Abstract copyright UK Data Service and data collection copyright owner.

The UK censuses took place on 27 March 2011. They were run by the Northern Ireland Statistics & Research Agency (NISRA), National Records of Scotland (NRS), and the Office for National Statistics (ONS) for both England and Wales. The UK comprises the countries of England, Wales, Scotland and Northern Ireland.

Statistics from the UK censuses help paint a picture of the nation and how we live. They provide a detailed snapshot of the population and its characteristics and underpin funding allocation to provide public services. This is the home for all UK census data.

The 2011 Census Ad Hoc Household Composition (Age Groups) Safeguarded Tables (Lower Layer Super Output Area): England and Wales study has been created as a request from a consortium of researchers working on the COVID-19 response and predictions.
Main Topics:


The study covers household composition by age groups for households in England and Wales.

Abstract

The 2010 Population and Housing Census was Conducted between 11-17 November 2010. Over 750,000 household forms were completed by over 12,000 enumerators. More than 30,000 persons were directly involved in census conducting. The Population and Housing Census is the biggest event organized by the National Statistical Office. The unique feature of the Census is that it covers a wide range of entities starting from the primary unit of the local government up to the highest levels of the government as well as all citizens and conducted with the highest levels of organization. For the 2010 Population and Housing Census, the management team to coordinate the preparatory work was established, a detailed work plan was prepared and the plan was successfully implemented. The preliminary condition for the successful conduct of the Census was the development of a detailed plan. The well thought-out, step by step plan and carefully evidenced estimation of the expenditure and expected results were crucial for the successful Census. Every stage of the Census including preparation, training, enumeration, data processing, analysis, evaluation and dissemination of the results to users should be reflected in the Census Plan.

Geographic coverage

National

Analysis unit

• Household;
• Indivudual.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Processing System

The introduction of internet technology and GIS in the 2010 Population and Housing Census has made the census more technically advanced than the previous ones. Compared to the data processing of the 2000 Population and Housing Census the techniques and technological abilities of the NSO have advanced. The central office - National Statistical Office has used an internal network with 1000 Mbps speed, an independent internet line with 2048 Kbps speed and server computers with special equipments to ensure the reliable function of internal and external networks and confidentiality. The Law on Statistics, the Law on Population and Housing Census, the guidelines of the safety of statistical information systems and policies, the provisional guidelines on the use of census and survey raw data by the users, the guidelines on receiving, entering and validating census data have created a legal basis for census data processing.

The data-entry network was set up separately from the network of the organization in order to ensure the safety and confidentiality of the data. The network was organized by using the windows platform and managed by a separate domain controller. Computers where the census data will be entered were linked to this server computer and a safety devise was set up to protect data loss and fixing. Data backup was done twice daily at 15:10 hour and 22:10 hour by auto archive and the full day archive was stored in tape at 23:00 hour everyday.

The essential resources of important equipments and tools were prepared in order to provide continuous function of all equipment, to be able to carry out urgent repairs when needed, and to return the equipment to normal function. The computer where the census data would be entered and other necessary equipment were purchased by the state budget. For the data processing, the latest packages of software programs (CSPro, SPSS) were used. Also, software programs for the computer assisted coding and checking were developed on NET within the network framework.

INTERNET CENSUS DATA PROCESSING

One of the specific features of the 2010 Population and Housing Census was e-enumeration of Mongolian citizens living abroad for longer period. The development of a web based software and a website, and other specific measures were taken in line with the coordination of the General Authority for State Registration, the National Data Centre, and the Central Intelligence Agency in relation to ensuring the confidentiality of data. Some difficulties were encountered in sharing information between government agencies and ensuring the safety and confidentiality of census data due to limited professional and organizational experience, also because it was the first attempt to enumerate its citizens online.

The main software to be used for online registration, getting permission to get login and filling in the census questionnaire online as well as receiving a reply was developed by the NSO using a symphony framework and the web service was provided by the National Data Centre. Due to the different technological conditions for citizens living and working abroad and the lack of certain levels of technological knowledge for some people the diplomatic representative offices from Mongolia in different countries printed out the online-census questionnaire and asked citizens to fill in and deliver them to the NSO in Mongolia. During the data processing stage these filled in questionnaires were key-entered into the system and checked against the main census database to avoid duplication.

CODING OF DATA, DATA-ENTRY AND VALIDATION

Additional 136 workers were contracted temporarily to complete the census data processing and disseminate the results to the users within a short period of time. Due to limited work spaces all of them were divided into six groups and worked in two shifts with equipments set up in three rooms and connected to the network. A total of six team leaders and 130 operators worked on data processing. The census questionnaires were checked by the ad hoc bureau staff at the respective levels and submitted to the NSO according to the intended schedule.

These organizational measures were taken to ensure continuity of the census data processing that included stages of receiving the census documents, coding the questionnaire, key-entering into the system and validating the data. Coding was started on December 13, 2010 and the data-entry on January 7, 2011. Data entering of the post-enumeration survey and verification were completed by April 16, 2011. Data checking and validation started on April 18, 2011 and was completed on May 5, 2011. The automatic editing and imputation based on scripts written by the PHCB staff was completed on May 10, 2011 and the results tabulation was started.

This dataset is a compilation of Frog Census records (citizen science program) and the preceding Frog Watch program for the Port Phillip and Westernport CMA Region. These presence-only records collected in an ad-hoc manner are combined with regional frog records form the Victorian Biodiversity Atlas (VBA) and results of Melbourne Water commissioned surveys for frogs. The latter data are largely targeting threatened species of frog.
NOTE: Whilst every effort has been taken in collecting, validating and providing the attached data, Melbourne Water Corporation makes no representations or guarantees as to the accuracy or completeness of this data. Any person or group that uses this data does so at its own risk and should make their own assessment and investigations as to the suitability and/or application of the data. Melbourne Water Corporation shall not be liable in any way to any person or group for loss of any kind including damages, costs, interest, loss of profits or special loss or damage, arising from any use, error, inaccuracy, incompleteness or other defect in this data.

Abstract

The Government of Liberia and its development partners recognized agriculture as a pivotal sector in fostering economic growth, reducing poverty, and achieving food security. Since the post-war period (insert dates) , the government in collaboration with development partners, has made substantial investments to develop and expand the agriculture sector. Over the years, policymakers and data users in the agriculture sector have experienced significant challenges in obtaining the data needed to monitor and evaluate these interventions and make informed decisions on new interventions. To address these challenges, the Liberia Institute of Statistics and Geo-Information Services (LISGIS) and the Ministry of Agriculture (MoA) conducted several ad hoc agricultural surveys. While valuable, these surveys have often been limited in scope and unable to provide the comprehensive data needed for effective policymaking and planning. To support the sector more robustly, the government decided to undertake a comprehensive agricultural census:the Liberia Agriculture Census 2024, the second agricultural census in Liberia since 1971 and the first to be conducted digitally, aimed to collect structural and reliable data on various aspects of the agricultural sector.

The main objectives of the LAC-2024 was to:

· Reduce the existing data gap in Liberia's agriculture sector.

· Provide comprehensive data on the agriculture sector for policy formulation and evaluation of existing programmes.

· Enable LISGIS to establish an agriculture master sampling frame for future agricultural surveys and research.

· Identify the structural changes in the agriculture sector over time.

· Provide information on crop, livestock, poultry, and aquaculture activities.

· Determine the size, composition, practices and related characteristics of Liberia's agricultural holdings.

· Generate disaggregated agriculture statistics.

· Provide statistics for advocacy on Liberia's agriculture sector.

· Identify agricultural practices and constraints at the community level.

To achieve these objectives, the LAC-2024 was designed to collect structural data at the household, non-household and community levels. The data provided a wealth of information on the state of agriculture in Liberia. This documentation provides information on how data was collected at the household level. The documentation also provides useful information on the household anonymized dataset.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The universe for the Liberia Agriculture Census 2024 household level data collection is all households in Liberia having at least one member engaged in agricultural activity during the 2022/2023 farming season.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The Liberia Agriculture Census 2024 (LAC-2024) was a sampled census conducted in all 15 counties of Liberia. The sampling frame used for the LAC-2024 is based on the 2022 National Population and Housing Census (2022-NPHC), conducted by the LISGIS. The sample design for the census was a stratified cluster sample with enumeration areas (EAs) as clusters and farming households as units of interest. In line with budget availability, a large sample of 4,800 EAs was considered for the LAC-2024. These EAs had a total of 269,652 agricultural households in the frame. The sample was allocated by strata (districts, urban/rural) proportional to the numbers of farming households in the frame. In total, about 78.8% of the sample was allocated to rural areas. The stratified sample of EAs was selected with a probability proportional to the number of farming households at EA level. A complete listing of all households (both agricultural and non-agricultural) was carried out in the selected EAs and detailed questions were addressed to all households that practiced agricultural activities during the 2022/2023 farming season. The results of the LAC-2024 are representative at the district level.

For more information on the LAC-2024 sampling methodology, see the methodology section of the Liberia Agriculture Census 2024 Household Report (available in the downloads tab).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The LAC-2024 employed three questionnaires: the Household Questionnaire, the Community Questionnaire and the Non-Household Questionnaire. These three questionnaires were based on the 50x2030 Initiative standard model questionnaires. The Liberia Agriculture Census Technical Working Group (LAC-TWG), comprising technical staff from LISGIS,Ministry of Agriculture (MOA), National Fishery and Aquaculture Authority (NaFAA), Cooperative Development Agency (CDA) and the Ministry of Internal Affairs (MIA) worked with technicians from the 50x2030 Initiative to adapt the questionnaires to Liberia's context and realities. Suggestions and inputs were solicited from various stakeholders representing government ministries, agencies and commissions(termed MACs by LISGIS), nongovernmental and international organizations as well as academic institutions researching agricultural issues. All questionnaires were finalized in English. Some questions in the questionnaires were translated into simple Liberian English, to ease administration. The household questionnaire included type of agricultural activities practiced, household members characteristics, housing conditions, hired labour practices, agricultural parcels and plots characteristics, types of crops and methods of crop cultivation, inputs, tools and equipment used, type and number of livestock and poultry. The household questionnaire was administered to the household head or an adult member of the household with knowledge of the household and its agricultural activities. The primary respondent (i.e., the household member that provided most of the information for the questionnaire or a given module, household member, or crop) sometimes varied across modules.

Cleaning operations

The data was edited using CSpro software, version 7.7.3. The appropriate edit rules were established by programmers and subject matter specialists at LISGIS and MOA. In a few cases, manual editing was applied to recode the “other specify” category. The SPSS software was used for this purpose.

Response rate

92.8%

This dataset shows the 2001 Census Output Areas (OAs) Household Weighted Centroids. An individual Output Area generally covers a sufficiently small area (subject to meeting minimum population / household counts) so that user defined or ad-hoc areas can be created while maintaining a sufficient level of quality. National Records of Scotland (NRS) produces only one set of OAs and creates all other output geographies using the OA as the building brick. Each OA is assigned to an area in a higher geography by first selecting one of the postcodes in the OA as a 'master' postcode. The OA inherits all of the characteristics of the master postcode including its assignments to higher areas and its grid reference. The master postcode was selected using an algorithm which calculates the postcode centroid within an OA which has grid references closest to the household-weighted centre of the OA.

The classification presented here is a classification created “ad hoc” by the City of Madrid in order to use it in the different administrative management procedures (urban licenses, trade, consumption and health control campaigns&), as well as in the new Census of Locals and Activities. The table is structured in three hierarchical levels: Section, Division and heading (activity code) for the purpose of facilitating its use: Section: coded with & A&A letters; to the “U” and each of which has a descriptive generic label of the activity headings it contains. Division: encoded with two numeric digits and have a descriptive label of the activities or headings it contains. Each is included in only one section. Heading: encoded with six numeric digits, the first two corresponding to the division to which it belongs while the remaining four are a specifically created sequential. Each heading is only included in one section and in a division. These headings are used in other datasets of this portal such as: Census of premises and their activities Consumption Inspections In the section ‘Associated documentation’ is an extensive explanatory document regarding this data set.

Abstract

In 1978, an agricultural survey was conducted using the 1977 Census of Population and Housing as the frame. Data was collected over a 15-month period, January 1978 to April 1979. Three strata were identified, namely:

• A comprehensive quarterly postal inquiry to all known large farmers
• A sample survey of small scale-producers of fruits and vegetables for sale
• A sample survey of small scale livestock holders

Actual coverage rates were lower than the design. The response rate of the postal enquiry of large farms declined from 82% at the start of the survey to 68% at the end. Of the 164 small-scale producers selected out of the total of 634, only 127 were enumerated. For livestock, out of 1,547 livestock holders, 106 were selected and 92 enumerated. There was also a supplementary sample of non-farming households set at 250 (Total 3,758) but the achieved sample size was only 144. The actual sample sizes were, therefore, very small casting some doubt as to the reliability of the estimates generated, particularly where these were disaggregated by region. In 1998 a second attempt was made to conduct an agricultural survey to update the 1978 database. This was not particularly successful and only limited, generally qualitative, data was generated. The main weakness was poor response. A report is available as part of its on-going programme on agricultural statistics, the Seychelles Agricultural Agency (SAA) collects data from the main livestock producers, on a regular basis. No regular data collection, however, is undertaken for the small livestock producers. The SAA has a list of registered farmers and crop area and production data is collected from these on an ‘ad hoc’ basis making the data difficult to analyse and interpret.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding (farm). Two types of holdings were distinguished: (i) agricultural enterprises (holdings in the non-household sector); and (ii) agricultural households (holdings in the household sector), farming mainly for sale.

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census The classical approach was applied in the 2011 CA.

ii. Frame The CPH 2010, which included an agriculture module, was used to establish the frame for the holdings in the household sector. The SAA provided the frame of large farms and registered farmers. To ensure that the large livestock producers would be included, lists of pig breeders and farms with broilers and/or layers were obtained from the livestock department.

iii. Complete and/or sample enumeration methods Non-household farms and agricultural households classified as "producing mainly for sale" in the CPH 2010 were covered by complete enumeration. Other farms were subject to sample surveys.

iv. Sample design This was not applied for the enumeration of farms in the CA 2011.

Mode of data collection

Face-to-face [f2f]

Research instrument

The "household/holding form" was used for CA data collection. It covered the following sections:

Section I - Household composition Section II - Purpose of growing crops Section III - Household agricultural income and credit Section IV - Livestock and other animals Section V - Number of holdings and method of operation Section VI - Parcel details Section VII - Fruit/nuts/other tree crops Section VIII - Vegetables and other temporary crops Section IX - Labour inputs Section X - Marketing Section XI - Use of fertilizers and agricultural chemicals Section XII - Equipment owned and hired/borrowed Section XIII - Irrigation Section XIV - Infrastructure Section XV - Area not cultivable Section XVI - Other economic activities of the holding

The CA 2011 questionnaire covered 13 of the 16 core items recommended for the WCA 2010 round. Items not covered were : (i) "Household size"; (ii) "Presence of aquaculture on the holding"; and (iii) "Presence of forests and other woodland on the holding".

Cleaning operations

Manual data entry was used to enter census data.

Data appraisal

DATA DISSEMINATION: The census results were released through printed reports, the NBS website, and a dissemination workshop with stakeholders. The census report includes thematic maps illustrating the distribution of farms, the land area under agricultural activities and livestock counts by district.

The avifauna is monitored with special emphasis on passerine birds representing the highest trophic level. Weekly counts of birds are carried out at 13 census points during the entire season. Other bird observations are recorded ad-hoc during the entire field season. For further details, please refer to the newest BioBasis manual available at the website.

Abstract

The Government of Liberia and its development partners recognized agriculture as a pivotal sector in fostering economic growth, reducing poverty, and achieving food security. Since the post-war period (insert dates), the government in collaboration with development partners, has made substantial investments to develop and expand the agriculture sector. Over the years, policymakers and data users in the agriculture sector have experienced significant challenges in obtaining the data needed to monitor and evaluate these interventions and make informed decisions on new interventions. To address these challenges, the Liberia Institute of Statistics and Geo-Information Services (LISGIS) and the Ministry of Agriculture (MoA) conducted several ad hoc agricultural surveys. While valuable, these surveys have often been limited in scope and unable to provide the comprehensive data needed for effective policymaking and planning. To support the sector more robustly, the government decided to undertake a comprehensive agricultural census: the Liberia Agriculture Census 2024, the second agricultural census in Liberia since 1971 and the first to be conducted digitally, aimed to collect structural and reliable data on various aspects of the agricultural sector.

The main objectives of the LAC-2024 was to: · Reduce the existing data gap in Liberia's agriculture sector. · Provide comprehensive data on the agriculture sector for policy formulation and evaluation of existing programmes. · Enable LISGIS to establish an agriculture master sampling frame for future agricultural surveys and research. · Identify the structural changes in the agriculture sector over time. · Provide information on crop, livestock, poultry, and aquaculture activities. · Determine the size, composition, practices and related characteristics of Liberia's agricultural holdings. · Generate disaggregated agriculture statistics. · Provide statistics for advocacy on Liberia's agriculture sector. · Identify agricultural practices and constraints at the community level.

To achieve these objectives, the LAC-2024 was designed to collect structural data at the household, non-household and community levels. The data provided a wealth of information on the state of agriculture in Liberia. This documentation provides information on how data was collected at the community level. The documentation also provides useful information on the community anonymized dataset.

Geographic coverage

National coverage

Analysis unit

Agricultural Communities

Universe

The universe for the Liberia Agriculture Census 2024 community operations is: all communities (localities) in Liberia that are located within an agricultural enumeration area.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Focus group interviews were conducted in communities in the EAs selected for the sample census. A sampled community had the same probability of selection and sample weight as the EA. If a community was linked to many EAs, additional adjustment for multiplicity was performed. The LAC-2024 community operations engaged 61,600 respondents across 7,193 sampled communities. Nationally, the distribution of respondents shows that males were 66.1% of the total 61,600 participants, while females were 33.9%.

Mode of data collection

Focus Group [foc]

Research instrument

The LAC-2024 employed three questionnaires: the Household Questionnaire, the Community Questionnaire and the Non-Household Questionnaire. These three questionnaires were based on the 50x2030 Initiative standard model questionnaires. The Liberia Agriculture Census Technical Working Group (LAC-TWG), comprising technical staff from LISGIS,Ministry of Agriculture (MOA), National Fishery and Aquaculture Authority (NaFAA), Cooperative Development Agency (CDA) and the Ministry of Internal Affairs (MIA) worked with technicians from the 50x2030 Initiative to adapt the questionnaires to Liberia's context and realities. Suggestions and inputs were solicited from various stakeholders representing government ministries, agencies and commissions (termed MACsby LISGIS), nongovernmental and international organizations as well as academic institutions researching agricultural issues. All questionnaires were finalized in English. Some questions in the questionnaires were translated into simple Liberian English, to ease administration.

The community questionnaire included the following sections: 1- respondents characteristics; 2- production and processing activities in the community; 3- land characteristics and irrigation in the community; 4- markets to sell agriculture products; 5- access to agricultural inputs, services and credits in the community; 6- social cohesion; 7- difficulties in agricultural activities; 8- livestock and Poultry Production; 9- environment; 10- disasters and shocks; 11- community infrastructure and transportation; 12- community organizations; 13- community resource management; 14- land prices and credit; 15- community key events; 16- labour and producer prices.

Cleaning operations

The data was edited using CSpro software, version 7.7.3. The appropriate edit rules were established by programmers and subject matter specialists at LISGIS and MOA. In a few cases, manual editing was applied to recode the "other specify" category. The SPSS software was used for this purpose.

Response rate

100%

The dataset contains all traffic counting points in Hamburg for motor vehicles, bicycles and foot traffic. A distinction is made between permanent counters, annual counters, levels and demand counters. At ‘permanent counting points’, motor vehicle traffic is automatically recorded by means of induction loops 24 hours a day and 365 days a year. At ‘Annual census points’, up to and including 2020, traffic was usually recorded at least once a year from 6 a.m. to 7 p.m., usually by means of a manual traffic census. Since 2021, these manual traffic counts have largely been replaced by the use of infrared sensors. ‘Levels’ are derived from the permanent counting points and from the annual counting points, at which the average daily traffic volumes (DTV, DTVw) determined for each year are included in the traffic statistics (observation of the long-term traffic development). At ‘demand counting points’, traffic is recorded irregularly and exclusively on an ad hoc basis (e.g. in connection with traffic planning or investigations), usually by manual traffic counting, usually from 6 a.m. to 7 p.m. The content of the data is the census number, the location description and the date of the last census. The results of the censuses (traffic strengths) are not published via this service. For the research of traffic strengths, the services can be used under the keyword search "traffic strength". With the transfer of the Bundesautobahnen and Bundesstraßen Freie Strecke to the Autobahn GmbH of the Federal Government on 1 January 2021, the counting data for these routes will no longer be published by the BVM.

This dataset is the definitive of the annually released meshblock boundaries as at 1 January 2024 as defined by Stats NZ. This version contains 57,539 meshblocks, including 16 with empty or null geometries (non-digitised meshblocks).

Stats NZ maintains an annual meshblock pattern for collecting and producing statistical data. This allows data to be compared over time.

A meshblock is the smallest geographic unit for which statistical data is collected and processed by Stats NZ. A meshblock is a defined geographic area, which can vary in size from part of a city block to a large area of rural land. The optimal size for a meshblock is 30–60 dwellings (containing approximately 60–120 residents).

Each meshblock borders on another to form a network covering all of New Zealand, including coasts and inlets and extending out to the 200-mile economic zone (EEZ) and is digitised to the 12-mile (19.3km) limit. Meshblocks are added together to build up larger geographic areas such as statistical area 1 (SA1), statistical area 2 (SA2), statistical area 3 (SA3), and urban rural (UR). They are also used to define electoral districts, territorial authorities, and regional councils.

Meshblock boundaries generally follow road centrelines, cadastral property boundaries, or topographical features such as rivers. Expanses of water in the form of lakes and inlets are defined separately from land.

Meshblock maintenance

Meshblock boundaries are amended by:

1. Splitting – subdividing a meshblock into two or more meshblocks.
2. Nudging – shifting a boundary to a more appropriate position.

Reasons for meshblock splits and nudges can include:

· to maintain meshblock criteria rules.

· to improve the size balance of meshblocks in areas where there has been population growth

· to maintain alignment to cadastre and other geographic features.

· Stats NZ requests for boundary changes so that statistical geography boundaries can be moved

· external requests for boundary changes so that administrative or electoral boundaries can be moved

· to separate land and water. Mainland, inland water, islands, inlets, and oceanic are defined separately

Meshblock changes are made throughout the year. A major release is made at 1 January each year with ad hoc releases available to users at other times.

While meshblock boundaries are continually under review, 'freezes' on changes to the boundaries are applied periodically. Such 'freezes' are imposed at the time of population censuses and during periods of intense electoral activity, for example, prior and during general and local body elections.

Meshblock numbering

Meshblocks are not named and have seven-digit codes.

When meshblocks are split, each new meshblock is given a new code. The original meshblock codes no longer exist within that version and future versions of the meshblock classification. Meshblock codes do not change when a meshblock boundary is nudged.

Meshblocks that existed prior to 2015 and have not changed are numbered from 0000100 to 3210003. Meshblocks created from 2015 onwards are numbered from 4000000.

Digitised and non-digitised meshblocks

The digital geographic boundaries are defined and maintained by Stats NZ.

Meshblocks cover the land area of New Zealand, the water area to the 12mile limit, the Chatham Islands, Kermadec Islands, sub-Antarctic islands, offshore oil rigs, and Ross Dependency. The following 16 meshblocks are not held in digitised form.

Meshblock / Location (statistical area 2 name)

• 0016901 / Oceanic Kermadec Islands
• 0016902 / Kermadec Islands
• 1588000 / Oceanic Oil Rig Taranaki
• 3166401 / Oceanic Campbell Island
• 3166402 / Campbell Island
• 3166600 / Oceanic Oil Rig Southland
• 3166710 / Oceanic Auckland Islands
• 3166711 / Auckland Islands
• 3195000 / Ross Dependency
• 3196001 / New Zealand Economic Zone
• 3196002 / Oceanic Bounty Islands
• 3196003 / Bounty Islands
• 3196004 / Oceanic Snares Islands
• 3196005 / Snares Island
• 3196006 / Oceanic Antipodes Islands
• 3196007 / Antipodes Island

​

For more information please refer to the Statistical standard for geographic areas 2023.

High definition version

This high definition (HD) version is the most detailed geometry, suitable for use in GIS for geometric analysis operations and for the computation of areas, centroids and other metrics. The HD version is aligned to the LINZ cadastre.

Digital Data

Digital boundary data became freely available on 1 July 2007.

This dataset is the definitive of the annually released meshblock boundaries as at 1 January 2023 as defined by Stats NZ (the custodian), clipped to the coastline. This clipped version has been created for cartographic purposes and so does not fully represent the official full extent boundaries. This version contains 57,539 meshblocks.

​

Stats NZ maintains an annual meshblock pattern for collecting and producing statistical data. This allows data to be compared over time.

​

A meshblock is the smallest geographic unit for which statistical data is collected and processed by Stats NZ. A meshblock is a defined geographic area, which can vary in size from part of a city block to a large area of rural land. The optimal size for a meshblock is 30–60 dwellings (containing approximately 60–120 residents).

​

Each meshblock borders on another to form a network covering all of New Zealand, including coasts and inlets and extending out to the 200-mile economic zone (EEZ) and is digitised to the 12-mile (19.3km) limit. Meshblocks are added together to build up larger geographic areas such as statistical area 1 (SA1), statistical area 2 (SA2), statistical area 3 (SA3), and urban rural (UR). They are also used to define electoral districts, territorial authorities, and regional councils.

​

Meshblock boundaries generally follow road centrelines, cadastral property boundaries, or topographical features such as rivers. Expanses of water in the form of lakes and inlets are defined separately from land.

​

Meshblock maintenance

Meshblock boundaries are amended by:

1. Splitting – subdividing a meshblock into two or more meshblocks.
2. Nudging – shifting a boundary to a more appropriate position.

​

Reasons for meshblock splits and nudges can include:

·to maintain meshblock criteria rules.

·to improve the size balance of meshblocks in areas where there has been population growth

·to maintain alignment to cadastre and other geographic features.

·Stats NZ requests for boundary changes so that statistical geography boundaries can be moved

·external requests for boundary changes so that administrative or electoral boundaries can be moved

·to separate land and water. Mainland, inland water, islands, inlets, and oceanic are defined separately

​

Meshblock changes are made throughout the year. A major release is made at 1 January each year with ad hoc releases available to users at other times.

​

While meshblock boundaries are continually under review, 'freezes' on changes to the boundaries are applied periodically. Such 'freezes' are imposed at the time of population censuses and during periods of intense electoral activity, for example, prior and during general and local body elections.

​

Meshblock numbering

Meshblocks are not named and have seven-digit codes.

​

When meshblocks are split, each new meshblock is given a new code. The original meshblock codes no longer exist within that version and future versions of the meshblock classification. Meshblock codes do not change when a meshblock boundary is nudged.

​

Meshblocks that existed prior to 2015 and have not changed are numbered from 0000100 to 3210003. Meshblocks created from 2015 onwards are numbered from 4000000.

​

Digitised and non-digitised meshblocks

The digital geographic boundaries are defined and maintained by Stats NZ.

​

Meshblocks cover the land area of New Zealand, the water area to the 12mile limit, the Chatham Islands, Kermadec Islands, sub-Antarctic islands, offshore oil rigs, and Ross Dependency. The following 16 meshblocks are not held in digitised form.

​

Meshblock / Location (statistical area 2 name)

• 0016901 / Oceanic Kermadec Islands
• 0016902 / Kermadec Islands
• 1588000 / Oceanic Oil Rig Taranaki
• 3166401 / Oceanic Campbell Island
• 3166402 / Campbell Island
• 3166600 / Oceanic Oil Rig Southland
• 3166710 / Oceanic Auckland Islands
• 3166711 / Auckland Islands
• 3195000 / Ross Dependency
• 3196001 / New Zealand Economic Zone
• 3196002 / Oceanic Bounty Islands
• 3196003 / Bounty Islands
• 3196004 / Oceanic Snares Islands
• 3196005 / Snares Island
• 3196006 / Oceanic Antipodes Islands
• 3196007 / Antipodes Island

​

For more information please refer to the Statistical standard for geographic areas 2023.

​

Generalised version

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

​

Digital data

Digital boundary data became freely available on 1 July 2007.

​

To download geographic classifications in table formats such as CSV please use Ariā

Refer to the current geographies boundaries table for a list of all current geographies and recent updates.

This dataset is the definitive version of the annually released meshblock boundaries as at 1 January 2025 as defined by Stats NZ. This version contains 57,551 meshblocks, including 16 with empty or null geometries (non-digitised meshblocks).

Stats NZ maintains an annual meshblock pattern for collecting and producing statistical data. This allows data to be compared over time.

A meshblock is the smallest geographic unit for which statistical data is collected and processed by Stats NZ. A meshblock is a defined geographic area, which can vary in size from part of a city block to a large area of rural land. The optimal size for a meshblock is 30–60 dwellings (containing approximately 60–120 residents).

Each meshblock borders on another to form a network covering all of New Zealand, including coasts and inlets and extending out to the 200-mile economic zone (EEZ) and is digitised to the 12-mile limit. Meshblocks are added together to build up larger geographic areas such as statistical area 1 (SA1), statistical area 2 (SA2), statistical area 3 (SA3), and urban rural (UR). They are also used to define electoral districts, territorial authorities, and regional councils.

Meshblock boundaries generally follow road centrelines, cadastral property boundaries, or topographical features such as rivers. Expanses of water in the form of lakes and inlets are defined separately from land.

Meshblock maintenance

Meshblock boundaries are amended by:

1. Splitting – subdividing a meshblock into two or more meshblocks.

2. Nudging – shifting a boundary to a more appropriate position.

Reasons for meshblock splits and nudges can include:

• to maintain meshblock criteria rules.
• to improve the size balance of meshblocks in areas where there has been population growth
• to maintain alignment to cadastre and other geographic features.
• Stats NZ requests for boundary changes so that statistical geography boundaries can be moved
• external requests for boundary changes so that administrative or electoral boundaries can be moved
• to separate land and water. Mainland, inland water, islands, inlets, and oceanic are defined separately

Meshblock changes are made throughout the year. A major release is made at 1 January each year with ad hoc releases available to users at other times.

While meshblock boundaries are continually under review, 'freezes' on changes to the boundaries are applied periodically. Such 'freezes' are imposed at the time of population censuses and during periods of intense electoral activity, for example, prior and during general and local body elections.

Meshblock numbering

Meshblocks are not named and have seven-digit codes.

When meshblocks are split, each new meshblock is given a new code. The original meshblock codes no longer exist within that version and future versions of the meshblock classification. Meshblock codes do not change when a meshblock boundary is nudged.

Meshblocks that existed prior to 2015 and have not changed are numbered from 0000100 to 3210003. Meshblocks created from 2015 onwards are numbered from 4000000.

Digitised and non-digitised meshblocks

The digital geographic boundaries are defined and maintained by Stats NZ.

Meshblocks cover the land area of New Zealand, the water area to the 12mile limit, the Chatham Islands, Kermadec Islands, sub-Antarctic islands, offshore oil rigs, and Ross Dependency. The following 16 meshblocks are not held in digitised form.

Meshblock

Location (statistical area 2 name)

• 0016901 / Oceanic Kermadec Islands
• 0016902 / Kermadec Islands
• 1588000 / Oceanic Oil Rig Taranaki
• 3166401 / Oceanic Campbell Island
• 3166402 / Campbell Island
• 3166600 / Oceanic Oil Rig Southland
• 3166710 / Oceanic Auckland Islands
• 3166711 / Auckland Islands
• 3195000 / Ross Dependency
• 3196001 / New Zealand Economic Zone
• 3196002 / Oceanic Bounty Islands
• 3196003 / Bounty Islands
• 3196004 / Oceanic Snares Islands
• 3196005 / Snares Island
• 3196006 / Oceanic Antipodes Islands
• 3196007 / Antipodes Islands

​

High-definition version

This high definition (HD) version is the most detailed geometry, suitable for use in GIS for geometric analysis operations and for the computation of areas, centroids and other metrics. The HD version is aligned to the LINZ cadastre.

​

Macrons

Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’.

​

Digital data

Digital boundary data became freely available on 1 July 2007.

​

Further information

To download geographic classifications in table formats such as CSV please use Ariā

For more information please refer to the Statistical standard for geographic areas 2023.

Contact: geography@stats.govt.nz

Abstract

The Estonian Labour Force Survey started in 1997 as an annual survey. Since 2000 the Estonian LFS has been organised as a continuous quarterly survey. The survey covers the whole country. Both private and collective households are surveyed.

Estonia Labour Force Survey provides population estimates for the main labour market characteristics, such as employment, unemployment, inactivity, hours of work, occupation, economic activity and much else, as well as important socio-demographic characteristics, such as sex, age, education, household characteristics and regions of residence. Estonia LFS uses methodology of the International Labour Organisation (ILO), which guarantees the comparability of the data.

The sample size per quarter is approximately 2,500 households, with a sampling rate of approximately 0.5% of the working age population. The sampling frame is based on the 2000 Population and Housing Census database, comprising all registered persons 15-74 years old.

Since 2001, the Estonian Labour Force Survey questionnaire includes an ad hoc module, the contents of which vary from year to year. The module is compiled in accordance with the relevant EU regulations. The aim of the added module is to gather detailed information about the aspect of life directly relevant to the labour market, which is comparable in all EU Member States. In 2004, the ad hoc module was work organisation and working time arrangements.

Geographic coverage

National

Universe

All persons 15-74 years old with permanent residence in Estonia.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size per quarter is approximately 2,500 households, with a sampling rate of approximately 0.5% of the working age population.

The sampling design is a stratified systematic two-phase sampling of individuals, whose households are included in the sample. The 15 counties of Estonia and Tallinn are divided into four strata according to the population numbers (I - Tallinn, II - four bigger counties, III - ten smaller counties, IV - Hiiu county) and different inclusion probabilities are used in strata, the highest being for Hiiu county. The sampling frame is based on the 2000 Population and Housing Census database, comprising all registered persons 15-74 years old.

In the first phase the sample is selected by systematic sampling inside strata and the information on the size of sampled households is collected. In the second phase the sample is grouped by the number of persons aged 15-74 years in the household of the sampled individual. The final sample is then selected by systematic sampling from each size group with inclusion probability inverse to the size. This yields an equal probability sample of households (and its 15-74 years old members) inside strata. All persons aged 15-74 years in the households of the final sample are interviewed. Every sampled household is interviewed for four quarters according to the rotation pattern 2-(2)-2.

Mode of data collection

Face-to-face [f2f]

Research instrument

The main objective of the Estonian Labour Force Survey is to get data about participation of population of Estonia in the labour market; therefore the main part of the questionnaire includes questions concerning the respondents’ activities in the reference week. The employed persons are asked about economic activity of the main job, occupation, labour status, work relations, place of work, usual and actual working time, about secondary jobs, etc. The unemployed persons are asked about the steps taken to find a job, the duration of job seeking, the characteristics of previous job and a job they are looking for, etc. The persons who do not work and do not look for a job (the inactive persons) are asked about the reason for inactivity, sources of subsistence, etc.

Eurostat’s ad hoc modules included in the Estonian Labour Force Survey 2004 is Work organisation and working time arrangements (Section D).

Cleaning operations

Starting with 2000 until the full implementation of CAPI-interviewing in the 4th quarter of 2005 the entry of the data was performed centrally. The co-ordinators of the interviewers' network collected the filled questionnaires from interviewers after every two weeks and sent the paper questionnaires to Statistics Estonia. In case of inconsistencies that appeared during data entering, the interviewer who had made a mistake was contacted and the data were adjusted. After the end of the field work and entering of all data, the Blaise data files were converted into SAS format and more complicated relations, which could not be made in Blaise, were checked. In case of inconsistencies corrections were made again by contacting the interviewer.

Since 2001, the data entry program was improved so that in case of inconsistencies operators had a possibility to add a short comment about the nature of the error to the database as a link to the corresponding question of the questionnaire. The aim of it was to analyze the errors made by interviewers and find out the reasons for that (lack of the interviewer's knowledge, unclear questions, etc.). The co-ordinators of the interviewers' network received the quarterly report of mistakes and dealt with the interviewers whose work quality was low.

Response rate

The response rate in 2004 was 72.7%.

Abstract

This report presents the findings of 2000-2001 Tanzania Household Budget Survey(HBS).It focuses on poverty- monitoring indicators and offers a set of baseline mesurements for the future.Data on key poverty indicators are presented for each region. Trends over the 1990s are also assessed by comparison with the 1991/92 HBS.

The HBS collected information on a range of individual and household characteristics. These included;

*household members, education,economic activities, and health status *household expenditure, consumption and income *ownership of consumer goods and assets *housing structure and materials *household access to services and facilities, and *food security

Geographic coverage

NATIONAL COVERAGE

Analysis unit

Individuals and households,

Universe

The survey covered all de jure household members

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of households interviewed in the 2000/2001 HBS was selected in two stage.In the first stage 1,161 small areas called Primary Sampling Units (PSUs) were selected throughout the country.In the second stage. 24 households were initialy selected in each PSU.

The sampled households are located in the National Master Sample (NMS) of PSUs. The NMS is a generalised set of area units that can be used as PSUs for conducting various household surveys. It is a fixed sample of rural and urban clusters, which among other things, make possible the performance of a continuous survey programme as well as ad hoc sample surveys. the NMS has four modules, A,A+B,A+B+C and A+B+C+D, which can provide urban and rural estimates at National, Zonal, Regional and District levels respectively.

The HBS 2000/01 used Module A+B+C of the NMS comprising 621 urban EAS and 540 rural villages drawn from each of the 20 regions of Mainland Tanzania. In the second stage,24 households were selected using systematic random sampling(SRS) from stratified lists of households complied from each of the sampled PSUs. These lists were stratified into high, middle and low socio-economic groups based on socio-economic data collected during the listing exercise. The stratification and selection of households was conducted in the NBS head office and interviewers were supplied with a list of pre-selected households for interview,

RURAL frame.The initial rural NMS frame was based on the 1978 Population Census and later updated with information from the 1988 Population Census.At the beginning, a ward or group of wards was used as Primary Sampling Unit (PSU), but later a village was used insted. The rural frame of the NMS was divided into :normal: large town surroundings; and Low density; strata. In total.150 strata were created and 2 to 8 PSUs (villages) were selected from each stratum to come up with the samp;e of villages that can provide estimates for each region of Mainland Tanzania (Module A+B+C).These villages were selected using the probability proportional to size (PPS) selection procedure. The PSUs (villages) for Module A of the rural NMS are automatically included in the regional sample.

URBAN frame: The urban frame for the NMS was the sample used for 1988 Population Census detailed questionnaire. For each district in a region, a list of the urban EAs was compiled and a specific number of EAs was selected from this frame using the systematic random sampling(SRS) procedure to produce the regional urban sample.

Sampling deviation

The final sample analysed for the 2000/01 HBS consisted of 22,178 households, a large sample for any household budget survey. Three PSUs were lost entirely from the sample. Households were included in the analysis if they had at least one record in both the roster and the monthly diary. The weights were calculated for this group of household.

Field supervisors were supplied with a list of twelve :replacement: households drawn as a separate sample at the same time as the main household sample, to be used if a sampled household could not be interviewed for the duration of the survey. The 2000/01 HBS sample had a high level of replacement of households that were not interviewed-around12 per cent.

A total of 4,823 households were analysed for the 1991/92 sample. Losses were higher; levels of replacement were lower (Table A1.2). In both surveys, households that were part of the initial selectionons constitute around 85 per cent of the sample analysed.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires contain information related to;Household Particulars, Household Facilities, Household Assets, Household Income, Distance to socio-Economic Facilities, Purchase of Durable items and other Services,Food security;

Cleaning operations

A number of data consistency cheks were undertaken early in the fieldwork to assess quality and to assist in the development of the data processing system.These identified a large number of problems in the data coming in from the field, which reflected in part the ambitious size of the survey.The errors identified included consumption unit miscoding,miscoding of transactions, out of range unit prices and problems in the identifier variables. As a consequence, automatic consistency cheking programmes were strengthened and a data editing team was created. where possible,errors were corrected at the data processing centre and the field teams were notified of the problems. This resolved a large number of problems.

Response rate

The 2000/01 HBS inteviewed 98 per cent of the (revised) intended sample size. It did so by relatively frequent use of replacement households, selected from a list provided by the head office. Almost 12 per cent of households included in the final analysis were replacements. The 1991/92 HBS Suffered higher levels of losses but used smaller proportion of replacements.The use of replacements is not usually considered good practice in sampling, since it runs the risk of estimates being biased by replacement with non comparable households.However,it was considered necessary because of the large sample size and demanding character of the data collection process.

Sampling error estimates

Table A1.4 shows standard errors and confidence intervals around a number of estimates, calculated in STATA. It also presents the results of statistical tests for a significant difference between the 2000/01 and 1991/92 estimates, for the total population and each of the three areas. While STATA allows the specification of sample design in the calculation of sampling errors, identifying the srata and PSUs used, it is not possible to specify fully the complexity of the design of the HBS 2000/01. The standard errors, confidence intervals and tests are therefore approximate.

Abstract

Tonga Household Income and Expenditure Survey 2009 (HIES), undertaken by the Tonga Statistics Department during the period from 1 January 2009 to 31 December 2009. This is the second survey of its kind in Tonga. The last one was carried out in 2000/01, and the results were used in November 2002 to rebase the Consumer Price Index (CPI). A report from that survey was produced in December 2002, and where possible, results from this report will be made to be comparable to the previous report.

• To provide updated information for the expenditure item weights for the CPI;

• To provide some data for the components of National Accounts; and

• To provide information on the nature and distribution of household income and expenditure for planners, policy makers, and the general public.

Geographic coverage

National Coverage and Island Division.

Analysis unit

Private Households, individuals, Income and expenditure items.

Universe

The survey covered all members of the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design was done in such a way that promoted estimates primarily at the national level, but also at the island division level. For that reason a higher sample fraction was selected in the smaller island divisions.

Rural Tongatapu received the smallest sample fraction (8.3%) as it had the highest population. On the other hand the Ongo Niua received the largest sample fraction (21.5%) as their population was the smallest. Overall a sample of roughly 10 per cent was selected for Tonga.

The sample was selected independently within each of the 6 target areas. Firstly, extremely remote areas were removed from the frame (and thus not given a chance of selection) as it was considered too expensive to cover these areas. These areas only represented about 3.5 per cent of the total population for Tonga, so the impact of their removal was considered very minimal.

The sampling in each area was then undertaken using a two-stage process. The first stage involved the selection of census blocks using Probability Proportional to Size (PPS) sampling, where the size measure was the expected number of households in that block. For the second stage, a fixed number (twelve) of households were selected from each selected census block using systematic sampling. The household lists for all selected blocks were updated just prior to the second stage of selection.

Given the sample was spread out over four quarters during the 2009 calendar year, every 4th selected census block was allocated to a respective quarter. To ensure an equally distribution of sample to each quarter, the number of census blocks selected for each of the six target group was made divisible by four. This therefore meant the sample size for each target group was adjusted so that it was divisible by (4*12)=48, as can be seen in Table 1 of Section 1 of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were 4 main survey schedules used to collect the information for the survey were published in English: 1) Household Questionnaire 2) Individual Questionnaire - Part 1 3) Individual Questionnaire - Part 2 4) Individual Diary (x2)

Household Questionnaire

This questionnaire is primarily used to collect information on large expenditure items, but also collects information about the dwelling characteristics. In total there are 14 sections to this uestionnaire which cover: 1 Dwelling Characteristics 2 Household Possessions 3 Dwelling Tenure 4 Construction of Dwellings 5 Household Bills 6 Transport Expenses 7 Major Consumer Durables 8 Education/Recreation 9 Medical & Health 10 Overseas Travel 11 Special Events 12 Subsistence Activity Sales 13 Remittances 14 Contributions to Church/Village/School As stated above, the first section is devoted to collecting information about key dwelling characteristics, whereas the second section collects information on household possessions. Sections 3-11, and Section 14, focus on expenses the household incurs, whereas Section 13 focuses on remittances both paid by and received by the household. Finally, Section 12 collects information from households about the income they generate from subsistence activities. This section is the main question collecting income from the household questionnaire, as was included here as it was considered more appropriate to collect this data at the household level. The front page of this Questionnaire is also used for collecting the Roster of Household Members.

Individual Questionnaire - Part 1

This questionnaire collects basic demographic information about each individual in the household, including: • Relationship to Household Head • Sex • Age • Ethnicity • Marital Status

Also collected in this form is information about health problems each individual may have encountered in the last 3 months, followed by education information. For the education section, if a person is currently attending an education institution, then current level is asked, whereas if the person attended an education institution but no longer attends, then the highest level completed is collected. The last main section of this form collects information about labour force and is only asked of individuals aged 10 years and above. These questions aim to classify each person in scope for this section as either: • In the Labour Force - Employed • In the Labour Force - Unemployed • Not in the Labour Force

Individual Questionnaire - Part 2

This questionnaire is focused on collecting information from individuals regarding their income. There are eight sections to this questionnaire of which six are devoted to income. They include: 1 Wages and Salary 2 Self-Employment
3 Previous Jobs
4 Ad-hoc Jobs 5 Pensions/Welfare Benefits 6 Other Income 7 Loan Information 8 Contributions to Benefit Schemes

As stated above, the first six sections of this questionnaire focus on income. Section 7 collects information pertaining to loans for i) households, ii) cars, iii) special events and iv) other, and finally the last question is an expense related question covering contributions to benefit schemes which was considered best covered at an individual level.

Individual Diary

The last form used for the survey was the Individual Diary which each individual aged 10 years and over was required to fill in for two weeks (two one-week diaries).

Each diary had 4 sections covering the following: 1) Items Purchased: This section had a separate page for each day and was for recording all items bought in a store, street vendors, market or any other place (including credit) 2) Home Grown/Produced Items: This section was for recording home grown/produced items consisting of items such as food grown at home or at the family plantation, self caught or gathered fish and homemade handicrafts and other goods grown and produced at home. Information is recorded for these items consumed by the household which they produced themselves, these items they gave away as a gift, and these items they received as a gift. 3) Gifts Given and Received: This section of the diary is for recording gifts given and received including both cash and purchased goods (but not home produced). If any member of the household receives a gift that meets this criteria during the diary keeping period from someone who is not a member of their household it is recorded here. 4) Winnings from Gambling: The last section of the Diary is for recording all winnings from gambling during the diary keeping period.

Cleaning operations

Batch edits in CSPro were performed on the data after data entry was completed. The batch edits were aimed at identifying any values falling outside acceptable ranges, as well as other inconsistencies in the data. As this process was done at the batch level, questionnaires were often referred to and manual changes to the data were performed to amend identified errors.

One significant problem which was identified during this process was the incorrect coding of phone card purchase to the purchase of actual phones. As there were many such cases, an automatic code change was applied to any purchase of phones which was less than $40 - recoding them to purchase of phone cards.

Response rate

The final Response Rates for the survey was high, which will assist in yielding statistically significant estimates. Across all six target groups the response rate was in excess of 95 per cent, with the exception of Ongo Niua who only reported 50 per cent. The reason the number was so low in the Ongo Niua was because this target area was only visited in the 2nd quarter, where half the total sample were enumerated (to make up for the sample loss in the first quarter), and was not visited again in quarter 3 and 4.

The reason behind the high response rates in other areas was due to the updated lists for selected census blocks excluding vacant dwellings. As such, it was mostly refusals that impacted on the final response rates.

Sampling error estimates

Sampling errors refer to those errors that are implicit in any sample survey, where only a portion of the population is covered. Non-sampling errors refer to all other types of error. These can arise at any stage of the survey process. Examples of activities that are likely to increase the level of non-sampling error are: failing to select a proper sample, poor questionnaire design, weak field supervision, inaccurate data entry, insufficient data editing, or failure to analyze or report on the data

Šī datu kopa parāda 2001. gada skaitīšanas rezultātu apgabalus (OAs) Mājsaimniecības svērtie Centroids.

Atsevišķa izlaides zona parasti aptver pietiekami mazu platību (ievērojot minimālo iedzīvotāju skaitu/mājsaimniecību skaitu), lai varētu izveidot lietotāju definētas vai ad hoc zonas, vienlaikus saglabājot pietiekamu kvalitātes līmeni. Skotijas nacionālajos reģistros (NRS) tiek ražots tikai viens OA kopums un izveidotas visas pārējās izejas ģeogrāfijas, izmantojot OA kā ēkas ķieģeļu. Katrs OA tiek piešķirts apgabalam augstākā ģeogrāfijā, vispirms izvēloties vienu no OA pasta indeksiem kā “galveno” pasta indeksu. OA manto visas galvenā pasta indeksa īpašības, tostarp tā uzdevumus uz augstākām zonām un tā koordinātu tīkla atsauci.

Galvenais pasta indekss tika izvēlēts, izmantojot algoritmu, kas aprēķina pasta indeksu centroid OA ietvaros, kuram ir koordinātu tīkla atsauces vistuvāk OA mājsaimniecības svērtajam centram.

Abstract

The 2003 National Demographic and Health Survey (NDHS) is a nationally representative survey of 13,945 women age 15-49 and 5,009 men age 15-54. The main purpose of the 2003 NDHS is to provide policymakers and program managers with detailed information on fertility, family planning, childhood and adult mortality, maternal and child health, and knowledge and attitudes related to HIV/AIDS and other sexually transmitted infections. The 2003 NDHS also collects high quality data on family health: immunizations, prevalence and treatment of diarrhea and other diseases among children under five, antenatal visits, assistance at delivery and breastfeeding.

The 2003 NDHS is the third national sample survey undertaken in Philippines under the auspices of the worldwide Demographic and Health Surveys program.

The 2003 Philippines National Demographic and Health Survey (NDHS) is designed to provide upto-date information on population, family planning, and health to assist policymakers and program managers in evaluating and designing strategies for improving health and family planning services in the country. In particular, the 2003 NDHS has the following objectives: - Collect data at the national level, which will allow the calculation of demographic rates and, particularly, fertility and under-five mortality rates. - Analyze the direct and indirect factors that determine the level and trends of fertility. Indicators related to fertility will serve to inform plans for social and economic development. - Measure the level of contraceptive knowledge and practice by method, urban-rural residence, and region. - Collect data on knowledge and attitudes of women and men about sexually transmitted infections and HIV/AIDS and evaluate patterns of recent behavior regarding condom use. - Collect high-quality data on family health, including immunizations, prevalence and treatment of diarrhea and other diseases among children under five, antenatal visits, assistance at delivery, and breastfeeding.

Geographic coverage

National

Analysis unit

• Household
• Women age 15-49
• Men age 15-54

Universe

The population covered by the 1998 Phillipines NDS is defined as the universe of all females age 15-49 years, who are members of the sample household or visitors present at the time of interview and had slept in the sample households the night prior to the time of interview, regardless of marital status and all men age 15-54 living in the household.

Kind of data

Sample survey data

Sampling procedure

The 2003 NDHS is the first survey that used the new master sample created for household surveys on the basis of the 2000 Census of Population and Housing. The 2003 NDHS used one of the four replicates of the master sample. The sample was designed to represent the country as a whole, urban and rural areas, and each of the 17 administrative regions. In each region, a stratified, three-stage cluster sampling design was employed. In the first stage, 819 primary sampling units (PSUs) were selected with probability proportional to the number of households in the 2000 census. PSUs consisted of a barangay or a group of contiguous barangays. In the second stage, in each PSU, enumeration areas (EAs) were selected with probability proportional to the number of EAs. An EA is defined as an area with discernable boundaries consisting of about 150 contiguous households. All households in the selected EAs were listed in a separate field operation conducted May 7 through 21, 2003. In the third stage, from each EA, an average of 17 households was selected using systematic sampling.

Mode of data collection

Face-to-face

Research instrument

The 2003 NDHS used four questionnaires: a) Household Questionnaire, b) Health Module, c) Women's Questionnaire, and d) Men's Questionnaire. The content of the Women's Questionnaire was based on the MEASURE DHS+ Model “A” Questionnaire, which was developed for use in countries with high levels of contraceptive use. To modify the questionnaire to reflect relevant family planning and health issues in the Philippines, program input was solicited from Department of Health (DOH), Commission on Population (POPCOM), the University of the Philippines Population Institute (UPPI), the Food and Nutrition Research Institute (FNRI), the Philippine Health Insurance Corporation (PhilHealth), USAID, the National Statistics Coordination Board (NSCB), the National Economic and Development Authority (NEDA), the United Nations Children's Fund (UNICEF), and Dr. Mercedes B. Concepcion, professor emeritus at the University of the Philippines, as well as managers of USAID-sponsored projects in the Philippines. The questionnaires were translated from English into six major languages: Tagalog, Cebuano, Ilocano, Bicol, Hiligaynon, and Waray.

a) The Household Questionnaire was used to list all of the usual members and visitors in the selected households. Basic information collected for each person listed includes age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. Information on characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor of the house, and ownership of various durable goods, was also recorded in the Household Questionnaire. These items are indicators of the household's socioeconomic status.

b) The Health Module was aimed at apprising concerned agencies on the health status, practices, and attitude of the population. The module included the following topics:
- Health facility utilization - Noncommunicable diseases - Infectious diseases -Traditional medicines, healing practices, and alternative health care modalities - Health care financing -Environmental health.

c) The Women's Questionnaire was used to collect information from all women age 15-49. These women were asked questions on the following topics: - Background characteristics (e.g., education, media exposure) - Reproductive history - Knowledge and use of family planning methods - Fertility preferences - Antenatal, delivery, and postnatal care - Breastfeeding and infant feeding practices - Vaccinations and childhood illnesses - Marriage and sexual activity - Woman's work and husband's background characteristics - Infant's and children's feeding practices - Childhood mortality - Awareness and behavior regarding AIDS and other sexually transmitted infections - Awareness and behavior regarding tuberculosis

d) The Men's Questionnaire was administered to all men age 15-54 living in every third household in the NDHS sample. The Men's Questionnaire collected much of the same information found in the Women's Questionnaire but was shorter because it did not contain questions on reproductive history, maternal and child health, and nutrition. Instead, men were asked about their knowledge and participation in health-seeking practices for their children.

Cleaning operations

All completed questionnaires and the control forms were returned to the NSO Central Office in Manila for data processing, which consisted of manual editing, data entry and verification, and editing of computer-identified errors. An ad hoc group of seven regular employees of DSSD was created to work full time in the NDHS Data Processing Center. This group was responsible for the different aspects of NDHS data processing. There were 10 manual processors and 25 data encoders hired to process the data.

Manual editing started on July 15, 2003, and data entry started on July 21, 2003. The computer package program called CSPro (Census and Survey Processing System) was used for data entry, editing, and tabulation. To prepare the data entry programs, two NSO staff members spent three weeks in ORC Macro offices in Calverton, Maryland, in April and May 2003. Data processing was completed in October 29, 2003.

Response rate

For the 2003 NDHS sample, 13,914 households were selected, of which 12,694 were occupied (Table). Of these households, 12,586 were successfully interviewed, yielding a household response rate of 99 percent. Household response rates are similar in rural areas and in urban areas (99 percent).

Among the households interviewed, 13,945 women were identified as eligible respondents, and interviews were completed for 13,633 women, yielding a response rate of 98 percent. In a subsample of every third household, 5,009 men were identified to be eligible for individual interview. Of these, 4,766 were successfully interviewed, yielding a response rate of 95 percent.

Sampling error estimates

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2003 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (e.g., mean, percentage), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from

Dette datasæt viser 2001 Census Output Areas (OAs) Household Weighted Centroids.

Et individuelt outputområde dækker generelt et tilstrækkeligt lille område (under forudsætning af, at minimumstallet for befolkning/husstand) opfyldes, således at der kan oprettes brugerdefinerede eller ad hoc-områder, samtidig med at der opretholdes et tilstrækkeligt kvalitetsniveau. National Records of Scotland (NRS) producerer kun et sæt OA'er og skaber alle andre outputgeografier ved hjælp af OA som byggesten. Hver OA tildeles et område i en højere geografi ved først at vælge et af postnumrene i OA som et "master" postnummer. OA arver alle karakteristika ved masterpostnummeret, herunder dets tildelinger til højere områder og dets gitterreference.

Hovedpostnummeret blev valgt ved hjælp af en algoritme, der beregner postnummeret centroid i en OA, som har gitterreferencer tættest på det husstandsvægtede centrum af OA.