Geodemographic Segmentation Data from Caliper Corporation contain demographic data in a way that is easy to visualize and interpret. We provide 8 segments and 32 subsegments for exploring the demographic makeup of neighborhoods across the country.

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

Pandemics such as Covid-19 pose tremendous public health communication challenges in promoting protective behaviours, vaccination, and educating the public about risks. Segmenting audiences based on attitudes and behaviours is a means to increase the precision and potential effectiveness of such communication. The present study reports on such an audience segmentation effort for the population of England, sponsored by the United Kingdom Health Security Agency (UKHSA) and involving a collaboration of market research and academic experts. A cross-sectional online survey was conducted between 4 and 24 January 2022 with 5525 respondents (5178 used in our analyses) in England using market research opt-in panel. An additional 105 telephone interviews were conducted to sample persons without online or smartphone access. Respondents were quota sampled to be demographically representative. The primary analytic technique was k means cluster analysis, supplemented with other techniques including multi-dimensional scaling and use of respondent ‐ as well as sample-standardized data when necessary to address differences in response set for some groups of respondents. Identified segments were profiled against demographic, behavioural self-report, attitudinal, and communication channel variables, with differences by segment tested for statistical significance. Seven segments were identified, including distinctly different groups of persons who tended toward a high level of compliance and several that were relatively low in compliance. The segments were characterized by distinctive patterns of demographics, attitudes, behaviours, trust in information sources, and communication channels preferred. Segments were further validated by comparing the segmentation variable versus a set of demographic variables as predictors of reported protective behaviours in the past two weeks and of vaccine refusal; the demographics together had about one-quarter the effect size of the single seven-level segment variable. With respect to managerial implications, different communication strategies for each segment are suggested for each segment, illustrating advantages of rich segmentation descriptions for understanding public health communication audiences. Strengths and weaknesses of the methods used are discussed, to help guide future efforts.

GapMaps Panorama Segmentation Data from Applied Geographic Solutions (AGS) is built on over three decades of experience in the creation and use of geodemographic segmentation systems in the United States and Canada. Building on and integrating the existing suite of AGS modeling and analytical tools, GapMaps Panorama Segmentation Data creates actionable perspective on an increasingly complex and rapidly churning demographic landscape.

GapMaps Segmentation Data consists of sixty eight segments currently paired with the industry leading GfK MRI survey, providing the essential linkage between neighborhood demographics and consumer preferences and attitudes.

The segments include: 01 One Percenters 02 Peak Performers 03 Second City Moguls 04 Sprawl Success 05 Transitioning Affluent Families 06 Best of Both Worlds 07 Upscale Diversity 08 Living the Dream 09 Successful Urban Refugees 10 Emerging Leaders 11 Affluent Newcomers 12 Mainstream Established Suburbs 13 Cowboy Country 14 American Playgrounds 15 Comfortable Retirement 16 Spacious Suburbs 17 New American Dreams 18 Small Town Middle Managers 19 Outer Suburban Affluence 20 Rugged Individualists 21 New Suburban Style 22 Up and Coming Suburban Diversity 23 Enduring Heartland 24 Isolated Hispanic Neighborhoods 25 Hipsters and Geeks 26 High Density Diversity 27 Young Coastal Technocrats 28 Asian-Hispanic Fusion 29 Big Apple Dreamers 30 True Grit 31 Working Hispania 32 Struggling Singles 33 Nor'Easters 34 Midwestern Comforts 35 Generational Dreams 36 Olde New England 37 Faded Industrial Dreams 38 Failing Prospects 39 Second City Beginnings 40 Beltway Commuters 41 Garden Variety Suburbia 42 Rising Fortunes 43 Classic Interstate Suburbia 44 Pacific Second City 45 Northern Blues 46 Recessive Singles 47 Simply Southern 48 Tex-Mex 49 Sierra Siesta 50 Great Plains, Great Struggles 51 Boots and Brews 52 Great Open Country 53 Classic Dixie 54 Off the Beaten Path 55 Hollows and Hills 56 Gospel and Guns 57 Cap and Gown 58 Marking Time 59 Hispanic Working Poor 60 Bordertown Blues 61 Communal Living 62 Living Here in Allentown 63 Southern Small City Blues 64 Struggling Southerners 65 Forgotten Towns 66 Post Industrial Trauma 67 Starting Out 68 Rust Belt Poverty

The dashboard was creating using Business Analyst Infographics. Read more about it here: https://www.esri.com/en-us/arcgis/products/data/overview?rmedium=www_esri_com_EtoF&rsource=/en-us/arcgis/products/esri-demographics/overview Data Source: U.S. Census Bureau, Census 2020 Summary File 1, 2021 American Community Survey(ACS), and ESRI 2022 Demographics and Tapestry Segmentation. For more information on Esri Demographics see HERE and for Tapestry see HERE.Geographies: The council district boundaries used in this dashboard are those that were effective as of May 6, 2023.Much of the science for determining the data for an irregular polygon is explained here:https://doc.arcgis.com/en/community-analyst/help/calculation-estimates-for-user-created-areas.htmCalculation estimates for user-created areasBusiness Analyst employs a GeoEnrichment service which uses the concept of a study area to define the location of the point or area that you want to enrich with additional information. If one or more points is input as a study area, the service will create a one-mile ring buffer around the points or points to collect and append enrichment data. You can optionally change the ring buffer size or create drive-time service areas around a point.The GeoEnrichment service uses a sophisticated geographic retrieval methodology to aggregate data for rings and other polygons. A geographic retrieval methodology determines how data is gathered and summarized or aggregated for input features. For standard geographic units, such as states, provinces, counties, or postal codes, the link between a designated area and its attribute data is a simple one-to-one relationship. For example, if an input study trade area contains a selection of ZIP Codes, the data retrieval is a simple process of gathering the data for those areas.Data Allocation MethodThe Data Allocation method allocates block group data to custom areas by examining where the population is located within the block group and determines how much of the population of a block group overlaps a custom area. This method is used in the United States, and similarly in Canada. The population data reported for census blocks, a more granular level of geography than block groups, is used to determine where the population is distributed within a block group. If the geographic center of a block falls within the custom area, the entire population for the block is used to weight the block group data. The geographic distribution of the population at the census block level determines the proportion of census block group data that is allocated to user specified areas as shown in the example.Note:Depending on the data, households, housing units or businesses at the block group level are used as weights. Employing block centriods is superior because it accounts for the possibility that the population may not be evenly distributed geographically throughout a block group.

A global database of population segmentation data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date audience targeting data trends for market research, audience targeting, and sales territory mapping.

Self-hosted consumer data curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The Consumer Data is standardized, unified, and ready to use.

Use cases for the Global Population Database (Consumer Data Data/Segmentation data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Segmentation data export methodology

Our location data packages are offered in variable formats, including GeoJSON, KML, and TopoJSON. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Population density

• Accurate at any level of granularity

• Global coverage

• Updated yearly

• Data spans over 55 years

• Standardized and reliable

• Self-hosted

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our Population Databases

• Standardized and unified demographic data structure

• Reduce integration time and cost by 30%

• Dedicated customer success manager

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

Abstract

The 1988 Egypt Demographic and Health Survey (EDHS) is part of the worldwide Demographic and Health Surveys (DHS) Program, which is designed to collect data on fertility, family planning and maternal and child health.

The 1988 EDHS is the most recent in a series of surveys carried out in Egypt to provide the information needed to study fertility behavior and its determinants, particularly contraceptive use. The EDHS findings are important in monitoring trends in these variables and in understanding the factors which contribute to differentials in fertility and contraceptive use among various population subgroups. The EDHS also provides a wealth of health-related information for mothers and their children, which was not available in the earlier surveys. These data are especially important for understanding the factors that influence the health and survival of infants and young children. In addition to providing insights into population and health issues in Egypt, the EDHS also hopefully will lead to an improved global understanding of population and health problems as it is one of 35 internationally comparable surveys sponsored by the Demographic and Health Surveys program.

The Egypt Demographic and Health Survey (EDHS) has as its major objective the provision of current and reliable information on fertility, mortality, family planning, and maternal and child health indicators. The information is intended to assist policy makers and administrators in Egyptian population and health agencies to: (1) assess the effect of ongoing family planning and maternal and child health programs and (2) improve planning for future interventions in these areas. The EDHS provides data on topics for which comparable data are not available from previous nationally representative surveys, as well as information needed to monitor trends in a number of indicators derived from earlier surveys, in particular, the 1980 Egypt Fertility Survey (EFS) and the 1980 and 1984 Egypt Contraceptive Prevalence Surveys (ECPS). Finally, as part of the worldwide Demographic and Health Surveys (DHS) program, the EDHS is intended to add to an international body of data, which can be used for cross-national research on these topics.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data

Sampling procedure

Geographical Coverage: The EDHS was carried out in 21 of the 26 governorates in Egypt. The Frontier Governorates (Red Sea, New Valley, Matrouh, North Sinai and South Sinai), which represent around two percent of the total population in Egypt, were excluded from coverage because a disproportionate share of EDHS resources would have been needed to survey the dispersed population in these governorates.

The EDHS sample was designed to provide separate estimates of all major parameters for: the national level, the Urban Governorates, Lower Egypt (total, urban and rural) and Upper Egypt (total, urban and rural). In addition, the sample was selected in such a fashion as to yield a sufficient number of respondents from each governorate to allow for governorate-level estimates of current contraceptive use. In order to achieve the latter objective, sample takes for the following governorates were increased during the selection process: Port Said, Suez, Ismailia, Damietta, Aswan, Kafr El-Sheikh, Beni Suef and Fayoum.

Sampling Plan: The sampling plan called for the EDHS sample to be selected in three stages. The sampling units at the first stage were shiakhas/towns in urban areas and villages in rural areas. The frame for the selection of the primary sampling units (PSU) was based on preliminary results from 1986 Egyptian census, which were provided by the Central Agency for Public Mobilization and Statistics. During the first stage selection, 228 primary sampling units (108 shiakhas/towns and 120 villages) were sampled.

The second stage of selection called for the PSUs chosen during the first stage to be segmented into smaller areal units and for two of the areal units to be sampled from each PSU. In urban PSUs, a quick count operation was carried out to provide the information needed to select the secondary sampling units (SSU) while for rural PSUs, maps showing the residential area within the selected villages were used.

Following the selection of the SSUs, a household listing was obtained for each of the selected units. Using the household lists, a systematic random sample of households was chosen for the EDHS. All ever-married women 15-49 present in the sampled households during the night before the interviewer's visit were eligible for the individual interview.

Quick Count and Listing: As noted in the discussion of the sampling plan, two separate field operations were conducted during the sample implementation phase of the EDHS. The first field operation involved a quick count in the shiakhas/towns selected as PSUs in urban areas. Prior to the quick count operation, maps for each of the selected shiakhas/towns were obtained and divided into approximately equal-sized segments, with each segment having well-defined boundaries. The objective of the quick count operation was to obtain an estimate of the number of households in each of the segments to serve as the measures of size for the second stage selection.

A review of the preliminary 1986 Census population totals for the selected shiakhas/towns showed that they varied greatly in total size, ranging from less than 10,000 to more than 275,000 residents. Experience in the 1984 Egypt Contraceptive Prevalence Survey, in which a similar quick count operation was carried out, indicated that it was very time-consuming to obtain counts of households in shiakhas/towns with large populations. In order to reduce the quick count workload during the EDHS, a subsample of segments was selected from the shiakhas/towns, with 50,000 or more population. The number of segments sub-sampled depended on the size of the shiakha. Only the sub-sampled segments were covered during the quick count operation in the large shiakhas/towns. For shiakhas with less than 50,000 populations, all segments were covered during the quick count.

Prior to the quick count, a one-week training was held, including both classroom instruction and practical training in shiakhas/towns not covered in the survey. The quick count operation, which covered all 108 urban PSUs, was carried out between June and August 1988. A group of 62 field staff participated in the quick count operation. The field staff was divided into ten teams each composed of one supervisor and three to four counters.

As a quality control measure, the quick count was repeated in 10 percent of the shiakhas. Discrepancies noted when the results of the second quick count operation were compared with the original counts were checked. No major problems were discovered in this matching process, with most differences in the counts attributed to problems in the identification of segment boundaries.

The second field operation during the sample implementation phase of the survey involved a complete listing of all of the households living in the 456 segments chosen during the second stage of the sample selection. Prior to the household listing, the listing staff attended a one-week training course, which involved both classroom lectures and field practice. After the training, the 14 supervisors and 32 listers were organized into teams; except in Damietta and Ismailia, where the listers work on their own, each listing team was composed of a supervisor and two listers. The listing operation began in the middle of September and was completed in October 1988.

Segments were relisted when the number of households in the listing differed markedly from that expected based on: (1) the quick count in urban areas or (2) the number of households estimated from the information on the size of the inhabited area for rural segments. Few discrepancies were noted for urban segments. Not surprisingly, more problems were noted for rural segments since the estimated size of the segment was not based on a recent count as it was for the urban segments. All segments where major differences were noted in the matching process were relisted in order to resolve the problems.

Note: See detailed description of sample design in APPENDIX B of the report which is presented in this documentation.

Mode of data collection

Face-to-face

Research instrument

The EDHS involved both a household and an individual questionnaire. These questionnaires were based on the DHS model "A" questionnaire for high contraceptive prevalence countries. Additional questions on a number of topics not covered in the DHS questionnaire were included in both the household and individual questionnaires. The questionnaires were pretested in June 1988, following a one-week training for supervisors and interviewers. Three supervisors and seven interviewers participated in the pretest. Interviewer comments and tabulations of the pretest results were reviewed during the process of modifying the questionnaires.

The EDHS household questionnaire obtained a listing of all usual household members and visitors and identified those present in the household during the night before the interviewer's visit. For each of the individuals included in the listing, information was collected on the relationship to the household head, age, sex, marital status, educational level, occupation and work status. In addition, questions were included on the mortality experience of sisters of all household members age 15 and over in order to obtain data to estimate the level of maternal mortality. The maternal mortality questions were administered in a

Success.ai’s Consumer Marketing Data API empowers your marketing, analytics, and product teams with on-demand access to a vast and continuously updated dataset of consumer insights. Covering detailed demographics, behavioral patterns, and purchasing histories, this API enables you to go beyond generic outreach and craft tailored campaigns that truly resonate with your target audiences.

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Introduction: White matter hyperintensities of presumed vascular origin (WMH) are an important magnetic resonance imaging marker of cerebral small vessel disease and are associated with cognitive decline, stroke, and mortality. Their relevance in healthy individuals, however, is less clear. This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.Methods: We evaluated the effect of varying training sample sizes on the accuracy and the robustness of the predicted white matter hyperintensity volume in a population (n = 201) with a low prevalence of confluent WMH and a substantial proportion of participants without WMH. BIANCA was trained with seven different sample sizes between 10 and 40 with increments of 5. For each sample size, 100 random samples of T1w and FLAIR images were drawn and trained with manually delineated masks. For validation, we defined an internal and external validation set and compared the mean absolute error, resulting from the difference between manually delineated and predicted WMH volumes for each set. For spatial overlap, we calculated the Dice similarity index (SI) for the external validation cohort.Results: The study population had a median WMH volume of 0.34 ml (IQR of 1.6 ml) and included n = 28 (18%) participants without any WMH. The mean absolute error of the difference between BIANCA prediction and manually delineated masks was minimized and became more robust with an increasing number of training participants. The lowest mean absolute error of 0.05 ml (SD of 0.24 ml) was identified in the external validation set with a training sample size of 35. Compared to the volumetric overlap, the spatial overlap was poor with an average Dice similarity index of 0.14 (SD 0.16) in the external cohort, driven by subjects with very low lesion volumes.Discussion: We found that the performance of BIANCA, particularly the robustness of predictions, could be optimized for use in populations with a low WMH load by enlargement of the training sample size. Further work is needed to evaluate and potentially improve the prediction accuracy for low lesion volumes. These findings are important for current and future population-based studies with the majority of participants being normal aging people.

Snapshot img

What is the Size of Burial Insurance Market?

The burial insurance market size is forecast to increase by USD 72 billion and is estimated to grow at a CAGR of 5.5% between 2024 and 2029. The market is experiencing significant growth due to several key factors. The geriatric population is expanding, leading to a rise in demand for burial insurance. Additionally, there is an increasing focus on digitalization in the insurance industry, making it more convenient for consumers to purchase policies online. However, the market is also facing challenges such as misleading advertisements that may misrepresent the true cost and coverage of burial insurance policies. As the population ages and consumers seek out more efficient ways to plan for end-of-life expenses, the market is poised for continued growth. Digitalization is playing a crucial role in making these policies more accessible, but it is essential for insurers to maintain transparency and accuracy in their advertising to build trust and credibility with consumers.

Request Free Market Sample

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments.

Age Group

  Seniors
  Pre-retirement individuals
  High-risk individuals


Product Type

  Final expense life insurance
  Pre-need burial insurance
  Whole life burial insurance
  Guaranteed issue burial insurance
  Term burial insurance


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  APAC

    Japan
    South Korea


  South America

    Brazil


  Middle East and Africa

Which is the largest segment driving market growth?

The seniors segment is estimated to witness significant growth during the forecast period. The market is gaining significance as the senior population continues to expand. Approximately 50% of the global population aged 60 and above is projected to reach 1.4 billion by 2030, and this demographic segment represents a substantial market opportunity.

Get a glance at the market share of various regions. Download the PDF Sample

The seniors segment was the largest segment and was valued at USD 126.40 billion in 2019. This trend presents a growing demand for burial insurance coverage. Burial insurance policies offer a predetermined coverage amount to cover funeral and burial expenses. Underwriting processes for these policies have been simplified, with some companies offering coverage without a medical exam requirement. This approach, known as simplified underwriting, caters to consumers with various health conditions. Agents play a crucial role in connecting consumers with the most suitable policies for their needs. Hence, such factors are fuelling the growth of this segment during the forecast period.

Which region is leading the market?

For more insights on the market share of various regions, Request Free Sample

North America is estimated to contribute 57% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. The North American market for burial insurance is witnessing notable expansion due to the increasing recognition among the aging population of the importance of long-term care planning. A recent survey involving over 1,700 participants underscored the significance of this issue, with 91% of respondents acknowledging the need to include long-term care in their retirement plans. This heightened awareness is fueling the demand for burial insurance in the region. In response to this trend, companies such as Transamerica are innovating to improve the customer experience. In March 2023, Transamerica introduced ConnectedClaimsSM, a range of customizable services aimed at streamlining access to workplace supplemental insurance benefits. This premium service offers policyholders a level of death benefit and guarantees acceptance without the need for a medical examination. With technological dependence on the rise, funeral cover continues to provide essential financial help for families during difficult times.

How do company ranking index and market positioning come to your aid?

Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.

AFLAC Inc: The company offers burial insurance such as immediate cash payout which provides a tax-free cash benefit to cover final expenses such as funeral costs.

Technavio provides the ranking index for the top 20 companies along with insights on the market positioning of:

American International Group Inc.
An Post Insuranc

Food festivals have been a growing tourism sector in recent years due to their contributions to a region’s economic, marketing, brand, and social growth. This study analyses the demand for the Bahrain food festival. The stated objectives were: i) To identify the motivational dimensions of the demand for the food festival, (ii) To determine the segments of the demand for the food festival, and (iii) To establish the relationship between the demand segments and socio-demographic aspects. The food festival investigated was the Bahrain Food Festival held in Bahrain, located on the east coast of the Persian Gulf. The sample consisted of 380 valid questionnaires and was taken using social networks from those attending the event. The statistical techniques used were factorial analysis and the K-means grouping method. The results show five motivational dimensions: Local food, Art, Entertainment, Socialization, and Escape and novelty. In addition, two segments were found; the first, Entertainment and novelties, is related to attendees who seek to enjoy the festive atmosphere and discover new restaurants. The second is Multiple motives, formed by attendees with several motivations simultaneously. This segment has the highest income and expenses, making it the most important group for developing plans and strategies. The results will contribute to the academic literature and the organizers of food festivals.

Average (mean) rating for 'Happy Yesterday' by LSOA in the First ONS Annual Experimental Subjective Wellbeing survey, April 2011 - March 2012

The Department for Communities and Local Government (DCLG) has estimated the expected wellbeing of residents at Lower-layer Super Output Area (LSOA) level. The purpose is to illustrate the likely degree of variation between neighbourhoods.

These are modelled estimates for local areas based on national findings from the ONS Annual Population Survey 2011-2012. They are not the actual survey responses of people living in those areas [1]. As such, DCLG encourage local areas to test these expected findings against their own local knowledge and data.

DCLG used CACI’s ACORN geo-demographic segmentation to estimate the likely wellbeing characteristics of each neighbourhood. Analysis of the APS provided a national profile of wellbeing by ACORN Type, with estimates of average subjective wellbeing and low subjective wellbeing for each of the 56 Types. The national profile was then applied to localities, to reflect their composition according to ACORN Type [2].

The method presumes the national profile of wellbeing for the ACORN types is broadly the same in each local authority. For all of the subjective wellbeing measures, DCLG tested this assumption broadly held across the nine regions. As a result, DCLG made a minimal number of adjustments to the profiles for life satisfaction, worthwhile, and happy yesterday, and determined that the method was not robust for modelling anxiety [3].

Feedback on the neighbourhood estimates and requests for further details of the methodology can sent to wellbeing@communities.gsi.gov.uk.

In October, DCLG will be producing wellbeing profiles to enable users to apply the same methodology using geo-demographic classifications: Experian’s MOSAIC and ONS’s Output Area Classification (OAC).

[1] This is because sample sizes from the APS do not permit reliable estimates of subjective wellbeing below the 90 unitary authorities and counties reported in the First ONS Annual Experimental Subjective Well-being Results.

[2] ACORN is a segmentation based on shared characteristics of people’s life-stage, income, profession and housing, as well as characteristics of places including whether they are urban, suburban or rural. Each respondent on the APS had been classified into one ACORN Type, based on the full postcode in which they live – approximately 16 addresses.) ACORN provided estimates of the population in each ACORN Type in each LSOA and local authority district.

[3] These adjustments were made only where there was reliable evidence (based on samples of more than 100 respondents) from APS that the national wellbeing ACORN profile was substantially different from the regional one, and where the implications for neighbourhood maps would be highly geographically clustered.

Percentage of responses in the range 0-6 for 'Worthwhile' by LSOA in the First ONS Annual Experimental Subjective Wellbeing survey, April 2011 - March 2012

The Department for Communities and Local Government (DCLG) has estimated the expected wellbeing of residents at Lower-layer Super Output Area (LSOA) level. The purpose is to illustrate the likely degree of variation between neighbourhoods.

These are modelled estimates for local areas based on national findings from the ONS Annual Population Survey 2011-2012. They are not the actual survey responses of people living in those areas [1]. As such, DCLG encourage local areas to test these expected findings against their own local knowledge and data.

DCLG used CACI’s ACORN geo-demographic segmentation to estimate the likely wellbeing characteristics of each neighbourhood. Analysis of the APS provided a national profile of wellbeing by ACORN Type, with estimates of average subjective wellbeing and low subjective wellbeing for each of the 56 Types. The national profile was then applied to localities, to reflect their composition according to ACORN Type [2].

The method presumes the national profile of wellbeing for the ACORN types is broadly the same in each local authority. For all of the subjective wellbeing measures, DCLG tested this assumption broadly held across the nine regions. As a result, DCLG made a minimal number of adjustments to the profiles for life satisfaction, worthwhile, and happy yesterday, and determined that the method was not robust for modelling anxiety [3].

Feedback on the neighbourhood estimates and requests for further details of the methodology can sent to wellbeing@communities.gsi.gov.uk.

In October, DCLG will be producing wellbeing profiles to enable users to apply the same methodology using geo-demographic classifications: Experian’s MOSAIC and ONS’s Output Area Classification (OAC).

[1] This is because sample sizes from the APS do not permit reliable estimates of subjective wellbeing below the 90 unitary authorities and counties reported in the First ONS Annual Experimental Subjective Well-being Results.

[2] ACORN is a segmentation based on shared characteristics of people’s life-stage, income, profession and housing, as well as characteristics of places including whether they are urban, suburban or rural. Each respondent on the APS had been classified into one ACORN Type, based on the full postcode in which they live – approximately 16 addresses.) ACORN provided estimates of the population in each ACORN Type in each LSOA and local authority district.

[3] These adjustments were made only where there was reliable evidence (based on samples of more than 100 respondents) from APS that the national wellbeing ACORN profile was substantially different from the regional one, and where the implications for neighbourhood maps would be highly geographically clustered.

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Additional...

Abstract

The 2000 Egypt Demographic and Health Survey is, part of the worldwide Demographic and Health Surveys project, carried out in Egypt that provide information on fertility behavior and its determinants, particularly contraceptive use. The EDHS findings are important in monitoring trends for key variables and in understanding the factors that contribute to differentials in fertility and contraceptive use among various population subgroups. The EDHS also provides a wealth of healthrelated information about mothers and their children. These data are of special importance for understanding the factors that influence the health and survival of infants and young children.

The 2000 EDHS was designed to provide estimates for key indicators such as fertility, contraceptive use, infant and child mortality, immunization levels, coverage of antenatal and delivery care, and maternal and child health and nutrition. The survey results are intended to assist policymakers and planners in assessing the current health and population programs and in designing new strategies for improving reproductive health and health services in Egypt.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN

The primary objective of the sample design for the 2000 EDHS was to provide estimates of key population and health indicators including fertility and child mortality rates for the country as a whole and for six major administrative regions (the Urban Governorates, urban Lower Egypt, rural Lower Egypt, urban Upper Egypt, rural Upper Egypt, and the Frontier Governorates). In the Urban Governorates, Lower Egypt, and Upper Egypt, the design allowed for governorate-level estimates of most of the key variables, with the exception of the fertility and mortality rates. In the Frontier Governorates, the sample size was not sufficiently large to provide separate estimates for the individual governorates. To meet the survey objectives, the number of households selected in the 2000 EDHS sample from each governorate was not proportional to the size of the population in the governorate. As a result, the 2000 EDHS sample is not self-weighting at the national level, and weights have to be applied to the data to obtain the national-level estimates presented in this report.

SAMPLE SELECTION

The sample for the 2000 EDHS was selected in three stages. The first stage included selecting the primary sampling units. The units of selection were shiakhas/towns in urban areas and villages in rural areas. Information from the 1996 census was used in constructing the frame from which the primary sampling units (PSUs) were selected. Prior to selecting the PSUs, the frame was updated to take into account administrative changes that had occurred since 1996. The updating process included both office work and field visits during a three-month period. After it was completed, urban and rural units were stratified by geographical location in a serpentine order from the northwest corner to the southeast corner within each governorate. During this process, shiakhas or villages with a population less than 2,500 were grouped with contiguous shiakhas or villages (usually within the same kism or marquez) to form units with a population of at least 5,000. After the frame was ordered, a total of 500 primary sampling units (228 shiakhas/towns and 272 villages) were selected.

The second stage of selection involved several steps. First, detailed maps of the PSUs chosen during the first stage were obtained and divided into parts of roughly equal population size (about 5,000). In shiakhas/towns or villages with a population of 20,000 or more, two parts were selected. In the remaining smaller shiakhas/towns or villages, only one part was selected. Overall, a total of 735 parts were selected from the shiakhas/towns and villages in the 2000 EDHS sample.

A quick count was then carried out to provide an estimate of the number of households in each part. This information was needed to divide each part into standard segments of about 200 households. A group of 37 experienced field workers participated in the quick count operation. They were organized into 13 teams, each consisting of 1 supervisor, 1 cartographer and 1 or 2 counters. A one-week training course conducted prior to the quick count included both classroom sessions and field practice in a shiakha/town and a village not covered in the survey. The quickcount operation took place between late March and May 1999.

As a quality control measure, the quick count was repeated in 10 percent of the parts. If the difference between the results of the first and second quick count was less than 2 percent, then the first count was accepted. No major discrepancies were found between the two counts in most of the areas for which the count was repeated.

After the quick count, a total of 1,000 segments were chosen from the parts in each shiakha/town and village in the 2000 EDHS sample (i.e., two segments were selected from each of the 500 PSUs). A household listing operation was then implemented in each of the selected segments. To conduct this operation, 12 supervisors and 24 listers were organized into 12 teams. Generally, each listing team consisted of a supervisor and two listers. A one-week training course for the listing staff was held in mid-September 1999. The training involved classroom lectures and two days of field practice in three urban and rural locations not covered in the survey. The listing operation began at the end of September and continued for about 40 days.

About 10 percent of the segments were relisted. Two criteria were used to select segments for relisting. First, segments were relisted when the number of households in the listing differed markedly from that expected according to the quick count information. Second, a number of segments were randomly selected to be relisted as an additional quality control test. Overall, few major discrepancies were found in comparisons of the listings. However, a third visit to the field was necessary in a few segments in the Cairo and Aswan governorates because of significant discrepancies between the results of the original listing and the relisting operation.

The third stage involved selecting the household sample. Using the household lists for each segment, a systematic random sample of households was selected for the 2000 EDHS sample. All ever-married women 15-49 who were usual residents or who were present in the sampled households on the night before the interview were eligible for the EDHS.

Note: See detailed description of sample design in APPENDIX B of the report which is presented in this documentation.

Mode of data collection

Face-to-face

Research instrument

The 2000 EDHS involved two questionnaires: a household questionnaire and an individual questionnaire. The household and individual questionnaires were based on the model survey instruments developed by MEASURE DHS+ for countries with high contraceptive prevalence. Questions on a number of topics not covered in the DHS model questionnaires were also included in the 2000 EDHS questionnaires. In some cases, those items were drawn from the questionnaires used for earlier rounds of the DHS in Egypt. In other cases, the questions were intended to collect information on topics not covered in the earlier surveys (e.g., schooling of children).

The household questionnaire consisted of three parts: a household schedule, a series of questions related to the socioeconomic status of the household, and height and weight measurement and anemia testing. The household schedule was used to list all usual household members and visitors and to identify those present in the household during the night before the interviewer’s visit. For each of the individuals included in the schedule, information was collected on the relationship to the household head, age, sex, marital status (for those 15 years and older), educational attainment, repetition and dropout (for those 6-24 years), and work status (for those 6 years and older). The second part of the household questionnaire obtained information on characteristics of the physical and social environment of the household (e.g., type of dwelling, availability of electricity, source of drinking water, household possessions, and the type of salt the household used for cooking). Height and weight measurements were obtained and recorded in the last part of the household questionnaire for all ever-married women age 15-49 years and all children born since January 1995 who were listed in the household schedule. In a subsample of households, all eligible women, all children born since January 1995, and all children age 11-19 years were eligible for anemia testing.

The individual questionnaire was administered to all ever-married women age 15-49 who were usual residents or who were present in the household during the night before the interviewer’s visit. It obtained information on the following topics: - Respondent’s background - Reproduction - Contraceptive knowledge and use - Fertility preferences and attitudes about family planning - Pregnancy and breastfeeding - Immunization and health - Schooling of children and child labor - Female circumcision - Marriage and husband’s background - Woman’s work and residence.

The individual questionnaire included a monthly calendar, which was used to record a history of the respondent’s fertility, contraceptive use (including the source where the method was obtained and the reason for discontinuation for each segment of use), and marriage status during each month of around a five-year period beginning

Abstract

The 1995 Egypt Demographic and Health Survey (EDHS-95) is part of the worldwide Demographic and Health Surveys project. It is the third survey in a series of Demographic and Health surveys that have been carried out in Egypt. The EDHS-95 collected information on fertility and child mortality, family planning awareness, approval and use, as well as basic information on maternal and child health.

The 1995 Egypt Demographic and Health Survey (EDHS-95) is aimed at providing policymakers and planners with important information for use in evaluating existing programs and formulating new programs and policies related to reproductive behavior and health. The survey was specifically designed to meet the following objectives: (1) Collect data on fertility and desired family size; (2) Monitor changes in family planning practice over time and investigate the availability and accessibility of family planning services in Egypt; (3) Determine reasons for nonuse and intention to use family planning; and (4) Measure the achievement of health policy objectives, particularly those concerning the GOE maternal and child health program.

In addition, because information on the status of women is of increasing interest to policymakers, the EDHS-95 included a special questionnaire to collect extensive data on the lives of Egyptian women. The questionnaire was administered to eligible women in one-third of the households in the EDHS-95 sample.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49

Kind of data

Sample survey data

Sampling procedure

Sample Design

The primary objective of the sample design for the EDHS-95 is to provide estimates of key population and health indicators including fertility and child mortality rates for the country as a whole and for six major administrative regions (Urban Governorates, urban Lower Egypt, rural Lower Egypt, urban Upper Egypt, rural Upper Egypt, and the Frontier Governorates). In addition, in the Urban Governorates, Lower Egypt and Upper Egypt, the design allows for governorate-level estimates of most key variables, with the exception of fertility and mortality rates and women's status indicators. In the Frontier Governorates, the sample size for individual governorates is not sufficiently large to allow for separate governorate-level estimates. However, separate estimates are possible for the western Frontier Governorates (Matrouh and New Valley) and the eastern Frontier Governorates (North Sinai, South Sinai and Red Sea). Finally, Assuit and Souhag governorates were oversampled in the EDHS-95 in order to provide sufficient cases for a special follow-up study of the reasons for nonuse of family planning in those areas.

In order to meet the survey objectives, the number of households selected in the EDHS-95 sample from each governorate was disproportional to the size of the population in the governorate. As a result, the EDHS-95 sample is not self-weighting at the national level, and weights had to be applied to the data to obtain the national-level estimates presented in this report.

Sample Implementation

Selection of PSUs: The EDHS-95 sample was selected in three stages. At the first or primary stage, the units of selection were shiakhas/towns in urban areas, and villages in rural areas. Information from the 1986 Census was used in constructing the frame from which the primary sampling units (PSU) were selected. Prior to the selection of the PSUs, the frame was updated to take into account all of the administrative changes which had occurred since 1986. The updating process included both office work and field visits during a three-month period. After it was completed, urban and rural units were stratified by geographical location in a serpentine order from the northwest comer to the southeast within each governorate. Shiakhas or villages with less than 2,500 populations were grouped with contiguous shiakhas or villages (usually within the same kism or marquez) to obtain the minimum size required (5,000 population). During the primary stage selection, a total of 467 units (204 shiakhas/towns and 263 villages) were sampled.

Quick Count: The second stage of selection involved several steps. First, detailed maps of the PSUs chosen during the first stage were obtained and divided into parts of roughly equal size. In shiakhas/towns or villages with 20,000 or more population, two parts were selected. In the remaining smaller shiakhas/towns or villages, only one part was selected. Overall, a total of 656 parts were selected from the shiakhas/towns and villages in the EDHS-95 sample.

A quick count was then carded out to divide each part into standard segments of about 200 households. This operation was conducted in order to provide an estimate of the number of households in each part so that the part could be divided into segments of roughly equal size. A group of 36 experienced field workers participated in the quick count operation. They were divided into 12 teams, each consisting of one supervisor, one cartographer and one or two counters. A one-week training course conducted prior to the quick count included both classroom sessions and field practice in a shiakha/town and a village not covered in the survey. The quick-count operation took place between late April and late July 1995.

As a quality control measure, the quick count was repeated in 10 percent of the parts. If the difference between the results of the first and second quick count were within 2 percent, then the first count was accepted. There were no major discrepancies between the two counts in most of the areas for which the count was repeated; however, in a few cases in Kafr El-Sheikh govemorate, a third visit was made to the field in order to resolve discrepancies between the counts.

Household Listing: Following the quick count, a total of 934 segments was chosen from the parts in each shiakha/town and village in the EDHS-95 sample (i.e., two segments were selected from each of the 467 PSUs). A household listing operation was then implemented in each of the selected segments. To conduct this operation, 16 supervisors and 32 listers were organized into 16 teams. Generally, each listing team consisted of a supervisor and two listers. A training course for the listing staff was held at the end of August for one week. The training involved classroom lectures and two days of field practice in two urban and rural locations. The listing operation began at the end of August and continued for about 40 days.

Around 10 percent of the segments were relisted. Two different criteria were used to select segments for relisting. First, segments were relisted when the number of households in the listing differed markedly from that expected according to the quick count information. Second, a number of segments were randomly selected to be relisted as an additional quality control test. Overall, few major discrepancies were found in comparisons of the two listings. However, a third visit to the field was necessary in a few segments in Gharbia governorate because of significant discrepancies between the results of the original listing and the relisting operation.

Selection of the Household Sample: Using the household lists for each segment, a systematic random sample of households was chosen to be interviewed in the EDHS-95. A subsample of one-third of these households was also selected for the woman's status survey, except in Assuit and Souhag governorates, where all households were included in the women's status survey. All ever-married women 15-49, who were usual residents or present in the household on the night before the interview, were eligible for the survey.

Note: See detailed description of sample design in APPENDIX B of the report which is presented in this documentation.

Mode of data collection

Face-to-face

Research instrument

The EDHS-95 involved three types of questionnaires: a household questionnaire, an individual questionnaire, and a women's status questionnaire. The household and individual questionnaires were based on the model survey instruments developed by the Demographic and Health Surveys program for high contraceptive prevalence countries. Additional questions on a number of topics not covered in the DHS mode/questionnaires were included in EDHS-95 questionnaires. In some cases, those items were drawn from the questionnaires used for the 1988 EDHS and the 1992 EDHS. In other cases, the questions were intended to collect information on topics not covered in the earlier surveys (e.g., schooling of children and female circumcision). The women's status questionnaire was based on a special set of modules developed in the DHS program to explore a number of dimensions of the status of women. The modules were modified to obtain data of interest in understanding the position of women in Egyptian society.

The household questionnaire consisted of two parts: a household schedule and a series of questions relating to the health and socioeconomic status of the household. The household schedule was used to list all usual household members and visitors and to identify those present in the household during the night before the interviewer's visit. For each of the individuals included in the schedule, information was collected on the relationship to the household head, age, sex, marital status (for those fifteen years and older), educational level and work status (for those six years and older). The second part of the household questionnaire included questions on characteristics of the physical and social environment of the household (e.g., type of dwelling, availability of electricity, source of drinking water, household possessions,

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Abstract

The Armenia Demographic and Health Survey (ADHS) was a nationally representative sample survey designed to provide information on population and health issues in Armenia. The primary goal of the survey was to develop a single integrated set of demographic and health data, the first such data set pertaining to the population of the Republic of Armenia. In addition to integrating measures of reproductive, child, and adult health, another feature of the DHS survey is that the majority of data are presented at the marz level.

The ADHS was conducted by the National Statistical Service and the Ministry of Health of the Republic of Armenia during October through December 2000. ORC Macro provided technical support for the survey through the MEASURE DHS+ project. MEASURE DHS+ is a worldwide project, sponsored by the USAID, with a mandate to assist countries in obtaining information on key population and health indicators. USAID/Armenia provided funding for the survey. The United Nations Children’s Fund (UNICEF)/Armenia provided support through the donation of equipment.

The ADHS collected national- and regional-level data on fertility and contraceptive use, maternal and child health, adult health, and AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well. Data are presented by marz wherever sample size permits.

The ADHS results are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of and health services for the people of Armenia. The ADHS also contributes to the growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-54

Kind of data

Sample survey data

Sampling procedure

The sample was designed to provide estimates of most survey indicators (including fertility, abortion, and contraceptive prevalence) for Yerevan and each of the other ten administrative regions (marzes). The design also called for estimates of infant and child mortality at the national level for Yerevan and other urban areas and rural areas.

The target sample size of 6,500 completed interviews with women age 15-49 was allocated as follows: 1,500 to Yerevan and 500 to each of the ten marzes. Within each marz, the sample was allocated between urban and rural areas in proportion to the population size. This gave a target sample of approximately 2,300 completed interviews for urban areas exclusive of Yerevan and 2,700 completed interviews for the rural sector. Interviews were completed with 6,430 women. Men age 15-54 were interviewed in every third household; this yielded 1,719 completed interviews.

A two-stage sample was used. In the first stage, 260 areas or primary sampling units (PSUs) were selected with probability proportional to population size (PPS) by systematic selection from a list of areas. The list of areas was the 1996 Data Base of Addresses and Households constructed by the National Statistical Service. Because most selected areas were too large to be directly listed, a separate segmentation operation was conducted prior to household listing. Large selected areas were divided into segments of which two segments were included in the sample. A complete listing of households was then carried out in selected segments as well as selected areas that were not segmented.

The listing of households served as the sampling frame for the selection of households in the second stage of sampling. Within each area, households were selected systematically so as to yield an average of 25 completed interviews with eligible women per area. All women 15-49 who stayed in the sampled households on the night before the interview were eligible for the survey. In each segment, a subsample of one-third of all households was selected for the men's component of the survey. In these households, all men 15-54 who stayed in the household on the previous night were eligible for the survey.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the ADHS: a Household Questionnaire, a Women’s Questionnaire, and a Men’s Questionnaire. The questionnaires were based on the model survey instruments developed for the MEASURE DHS+ program. The model questionnaires were adapted for use during a series of expert meetings hosted by the Center of Perinatology, Obstetrics, and Gynecology. The questionnaires were developed in English and translated into Armenian and Russian. The questionnaires were pretested in July 2000.

The Household Questionnaire was used to list all usual members of and visitors to a household and to collect information on the physical characteristics of the dwelling unit. The first part of the household questionnaire collected information on the age, sex, residence, educational attainment, and relationship to the household head of each household member or visitor. This information provided basic demographic data for Armenian households. It also was used to identify the women and men who were eligible for the individual interview (i.e., women 15-49 and men 15-54). The second part of the Household Questionnaire consisted of questions on housing characteristics (e.g., the flooring material, the source of water, and the type of toilet facilities) and on ownership of a variety of consumer goods.

The Women’s Questionnaire obtained information on the following topics: - Background characteristics - Pregnancy history - Antenatal, delivery, and postnatal care - Knowledge and use of contraception - Attitudes toward contraception and abortion - Reproductive and adult health - Vaccinations, birth registration, and health of children under age five - Episodes of diarrhea and respiratory illness of children under age five - Breastfeeding and weaning practices - Height and weight of women and children under age five - Hemoglobin measurement of women and children under age five - Marriage and recent sexual activity - Fertility preferences - Knowledge of and attitude toward AIDS and other sexually transmitted infections.

The Men’s Questionnaire focused on the following topics: - Background characteristics - Health - Marriage and recent sexual activity - Attitudes toward and use of condoms - Knowledge of and attitude toward AIDS and other sexually transmitted infections.

Cleaning operations

After a team had completed interviewing in a cluster, questionnaires were returned promptly to the National Statistical Service in Yerevan for data processing. The office editing staff first checked that questionnaires for all selected households and eligible respondents had been received from the field staff. In addition, a few questions that had not been precoded (e.g., occupation) were coded at this time. Using the ISSA (Integrated System for Survey Analysis) software, a specially trained team of data processing staff entered the questionnaires and edited the resulting data set on microcomputers. The process of office editing and data processing was initiated soon after the beginning of fieldwork and was completed by the end of January 2001.

Response rate

A total of 6,524 households were selected for the sample, of which 6,150 were occupied at the time of fieldwork. The main reason for the difference is that some of the dwelling units that were occupied during the household listing operation were either vacant or the household was away for an extended period at the time of interviewing. Of the occupied households, 97 percent were successfully interviewed.

In these households, 6,685 women were identified as eligible for the individual interview (i.e., age 15-49). Interviews were completed with 96 percent of them. Of the 1,913 eligible men identified, 90 percent were successfully interviewed. The principal reason for non-response among eligible women and men was the failure to find them at home despite repeated visits to the household. The refusal rate was low.

The overall response rates, the product of the household and the individual response rates, were 94 percent for women and 87 percent for men.

Note: See summarized response rates by residence (urban/rural) in Table 1.1 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2000 Armenia Demographic and Health Survey (ADHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the ADHS 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

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Champagne Market Size 2025-2029

The champagne market size is forecast to increase by USD 3.02 billion at a CAGR of 6.7% between 2024 and 2029.

The market is experiencing significant growth driven by the increasing demand from the millennial demographic. This generation's preference for premium and celebratory beverages, coupled with their affinity towards social media and e-commerce platforms, is fueling the market's expansion. Furthermore, the e-commerce sector's continued growth provides new opportunities for champagne brands to reach consumers directly and expand their customer base. However, this market is not without challenges. Increasing competition from other alcoholic beverages, particularly sparkling wines and cocktails, puts pressure on champagne producers to differentiate their offerings and maintain their market share. To capitalize on the market's opportunities and navigate these challenges effectively, companies must focus on innovation, branding, and targeted marketing strategies. By the evolving consumer preferences and market dynamics, champagne producers can position themselves for long-term success.

What will be the Size of the Champagne Market during the forecast period?

Request Free SampleThe market is a thriving sector of the global alcoholic beverage industry, characterized by its production of sparkling wines using the traditional Champagne method. This market primarily utilizes three grape varieties: Pinot Noir, Pinot Meunier, and Pinot Blanc. Demand for champagne is driven by its association with celebratory occasions, such as sporting events and joyous milestones, as well as corporate gatherings, office parties, and food service restaurants. Vintage wines, ruinart champagne, and champagne cocktails are popular offerings within this market. The market's growth is influenced by increasing consumer disposable income, changing demographics, and evolving consumer preferences. Overall, the market is expected to maintain a steady expansion, driven by its unique position as a premium and versatile alcoholic beverage.

How is this Champagne Industry segmented?

The champagne industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Distribution ChannelOfflineOnlinePrice RangeEconomyMid-rangeLuxuryGeographyEuropeFranceGermanyItalyUKNorth AmericaUSCanadaAPACChinaIndiaJapanSouth AmericaBrazilMiddle East and Africa

By Distribution Channel Insights

The offline segment is estimated to witness significant growth during the forecast period.The market encompasses the production, sales, and distribution of this iconic alcoholic beverage, primarily made from Pinot Noir, Pinot Meunier, and Pinot Blanc grape varieties. Champagne is renowned for its association with joyous occasions, sporting events, corporate gatherings, and luxury brands. The global market for Champagne and other sparkling wines is expanding, driven by increasing consumer demand for authentic flavors and eco-friendly packaging. Specialty stores, pubs, and restaurants are significant distribution channels, while online retailing, direct-to-consumer (DTC) channels, and airport expansions broaden the market's reach. Rosé Champagne, luxury brands, and vintage wines remain popular choices, as do non-alcoholic, low carb, and health-friendly beverages. Champagne houses and manufacturers prioritize sustainable production using artisanal methods and organic or natural ingredients. Key players include Ruinart Champagne, among others, offering gift packaging, wine cellars, and tasting events. The market research landscape includes reports from reputable firms like , , and .

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The Offline segment was valued at USD 5.22 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 35% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market size of various regions, Request Free Sample

The European region dominates The market, with France, Germany, and Italy being the leading consumers. This is largely due to the significant production of grape varieties such as Pinot Noir, Pinot Meunier, and Pinot Blanc in France. However, consumer preferences are evolving, with a trend towards lower alcohol content and healthier options. Many champagne manufacturers are responding by producing champagnes with authentic flavors, including fruity, sweet, and nutty notes. Champagne is often consumed during joyous occasions, sporting events, corporate gatherings, and office parties. The

Multi-atlas bundle segmentation

This data is made to be used with the following script:https://github.com/scilus/scilpy/blob/master/scripts/scil_tractogram_segment_with_bundleseg.pyOr the following Nextflow pipeline:https://github.com/scilus/rbx_flow

Etienne St-Onge, Kurt Schilling, Francois Rheault, "BundleSeg: A versatile, reliable and reproducible approach to whitte matter bundle segmentation.", arXiv, 2308.10958 (2023)Rheault, François. "Analyse et reconstruction de faisceaux de la matière blanche." Computer Science (Université de Sherbrooke) (2020), https://savoirs.usherbrooke.ca/handle/11143/17255

UsageHere is an example (for more details use scil_tractogram_segment_with_bundleseg.py -h) :

antsRegistrationSyNQuick.sh -d 3 -f ${T1} -m mni_masked.nii.gz -t a -n 4scil_tractogram_segment_with_bundleseg.py ${TRACTOGRAM} config_fss_1.json atlas/*/ output0GenericAffine.mat --out_dir ${OUTPUT_DIR}/ --log_level DEBUG --minimal_vote 0.4 --processes 8 --seed 0 --inverse -f

To facilitate interpretation, all endpoints were uniformized head/tail. To see, which side of a bundle is head or tail, you can load the atlas bundle into the software MI-Brain

Notes on bundles- AC and PC were added mostly in case the atlas is used for lesion-mapping or figures. Likely, segmentation won't produce good results. This is mostly due to difficult tracking for these bundles.- The CC are split for each lobe. However, for technical consideration, the frontal portion was split in two to facilitate clustering and segmentation. For the same reason, the portion fanning to the pre/post central gyri were separated.- The streamlines present in the CC are homotopic, Recobundles will allow for variation and thus lead to 'some' heterotopy. However, it is expected that the results will be mostly homotopic.- CG has 3 possible endpoint locations. However, the full extent of the tail is difficult to track and is often missing.- FPT and POPT should terminate in the pons. However, to fully capture candidate streamlines and improve segmentation quality even streamlines reaching down the brainstem are selected. - PYT should reach down the brainstem. For similar reasons to the FPT/POPT, streamlines ending in the pons are selected. Otherwise, fanning is affected and bundles is too skinny. - OR_ML will most likely have difficulty capturing the full ML. However, this is often due to difficult tracking.- The cerebellum is often cut due to acquisition FOV. In such a case, all projection bundles will be more difficult to recognize and most cerebellum bundles will be missing (ICP, MCP, SCP).

See Mosaic of bundles here.

AcronymAC - Anterior commisureAF - Arcuate fasciculusCC_Fr_1 - Corpus callosum, Frontal lobe (most anterior part)CC_Fr_2 - Corpus callosum, Frontal lobe (most posterior part)CC_Oc - Corpus callosum, Occipital lobeCC_Pa - Corpus callosum, Parietal lobeCC_Pr_Po - Corpus callosum, Pre/Post central gyriCC_Te - Corpus callosum, Temporal lobeCG - CingulumFAT - Frontal aslant tractFPT - Fronto-pontine tractFX - FornixICP - Inferior cerebellar peduncleIFOF - Inferior fronto-occipital fasciculusILF - Inferior longitudinal fasciculusMCP - Middle cerebellar peduncleMdLF - Middle longitudinal fascicleOR_ML - Optic radiation and Meyer's loopPC - Posterior commisurePOPT - parieto-occipito pontine tractPYT - Pyramidal tractSCP - Superior cerebellar peduncleSLF - Superior longitudinal fasciculusUF - Uncinate fasciculus