GapMaps GIS Data sourced from Applied Geographic Solutions includes over 40k Demographic variables across topics including estimates & projections on population, demographics, neighborhood segmentation, consumer spending, crime index & environmental risk available at census block level.

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.

Segments and demographic variables predicting Covid-19 protective behaviors.

Vaccination status and past two-week protective behavior by segment.

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.

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

Hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

ABSTRACT The electronic games industry is a new, dynamic, and fast-growing economic sector. However, organizations in this industry do not know the profile of their consumers. In view of this knowledge gap, the objective of this research paper is to analyze groups of electronic games consumers in the Brazilian market, in terms of their socio-demographic, behavioral, and expenditure characteristics. Using market segmentation literature and motivational variables found in games literature, this paper uses self-organizing maps and analysis of variance to segment 601 Brazilian gamers. The results demonstrate the existence of five different groups of games players and that, in order to reach each group, different strategies need to be used. The first group consists of t players who play all the time. The second has the same features as the first, but they do not have the same amount of time available to play. The third group consists of pro players. The fourth group and fifth group are the new challenge for games companies.

Patient demographics and CT information.

Aerial photography is a long-range, non-contact method of target detection technology that enables qualitative or quantitative analysis of the target. However, aerial photography images generally have certain chromatic aberration and color distortion. Therefore, effective segmentation of aerial images can further enhance the feature information and reduce the computational difficulty for subsequent image processing. In this paper, we propose an improved version of Golden Jackal Optimization, which is dubbed Helper Mechanism Based Golden Jackal Optimization (HGJO), to apply multilevel threshold segmentation to aerial images. The proposed method uses opposition-based learning to boost population diversity. And a new approach to calculate the prey escape energy is proposed to improve the convergence speed of the algorithm. In addition, the Cauchy distribution is introduced to adjust the original update scheme to enhance the exploration capability of the algorithm. Finally, a novel “helper mechanism” is designed to improve the performance for escape the local optima. To demonstrate the effectiveness of the proposed algorithm, we use the CEC2022 benchmark function test suite to perform comparison experiments. the HGJO is compared with the original GJO and five classical meta-heuristics. The experimental results show that HGJO is able to achieve competitive results in the benchmark test set. Finally, all of the algorithms are applied to the experiments of variable threshold segmentation of aerial images, and the results show that the aerial photography images segmented by HGJO beat the others. Noteworthy, the source code of HGJO is publicly available at https://github.com/Vang-z/HGJO.

The mean and standard deviation of the DSC, LVR, and H95 for FreeSurfer, RUDOLPH and the proposed CNN pipeline on the entire ventricular system (Entire), the left lateral ventricle (LLV), the right lateral ventricle (RLV), the third ventricle (3rd) and the fourth ventricle (4th). A paired Wilcoxon signed-rank test was used to obtain the p-values for determining statistical significance.

The mean and standard deviation for DSC, LVR, and L-F1 for the WMH segmentations from FreeSurfer, LGA, LPA, and SegAE. A paired Wilcoxon signed-rank test was used to obtain the p-values for determining statistical significance.