Description:This online mapping tool, provided by the Western Cape Government, is designed to assist with spatial information queries in the fields of population and demographics. The dashboard is provided through the Western Cape Government Open Data Portal For more information, please contact the Provincial Data Office (mailto://pdo@westerncape.gov.za).Linage:The data presented on this site originates from various sources and custodians. The demographic data is updated annually.Data Sources:StatsSA Census boundaries (2011)Demogaphic data supplied by ©GEOTERRAIMAGE – 2022Contact Person:julie.verhulp@westerncape.gov.za

This data set contains ratings of perceived and actual gender composition by 2010 U.S. Census codes. Perception data were collected via an online Qualtrics panel of 2,340 individual respondents averaged to the occupation level. Estimates of actual gender composition were derived from the CPS annual ASEC samples. The gender of 930,610 individuals were averaged to the level of the occupation for each year between 2015 and 2019 then each year's value was averaged across the five years.

Abstract

The CPS survey is a follow up survey designed to provide information that will help to evaluate the current situation and status of the police services which had been delivered to the public since 2010 which was conducted by NUS Consult Ltd. It aims to provide an enabling environment to facilitate quality policing services, and, for policing services to deliver the most effective and efficient services pertaining to public safety, crime, and, general policing within Samoa. The CPS 2013 is a nationally representative sample survey designed to collect data that would help to evaluate policing programs over the previous three years (2011-2013), and, to provide feedback and views from the community or public on the delivery of policing services to all.

Geographic coverage

National coverage Region

Analysis unit

Individuals 18 years of age and over were the units of analysis.

Universe

The target population was adults 18 years and older residing in the community.

Kind of data

Sample survey data [ssd]

Sampling procedure

In national statistical surveys, the region of Apia Urban Area (AUA) represented the urban population while the regions of North West Upolu (NWU), Rest of Upolu (ROU) and Savaii represented the rural population. The target population was adults 18 years and older residing in the community. Hence all persons 18+ who were usual residents of the household were eligible in the survey. The sample for the CPS 2013 was drawn from the master sample frame of the List of occupied households compiled in the most recent Population and Housing Census 2011. The sample size was based on a 95 percent confidence interval of ± 5 percent margin of error; assuming an 80 percent response rate; a design-effect of 1.5 to allow for clustering of the complex design; and; an average of 3 persons 18+ per household. The design also considered the features of a follow-up survey assuming that the policing services had improved by at least 10 percent since the baseline CPS 2010 results. After taking into account all those features, it resulted in the required sample size of 540 persons aged 18 years and older.

Therefore in order to achieve the sample size, a representative probability sample of households was selected in two stages The first stage involved the selection of clusters from the master sample frame using stratified systematic sampling with probability proportional to size. A total of 45 primary sampling units or clusters were selected in which 10 clusters were selected from the urban areas and 35 clusters were selected from the rural areas. The design did not allow for replacement of clusters or households.

In the second stage, a total of 5 households were selected from each cluster using systematic equal probability selection for inclusion in the survey. Normally an updated household listing from selected clusters could have been done to select 5 households. However, due to the unexpected request of the MPP survey in February 2013, and the fact that the census 2011 was just completed in the previous two years, it was seen not necessary to conduct a fresh household listing which would have taken SBS another two months to carry out causing delay to the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Based on the request by MPP, a structured English questionnaire which was used in the CPS 2010 was also implemented in the CPS 2013. However, SBS made some improvements in terms of instructions between questionnaire sections in order to make the interviewing flow properly from beginning to end. The questionnaire was also translated into the Samoan language to complement the English questionnaire so that the interpretation of questions by the field enumerators was consistent on the field. In addition, since probability sampling and complex design was used, a new cover page of the questionnaire was developed so that selected clusters and household identification were clearly made and that features to account for non-coverage of the households and non-response of eligible persons 18 years and older were also accounted for during the fieldwork. The questionnaire therefore has five parts. Section A has 5 questions about the respondent details. Section B contains 7 questions intended to find out the community's views on safety and crime plus an open question for the respondents to express their own opinions about the topic. Section C contains 7 questions to find out the respondents personal awareness and experience (if any) of crime and crime management. An open question was also added for their own opinions. Section D contains 4 sub-sections (D1 to D4) which seek questions on the overall community perception of the MPP services whereby each sub-section also contained open-questions. D1 contains 3 questions about the assistance sought by the community at the MPP. D2 contains 2 questions about the control of crime. D3 contains 4 questions about police service in general and D4 contains 2 questions about the access to police information, education and communication. Section E is the last section which was again open to the respondents to make their own suggestions on how police can improve their job to assist the community.

Cleaning operations

The data editing, cleaning and weighting of the data took another two weeks (June 4-14) to complete, leaving only two weeks (June 17-30) to analyse and write the analysis report to meet the deadline.

Response rate

A total of 225 households were selected for the sample of which 216 were found occupied at the time of the survey. Of the occupied households, only 209 were successfully interviewed resulting in a household response rate of 96.8 percent. The other households were not available throughout the survey period despite repeated visits by the field teams. The total eligible persons 18 years and older who were found in the interviewed households were 731. Out of this total, only 645 were successfully interviewed yielding the Individual response rate of 88.2 percent. The response rates by sex shows 90 percent for females and 87 percent for males which is not surprising as males usually spent more time away from home than females. That situation was reflected more in the regions of North West Upolu and Rest of Upolu than in Savaii and Apia Urban Area. The high response rate in Savaii was due mainly to the field teams sleeping over in Savaii for one week. Therefore, the Savaii team had extra time after hours to make call-backs while the team in Upolu had to commute daily until 7pm for repeated visits, hence missed out on those who come home later.

Summary

The 2024 APS Employee Census was administered to eligible Australian Public Service (APS) employees between 6 May and 7 June 2024. Overall, 140,396 APS employees responded to the APS Employee Census in 2024, a response rate of 81%.

The APS Employee Census is an annual employee perception survey of the Australian Public Service workforce. The APS Employee Census has been conducted since 2012 and collects APS employee opinions and perspectives on a range of topics, including employee engagement, wellbeing, and leadership.

The APS Employee Census provides a comprehensive view of the APS and ensures no eligible respondents are omitted from the survey sample, removing sampling bias and reducing sample error.

Please be aware that the very large number of respondents to the APS Employee Census means these files are over 200MB in size.

Downloading and opening these files may take some time.

Technical notes

Three files are available for download.

• 2024 APS Employee Census - Questionnaire: This contains the 2024 APS Employee Census questionnaire.

• 2024 APS Employee Census - 5 point dataset with data values: This CSV file contains individual responses to the 2024 APS Employee Census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document. Data in this file are presented as data values.

• 2024 APS Employee Census - 5 point dataset with data labels: This CSV file contains individual responses to the 2024 APS Employee Census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document. Data in this file are presented as data labels.

• 2024 APS Employee Census - 5 point dataset.sav: This file contains individual responses to the 2024 APS Employee Census for use with the SPSS software package.

• 2024 APS Employee Census - data dictionary: This file contains a list of variables and labels within the APS Employee Census.

To protect the privacy and confidentiality of respondents to the 2024 APS Employee Census, the datasets provided on data.gov.au include responses to a limited number of demographic or other attribute questions.

Full citation of this dataset should list the Australian Public Service Commission (APSC) as the author.

A recommended short citation is: 2024 APS Employee Census, Australian Public Service Commission.

Any queries can be directed to research@apsc.gov.au.

Abstract (en): The Tangail Survey (TS) constitutes a household level census of three contiguous villages in the Tangail district of Dhaka, Bangladesh. In addition to standard socioeconomic and demographic information (e.g. marital status, employment, age, gender, living conditions, etc.), the TS gathers information on international migration, perceptions of respondent's economic position relative to different reference groups (e.g. neighbors, colleagues, siblings, etc.), subjective well-being, and household GPS location coordinates. Data on roughly 1,400 households were collected in a single wave during the summer of 2013. On 2016-08-20, the openICPSR web site was moved to new software. In the migration process, some projects were not published in the new system because the decisions made in the old site did not map easily to the new setup. An ICPSR staff member manually published these projects, taking care to preserve the original wishes of the depositor. Funding insitution(s): Carleton College (Dean's Office Discretionary Grant). Carleton College (Department of Economics).

This study provides cross-sectional data from a nationally representative survey that collected data on public perceptions of artists. The data were collected in March 2017, August 2019, and April 2022; the development of the survey instrument and results from its pilot test are documented in Novak-Leonard and Skaggs (2017). In each wave, a core set of questions is repeated while additional wave-specific questions are uniquely added on matters germane to arts policy within the United States. Each survey wave has been administered through the AmeriSpeak Panel, a probability-based panel designed to be representative of the U.S. household population, operated by NORC at the University of Chicago. The survey was offered in both in English and Spanish and fielded through both a web-based survey and phone interviews. The data are weighted using sampling weights provided by AmeriSpeak, accounting for age, gender, U.S. Census geographic divisions, education levels, and race/ethnicity. In 2017, the resulting sample was 1,110 adult respondents (age 18 and older); in 2019, the resulting sample contained 1,002 adult respondents; and, in 2022, 1,033 adult respondents.

Street tree data from the TreesCount! 2015 Street Tree Census, conducted by volunteers and staff organized by NYC Parks & Recreation and partner organizations. Tree data collected includes tree species, diameter and perception of health. Accompanying blockface data is available indicating status of data collection and data release citywide.

The 2015 tree census was the third decadal street tree census and largest citizen science initiative in NYC Parks’ history. Data collection ran from May 2015 to October 2016 and the results of the census show that there are 666,134 trees planted along NYC's streets. The data collected as part of the census represents a snapshot in time of trees under NYC Parks' jurisdiction.

The census data formed the basis of our operational database, the Forestry Management System (ForMS) which is used daily by our foresters and other staff for inventory and asset management: https://data.cityofnewyork.us/browse?sortBy=most_accessed&utf8=%E2%9C%93&Data-Collection_Data-Collection=Forestry+Management+System+%28ForMS%29

To learn more about the data collected and managed in ForMS, please refer to this user guide: https://docs.google.com/document/d/1PVPWFi-WExkG3rvnagQDoBbqfsGzxCKNmR6n678nUeU/edit. For information on the city's current tree population, use the ForMS datasets.

All taken form site: https://data.cityofnewyork.us/Environment/2015-Street-Tree-Census-Tree-Data/uvpi-gqnh/about_data

Summary The 2024 APS Employee Census was administered to eligible Australian Public Service (APS) employees between 6 May and 7 June 2024. Overall, 140,396 APS employees responded to the APS …Show full description##Summary The 2024 APS Employee Census was administered to eligible Australian Public Service (APS) employees between 6 May and 7 June 2024. Overall, 140,396 APS employees responded to the APS Employee Census in 2024, a response rate of 81%. The APS Employee Census is an annual employee perception survey of the Australian Public Service workforce. The APS Employee Census has been conducted since 2012 and collects APS employee opinions and perspectives on a range of topics, including employee engagement, wellbeing, and leadership. The APS Employee Census provides a comprehensive view of the APS and ensures no eligible respondents are omitted from the survey sample, removing sampling bias and reducing sample error. Please be aware that the very large number of respondents to the APS Employee Census means these files are over 200MB in size. Downloading and opening these files may take some time. ##Technical notes Three files are available for download. • 2024 APS Employee Census - Questionnaire: This contains the 2024 APS Employee Census questionnaire. • 2024 APS Employee Census - 5 point dataset with data values: This CSV file contains individual responses to the 2024 APS Employee Census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document. Data in this file are presented as data values. • 2024 APS Employee Census - 5 point dataset with data labels: This CSV file contains individual responses to the 2024 APS Employee Census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document. Data in this file are presented as data labels. • 2024 APS Employee Census - 5 point dataset.sav: This file contains individual responses to the 2024 APS Employee Census for use with the SPSS software package. • 2024 APS Employee Census - data dictionary: This file contains a list of variables and labels within the APS Employee Census. To protect the privacy and confidentiality of respondents to the 2024 APS Employee Census, the datasets provided on data.gov.au include responses to a limited number of demographic or other attribute questions. Full citation of this dataset should list the Australian Public Service Commission (APSC) as the author. A recommended short citation is: 2024 APS Employee Census, Australian Public Service Commission. Any queries can be directed to research@apsc.gov.au.

Abstract

The survey was one of three components of a World Bank project implemented to provide information on the size and composition of the civil service, improve systems and control mechanisms, institutional capacity, and provide information on policy-formulation and decision-making processes. Other components included a census of Guatemalan civil servants and contractors, and the continuous updating and use of this information to strengthen checks and improve transparency, and a new policy framework aimed at strengthening the institutional capacity of the Guatemalan civil service.

The aim of the survey was to assess the characteristics and quality of human resource management in the public administration, as well as to capture the attitudes, motivations, and experiences of public officials. In particular, the survey focused on the priority areas for reform identified by the Government of Guatemala and the World Bank. The data collected was used to support the World Bank’s diagnostic of key problem areas in the human resource management of the public administration in Guatemala. It was used to inform the design of institution-level interventions, as well as the new public policy framework.

Geographic coverage

The target population were civil servants across 18 institutions in Guatemala at the central, and their respective departmental and municipal branches.

Analysis unit

Public servants (managers and non-managers) across 18 institutions in Guatemala at the central, and their respective departmental and municipal branches.

Kind of data

Aggregate data [agg]

Sampling procedure

The sample frame used comes from the frame used for the Human Resources National Census. It has the list of positions in all the units of the 18 institutions selected for this study. The sample size for the managerial level was calculated with a 95% confidence level and a 5% margin error for each institution. For the non-managers, it was calculated with the same confidence level and margin error. The sample sizes are adjusted so the sample would have an even number for each study domain for the experiment which will assign a different questionnaire to half of the respondents.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey questionnaire comprises following modules: 1- Pre-interview questions, 2- Demographic and work history information, 3- Management practices, 4- Performance evaluation, 5- perceptions about discrimination, 6- Human resources management practices, 7- Perceptions of the national office of the civil service, 8- Perception of acts of corruption, and 9- Review of surveys.

The questionnaire was prepared in English and Spanish.

Response rate

Response rate was 96%.

Attitude of the German population to the census after census day, 31 May 1987. Political attitudes.

Topics: political interest; satisfaction with democracy in the Federal Republic; difficulties in taking care of official matters; sympathy scale for political parties; attitude to the census; conversations about the census in social surroundings and topics discussed; attitude to the census in circle of friends and acquaintances; perceived attitude of the population to the census; conversation contact with the canvasser; questionnaire filled out oneself or by canvasser; individually filling out questionnaire or information for entire household from a single person; duration of filling it out; response or boycott behavior during the survey; type of questionnaire return; attitude to the question program of the census and personal change in attitude from knowledge of the questionnaire; intent to participate; response or boycott behavior in circle of friends and acquaintances; topics noticed in media reporting about the census and tendency of content of these articles; attitude to use of canvassers; assessment and importance of trustworthiness of canvasser; attitude to punishment of census boycotters as well as persons calling for boycott; attitude to the requirement to provide information in the census (scale); attitude to the boycott and to criticism of the census (scale); attitude to government statistics; personal concerns regarding misuse of personal census data; trust in observance of data protection; knowledge about the office for data protection and the office of the data protection official.

Demography: month of birth of respondent; sex; marital status; number of children; ages of children (classified); religious denomination; frequency of church attendance; school education; vocational training; occupation; occupational position; employment; monthly gross income of respondent and household altogether; number of persons contributing to household income; size of household; position of respondent in household; characteristics of head of household; number of persons eligible to vote in household; persons in household who do not have German citizenship; self-assessment of social class; union membership of respondent and other members of household; possession of a telephone.

Interviewer rating: presence of third persons during interview and person desiring this presence; intervention of others in interview and person introducing this intervention; attitude to the census of additional persons present during interview; presence of further persons in other rooms; willingness to cooperate and reliability of respondent.

Also encoded was: length of interview; date of interview; identification of interviewer; sex of interviewer; age of interviewer.

Distribution of demographics in the two sample splits (evaluation of single pig and pen pictures first (n = 489) and evaluation of combined pictures first (n = 530)) in comparison to census data from Germany.

Researchers at Utah State University created a short survey instrument to gather information about the views and concerns of Utah residents related to water issues. This survey was designed to give the public a chance to share their perceptions and concerns about water supply, water quality, and other related issues. While finding out what the ‘average citizen’ feels about key water issues was one goal of the project, the most interesting and important results are found in exploring ways in which perspectives about water vary across the population based on where people live and their demographic background (gender, age, education, etc.). This survey helps bring a voice to groups of citizens typically not represented in water policy debates. The findings have been and continue to be shared with water managers and decision makers who are planning for local and state water system sustainability.

This survey effort is also a key outreach and education component of the iUTAH project. High school groups, college and university classes, and others are invited to collaborate with iUTAH faculty to conduct public intercept surveys. Co-collection and analysis of survey data provides a hands-on learning opportunity about the principles of social science research. This effort helps increase awareness about the complexity of water issues in Utah, and the methods through which scientists learn about the public’s thoughts and concerns. Between July 2014 and April 2016, the survey has been implemented with collaborating students and faculty from the University of Utah, Utah Valley University, Weber State University, Salt Lake Community College, Southern Utah University, Dixie State University, and Snow College.

The survey involved using a structured protocol to randomly approach adults entering grocery stores in communities across the state, and inviting them to complete a 3-minute questionnaire about thier perceptions and concerns about water issues in Utah. The survey was self-administered on an iPad tablet and uploaded to a web server using the Qualtrics Offline App.

The project generated responses from over 7,000 adults, with a response rate of just over 42% . Comparisons of the respondents with census data suggest that they are largely representative of the communities where data were collected and of the state's adult population.

The data are anonymous and are available as a public dataset here. The data also served as the basis for the development of an open-source web-based survey data viewer that can be found at: http://data.iutahepscor.org/surveys/ and were also reported in Jones et al. (2016). We encourage users to use the viewer to explore the survey results.

The files below include a document describing in detail the method/protocol used in the study, and copies of field materials we used to implement the project. We also include copies of the full dataset and a codebook in various formats.

Street tree data from the TreesCount! 2015 Street Tree Census, conducted by volunteers and staff organized by NYC Parks & Recreation and partner organizations. Tree data collected includes tree species, diameter and perception of health. Accompanying blockface data is available indicating status of data collection and data release citywide. The 2015 tree census was the third decadal street tree census and largest citizen science initiative in NYC Parks’ history. Data collection ran from May 2015 to October 2016 and the results of the census show that there are 666,134 trees planted along NYC's streets. The data collected as part of the census represents a snapshot in time of trees under NYC Parks' jurisdiction. The census data formed the basis of our operational database, the Forestry Management System (ForMS) which is used daily by our foresters and other staff for inventory and asset management. To learn more about the data collected and managed in ForMS, please refer to this user guide: https://docs.google.com/document/d/1PVPWFi-WExkG3rvnagQDoBbqfsGzxCKNmR6n678nUeU/edit. For information on the city's current tree population, use the ForMS datasets.

Attitude of the West German population to the census.

Summary of the data sets of a five-wave trend survey from ZA Study Nos. 1583 to 1587, insofar as it touches on the census topic.

Topics: 1. From the first wave (ZA Study No. 1583): political interest; satisfaction with democracy in the Federal Republic; difficulties in completing official matters; sympathy scale for political parties; attitude to the census and intent to participate; knowledge about the legal obligation to participate; personal consequences of non-participation; readiness to participate given threat of fine; response or boycott conduct in the survey; time of last noticed media reports about the census and content tendency of these articles; conversations about the census in social surroundings and time of last conversation; living together with a partner and his attitude to the census; assumed participation of partner in the census; attitude to the census in circle of friends and acquaintances as well as their assumed willingness to participate; perceived attitude of the population as well as parties, churches and trade unions on the census; knowledge about the survey procedure; attitude to government statistics; personal fears regarding misuse of personal census data; trust in observance of data protection; attitude to the census in 1983; willingness to participate in a microcensus survey; attitude to technology; self-assessment on a left-right continuum; knowledge about the office for data protection and the data protection official; earlier participation in a survey and type of survey; attitude to opinion polls (scale).

Demography: month of birth of respondent; sex; marital status; number of children; ages of children (classified); religious denomination; frequency of church attendance; school education; vocational training; occupation; occupational position; employment; monthly net income of respondent and household altogether; number of persons contributing to household income; size of household; position of respondent in household; characteristics of head of household; number of persons eligible to vote in household; persons in household who do not have German citizenship; self-assessment of social class; union membership of respondent and other members of household; possession of a telephone.

Interviewer rating: presence of third persons during interview and person desiring this presence; intervention of others in interview and person introducing intervention; attitude to the census of additional persons present during interview; presence of further persons in other rooms; willingness to cooperate and reliability of respondent.

Also encoded was: length of interview; date of interview; identification of interviewer; sex of interviewer; age of interviewer Test.

1. From the second wave (ZA Study No. 1584): political interest; satisfaction with democracy in the Federal Republic; difficulties in completing official matters; sympathy scale for political parties; attitude to the census; intent to participate in the census; knowledge about the legal obligation to participate; personal consequences of non-participation; readiness to participate given threat of fine; response or boycott conduct in the survey; time of last noticed media reports about the census and content tendency of these articles; conversations about the census in social surroundings and time of last conversation; living together with a partner and his attitude to the census; assumed participation of partner in the census; attitude to the census in circle of friends and acquaintances as well as their assumed willingness to participate; perceived attitude of the population as well as parties, churches and trade unions to the census; knowledge about the contents of the census survey; knowledge about the survey procedure; attitude to government statistics; personal fears regarding misuse of personal census data; trust in observance of data protection; attitude to the census in 1983; perception of advertising actions for and against the census and judgement on the argumentation of supporters and opponents; attitude to technology; self-assessment on a left-right continuum; knowledge about the office for data protection and the data protection official; frequency of conversations about politics; ability to establish contacts; party preference; behavior at the polls in the last Federal Parliament election.

Demography: month of birth of respondent; sex; marital status; number of ch...

Street tree data from the TreesCount! 2015 Street Tree Census, conducted by volunteers and staff organized by NYC Parks & Recreation and partner organizations. As of June 2016, mapping is still in progress – this is a partial release. Tree data collected includes tree species, diameter and perception of health. Accompanying blockface data is available indicating status of data collection and data release citywide.

BackgroundTuberculosis (TB) is the world’s major public health problem. We assessed the proportion, reasons, and associated factors for anti-TB treatment nonadherence in the communities in Indonesia.MethodsThis national coverage cross-sectional survey was conducted from 2013 to 2014 with stratified multi-stage cluster sampling. Based on the region and rural-urban location. The 156 clusters were distributed in 136 districts/cities throughout 33 provinces, divided into three areas. An eligible population of age ≥15 was interviewed to find TB symptoms and screened with a thorax x-ray. Those whose filtered result detected positive followed an assessment of Sputum microscopy, LJ culture, and Xpert MTB/RIF. Census officers asked all participants about their history of TB and their treatment—defined Nonadherence as discontinuation of anti-tuberculosis treatment for

New York City’s tree inventory includes 592,130 publicly managed street trees. This represents 584,036 live trees and 8,036 standing dead trees tallied over the course of two summer inventory periods. The inventory contains 168 tree species with London planetree (Platanus acerifolia), Norway maple (Acer platanoides), callery pear (Pyrus calleryana), honeylocust (Gleditsia triacanthos) and pin oak (Quercus palustris) as the predominant species. 2015 Street Tree Census, conducted by volunteers and staff organized by NYC Parks & Recreation and partner organizations. Tree data collected includes tree species, diameter and perception of health. Ref: https://data.cityofnewyork.us/Environment/2015-Street-Tree-Census-Tree-Data/uvpi-gqnh/data See also: https://www.nycgovparks.org/trees/treescount

Definitions of “urban” and “rural” are abundant in government, academic literature, and data-driven journalism. Equally abundant are debates about what is urban or rural and which factors should be used to define these terms. Absent from most of this discussion is evidence about how people perceive or describe their neighborhood. Moreover, as several housing and demographic researchers have noted, the lack of an official or unofficial definition of suburban obscures the stylized fact that a majority of Americans live in a suburban setting. In 2017, the U.S. Department of Housing and Urban Development added a simple question to the 2017 American Housing Survey (AHS) asking respondents to describe their neighborhood as urban, suburban, or rural. This service provides a tract-level dataset illustrating the outcome of analysis techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural.

To create this data, analysts first applied machine learning techniques to the AHS neighborhood description question to build a model that predicts how out-of-sample households would describe their neighborhood (urban, suburban, or rural), given regional and neighborhood characteristics. Analysts then applied the model to the American Community Survey (ACS) aggregate tract-level regional and neighborhood measures, thereby creating a predicted likelihood the average household in a census tract would describe their neighborhood as urban, suburban, and rural. This last step is commonly referred to as small area estimation. The approach is an example of the use of existing federal data to create innovative new data products of substantial interest to researchers and policy makers alike.

If aggregating tract-level probabilities to larger areas, users are strongly encouraged to use occupied household counts as weights.

We recommend users read Section 7 of the working paper before using the raw probabilities. Likewise, we recognize that some users may:

prefer to use an uncontrolled classification, or

prefer to create more than three categories.

To accommodate these uses, our final tract-level output dataset includes the "raw" probability an average household would describe their neighborhood as urban, suburban, and rural. These probability values can be used to create an uncontrolled classification or additional categories.

The final classification is controlled to AHS national estimates (26.9% urban; 52.1% suburban, 21.0% rural).

  For more information about the 2017 AHS Neighborhood Description Study click on the following visit: https://www.hud.gov/program_offices/comm_planning/communitydevelopment/programs/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. 

Data Dictionary: DD_Urbanization Perceptions Small Area Index.

Background and aimPublic health and social measures (PHSM) are critical aspects of limiting the spread of infections in pandemics. Compliance with PHSM depends on a wide range of factors, including behavioral determinants such as emotional response, trust in institutions or risk perceptions. This study examines self-reported compliance with PHSM during the COVID-19 pandemic in the Federation of Bosnia and Herzegovina (FBIH).Materials and methodsWe analyze the association between compliance and behavioral determinants, using data from five cross-sectional surveys that were conducted between June 2020 and August 2021 in FBIH. Quota-based sampling ensured that the 1000 people per wave were population representative regarding age, sex, and education level based on the data from the latest census in Bosnia and Herzegovina. One-way analysis of variance (ANOVA) was used to identify significant changes between studies on determinants and PHSM measures. Regression was used to find relations between behavioral determinants and PHSM.ResultsParticipants reported strong emotional responses to the rapid spread of the virus and its proximity to them. Risk perception was spiking in December 2020 when rates of infection and death were particularly high. Trends in policy acceptance were divergent; participants did not rate PHSM as exaggerated, but perceived fairness was low. Trust in institutions was low across all waves and declined for specific institutions such as the health ministry. In five wave-specific regression analyses, emotional response (βmin/max = .11*/.21*), risk perception (βmin/max = .06/.18*), policy acceptance (βmin/max = .09/.20*), and trust in institutions (βmin/max = .06/.21*) emerged as significant predictors of PHSM.ConclusionsThis study contributes to the body of research on factors influencing compliance with PHSM. It emphasizes the importance of behavioral monitoring through repeated surveys to understand and improve compliance. The study also affirms the impact of public trust on compliance, the risk of eroding compliance over time, and the need for health literacy support to help reinforce protective behaviors.

Frequency of participation in surveys. Experiences with market and opinion research institutes.

Topics: Frequency of participation in surveys up to now; type of interview and institute conducting; survey topic; judgement on the interview as pleasant or unpleasant; positive or negative characteristics of the interview; preferred survey technique; comparison of perceived disturbance from a hypothetical telephone question from the census bureau, a charity organization, a political party, an opinion research institute, an insurance salesman or a car dealer; calls received in the framework of telephone marketing; general willingness to respond to selected topic areas; interest in responding to surveys; assessment of data protection with surveys; perceived annoyances from surveys; attitude to private market research institutes; degree of familiarity of selected market and opinion research institutes; party preference; city size.