The population count of Pearl City, HI was 46,259 in 2017.


Population Change

Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API - Notes:

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Demographics and Population Datasets Involving Pearl City, HI

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    Virginia Beach Demographics | Last Updated 2017-10-12T13:51:45.000Z

    This dataset provides demographic information from the American Community Survey about residents of Virginia Beach. This data was originally provided in the executive summary of the City of Virginia Beach’s Operating Budget.

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    Uninsured Population Census Data CY 2009-2014 Human Services | Last Updated 2019-04-01T15:15:07.000Z

    This data is pulled from the U.S. Census website. This data is for years Calendar Years 2009-2014. Product: SAHIE File Layout Overview Small Area Health Insurance Estimates Program - SAHIE Filenames: SAHIE Text and SAHIE CSV files 2009 – 2014 Source: Small Area Health Insurance Estimates Program, U.S. Census Bureau. Internet Release Date: May 2016 Description: Model‐based Small Area Health Insurance Estimates (SAHIE) for Counties and States File Layout and Definitions The Small Area Health Insurance Estimates (SAHIE) program was created to develop model-based estimates of health insurance coverage for counties and states. This program builds on the work of the Small Area Income and Poverty Estimates (SAIPE) program. SAHIE is only source of single-year health insurance coverage estimates for all U.S. counties. For 2008-2014, SAHIE publishes STATE and COUNTY estimates of population with and without health insurance coverage, along with measures of uncertainty, for the full cross-classification of: •5 age categories: 0-64, 18-64, 21-64, 40-64, and 50-64 •3 sex categories: both sexes, male, and female •6 income categories: all incomes, as well as income-to-poverty ratio (IPR) categories 0-138%, 0-200%, 0-250%, 0-400%, and 138-400% of the poverty threshold •4 races/ethnicities (for states only): all races/ethnicities, White not Hispanic, Black not Hispanic, and Hispanic (any race). In addition, estimates for age category 0-18 by the income categories listed above are published. Each year’s estimates are adjusted so that, before rounding, the county estimates sum to their respective state totals and for key demographics the state estimates sum to the national ACS numbers insured and uninsured. This program is partially funded by the Centers for Disease Control and Prevention's (CDC), National Breast and Cervical Cancer Early Detection ProgramLink to a non-federal Web site (NBCCEDP). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the NBCCEDP. Most state NBCCEDP programs define low-income as 200 or 250 percent of the poverty threshold. Also included are IPR categories relevant to the Affordable Care Act (ACA). In 2014, the ACA will help families gain access to health care by allowing Medicaid to cover families with incomes less than or equal to 138 percent of the poverty line. Families with incomes above the level needed to qualify for Medicaid, but less than or equal to 400 percent of the poverty line can receive tax credits that will help them pay for health coverage in the new health insurance exchanges. We welcome your feedback as we continue to research and improve our estimation methods. The SAHIE program's age model methodology and estimates have undergone internal U.S. Census Bureau review as well as external review. See the SAHIE Methodological Review page for more details and a summary of the comments and our response. The SAHIE program models health insurance coverage by combining survey data from several sources, including: •The American Community Survey (ACS) •Demographic population estimates •Aggregated federal tax returns •Participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program •County Business Patterns •Medicaid •Children's Health Insurance Program (CHIP) participation records •Census 2010 Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level.

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    DBEDT Hawaii De Facto Population By County 2000-2010 | Last Updated 2012-09-05T00:34:45.000Z

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    Hawaii Directory Of Green Employers | Last Updated 2019-04-20T08:38:25.000Z

    The Hawai'i Directory of Green Employers is a growing online directory of green employers in Hawai'i. The Hawaii Department of Labor and Industrial Relations (DLIR) defines green employers as businesses that employ workers in occupations in these core areas: • Generate clean, renewable, sustainable Energy • Reduce pollution and waste; conserve natural resources; recycle • Energy efficiency • Education, training and support of green workforce • Natural, sustainable, environmentally-friendly production The Directory contains employers’ self-posted profiles that describe their operations, specify their core occupations, and describe the skills and education they want in employees. Jobseekers, students, their counselors and advisors, and others can access the employer profiles to learn about these companies and the workers they require.

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    Median Household Income All States 2000-2012 | Last Updated 2019-04-19T06:16:01.000Z

    Median Household Income All States 2000-2012

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    SBIR Awards in Hawaii - Phase 1 - 2000 to 2013 | Last Updated 2014-12-11T21:34:31.000Z

    Phase 1 SBIR Awards in State of Hawaii from 2000 to 2013

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    Number Of Minor Effect Illnesses From Exposure To All Pesticides By States | Last Updated 2019-04-19T02:39:04.000Z

    Number Of Minor Effect Illnesses From Exposure To All Pesticides By States

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    Phase 2 SBIR Awards in Hawaii - 2000~2013 | Last Updated 2014-12-11T23:30:29.000Z

    Phase 2 SBIR Awards in Hawaii from 2000 to 2013

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    OPD Response To Resistance | Last Updated 2019-02-15T19:21:15.000Z

    Any time an Officer must respond to resistance or aggression by using certain levels of force, it is recorded and investigated by the Orlando Police Department in a Response to Resistance report. This data set comes from those reports, which are entered into OPD’s records management system. Officer involved shooting data is maintained separately by Internal Affairs and is not included in this data set. The first three fields provide basic information about the incident Incident Number - Automatically assigned at the time the incident; is created in OPD records management system. First four characters indicate the year the incident was created in the system. Incident Date Time - Day and time of the incident Incident Location - Block level address or intersection of the location The next seven fields provide information about the officer(s) involved in the incident. Officers Involved - The number of officers involved in the entire incident Officers Race - Race of the officers involved, multiple officers separated by comma (W = White, A = Asian, B = Black) Officers Ethnicity - Ethnicity of the officers involved, multiple officers separated by comma (NH = Non-hispanic, HI = Hispanic) Officers Sex - Sex of the officers involved, multiple officers separated by comma (M = Male, F = Female) Officer Battered - Did the officer report being battered during the incident Officer Injured - Did the officer report being injured during the incident Officer Medical Treatment - Did any of the officers receive medical treatment for injuries sustained during the incident The next eight fields reference what responses to resistance any officer may have used during the incident. A “Yes” indicates at least one officer used that tactic. A “No” indicates no officers used that tactic. Electronic Device Used - Commonly referred to as a Taser. This device uses propelled wires or direct contact to conduct energy to affect the sensory and motor functions of the nervous system. Chemical Agent Used - Chemical Agent, or Pepper Spray, may cause burning and tearing of the eyes, and disorientation. Tackle/ Take Down – Tactic used to gain compliance by taking a person to the ground Impact Weapon Used - Impact weapon is most commonly a baton. OPD also has a weapon called a Sage SL6 which launches rubber projectiles. Physical Strikes Made - . Strikes made with the hand, forearm, knee, or foot to assist with controlling a person. Deflation Device Used - Tire deflation devices - commonly known as "stop sticks". The use of this device is intended to deflate a vehicle’s tire(s). K9 Unit Involved - A Response to Resistance is required if a K9 bites a subject. The next seven fields provide information about the offender(s) involved in the incident. Race and ethnicity may be unknown. OPD policy is that officers not ask for information on race or ethnicity if it’s not related to the police encounter. Offenders Involved - Number of offenders involved in the entire incident Offenders Race - Race of the offenders involved, multiple offenders separated by comma (W = White, A = Asian, B = Black) Offenders Ethnicity - Ethnicity of the offenders involved, multiple offenders separated by comma (NH = Non-hispanic, HI = Hispanic) Offenders Sex - Sex of the offenders involved, multiple offenders separated by comma (M = Male, F = Female) Offender Arrested - Was the offender arrested during the incident (Not Arrested = Offender was not arrested during the incident, Misdemeanor = Offender was arrested for a misdemeanor, Felony = Offender was arrested for a Felony) Offender Injured - Did the offenders report being injured during the incident Offender Medical Treatment - Did any of the offenders receive medical treatment for injuries sustained during the incident Witnesses Involved - Count of the number of witnesses indicated in the report Status - This is an information only field to designate if the City was able to map the Incident Location

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    Hate Crimes by County and Bias Type: Beginning 2010 | Last Updated 2018-08-07T23:35:10.000Z

    Under New York State’s Hate Crime Law (Penal Law Article 485), a person commits a hate crime when one of a specified set of offenses is committed targeting a victim because of a perception or belief about their race, color, national origin, ancestry, gender, religion, religious practice, age, disability, or sexual orientation, or when such an act is committed as a result of that type of perception or belief. These types of crimes can target an individual, a group of individuals, or public or private property. DCJS submits hate crime incident data to the FBI’s Uniform Crime Reporting (UCR) Program. Information collected includes number of victims, number of offenders, type of bias motivation, and type of victim.