance coverage17,146,763+/-32,076No health insurance coverage3,133,138+/-31,65945 to 54 years:21,975,842+/-5,087With health insurance coverage19,083,932+/-27,415No health insurance coverage2,891,910+/-25,02255 to 64 years:20,665,987+/-3,867With health insurance coverage18,537,874+/-18,484No health insurance coverage2,128,113+/-16,61465 to 74 years:13,896,484+/-3,882With health insurance coverage13,730,727+/-6,177No health insurance coverage165,757+/-3,85775 years and over:11,110,318+/-3,977With health insurance coverage11,037,661+/-4,391No health insurance coverage72,657+/-2,120Data note from the US Census Bureau:[ACS] data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.

Link to the Library of Congress Sanborn Fire Insurance Map dated May 1916 for Sioux Falls, South Dakota.Sanborn Fire Maps were originally prepared for the use of fire insurance companies. The maps include parcel boundaries, building information, business names, street names, house numbers, fire hydrants, utilities, and more.

Historical holdings data showing quarterly positions, market values, shares held, and portfolio percentages for GIS held by Cincinnati Specialty Underwriters Insurance CO from Q3 2013 to Q4 2017

This layer shows health insurance coverage by type and by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.

The newGeoSure Insurance Product (newGIP) provides the potential insurance risk due to natural ground movement. It incorporates the combined effects of the 6 GeoSure hazards on (low-rise) buildings. This data is available as vector data, 25m gridded data or alternatively linked to a postcode database the Derived Postcode Database. A series of GIS (Geographical Information System) maps show the most significant hazard areas. The ground movement, or subsidence, hazards included are landslides, shrink-swell clays, soluble rocks, running sands, compressible ground and collapsible deposits. The newGeoSure Insurance Product uses the individual GeoSure data layers and evaluates them using a series of processes including statistical analyses and expert elicitation techniques to create a derived product that can be used for insurance purposes such as identifying and estimating risk and susceptibility. The Derived Postcode Database (DPD) contains generalised information at a postcode level. The DPD is designed to provide a summary value representing the combined effects of the GeoSure dataset across a postcode sector area. It is available as a GIS point dataset or a text (.txt) file format. The DPD contains a normalised hazard rating for each of the 6 GeoSure themes hazards (i.e. each GeoSure theme has been balanced against each other) and a combined unified hazard rating for each postcode in Great Britain. The combined hazard rating for each postcode is available as a standalone product. The Derived Postcode Database is available in a point data format or text file format. It is available in a range of GIS formats including ArcGIS (.shp), ArcInfo Coverages and MapInfo (.tab). More specialised formats may be available but may incur additional processing costs. The newGeoSure Insurance Product dataset has been created as vector data but is also available as a raster grid. This data is available in a range of GIS formats, including ArcGIS (.shp), ArcInfo coverages and MapInfo (.tab). More specialised formats may be available but may incur additional processing costs. Data for the newGIP is provided for national coverage across Great Britain. The newGeoSure Insurance Product dataset is produced for use at 1:50 000 scale providing 50 m ground resolution. This dataset has been specifically developed for the insurance of low-rise buildings. The GeoSure datasets have been developed to identify the potential hazard for low-rise buildings and those with shallow foundations of less than 2 m deep. The identification of ground instability and other geological hazards can assist regional planners; rapidly identifying areas with potential problems and aid local government offices in making development plans by helping to define land suited to different uses. Other users of these data may include developers, homeowners, solicitors, loss adjusters, the insurance industry, architects and surveyors. Version 7 released June 2015.

Compilations of digital GIS data representing the same information presented on FIRMs, and in the Flood Insurance Study Report.rn

Analysis of ‘1.15 Insurance Services Organization (summary)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/0905a875-5513-4591-aeaf-370103dc476a on 11 February 2022.

--- Dataset description provided by original source is as follows ---

ISO is an independent advisory organization that collects information on a community's building-code adoption and enforcement services in order to provide a ranking for insurance companies. ISO assigns a Building Code Effectiveness Classification from 1 to 10 based on the data collected. Class 1 represents exemplary commitment to building-code enforcement.

Municipalities with better rankings are lower risk, and their residents' insurance rates can reflect that. The prospect of minimizing catastrophe-related damage and ultimately lowering insurance costs gives communities an incentive to enforce their building codes rigorously.

This page provides data for the Insurance Services Organization (ISO) performance measure.

This data includes residential and commercial building code enforcement ratings for the City of Tempe.

The performance measure dashboard is available at 1.15 Insurance Services Organization (ISO) Rating

Additional Information

Source: Insurance Service Organization Rating

Contact: Chris Thompson

Contact E-Mail: Christopher_Thompson@tempe.gov

Data Source Type: Excel

Preparation Method: Information added to Excel spreadsheet from rating report

Publish Frequency: Every 5 Years

Publish Method: Manual

Data Dictionary

--- Original source retains full ownership of the source dataset ---

FIRM Flood Insurance Rate Map Official map of a community on which FEMA has delineated the Special Flood Hazard Areas (SFHAs), the Base Flood Elevations (BFEs) and the risk premium zones applicable to the community.

REQUIRED: A brief narrative summary of the data set.