This dataset is used to track wildfire information, assess wildfire risks, and to plan wildfire prevention activities.

It includes information about wildfires that have occurred on lands protected by the Washington State Department of Natural Resources, 2008 to present.This dataset is used to track wildfire information, assess wildfire risks, and to plan wildfire prevention activities.

Nonemployer Statistics is an annual series that provides subnational economic data for businesses that have no paid employees and are subject to federal income tax. The data consist of the number of businesses and total receipts by industry. Most nonemployers are self-employed individuals operating unincorporated businesses (known as sole proprietorships), which may or may not be the owner's principal source of income. The majority of all business establishments in the United States are nonemployers, yet these firms average less than 4 percent of all sales and receipts nationally. Due to their small economic impact, these firms are excluded from most other Census Bureau business statistics (the primary exception being the Survey of Business Owners). The Nonemployers Statistics series is the primary resource available to study the scope and activities of nonemployers at a detailed geographic level. For complementary statistics on the firms that do have paid employees, refer to the County Business Patterns. Additional sources of data on small businesses include the Economic Census, and the Statistics of U.S. Businesses.

The National Crime Victimization Survey (NCVS) Series, previously called the National Crime Surveys (NCS), has been collecting data on personal and household victimization through an ongoing survey of a nationally-representative sample of residential addresses since 1973. The NCVS was designed with four primary objectives: (1) to develop detailed information about the victims and consequences of crime, (2) to estimate the number and types of crimes not reported to the police, (3) to provide uniform measures of selected types of crimes, and (4) to permit comparisons over time and types of areas. The survey categorizes crimes as "personal" or "property." Personal crimes include rape and sexual attack, robbery, aggravated and simple assault, and purse-snatching/pocket-picking, while property crimes include burglary, theft, motor vehicle theft, and vandalism. Each respondent is asked a series of screen questions designed to determine whether she or he was victimized during the six-month period preceding the first day of the month of the interview. A "household respondent" is also asked to report on crimes against the household as a whole (e.g., burglary, motor vehicle theft). The data include type of crime, month, time, and location of the crime, relationship between victim and offender, characteristics of the offender, self-protective actions taken by the victim during the incident and results of those actions, consequences of the victimization, type of property lost, whether the crime was reported to police and reasons for reporting or not reporting, and offender use of weapons, drugs, and alcohol. Basic demographic information such as age, race, gender, and income is also collected, to enable analysis of crime by various subpopulations. This version of the NCVS, referred to as the collection year, contains records from interviews conducted in the 12 months of the given year.

The 2008 Yearbook of Immigration Statistics is a compendium of tables that provide data on foreign nationals who are granted lawful permanent residence (i.e., immigrants who receive a -green card-), admitted as temporary nonimmigrants, granted asylum or refugee status, or are naturalized. The Yearbook also presents data on immigration enforcement actions, including apprehensions and arrests, removals, and returns.

The data contain records of criminal appeals cases filed in United States Courts of Appeals during fiscal year 2008. The data were constructed from the Administrative Office of the United States Courts' (AOUSC) Court of Appeals file. These contain variables on the nature of the criminal appeal, the underlying offense, and the disposition of the appeal. An appeal can be filed by the government or the offender, and the appellant can appeal the sentence, the verdict, or both sentence and verdict. The data file contains variables from the original AOUSC files as well as additional analysis variables, or "SAF" variables, that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics, Tables 6.1-6.5. Variables containing information (e.g., name, Social Security number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute.

Description to be added

Land Registry yearly statistical summary.

The JSON file contains statistics for the year of 2007/2008.

State-reported annual data collected on the presence of elderly, disabled, and young children in eligible households receiving Low Income Home Energy Assistance Program (LIHEAP) heating assistance, cooling assistance, crisis assistance or weatherization assistance.

The FTC produces the Consumer Sentinel Network Data Book annually using a data set of fraud, identity theft, and other reports from consumers received by the Consumer Sentinel Network. These include reports made directly by consumers to the FTC, as well as reports received by federal, state, local, and international law enforcement agencies and other non-governmental organizations. This data set includes national statistics, as well as a state-by-state listing of top report categories in each state and a listing of metropolitan areas that generated the most complaints per capita, for calendar year 2008.

Market Statistics - G17c Statistics on Pure Reinsurers' Business - Commission Ratio

Abstract

EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labour, education and health observations only apply to persons 16 and older. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

The 7th version of the 2008 Cross-Sectional User Database (UDB) as released in July 2015 is documented here.

Geographic coverage

The survey covers following countries: Austria, Belgium, Bulgaria, Czech Republic, Denmark, Germany, Estonia, Greece, Spain, France, Ireland, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Malta, Netherlands, Poland, Portugal, Romania, Slovenia, Slovakia, Finland, Sweden, United Kingdom, Iceland, Norway.

Small parts of the national territory amounting to no more than 2% of the national population and the national territories listed below may be excluded from EU-SILC: France - French Overseas Departments and territories; Netherlands - The West Frisian Islands with the exception of Texel; Ireland - All offshore islands with the exception of Achill, Bull, Cruit, Gorumna, Inishnee, Lettermore, Lettermullan and Valentia; United kingdom - Scotland north of the Caledonian Canal, the Scilly Islands.

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the cross-sectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Detailed information about sampling is available in Quality Reports in Related Materials.

Mode of data collection

Mixed

Under the mining legislation in Northern Territory production data on mining leases is collected and a summary published in the annual reports of the department. Prior to July 2009 the data is available as an 11 year compilation.

UCR IBR Statistics Summary 1999-2008 - York County, Virginia

The information in this bulletin, about immunisation statistics in England, comes from: The Health Protection Agency (HPA) Centre for Infections (CfI) for information on childhood immunisation uptake at ages 1, 2 and 5 collected through the Cover of Vaccination Evaluated Rapidly (COVER) data collection for PCTs The NHS Information Centre (The NHS IC) for information about the BCG programme and reinforcing doses on the KC50 return from known providers of immunisation services. The Department of Health for: the new HPV vaccination programme for teenage girls persons aged 65 and over immunised against influenza for all PCTs (in conjunction with Health Protection Agency) Subsequent to publishing this report on 3 September 2009, a number of changes were identified as being needed. These are detailed in the Errata note document above. March 2013: Following investigation of KC50 data submitted by some Trusts from 2008-09 through to 2010-11, the HSCIC is recommending that Td/IPV and BCG data reported in tables 5, 6, 12 and 12a of this publication should be treated with some caution. See Errata note above for more information.

Market Statistics - G18h Claims Statistics - General Liability (including Employees' Compensation) Direct Business (Accident Year Basis) - Net Claims Paid in each year of development

This statistic shows the most common reasons for cost overrun in construction projects around the world as of 2008. Data is based on a survey among leaders in the construction industry. ** percent of respondents referenced material price escalation as a reason for cost overruns in a project making it the most commonly cited reason.

Housing statistics on routes 5 000 m X 5 000 m for all vintages from 2008, in separate CSV files. Newer vintages available in several formats. The data sets contain statistics on the number of dwellings and associated variables as of 1 January. Housing statistics on routes belong to the thematic group “Construction/Housing” in Statistics Norway’s product group “Statistics on grids”. In the same Theme group there are also dataset Building mass statistics on routes Other themes available are “Population”, “Businesses”, and “Earth, Forests, Hunting and Fisheries”

Market Statistics - G18n Claims Statistics - Goods In Transit Reinsurance Inward Facultative Business (Accident Year Basis) - Gross Claims Paid in each year of development