LINZ maintains a point layer of primary address points allocated by local councils for rateable properties. The principle purpose of this dataset is to allocate voters to the correct electorate. The set is actively maintained, but is still incomplete and some locations are incorrect. Nevertheless it is by far the most comprehensive address database available.

It includes all (allocated) rural address points (RAPID numbers), commercial addresses and many flat numbers. So numbers are not numeric, there are all sorts of formats included here, sorry. Addresses are not unique. The points are "location addresses", not "postal addresses". For residential town addresses this is normally the same, but for commercial and rural locations they are not the same.

Primary addresses are only the number and alpha parts. Not included is a flat, unit, apartment, floor or other subdivision of the main property address. They should also not be a range, simply the entrance to the property.

Address points only have a number and a key to a road centreline segment. They did not contain a full address or postcode as you see here.

Road names in the address are joined from the road centreline segments All road names in this database are official, with a locality (suburb or town) allocated to make the complete address unique within a local council district. Road names are unique if you include the location and local authority name as part of the name. The postcode alone does not make an address unique because they cover too large an area and NZPost use a different surburb/mailtown/postcode composite key.

The locality is not derived from the road centrelines. These are not useful because the do not have a left and right and do not reflect common usage. Instead the Fire Service locality polygons have been used to tag the addresses with the preferred name. I know it may not be the name used elsewhere, so a geocoder allows for alternatives.

These addresses are a "situation" or "location" address, not a "delivery address" or "property identifier". It does not have complete flat or unit numbers, although there are some due to confusion in the purpose of the database, so you will see some.

NZ Post uses this dataset to maintain their GeoPAF file which is a subset of this data because they only supply 'deliverable' addresses where they deliver mail. Therefore no commercial or rural addresses are included in the PAF (PO Boxes are the postal address for these properties). The postcode has been added from an authoritative postcode map. Postcodes are for bulk mail sorting, not for defining a unique location address. (NZPost will supplement the PAF with all address points for a significant fee.)

Note that an address number is related to the road centreline. No road - no address. It is a linear referencing system, starting at one end, continuing in sequence to the end of the road with odd numbers on one side and even numbers on the other. In towns the spacing is approximately 20 metres, and in the country it is 200 metres.

Addresses are NOT related to parcels and should not be a property key because they are not unique, consider a corner section. They do not define property boundaries. Think of addresses as the location of the letterbox marking the entrance to the property, not the building. The mapped point is generally located 15 metres from the centreline at the entrance or at the neck of a rear section. Address ranges on a point are deprecated in the NZ address standard, a single number should be allocated to the principle entrance so the fire service can find it quickly and unambiguously.

This is different from base address ranges with parity and direction on a road centreline which would be really useful and are common overseas but do not exist for NZ. Even private sets are not done properly. A base address is a simple integer with a range of 1 - 99999.

See Where The Hell Are You? for more explanation on the confusion between an address and a property and the NZ Address Standard AS/NZS 4819:2003.

Meshblock codes for the 2013 census have been added.

Source LINZ Bulk Data Extract August 2014, Postcodes Feb 2014

Donated by Ollivier & Co for Christchurch post-earthquake efforts.

LINZ maintains a point layer of primary address points allocated by local councils for rateable properties. The principle purpose of this dataset is to allocate voters to the correct electorate. The set is actively maintained, but is still incomplete and some locations are incorrect. Nevertheless it is by far the most comprehensive address database available.

It includes all rural address points (RAPID numbers), commercial addresses and many flat numbers. So numbers are not numeric, there are all sorts of formats included here, sorry. Addresses are not unique. The points are "location addresses", not "postal addresses". For residential town addresses this is normally the same, but for commercial and rural locations they are not the same.

Primary addresses are only the number and alpha parts. Not included is a flat, unit, apartment, floor or other subdivision of the main property address. They should also not be a range, simply the entrance to the property.

Address points only have a number and a key to a road centreline segment. They did not contain a full address or postcode as you see here.

Road names in the address are joined from the road centreline segments, in turn derived from the ASP (Authoritative Streets and Places) database (downloadable from the LINZ site). All road names in this database are official, with a locality (suburb or town) allocated to make the complete address unique within a local council district. There are no postcodes in the ASP of course. Unfortunately there is only one entry linked per road name, which is not always correct for long roads, where the road is a suburb boundary or a road is cut by a TLA boundary. Road names are unique if you include the location and local authority name as part of the name. The postcode alone does not make an address unique because they cover too large an area and NZPost use a different surburb/mailtown/postcode composite key.

These addresses are a "situation" or "location" address, not a "delivery address" or "property identifier". It does not have complete flat or unit numbers, although there are some due to confusion in the purpose of the database, so you will see some.

NZ Post uses this dataset to maintain their GeoPAF file which is a subset of this data because they only supply 'post' addresses where they deliver mail. Therefore no commercial or rural addresses are included in the PAF (PO Boxes are the postal address for these properties). The postcode has been added from an original postcode map, not from the PAF. It is not part of the LINZ or ASP. Postcodes are for bulk mail sorting, not for defining a unique location address. (NZPost will supplement the PAF with all address points for a significant fee.)

Note that an address number is related to the road centreline. No road - no address. It is a linear referencing system, starting at one end, continuing in sequence to the end of the road with odd numbers on one side and even numbers on the other. In towns the spacing is approximately 20 metres, and in the country it is 200 metres.

Addresses are NOT related to parcels and should not be a property key because they are not unique, consider a corner section. They do not define property boundaries. Think of addresses as the location of the letterbox marking the entrance to the property, not the building. The mapped point is generally located 15 metres from the centreline at the entrance or at the neck of a rear section. Address ranges on a point are deprecated in the NZ address standard, a single number should be allocated to the principle entrance so the fire service can find it quickly and unambiguously.

This is different from base address ranges with parity and direction on a road centreline which would be really useful and are common overseas but do not exist for NZ. Even private sets are not done properly. A base address is a simple integer with a range of 1 - 9999.

See Where The Hell Are You? for more explanation on the confusion between an address and a property and the NZ Address Standard AS/NZS 4819:2003.

Source LINZ Bulk Data Extract February 2011, ASP, Postcodes Nov 2006

This dataset shows secondary school place allocations based on distance (in miles).

Dataset Guidance

• Nearest: The furthest distance from which a pupil was admitted if it was their nearest school.
• Non nearest: The furthest distance from which a pupil was admitted if it was not their nearest school.

Information

• Distances are measured using the midpoint of the main school building and the Royal Mail Postcode Address File (PAF) which creates a set of o/s coordinates for each property in Leeds and the surrounding area. These coordinates are unique to each individual property and are used to then measure the distance, in a straight line, from a property to the central point on the main school building.
• Please go to http://www.leeds.gov.uk/residents/Pages/Admissions.aspx for further information.

This project was designed to identify the principal general categories of consumers in Great Britain, on the basis of their different levels of engagement in and use of electronic information technologies. A key purpose of the classification was to allow surveys and other data files to be coded with a consumer segmentation relevant to this particular area of behaviour. Around 40,000 electors in Great Britain were thereby allocated to one of 23 distinct clusters in the data. The classification system was built using a series of publicly-available data items known about virtually all British electors. These have been combined in such a way as to optimise discrimination across a series of 80 different measures of consumer behaviour sources, taken from a lifestyle questionnaire database. Thus, the classification was compiled using results of some 80 response codes on over 500,000 lifestyle questionnaires collected over the three years prior to the project.

The classification system is in the form of a look-up table relating the cluster codes to a set of values on two multivariate classification systems, one at the person level (Pixel), the other operating at the postcode level (Mosaic). The information used to compile the classification included: length of residence and household composition obtained from the electoral registers of Great Britain local authorities; estimates of age based partly on attributes from said electoral registers; textual parsing of information from the Post Office Postal Address File (PAF); and names and addresses of company directors supplied by Companies House and from a number of shareholder registers. Because of their proprietary nature, these source files are not included in the dataset.

An arrangement has been negotiated between the Economic and Social Research Council (ESRC) and Experian Ltd., whereby Experian will append their proprietary Mosaic and Pixel codes to survey respondent and/or customer/client records. The look-up table contained in this dataset thus allows the cluster code to be derived from these two data items. As a result of the annual updating of Experian's electoral register, there should be no obstacle to the coding of files containing new electors or those who have recently moved house.

Abstract

The Labour Force Survey (LFS) is a study of the employment circumstances of the UK population. It is the largest household study in the UK and provides the official measures of employment and unemployment.The first Labour Force Survey (LFS) in the United Kingdom was conducted in 1973, under the terms of a Regulation derived from the Treaty of Rome. The provision of information for the Statistical Office of the European Communities (SOEC) continued to be one of the reasons for carrying out the survey on an annual basis. SOEC co-ordinated information from labour force surveys in the member states in order to assist the EC in such matters as the allocation of the Social Fund. The survey was carried out biennially from 1973 to 1983 and was increasingly used by UK government departments to obtain information which would assist in the framing of social and economic policy. By 1983 it was being used by the Employment Department (now the Department for Work and Pensions) to obtain information which was not available from other sources or was only available for Census years. From 1984 the survey was carried out annually, and since that time the LFS has consisted of two elements:

• a quarterly survey conducted in Great Britain throughout the year, in which each sampled address was called on five times at quarterly intervals, and which yielded about 15,000 responding households in every quarter;
• a 'boost' survey in the spring quarter (March-May), which produced interviews at over 44,000 households in Great Britain and over 4,000 households in Northern Ireland.

Users should note that only the data from the spring quarter and the 'boost' survey were included in the annual datasets for public release, and that only data from 1975-1991 are available from the UK Data Archive. The depositor recommends only considered use of data for 1975 and 1977 (SNs 1757 and 1758), as the concepts behind the definitions of economic activity changed and are not comparable with later years. Also the survey methodology was being developed at the time and so the estimates may not be reliable enough to use.

During 1991 the survey was developed, so that from spring 1992 the data were made available quarterly, with a quarterly sample size approximately equivalent to that of the previous annual data. The Quarterly Labour Force Survey series therefore superseded the annual LFS series, and is held at the Data Archive under GN 33246.

The study is being conducted by the Office for National Statistics (ONS), the government's largest producer of statistics. They compile independent information about the UK's society and economy which provides evidence for policy and decision making, and for directing resources to where they are needed most. The ten-yearly census, measures of inflation, the National Accounts, and population and migration statistics are some of our highest-profile outputs.

Geographic coverage

The whole country.

Analysis unit

• Individuals
• Families/households

Universe

• Households
• All persons normally resident in private households in the United Kingdom

Kind of data

Sample survey data [ssd]

Sampling procedure

Stratified multi-stage sample; for further details see annual reports. Until 1983 two sampling frames were used; in England, Northern Ireland and Wales, the Valuation Roll provided the basis for a sample which, in England and Wales, included all 69 metropolitan districts, and a two-stage selection from among the remaining non-metropolitan districts. In Northern Ireland wards were the primary sampling units. In Scotland, the Address File (i.e. post codes) was used as the basis for a stratified sample.From 1983 the Postoffice Address File has been used instead of the Valuation Roll in England and Wales. In 1984 sample rotation was introduced along with a panel element, the quarterly survey, which uses a two-stage clustered sample design.

The sample comprises about 90,000 addresses drawn at random from the rating lists in 190 different areas of England and Wales With such a large sample, it Will happen by chance that a small number of addresses which were selected at random for the 1979 survey Will come up again In addition 2,000 addresses in 8 of the areas selected in 1979 have been deliberately re-selected again this time (me Interviewers who get these addresses In their work w,ll receive a special letter to take with them.)

The sample is drawn from the "small users" sub-file of the Postcode Address File (PAF), which is a list of all addresses (delivery points) to which mail is delivered, prepared by the Post OffIce and held on computer. "Small users" are delivery points that receive less than 25 afiicles of mail a day and include all but a small proportion of private households. The PAF is updated regularly by the Post Office but, as mentioned in Chapter 1, there was an interruption in the supply of updates in the period leading up to the 1988 msurvey. As a result one third of the sample was drawn from the PAF as at March 1986 and two thirds from the sample as at September 1986. Although the PAF includes newly built properties ahead of their actual occupation, the 1988 sample does seem to have been light in the most recently built properties. The 1991 sample was drawn from the PAF as at May 1990 and should include most newly built houses.

Sample sizes and response rates Numbers of households who answered the questions in the Housing. Trailer were 37,175 in 1991. The corresponding response rates were 81.9 percent. Response rates were highest in East Anglia with nearly 87 percent in 1991, lowest in Inner London with only 66 percent in 1991.

Sampling deviation

One of the limitations of the LFS is that the sample design provides no guarantee of adequate coverage of any industry, as the survey is not industrially stratified. The LFS coverage also omits communal establishments, except NHS housing, students in halls of residence and at boarding schools. Members of the armed forces are only included if they live in private accommodation. Also, workers under 16 are not covered. As in previous years, the sample for the boost survey was drawn in a single stage in the most densely populated areas, in two stages elsewhere. The areas where the sample was drawn in a single stage were:

(I) local authority districts in the metropolitan counties and Greater London; (II) districts which, based on the 1981 Census.

Mode of data collection

Face-to-face [f2f]

Research instrument

All questions in the specification are laid out using the same format. Some questions (for instance USUWRKM) have a main group routed to them, but subsets of this group are asked variations of the question. In such cases the main routing is at the foot of the question as usual, and the subsets are listed separately above it, with the individual aspect of the routing indented slightly from the left of the page.

Cleaning operations

Information Technology Centres provides one-year training and practical work experience course in the use of computers and word processors and other aspects of information technology (eg teletex, editing, computer maintenance).

Response rate

The response rate achieved averaged between 79 percent. The method of calculating response rates is the following: The response rate indicates how many interviews were achieved as a proportion of those eligible for the survey. The formula used is as follows: RR = (FR + PR)/(FR + PR + OR + CR + RHQ + NC + RRI*) where RR = response rate, FR = full response, PR = partial response, OR = outright refusal, CR = circumstantial refusal, RHQ = refusal to HQ, NC = non contact, RRI = refusal to re-interview, *applies to waves two to five only.

Sampling error estimates

As with any sample survey, the results of the Labour Force Survey are subject to sampling errors. In addition, the results of any sample survey are affected by non-sampling errors, i.e. the whole variety of errors other then those due to sampling.

Data appraisal

Day of birth and date of birth variables have been removed from the annual LFS datasets, in the same way that they have been removed from the quarterly LFS datasets from 1992 onwards, as this information is now considered to be disclosive. The variable AGEDFE (age at proceeding 31 August) has been added to all annual datasets.

The ORs of AD in CSF AD cases compared with population controls: multivariable logistic regression analysis.

Relative risk of key characteristics and anthropometric factors in pre- and post- menopausal females.

Note A new version of the report was added on 14 July 2011 to reflect the fact that the report was designated as a National Statistic by the United Kingdom Statistics Authority. We would be interested to hear some feedback from users on how you've used the results from the survey and what you thought of the report. So we would be grateful if you could email us at socialcarequeries@ic.nhs.uk with your views. This report contains the results of a detailed survey of carers in households in 2009/10, commissioned by the Department of Health as part of the Government's Carers' Strategy programme. The NHS Information Centre for health and social care (NHS IC) undertook responsibility for this survey which was funded by the Department of Health and the Department for Work and Pensions. GfK NOP was commissioned to carry out face-to-face interviews over 11 months of fieldwork in a representative sample of homes in England. The report contains details on the prevalence of caring in England, the demographic profile of carers, the impact of caring duties upon the carer, details of the services carers receive and a profile of the cared for people. Carers who were under 16 years of age were excluded from the Survey of Carers in Households 2009/10, as were people in communal establishments. This will be of interest to all who share the vision and responsibility for implementing the Carers' Strategy, including Central and Local Government, the public sector, third sector organisations, families and communities. These groups will be able to align the results of this survey with the Strategy. Carers were identified via a short screening questionnaire at addresses which were randomly selected from the Postcode Address File (PAF). Carers were defined as those people who identified themselves as having extra responsibilities of looking after someone who has a long-term physical or mental ill health or disability, or problem related to old age. People providing care in a professional capacity were excluded. The main questionnaire, covered in Chapters 3 to 7 of this report, asked a series of detailed questions about the caring role and was concerned only with Carers who also fitted the General Household Survey (GHS) definition of Carers (which excludes those caring as volunteers for a charity or organisation, those caring for someone in an institution, those providing financial support only and those caring for someone with a temporary illness or disability).

CSF AD cases were compared with population controls and with CSF controls using logistic regression analysis.

During Alzheimer's Disease, sustained exposure to amyloid-β42 oligomers perturbs metabolism of ether-linked glycerophospholipids defined by a saturated 16 carbon chain at the sn-1 position. The intraneuronal accumulation of 1-O-hexadecyl-2-acetyl-sn-glycerophosphocholine (C16:0 PAF), but not its immediate precursor 1-O-hexadecyl-sn-glycerophosphocholine (C16:0 lyso-PAF), participates in signaling tau hyperphosphorylation and compromises neuronal viability. As C16:0 PAF is a naturally occurring lipid involved in cellular signaling, it is likely that mechanisms exist to protect cells against its toxic effects. Here, we utilized a chemical genomic approach to identify key processes specific for regulating the sensitivity of Saccharomyces cerevisiae to alkyacylglycerophosphocholines elevated in Alzheimer's Disease. We identified ten deletion mutants that were hypersensitive to C16:0 PAF and five deletion mutants that were hypersensitive to C16:0 lyso-PAF. Deletion of YDL133w, a previously uncharacterized gene which we have renamed SRF1 (Spo14 Regulatory Factor 1), resulted in the greatest differential sensitivity to C16:0 PAF over C16:0 lyso-PAF. We demonstrate that Srf1 physically interacts with Spo14, yeast phospholipase D (PLD), and is essential for PLD catalytic activity in mitotic cells. Though C16:0 PAF treatment does not impact hydrolysis of phosphatidylcholine in yeast, C16:0 PAF does promote delocalization of GFP-Spo14 and phosphatidic acid from the cell periphery. Furthermore, we demonstrate that, similar to yeast cells, PLD activity is required to protect mammalian neural cells from C16:0 PAF. Together, these findings implicate PLD as a potential neuroprotective target capable of ameliorating disruptions in lipid metabolism in response to accumulating oligomeric amyloid-β42.

BackgroundTraumatic brain injury (TBI) is the leading cause of disability and mortality in children and young adults worldwide. It remains unclear, however, how TBI in childhood and adolescence is associated with adult mortality, psychiatric morbidity, and social outcomes.Methods and FindingsIn a Swedish birth cohort between 1973 and 1985 of 1,143,470 individuals, we identified all those who had sustained at least one TBI (n = 104,290 or 9.1%) up to age 25 y and their unaffected siblings (n = 68,268) using patient registers. We subsequently assessed these individuals for the following outcomes using multiple national registries: disability pension, specialist diagnoses of psychiatric disorders and psychiatric inpatient hospitalisation, premature mortality (before age 41 y), low educational attainment (not having achieved secondary school qualifications), and receiving means-tested welfare benefits. We used logistic and Cox regression models to quantify the association between TBI and specified adverse outcomes on the individual level. We further estimated population attributable fractions (PAF) for each outcome measure. We also compared differentially exposed siblings to account for unobserved genetic and environmental confounding. In addition to relative risk estimates, we examined absolute risks by calculating prevalence and Kaplan-Meier estimates. In complementary analyses, we tested whether the findings were moderated by injury severity, recurrence, and age at first injury (ages 0–4, 5–9, 6–10, 15–19, and 20–24 y).TBI exposure was associated with elevated risks of impaired adult functioning across all outcome measures. After a median follow-up period of 8 y from age 26 y, we found that TBI contributed to absolute risks of over 10% for specialist diagnoses of psychiatric disorders and low educational attainment, approximately 5% for disability pension, and 2% for premature mortality. The highest relative risks, adjusted for sex, birth year, and birth order, were found for psychiatric inpatient hospitalisation (adjusted relative risk [aRR] = 2.0; 95% CI: 1.9–2.0; 6,632 versus 37,095 events), disability pension (aRR = 1.8; 95% CI: 1.7–1.8; 4,691 versus 29,778 events), and premature mortality (aRR = 1.7; 95% CI: 1.6–1.9; 799 versus 4,695 events). These risks were only marginally attenuated when the comparisons were made with their unaffected siblings, which implies that the effects of TBI were consistent with a causal inference. A dose-response relationship was observed with injury severity. Injury recurrence was also associated with higher risks—in particular, for disability pension we found that recurrent TBI was associated with a 3-fold risk increase (aRR = 2.6; 95% CI: 2.4–2.8) compared to a single-episode TBI. Higher risks for all outcomes were observed for those who had sustained their first injury at an older age (ages 20–24 y) with more than 25% increase in relative risk across all outcomes compared to the youngest age group (ages 0–4 y). On the population level, TBI explained between 2%–6% of the variance in the examined outcomes.Using hospital data underestimates milder forms of TBI, but such misclassification bias suggests that the reported estimates are likely conservative. The sibling-comparison design accounts for unmeasured familial confounders shared by siblings, including half of their genes. Thus, residual genetic confounding remains a possibility but will unlikely alter our main findings, as associations were only marginally attenuated within families.ConclusionsGiven our findings, which indicate potentially causal effects between TBI exposure in childhood and later impairments across a range of health and social outcomes, age-sensitive clinical guidelines should be considered and preventive strategies should be targeted at children and adolescents.

Many human cancers develop as a result of exposure to risk factors related to the environment and ways of life. The aim of this study was to estimate attributable fractions of 25 types of cancers resulting from exposure to modifiable risk factors in Brazil. The prevalence of exposure to selected risk factors among adults was obtained from population-based surveys conducted from 2000 to 2008. Risk estimates were based on data drawn from meta-analyses or large, high quality studies. Population-attributable fractions (PAF) for a combination of risk factors, as well as the number of preventable deaths and cancer cases, were calculated for 2020. The known preventable risk factors studied will account for 34% of cancer cases among men and 35% among women in 2020, and for 46% and 39% deaths, respectively. The highest attributable fractions were estimated for tobacco smoking, infections, low consumption of fruits and vegetables, excess weight, reproductive factors, and physical inactivity. This is the first study to systematically estimate the fraction of cancer attributable to potentially modifiable risk factors in Brazil. Strategies for primary prevention of tobacco smoking and control of infection and the promotion of a healthy diet and physical activity should be the main priorities in policies for cancer prevention in the country.