analyze the american community survey (acs) with r and monetdb experimental. think of the american community survey (acs) as the united states' census for off-years - the ones that don't end in zero. every year, one percent of all americans respond, making it the largest complex sample administered by the u.s. government (the decennial census has a much broader reach, but since it attempts to contact 100% of the population, it's not a sur vey). the acs asks how people live and although the questionnaire only includes about three hundred questions on demography, income, insurance, it's often accurate at sub-state geographies and - depending how many years pooled - down to small counties. households are the sampling unit, and once a household gets selected for inclusion, all of its residents respond to the survey. this allows household-level data (like home ownership) to be collected more efficiently and lets researchers examine family structure. the census bureau runs and finances this behemoth, of course. the dow nloadable american community survey ships as two distinct household-level and person-level comma-separated value (.csv) files. merging the two just rectangulates the data, since each person in the person-file has exactly one matching record in the household-file. for analyses of small, smaller, and microscopic geographic areas, choose one-, three-, or fiv e-year pooled files. use as few pooled years as you can, unless you like sentences that start with, "over the period of 2006 - 2010, the average american ... [insert yer findings here]." rather than processing the acs public use microdata sample line-by-line, the r language brazenly reads everything into memory by default. to prevent overloading your computer, dr. thomas lumley wrote the sqlsurvey package principally to deal with t his ram-gobbling monster. if you're already familiar with syntax used for the survey package, be patient and read the sqlsurvey examples carefully when something doesn't behave as you expect it to - some sqlsurvey commands require a different structure (i.e. svyby gets called through svymean) and others might not exist anytime soon (like svyolr). gimme some good news: sqlsurvey uses ultra-fast monetdb (click here for speed tests), so follow the monetdb installation instructions before running this acs code. monetdb imports, writes, recodes data slowly, but reads it hyper-fast . a magnificent trade-off: data exploration typically requires you to think, send an analysis command, think some more, send another query, repeat. importation scripts (especially the ones i've already written for you) can be left running overnight sans hand-holding. the acs weights generalize to the whole united states population including individuals living in group quarters, but non-residential respondents get an abridged questionnaire, so most (not all) analysts exclude records with a relp variable of 16 or 17 right off the bat. this new github repository contains four scripts: 2005-2011 - download all microdata.R create the batch (.bat) file needed to initiate the monet database in the future download, unzip, and import each file for every year and size specified by the user create and save household- and merged/person-level replicate weight complex sample designs create a well-documented block of code to re-initiate the monet db server in the future fair warning: this full script takes a loooong time. run it friday afternoon, commune with nature for the weekend, and if you've got a fast processor and speedy internet connection, monday morning it should be ready for action. otherwise, either download only the years and sizes you need or - if you gotta have 'em all - run it, minimize it, and then don't disturb it for a week. 2011 single-year - analysis e xamples.R run the well-documented block of code to re-initiate the monetdb server load the r data file (.rda) containing the replicate weight designs for the single-year 2011 file perform the standard repertoire of analysis examples, only this time using sqlsurvey functions 2011 single-year - variable reco de example.R run the well-documented block of code to re-initiate the monetdb server copy the single-year 2011 table to maintain the pristine original add a new age category variable by hand add a new age category variable systematically re-create then save the sqlsurvey replicate weight complex sample design on this new table close everything, then load everything back up in a fresh instance of r replicate a few of the census statistics. no muss, no fuss replicate census estimates - 2011.R run the well-documented block of code to re-initiate the monetdb server load the r data file (.rda) containing the replicate weight designs for the single-year 2011 file match every nation wide statistic on the census bureau's estimates page, using sqlsurvey functions click here to view these four scripts for more detail about the american community survey (acs), visit: < ul> the us census...

analyze the survey of income and program participation (sipp) with r if the census bureau's budget was gutted and only one complex sample survey survived, pray it's the survey of income and program participation (sipp). it's giant. it's rich with variables. it's monthly. it follows households over three, four, now five year panels. the congressional budget office uses it for their health insurance simulation . analysts read that sipp has person-month files, get scurred, and retreat to inferior options. the american community survey may be the mount everest of survey data, but sipp is most certainly the amazon. questions swing wild and free through the jungle canopy i mean core data dictionary. legend has it that there are still species of topical module variables that scientists like you have yet to analyze. ponce de león would've loved it here. ponce. what a name. what a guy. the sipp 2008 panel data started from a sample of 105,663 individuals in 42,030 households. once the sample gets drawn, the census bureau surveys one-fourth of the respondents every four months, over f our or five years (panel durations vary). you absolutely must read and understand pdf pages 3, 4, and 5 of this document before starting any analysis (start at the header 'waves and rotation groups'). if you don't comprehend what's going on, try their survey design tutorial. since sipp collects information from respondents regarding every month over the duration of the panel, you'll need to be hyper-aware of whether you want your results to be point-in-time, annualized, or specific to some other period. the analysis scripts below provide examples of each. at every four-month interview point, every respondent answers every core question for the previous four months. after that, wave-specific addenda (called topical modules) get asked, but generally only regarding a single prior month. to repeat: core wave files contain four records per person, topical modules contain one. if you stacked every core wave, you would have one record per person per month for the duration o f the panel. mmmassive. ~100,000 respondents x 12 months x ~4 years. have an analysis plan before you start writing code so you extract exactly what you need, nothing more. better yet, modify something of mine. cool? this new github repository contains eight, you read me, eight scripts: 1996 panel - download and create database.R 2001 panel - download and create database.R 2004 panel - download and create database.R 2008 panel - download and create database.R since some variables are character strings in one file and integers in anoth er, initiate an r function to harmonize variable class inconsistencies in the sas importation scripts properly handle the parentheses seen in a few of the sas importation scripts, because the SAScii package currently does not create an rsqlite database, initiate a variant of the read.SAScii function that imports ascii data directly into a sql database (.db) download each microdata file - weights, topical modules, everything - then read 'em into sql 2008 panel - full year analysis examples.R< br /> define which waves and specific variables to pull into ram, based on the year chosen loop through each of twelve months, constructing a single-year temporary table inside the database read that twelve-month file into working memory, then save it for faster loading later if you like read the main and replicate weights columns into working memory too, merge everything construct a few annualized and demographic columns using all twelve months' worth of information construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half, again save it for faster loading later, only if you're so inclined reproduce census-publish ed statistics, not precisely (due to topcoding described here on pdf page 19) 2008 panel - point-in-time analysis examples.R define which wave(s) and specific variables to pull into ram, based on the calendar month chosen read that interview point (srefmon)- or calendar month (rhcalmn)-based file into working memory read the topical module and replicate weights files into working memory too, merge it like you mean it construct a few new, exciting variables using both core and topical module questions construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half reproduce census-published statistics, not exactly cuz the authors of this brief used the generalized variance formula (gvf) to calculate the margin of error - see pdf page 4 for more detail - the friendly statisticians at census recommend using the replicate weights whenever possible. oh hayy, now it is. 2008 panel - median value of household assets.R define which wave(s) and spe cific variables to pull into ram, based on the topical module chosen read the topical module and replicate weights files into working memory too, merge once again construct a replicate-weighted complex sample design with a...

Abstract

The 2000 Republic of Palau Census of Population and Housing was the second census collected and processed entirely by the republic itself. This monograph provides analyses of data from the most recent census of Palau for decision makers in the United States and Palau to understand current socioeconomic conditions. The 2005 Census of Population and Housing collected a wide range of information on the characteristics of the population including demographics, educational attainments, employment status, fertility, housing characteristics, housing characteristics and many others.

Geographic coverage

National

Analysis unit

• Household;
• Individual.

Universe

The 1990, 1995 and 2000 censuses were all modified de jure censuses, counting people and recording selected characteristics of each individual according to his or her usual place of residence as of census day. Data were collected for each enumeration district - the households and population in each enumerator assignment - and these enumeration districts were then collected into hamlets in Koror, and the 16 States of Palau.

Kind of data

Census/enumeration data [cen]

Sampling procedure

No sampling - whole universe covered

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000 censuses of Palau employed a modified list-enumerate procedure, also known as door-to-door enumeration. Beginning in mid-April 2000, enumerators began visiting each housing unit and conducted personal interviews, recording the information collected on the single questionnaire that contained all census questions. Follow-up enumerators visited all addresses for which questionnaires were missing to obtain the information required for the census.

Cleaning operations

The completed questionnaires were checked for completeness and consistency of responses, and then brought to OPS for processing. After checking in the questionnaires, OPS staff coded write-in responses (e.g., ethnicity or race, relationship, language). Then data entry clerks keyed all the questionnaire responses. The OPS brought the keyed data to the U.S. Census Bureau headquarters near Washington, DC, where OPS and Bureau staff edited the data using the Consistency and Correction (CONCOR) software package prior to generating tabulations using the Census Tabulation System (CENTS) package. Both packages were developed at the Census Bureau's International Programs Center (IPC) as part of the Integrated Microcomputer Processing System (IMPS).

The goal of census data processing is to produce a set of data that described the population as clearly and accurately as possible. To meet this objective, crew leaders reviewed and edited questionnaires during field data collection to ensure consistency, completeness, and acceptability. Census clerks also reviewed questionnaires for omissions, certain inconsistencies, and population coverage. Census personnel conducted a telephone or personal visit follow-up to obtain missing information. The follow-ups considered potential coverage errors as well as questionnaires with omissions or inconsistencies beyond the completeness and quality tolerances specified in the review procedures.

Following field operations, census staff assigned remaining incomplete information and corrected inconsistent information on the questionnaires using imputation procedures during the final automated edit of the data. The use of allocations, or computer assignments of acceptable data, occurred most often when an entry for a given item was lacking or when the information reported for a person or housing unit on an item was inconsistent with other information for that same person or housing unit. In all of Palau’s censuses, the general procedure for changing unacceptable entries was to assign an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. The assignment of acceptable data in place of blanks or unacceptable entries enhanced the usefulness of the data.

Sampling error estimates

Human and machine-related errors occur in any large-scale statistical operation. Researchers generally refer to these problems as non-sampling errors. These errors include the failure to enumerate every household or every person in a population, failure to obtain all required information from residents, collection of incorrect or inconsistent information, and incorrect recording of information. In addition, errors can occur during the field review of the enumerators' work, during clerical handling of the census questionnaires, or during the electronic processing of the questionnaires. To reduce various types of non-sampling errors, Census office personnel used several techniques during planning, data collection, and data processing activities. Quality assurance methods were used throughout the data collection and processing phases of the census to improve the quality of the data.

Census staff implemented several coverage improvement programs during the development of census enumeration and processing strategies to minimize under-coverage of the population and housing units. A quality assurance program improved coverage in each census. Telephone and personal visit follow-ups also helped improve coverage. Computer and clerical edits emphasized improving the quality and consistency of the data. Local officials participated in post-census local reviews. Census enumerators conducted additional re-canvassing where appropriate.

Abstract

A census gives a complete and comprehensive picture of the nation as well as groups of people living in specific areas. In what type of buildings and housing units are we living? What are the amenities and facilities that are available therein? How many rooms are there and what is the extent of overcrowding? How many people live in a given town or locality? How many children are there? How many women are there? How many are old enough to vote? What kind of jobs are we doing? What is our level of education? Do we have the required qualifications or skills to satisfy the needs of the labour market? The census helps to answer these questions and many others.

It provides up-to-date and disaggregated data on the housing conditions, the spatial distribution, and the demographic and socio-economic characteristics of the population. These data are essential for assessing the country's demographic, social and economic performance and for developing sound policies and programmes aimed at fostering the welfare of the country and its population.

Census data are also useful to business, industrial and commercial organisations to estimate and forecast demand for their products and services, and to assess the supply of manpower with the relevant skills to run their activities.

Furthermore, census data are used in the derivation of many important and meaningful social indicators that are needed by local and international organizations. Thus, many social indicators, as defined in the set of indicators recommended by the United Nations Statistics Division, can only be worked out from census data.

Legal framework Census 2000 was conducted according to provisions of the Statistics Act of 7 April 1951. The underlying procedures are given in Sections 5, 6 and 13 of the Act. In March 1998, the Cabinet agreed to the conduct of a housing and population census in year 2000. In June 1999, it gave its approval to the census dates and to the topics to be investigated. The regulations for the Housing Census, prescribing the particulars and information to be collected, were subsequently prepared and approved by the President in November 1999. The regulations were published as Government Notice 170 of 1999. In December 1999, the President made an order to the effect that a census of the population be taken between 19 June and 16 July 2000 in respect of all persons alive at midnight on 2 July 2000. The Order was gazetted in December 1999. The regulations for the Population Census, prescribing the particulars and information to be collected were approved by the President in April 2000 and published as Government Notice 57 of 2000.

Geographic coverage

Housing and population enumerations were conducted on the Islands of Mauritius, Rodrigues and Agalega. As regards St Brandon islands, only a count of persons spending census night on the islands was made, these islands being fishing stations with no resident population.

Analysis unit

• Household
• Individual
• Housing unit

Universe

The Housing Census enumerated all buildings, housing units, households, commercial and industrial establishments, hotels and boarding houses as well as fruit trees of bearing age on residential premises.

The Population Census enumerated all persons present on census night in all households and communal establishments, as well as usual residents who were away on census night.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Self administered and face to face

Research instrument

Questionnaire Design Consultation with stakeholders from Government Ministries and Departments started in 1998. Heads of Government Ministries and Departments were invited via a circular letter to submit a list of demographic, social and economic data they considered essential for administration, planning and policy-making and which could be collected at the census. The proposals received were discussed at various levels. In the light of these discussions and taking into account recommendations of the United Nations Statistics Division on subject matters that can be investigated at a census, final selection of topics was made at a meeting with subject matter specialists from our parent Ministry.

The main considerations in the final selection of topics were: - the importance of the topics to the country - the cost for collecting and processing data on a given item - where it was possible by other means to obtain satisfactory information more cheaply, the topic was not selected - the suitability of topics - sensitive and controversial issues as well as questions that were too complicated or difficult for the average respondent to answer were avoided - whether the census was the appropriate method for data collection - topics that required detailed investigation or highly qualified staff were not included since they would be best canvassed by sample surveys.

Housing Census Questionnaire All topics investigated at the 1990 Census were included in the 2000 Housing Census questionnaire. Three new items were however added. These were: “Availability of domestic water tank/reservoir”, “Principal fuel used in bathroom” and “Fruit trees on premises”.

The housing census questionnaire was divided into seven parts. A list of topics and items included in the questionnaire is given below: Part I - Location Part II - Type of Building Part III - Characteristics of buildings - Storeys above ground floor
- Year of completion
- Principal material of construction used for roof and walls
Part IV - Characteristics of housing units - Ownership
- Occupancy
- Water supply
- Domestic water tank/reservoir - Availability of electricity
- Toilet facilities
- Bathing facilities
- Availability of kitchen - Refuse disposal Part V - Characteristics of households - Household type - Name and address of head of household - Number of persons by sex - Tenure - Number of rooms for living purposes - Number of rooms for business or profession - Monthly rent - Principal fuel used for cooking - Principal fuel used in bathroom Part VI - Commercial and industrial establishments, hotels and boarding houses - Name and address of establishment or working proprietor/manager - Main activity in which the establishment is engaged - Number of persons engaged at the time of enumeration Part VII - Fruit-trees on premises - Number of fruit trees of bearing age by type

Population Census Questionnaire The 2000 Population Census questionnaire covered most of the topics investigated at the 1990 Population Census. A question on income was added while the questions on education were reviewed to include qualifications, other than those of the primary and secondary levels, of the respondent. The topic, main activity status of person during the year, which was investigated at the previous census was not included.

Topics and items included in the population census questionnaire are given below: (i) Location (ii) Names of persons These information were asked only to ensure that all members of the household were enumerated. Also, the listing of names of each person facilitated the checking for accuracy and completeness of each entry at the time of enumeration and later, if errors or missing information still persisted on the form. It should be pointed out that names were not captured at the data entry stage, so that data collected could not be identified with any individual person, in line with the requirements of the Statistics Act. (iii) Demographic and social characteristics - Relationship to head (only one head is allowed for each household) - Sex - Age - Date of birth (This question served as a verification to the age reported earlier) - Citizenship - Marital Status - Religion - Linguistic group - Language usually spoken (iv) Whether disabled or not - Type of disability, if disabled (v) Migration characteristics - Whereabouts on Census night - Usual address - Usual address five years ago (vi) Fertility - For persons not single: - Age at first marriage - Whether married more than once - Number of children ever born (for women only) (vii) Education characteristics - For persons 2 years and above: - Languages read and written - School attendance - Primary and secondary education (viii) Current economic characteristics (ix) Income

Census Guide and Instructions A census guide and instructions booklet was prepared and distributed to all heads of households. The booklet contained extensive explanations on how to fill in the census form and answered questions that people usually asked about censuses. Thus the objectives of the census, what happened to the census forms once the enumeration was over, the confidential aspect of collected information as well as the usefulness of each item were explained.

Printing of Census Questionnaires and Guides
The census questionnaires, and the census guide and instructions booklets were printed by the Government Printer. The numbers printed were as follows: (i) Housing Census questionnaires - 16,000 booklets of 25 questionnaires (ii) Population Census questionnaires - 375,000 (iii) Census guide and instructions booklets - 312,000

Cleaning operations

Recruitment and Training of Editors and Coders About 15 clerical officers who were previously engaged in the various units of the Office and 10 newly recruited statistical officers were called on to the editing and coding of the census forms while a request for the services of 50 additional clerical officers was made to the Ministry for Civil Service Affairs and Administrative Reform. Between March 2000 and May 2001, small groups of clerical officers from the ministry joined the

The data on relationship to householder were derived from answers to Question 2 in the 2015 American Community Survey (ACS), which was asked of all people in housing units. The question on relationship is essential for classifying the population information on families and other groups. Information about changes in the composition of the American family, from the number of people living alone to the number of children living with only one parent, is essential for planning and carrying out a number of federal programs.

The responses to this question were used to determine the relationships of all persons to the householder, as well as household type (married couple family, nonfamily, etc.). From responses to this question, we were able to determine numbers of related children, own children, unmarried partner households, and multi-generational households. We calculated average household and family size. When relationship was not reported, it was imputed using the age difference between the householder and the person, sex, and marital status.

Household – A household includes all the people who occupy a housing unit. (People not living in households are classified as living in group quarters.) A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied (or if vacant, is intended for occupancy) as separate living quarters. Separate living quarters are those in which the occupants live separately from any other people in the building and which have direct access from the outside of the building or through a common hall. The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated people who share living arrangements.

Average Household Size – A measure obtained by dividing the number of people in households by the number of households. In cases where people in households are cross-classified by race or Hispanic origin, people in the household are classified by the race or Hispanic origin of the householder rather than the race or Hispanic origin of each individual.

Average household size is rounded to the nearest hundredth.

Comparability – The relationship categories for the most part can be compared to previous ACS years and to similar data collected in the decennial census, CPS, and SIPP. With the change in 2008 from “In-law” to the two categories of “Parent-in-law” and “Son-in-law or daughter-in-law,” caution should be exercised when comparing data on in-laws from previous years. “In-law” encompassed any type of in-law such as sister-in-law. Combining “Parent-in-law” and “son-in-law or daughter-in-law” does not represent all “in-laws” in 2008.

The same can be said of comparing the three categories of “biological” “step,” and “adopted” child in 2008 to “Child” in previous years. Before 2008, respondents may have considered anyone under 18 as “child” and chosen that category. The ACS includes “foster child” as a category. However, the 2010 Census did not contain this category, and “foster children” were included in the “Other nonrelative” category. Therefore, comparison of “foster child” cannot be made to the 2010 Census. Beginning in 2013, the “spouse” category includes same-sex spouses.

Context

This dataset expands on my earlier New York City Census Data dataset. It includes data from the entire country instead of just New York City. The expanded data will allow for much more interesting analyses and will also be much more useful at supporting other data sets.

Content

The data here are taken from the DP03 and DP05 tables of the 2015 American Community Survey 5-year estimates. The full datasets and much more can be found at the American Factfinder website. Currently, I include two data files:

1. acs2015_census_tract_data.csv: Data for each census tract in the US, including DC and Puerto Rico.
2. acs2015_county_data.csv: Data for each county or county equivalent in the US, including DC and Puerto Rico.

The two files have the same structure, with just a small difference in the name of the id column. Counties are political subdivisions, and the boundaries of some have been set for centuries. Census tracts, however, are defined by the census bureau and will have a much more consistent size. A typical census tract has around 5000 or so residents.

The Census Bureau updates the estimates approximately every year. At least some of the 2016 data is already available, so I will likely update this in the near future.

Acknowledgements

The data here were collected by the US Census Bureau. As a product of the US federal government, this is not subject to copyright within the US.

Inspiration

There are many questions that we could try to answer with the data here. Can we predict things such as the state (classification) or household income (regression)? What kinds of clusters can we find in the data? What other datasets can be improved by the addition of census data?

Abstract

The five-yearly Census of Population and Dwellings is a very important item on Tokelau’s agenda. Its results provide the most authoritative data on how many people we have, what the composition of their households is, what education level they have, how they contribute to Tokelau’s economy, and so on. As a non-self- governing territory, Tokelau has a special constitutional relationship with New Zealand. This special relationship is strengthened by connections between the tiny Tokelau National Statistics Office (TNSO) and Statistics NZ. It is the latter organisation that has been largely responsible for the excellent Tokelau Censuses in 2006, 2011, and again in 2016.

Geographic coverage

National coverage. Tokelauan employees of the Tokelau Public Service based in Apia (and their immediate families), were also interviewed in Apia on census day.

Analysis unit

Individuals and Households.

Universe

The Census covers residents of the non-self-governing New Zealand territory of Tokelau and includes Tokelau public servants and their families who are employed in Apia, Samoa. While visitors to Tokelau on Census night are also included, the ultimate aim of the Census is to provide an accurate assessment of the de jure population. This has in the Censusus of 2006, 2011 and 2016 been done to an exact definition who is included. Previous definitions have been less precise which makes long-term time serie less reliable.

Kind of data

Census/enumeration data [cen]

Sampling procedure

N/A: Census.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questions matched the previous Censuses' format in Paper Assisted Personal Interview (PAPI) as much as possible. The "skips" in PAPI proved a big time saver, and the internal checks for suitability of answers made quality control much faster.

The questionnaire was published in English with the Tokelauan translation for each question. It was divided into two sections: - Dwelling questions - Individual questions.

Cleaning operations

Thanks to the Computer Assisted Personal Interview (CAPI) data collection method, it was possible to quality check census forms on census day as soon as the interviewers uploaded them. Supervisors helped the census management team to quality check every census form and if there were missing answers or errors found, the forms were sent back to the interviewers to fix. The ability to check the quality of answers was one of the major benefits of using tablets for data collection; it made the checking process faster and more thorough. This checking also ensured that the final population counts were able to be released only three weeks after census.

Sampling error estimates

Not applicable: Census.

Data appraisal

Given the small population size, no post-enumeration survey was done.

Measure and Map Access to Grocery StoresFrom the perspective of the people living in each neighborhoodHow do people in your city get to the grocery store? The answer to that question depends on the person and where they live. This web map helps answer the question in this app.Some live in cities and stop by a grocery store within a short walk or bike ride of home or work. Others live in areas where car ownership is more prevalent, and so they drive to a store. Some do not own a vehicle, and rely on a friend or public transit. Others rely on grocery delivery for their needs. And, many live in rural areas far from town, so a trip to a grocery store is an infrequent event involving a long drive.This map from Esri shows which areas are within a ten minute walk or ten minute drive of a grocery store in the United States and Puerto Rico. Darker color indicates access to more stores. The chart shows how many people can walk to a grocery store if they wanted to or needed to.It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store.Look up your city to see how the numbers change as you move around the map. Or, draw a neighborhood boundary on the map to get numbers for that area.Every census block is scored with a count of walkable and drivable stores nearby, making this a map suitable for a dashboard for any city, or any of the 50 states, DC and Puerto Rico. Two colorful layers visualize this definition of access, one for walkable access (suitable for looking at a city neighborhood by neighborhood) and one for drivable access (suitable for looking across a city, county, region or state).On the walkable layer, shades of green define areas within a ten minute walk of one or more grocery stores. The colors become more intense and trend to a blue-green color for the busiest neighborhoods, such as downtown San Francisco. As you zoom in, a layer of Census block points visualizes the local population with or without walkable access.As you zoom out to see the entire city, the map adds a light blue - to dark blue layer, showing which parts of the region fall within ten minutes' drive of one or more grocery stores. As a result, the map is useful at all scales, from national to regional, state and local levels. It becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This MapUse this map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying.The map was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.There is data behind the map, which can be summarized to show how many people have walkable access to local grocery stores. The map includes a feature layer of population in Census block points, which are visible when you zoom in far enough. This feature layer can be plugged into an app like this one that summarizes the population with/without walkable or drivable access.Lastly, this map can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The map is a useful visual and analytic resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a

California is home to 12 percent of the nation's population yet accounts for more than 20 percent of the people living in the nation’s hardest-to-count areas, according to the United States Census Bureau (U.S. Census Bureau). California's unique diversity, large population distributed across both urban and rural areas, and sheer geographic size present significant barriers to achieving a complete and accurate count. The state’s population is more racially and ethnically diverse than ever before, with about 18 percent of Californians speaking English “less than very well,” according to U.S. Census Bureau estimates. Because the 2020 Census online form was offered in only twelve non-English languages, which did not correspond with the top spoken language in California, and a paper questionnaire only in English and Spanish, many Californians may not have been able to access a census questionnaire or written guidance in a language they could understand. In order to earn the confidence of California’s most vulnerable populations, it was critical during the 2020 Census that media and trusted messengers communicate with them in their primary language and in accessible formats. An accurate count of the California population in each decennial census is essential to receive its equitable share of federal funds and political representation, through reapportionment and redistricting. It plays a vital role in many areas of public life, including important investments in health, education, housing, social services, highways, and schools. Without a complete count in the 2020 Census, the State faced a potential loss of congressional seats and billions of dollars in muchneeded federal funding. An undercount of California in 1990 cost an estimated $2 billion in federal funding. The potential loss of representation and critically needed funding could have long-term impacts; only with a complete count does California receive the share of funding the State deserves with appropriate representation at the federal, state, and local government levels. The high stakes and formidable challenges made this California Complete Count Census 2020 Campaign (Campaign) the most important to date. The 2020 Census brought an unprecedented level of new challenges to all states, beyond the California-specific hurdles discussed above. For the first time, the U.S. Census Bureau sought to collect data from households through an online form. While the implementation of digital forms sought to reduce costs and increase participation, its immediate impact is still unknown as of this writing, and it may have substantially changed how many households responded to the census. In addition, conditions such as the novel Coronavirus (COVID-19) pandemic, a contentious political climate, ongoing mistrust and distrust of government, and rising concerns about privacy may have discouraged people to open their doors, or use computers, to participate. Federal immigration policy, as well as the months-long controversy over adding a citizenship question to the census, may have deterred households with mixed documentation status, recent immigrants, and undocumented immigrants from participating. In 2017, to prepare for the unique challenges of the 2020 Census, California leaders and advocates reflected on lessons learned from previous statewide census efforts and launched the development of a high-impact strategy to efficiently raise public awareness about the 2020 Census. Subsequently, the State established the California Complete Count – Census 2020 Office (Census Office) and invested a significant sum for the Campaign. The Campaign was designed to educate, motivate, and activate Californians to respond to the 2020 Census. It relied heavily on grassroots messaging and outreach to those least likely to fill out the census form. One element of the Campaign was the Language and Communication Access Plan (LACAP), which the Census Office developed to ensure that language and communication access was linguistically and culturally relevant and sensitive and provided equal and meaningful access for California’s vulnerable populations. The Census Office contracted with outreach partners, including community leaders and organizations, local government, and ethnic media, who all served as trusted messengers in their communities to deliver impactful words and offer safe places to share information and trusted messages. The State integrated consideration of hardest-to-count communities’ needs throughout the Campaign’s strategy at both the statewide and regional levels. The Campaign first educated, then motivated, and during the census response period, activated Californians to fill out their census form. The Census Office’s mission was to ensure that Californians get their fair share of resources and representation by encouraging the full participation of all Californians in the 2020 Census. This report focuses on the experience of the Census Office and partner organizations who worked to achieve the most complete count possible, presenting an evaluation of four outreach and communications strategies.

Abstract

This study is an experiment designed to compare the performance of three methodologies for sampling households with migrants:

• a stratified sample using the census to sample census tracts randomly, in which each household is then listed and screened to determine whether or not it has a migrant, with the full length questionnaire then being applied in a second phase only to the households of interest;
• a snowball survey in which households are asked to provide referrals to other households with migrant members;
• an intercept point survey (or time-and-space sampling survey), in which individuals are sampled during set time periods at a prespecified set of locations where households in the target group are likely to congregate.

Researchers from the World Bank applied these methods in the context of a survey of Brazilians of Japanese descent (Nikkei), requested by the World Bank. There are approximately 1.2-1.9 million Nikkei among Brazil’s 170 million population.

The survey was designed to provide detail on the characteristics of households with and without migrants, to estimate the proportion of households receiving remittances and with migrants in Japan, and to examine the consequences of migration and remittances on the sending households.

The same questionnaire was used for the stratified random sample and snowball surveys, and a shorter version of the questionnaire was used for the intercept surveys. Researchers can directly compare answers to the same questions across survey methodologies and determine the extent to which the intercept and snowball surveys can give similar results to the more expensive census-based survey, and test for the presence of biases.

Geographic coverage

Sao Paulo and Parana states

Analysis unit

Japanese-Brazilian (Nikkei) households and individuals

The 2000 Brazilian Census was used to classify households as Nikkei or non-Nikkei. The Brazilian Census does not ask ethnicity but instead asks questions on race, country of birth and whether an individual has lived elsewhere in the last 10 years. On the basis of these questions, a household is classified as (potentially) Nikkei if it has any of the following: 1) a member born in Japan; 2) a member who is of yellow race and who has lived in Japan in the last 10 years; 3) a member who is of yellow race, who was not born in a country other than Japan (predominantly Korea, Taiwan or China) and who did not live in a foreign country other than Japan in the last 10 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

1) Stratified random sample survey

Two states with the largest Nikkei population - Sao Paulo and Parana - were chosen for the study.

The sampling process consisted of three stages. First, a stratified random sample of 75 census tracts was selected based on 2000 Brazilian census. Second, interviewers carried out a door-to-door listing within each census tract to determine which households had a Nikkei member. Third, the survey questionnaire was then administered to households that were identified as Nikkei. A door-to-door listing exercise of the 75 census tracts was then carried out between October 13th, 2006, and October 29th, 2006. The fieldwork began on November 19, 2006, and all dwellings were visited at least once by December 22, 2006. The second wave of surveying took place from January 18th, 2007, to February 2nd, 2007, which was intended to increase the number of households responding.

2) Intercept survey

The intercept survey was designed to carry out interviews at a range of locations that were frequented by the Nikkei population. It was originally designed to be done in Sao Paulo city only, but a second intercept point survey was later carried out in Curitiba, Parana. Intercept survey took place between December 9th, 2006, and December 20th, 2006, whereas the Curitiba intercept survey took place between March 3rd and March 12th, 2007.

Consultations with Nikkei community organizations, local researchers and officers of the bank Sudameris, which provides remittance services to this community, were used to select a broad range of locations. Interviewers were assigned to visit each location during prespecified blocks of time. Two fieldworkers were assigned to each location. One fieldworker carried out the interviews, while the other carried out a count of the number of people with Nikkei appearance who appeared to be 18 years old or older who passed by each location. For the fixed places, this count was made throughout the prespecified time block. For example, between 2.30 p.m. and 3.30 p.m. at the sports club, the interviewer counted 57 adult Nikkeis. Refusal rates were carefully recorded, along with the sex and approximate age of the person refusing.

In all, 516 intercept interviews were collected.

3) Snowball sampling survey

The questionnaire that was used was the same as used for the stratified random sample. The plan was to begin with a seed list of 75 households, and to aim to reach a total sample of 300 households through referrals from the initial seed households. Each household surveyed was asked to supply the names of three contacts: (a) a Nikkei household with a member currently in Japan; (b) a Nikkei household with a member who has returned from Japan; (c) a Nikkei household without members in Japan and where individuals had not returned from Japan.

The snowball survey took place from December 5th to 20th, 2006. The second phase of the snowballing survey ran from January 22nd, 2007, to March 23rd, 2007. More associations were contacted to provide additional seed names (69 more names were obtained) and, as with the stratified sample, an adaptation of the intercept survey was used when individuals refused to answer the longer questionnaire. A decision was made to continue the snowball process until a target sample size of 100 had been achieved.

The final sample consists of 60 households who came as seed households from Japanese associations, and 40 households who were chain referrals. The longest chain achieved was three links.

Mode of data collection

Face-to-face [f2f]

Research instrument

1) Stratified sampling and snowball survey questionnaire

This questionnaire has 36 pages with over 1,000 variables, taking over an hour to complete.

If subjects refused to answer the questionnaire, interviewers would leave a much shorter version of the questionnaire to be completed by the household by themselves, and later picked up. This shorter questionnaire was the same as used in the intercept point survey, taking seven minutes on average. The intention with the shorter survey was to provide some data on households that would not answer the full survey because of time constraints, or because respondents were reluctant to have an interviewer in their house.

2) Intercept questionnaire

The questionnaire is four pages in length, consisting of 62 questions and taking a mean time of seven minutes to answer. Respondents had to be 18 years old or older to be interviewed.

Response rate

1) Stratified random sampling 403 out of the 710 Nikkei households were surveyed, an interview rate of 57%. The refusal rate was 25%, whereas the remaining households were either absent on three attempts or were not surveyed because building managers refused permission to enter the apartment buildings. Refusal rates were higher in Sao Paulo than in Parana, reflecting greater concerns about crime and a busier urban environment.

2) Intercept Interviews 516 intercept interviews were collected, along with 325 refusals. The average refusal rate is 39%, with location-specific refusal rates ranging from only 3% at the food festival to almost 66% at one of the two grocery stores.

Abstract

The 1996 Papua New Guinea household survey is designed to measure the living standards of a random sample of PNG households. As well as looking at the purchases, own-production, gift giving/receiving and sales activities of households over a short period (usually 14 days), the survey also collects information on education, health, nutrition, housing conditions and agricultural activities. The survey also collects information on community level access to services for education, health, transport and communication, and on the price levels in each community so that the cost of living can be measured.

There are many uses of the data that the survey collects, but one main aim is for the results to help government, aid agencies and donors have a better picture of living conditions in all areas of PNG so that they can develop policies and projects that help to alleviate poverty. In addition, the survey will provide a socio-economic profile of Papua New Guinea, describing the access that the population has to agricultural, educational, health and transportation services, their participation in various economic activities, and household consumption patterns.

The survey is nationwide and the same questionnaire is being used in all parts of the country, including the urban areas. This fact can be pointed out if households find that some of the questions are irrelevant for their own living circumstances: there are at least some Papua New Guinean households for which the questions will be relevant and it is only by asking everyone the same questions that living standards can be compared.

Geographic coverage

The survey covers all provinces except Noth Solomons.

Analysis unit

• Household
• Individual
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Listing Form and Selection of the Sample Listing of households is the first job to be done after the team has settled in and completed the introductions to the community. Listing is best done by the whole team working together. This way they all get to know the community and its lay-out. However, if the census unit is too large this wastes too much time. So before beginning asks how many households there are, very roughly, in the census unit (noting that teams are supplied with the number of households that were there in the 1990 census). If the answer is 80 or more, divide the team into two and have each half-team work on one sector of the community/village. See the section below on what to do when the listing work is divided up.

If the census unit is a "line-up point" that does not correspond to any single village or community the number of households will often exceed 200 and frequently they are also quite dispersed. In this case it is not practical to attempt to list the whole census unit, so a decision is made in advance to split the census unit into smaller areas (perhaps groupings of clans). First, a local informant must communicate the boundaries of the census unit and for natural or administrative sub-units with the larger census unit (such as hamlets; or canyons/valleys). The sub-units should be big enough to allow for the selection of a set of households (about 30 or more), but should not be so large that excessive transport time will be needed each day just to find the household. Once the subunit is defined, its boundaries should be clearly described. Then one of the smaller units is randomly selected and the procedures outlined above are then followed to complete the listing. Note: only one of the sub-units are listed, sample chosen, and interviews undertaken.

The most important thing in the listing is to be sure that you list all the households and only the households belonging to the named village or census unit (or subset of the census unit if it is a line-up point). In rural areas, explain to village leaders at the beginning: "We have to write down all the households belonging to (Name) village." In case of doubt, always ask: "Does this household belong to (Name) village?" In the towns, the selected area is shown on a map. Check that the address where you are listing is within the same area shown.

Also explain: "We only write down the name of the head of household. When we have the list of all the households, we will select 12 by chance, for interview."

Procedure for Listing The listing team walks around in every part of the village, accompanied by a guide who is a member of the village. If possible, find a person who conducted the 1990 Census in this community or someone with similar knowledge of the community and ask them to be your guide. Make sure you go to all parts of the village, including outlying hamlets. In hamlets, on in any place far from the centre, always check: "Do these people belong to (Name) village?"

In every part of the village, ask the guide about every house: "Who lives in this house? What is the name of the household head?" Note that you do not have to visit every household. At best, you just need to see each house but you do not need to go inside it or talk to anyone who lives there. Even the rule of seeing each house may be relaxed if there are far away household for which good information can be provided by the guide.

Enter the names of household heads in the lines of the listing form. One line is used for each household. As the lines are numbered, the procedure gives a number to each household. When you come to the last house, check with the guide: "Are you sure we have seen all the houses in the village?"

NOTE: It does not matter in what order you list the households as long as they are all listed. After the listing is complete, check that all lines are numbered consecutively with no gaps, from start to finish. The number on the last line should be exactly the number of households listed.

Note: If the list is long (say more than 30 households) interviewer may encounter difficulties when looking for their selected household. One useful way to avoid this is to show the approximately the place in the list here certain landmarks come. This can be done by writing in the margin, CHURCH or STORE or whatever. You can also indicate where the lister started in a hamlet, for example.

Sample Selection The sampling work is done by the supervisor. The first steps are done at the foot of the first page of the listing form. The steps to be taken are as follows:

1. Fill in the numbers asked for at the foot of the last listing page, as follows:
2. M: enter the total number of households listed (same as last household number shown).
3. Interval L: calculate (M / 15) to the nearest whole number.
4. R: This is a random number with 3-digit decimals between 0.000 and 0.999.

5. MR: multiply M by R and round to the nearest whole number. (If decimal 0.5, round up).

6. MR gives the 1st selection. (Exception: If MR=0, L gives the first selection.) Enter S against this line in the selection column of the list.

7. Count down the list, beginning after the 1st selection, a distance of L lines to get the 2nd selection, then another L to get the 3rd, etc. When you come to the bottom of the list, jump back to the top as if the list were circular. Stop after the 15th selection. Mark the 13th, 14th, and 15th selections "RES" (for reserve). Mark the 1st - 12th selection "S" (for selection).

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1996 Papua New Guinea Household Survey questionnaire consists of three basic parts:

Household questionnaire first visit: asks a series of questions about the household, discovering who lives there, what they do, their characteristics, where they live, and a little about what kinds of things they consume. This questionnaire consists of the following sections. - Section 1. Household Roster - Section 2. Education - Section 3. Income Sources - Section 4. Health - Section 5. Foods in the Diet - Section 6. Housing Conditions - Section 7. Agricultural Assets, Inputs and Services - Section 8. Anthropometrics - Section 9. Household Stocks

Consumption recall (second visit questionnaire): is focused primarily on assessing the household's expenditure, gift giving and recieving, production, and level of wealth. The information in the first and second visits will provide information that can determine the household's level of consumption, nutrition, degree of food security, and ways in which it organizes its income earning activities. This questionnaire consists of the following sections. - Section 1. Purchases of Food - Section 2. Other Frequent Purchases - Section 3. Own-production of Food - Section 4. Gifts Received: Food and Frequent Purchases (START) - Section 5. Annual Expenses and Gifts - Section 6. Inventory of Durable Goods - Section 7. Inward Transfers of Money - Section 8. Outward Transfers of Money - Section 9. Prices - Section 10. Repeat of Anthropometric Measurements - Section 11. Quality of Life

Community Questionnaire: which is completed by the interview team in consultation with community leaders. This questionnaire also includes market price surveys that are carried out by the team when they are working in the community. Associated with this is a listing of all households in the community, which has to be done prior to the selection of the 12 households. This questionnaire consists of the following sections. - Section A. Listing of Community Assets - Section B. Education - Section C. Health - Section D. Town or Government Station - Section E: Transport and Communications - Section F. Prices - Section G. Changes in Economic Activity, Infrastructure, and Services

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden. The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

The survey covers Egypt.

Analysis unit

• Household
• Individual

Universe

The WVS for Egypt covers national population aged 18 years and over, for both sexes.

Kind of data

Sample survey data [ssd]

Sampling procedure

1- The sampling frame is the extended roaster of households for the post enumeration survey of the 2006 Census.

2- This frame covered all governorates (except the five frontiers Governorates hosting about 1.8% of the total population), within about 480 segments (average 100 HH).

3- To reduce sampling error, it was only to select 25 HH from each segment to increase the number of segments selected from each Governorate and that Number of segments was proportional to its size according to the 2006 population census.

1. Within Governorates, rural/urban parts were represented by selecting separately the number of segments proportional to its population share according to the 2006 census. Overall, a total of 122 segments were selected (out of which 56 from Urban areas and the balance from Rural areas of each Governorate), thus drawing a self-weighted sample for each Governorate based on its share of the 2006 population census.

2. The sample size was set to be 3000 individuals, to reduce sampling error and to ensure having estimates of adequate precision. The sampling unit would be the individuals 18 years old and over.

3. Both segments were selected separately from the frame of Urban/Rural area for each Governorate using systematic random sampling.

4. Households were also selected within segments (25 households from each segment) using systematic random sampling.

5. Due to rounding, the total number of segments rose to 122 segment, (and the sample size rose to 3050 individuals).

Remarks about sampling:

• The sample tended to be biased to females, as they were represented by 62% of the total sample. So weights was computed to modify the distribution of males and females, according to their distribution in the population. Basically, interviews would be conducted with the head of the household, if he/she exists and is able to answer the questionnaire. If not, another member of the household (who is 18 years or older) would answer the questionnaire. In every segment there was an alternative sample, that was also randomly selected, if an appropriate person within the household, was not available to answer the questionnaire, an alternative sampling unit would be selected. The alternative sample represented only 3.3% of the total sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

For each wave, suggestions for questions are solicited by social scientists from all over the world and a final master questionnaire is developed in English. Since the start in 1981 each successive wave has covered a broader range of societies than the previous one. Analysis of the data from each wave has indicated that certain questions tapped interesting and important concepts while others were of little value. This has led to the more useful questions or themes being replicated in future waves while the less useful ones have been dropped making room for new questions. The questionnaire is translated into the various national languages and in many cases independently translated back to English to check the accuracy of the translation. In most countries, the translated questionnaire is pre-tested to help identify questions for which the translation is problematic. In some cases certain problematic questions are omitted from the national questionnaire. WVS requires implementation of the common questionnaire fully and faithfully, in all countries included into one wave. Any alteration to the original questionnaire has to be approved by the EC. Omission of no more than a maximum of 12 questions in any given country can be allowed.

Response rate

3050 Total number of starting names/addresses 3050 - full productive interview

This data collection is comprised of responses from two sets of survey questionnaires, the basic Current Population Survey (CPS) and a survey on the topic of voting and registration in the United States, which was administered as a supplement to the November 2010 CPS questionnaire. The Housing and Household Economic Statistics Division of the Census Bureau sponsored the supplemental questions for November.The CPS, administered monthly, is a labor force survey providing current estimates of the economic status and activities of the population of the United States. Specifically, the CPS provides estimates of total employment (both farm and nonfarm), nonfarm self-employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment. Data from the CPS are provided for the week prior to the survey.The voting and registration supplement data are collected every two years to monitor trends in the voting and nonvoting behavior of United States citizens in terms of their different demographic and economic characteristics. The supplement was designed to be a proxy response supplement, meaning a single respondent could provide answers for all eligible household members. The supplement questions were asked of all persons who were both United States citizens and 18 years of age or older. The CPS instrument determined who was eligible for the voting and registration supplement through the use of check items that referred to basic CPS items, including age and citizenship.Respondents were queried on whether they were registered to vote in the November 2, 2010, election, main reasons for not being registered to vote, main reasons for not voting, whether they voted in person or by mail, and method used to register to vote. Demographic variables include age, sex, race, Hispanic origin, marital status, veteran status, educational attainment, occupation, and income.

This dataset provides Census 2022 estimates for the Religion (in 12 categories) by sex by age (in 6 categories) by Individuals in Scotland.

Age

A person's age on Census Day, 20 March 2022. Infants aged under 1 year are classified as 0 years of age.

Sex

This is the sex recorded by the person completing the census. The options were "Female" and "Male". Guidance on answering the question can be found here

Religion

This is a person’s current religious denomination or body that they belong to, or if the person does not have a religion, ‘No Religion’. No determination is made about whether a person was a practising member of a religion.

Religion is a voluntary question and 6.2% of the population did not provide a response. Please be aware that when we state percentages these are out of the whole population, not just those that provided a response. Our approach to imputation is also different for voluntary questions. Not stating a religion is considered to be a valid response, so we do not impute a religion for those who responded to the census but did not answer the religion question. However, we do impute religion for those who did not respond at all to the census. 'Not stated’ is one of the values that can be imputed for religion. More information on our edit and imputation method is available on the Scotland’s Census website.

Classification and comparison with 2011 census can be found here

The quality assurance report can be found here

Abstract

The 1980 South African Population Census was a count of all persons present on Republic of South African territory during census night (i.e. at midnight between 6 and 7 May 1980). The purpose of the population census was to collect, process and disseminate detailed statistics on population size, composition and distribution at small area level. The 1980 South African Population Census contains data collected on HOUSEHOLDS: household goods and dwelling characteristics as well as employment of domestic workers; INDIVIDUALS: population group, citizenship/nationality, marital status, fertility and infant mortality, education, employment, religion, language and disabilities, as well as mode of transport used and participation in sport and other recreational activities

Geographic coverage

The 1980 census covered the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal. It also covered areas in the following so-called National States of Ciskei, KwaZulu, Gazankulu, Lebowa, Qwaqwa, Kangwane, and Kwandebele. The 1980 South African census excluded the areas of the Transkei and Bophuthatswana. A census data file for Bophuthatswana was released with the final South African Census 1980 dataset.

Analysis unit

The units of analysis of the 1980 census includes households, individuals and institutions

Universe

The 1980 South African census covered all household members (usual residents).

The 1980 South African Population Census was enumerated on a de facto basis, that is, according to the place where persons were located during the census. All persons who were present on Republic of South African territory during census night (i.e. at midnight between 6 and 7 May 1980) were enumerated and included in the data. Visitors from abroad who were present in the RSA on holiday or business on the night of the census, as well as foreigners (and their families) who were studying or economically active, were not enumerated and included in the figures. Likewise, members of the Diplomatic and Consular Corps of foreign countries were not included. However, the South African personnel linked to the foreign missions including domestic workers were enumerated. Crews and passengers of ships were also not enumerated, unless they were normally resident in the Republic of South Africa. Residents of the RSA who were absent from the night were as far as possible enumerated on their return and included in the region where they normally resided. Personnel of the South African Government stationed abroad and their families were, however enumerated. Such persons were included in the Transvaal (Pretoria).

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1980 Population Census questionnaire was administered to all household members and covered household goods and dwelling characteristics, and employment of domestic workers. Questions concerning individuals included those on citizenship/nationality, marital status, fertility and infant mortality, education, employment, religion, language and disabilities, as well as mode of transport used and participation in sport and other recreational activities.

The following questions appear in the questionnaire but the corresponding data has not been included in the data set: PART C: PARTICULARS OF DWELLING: 2. How many separate families (i) Number of families (ii) Number of non-family persons (iii) total number of occupants [i.e. persons in families shown against (i) plus persons shown against 3. Persons employed by household Full-time, Part-time (a) How many persons are employed as domestics by you? (Include garden workers) (b) Total cash wages paid to above –mentioned persons for April 1980 4. Ownership – Do not answer this question if your dwelling is on a farm. (i) Own dwelling – (Including hire-purchase, sectional title property or property of wife): (a) Is the dwelling Fully paid Partly paid-off (b) If partly paid-off, state monthly repayment (include housing subsidy, but exclude insurance. (ii) Rented or occupied free dwelling : (a) Is the dwelling occupied free, rented furnished, rented unfurnished (b) If rented, state monthly rent (c) Is the dwelling owned by the employer? (d) Does it belong to the state, SA Railways, a provincial administration, a divisional council, or a municipality or other local authority? PART D: PARTICULARS OF THE FAMILY 1. Number of members in the family 2. Occupation. (Nature of work done) (a) Head of family (b) Wife 3. Annual income of head of family and wife. Annual income of: Head, Wife (if applicable)

description: The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. The first wave of results for sub-state geographic areas in New Mexico was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics were released in the summer of 2011. The data in these particular RGIS Clearinghouse tables are for all census tracts in New Mexico. This table provides total counts of population. This file, along with file-specific descriptions (in Word and text formats) are available in a single zip file.; abstract: The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. The first wave of results for sub-state geographic areas in New Mexico was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics were released in the summer of 2011. The data in these particular RGIS Clearinghouse tables are for all census tracts in New Mexico. This table provides total counts of population. This file, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

Abstract (en): This data collection is comprised of responses from two sets of survey questionnaires, the basic Current Population Survey (CPS) and a survey on the topic of voting and registration in the United States, which was administered as a supplement to the November 2008 CPS questionnaire. The Housing and Household Economic Statistics Division of the Census Bureau sponsored the supplemental questions for November.The CPS, administered monthly, is a labor force survey providing current estimates of the economic status and activities of the population of the United States. Specifically, the CPS provides estimates of total employment (both farm and nonfarm), nonfarm self-employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment. Data from the CPS are provided for the week prior to the survey.The voting and registration supplement data are collected every two years to monitor trends in the voting and nonvoting behavior of United States citizens in terms of their different demographic and economic characteristics. The supplement was designed to be a proxy response supplement, meaning a single respondent could provide answers for all eligible household members. The supplement questions were asked of all persons who were both United States citizens and 18 years of age or older. The CPS instrument determined who was eligible for the voting and registration supplement through the use of check items that referred to basic CPS items, including age and citizenship.Respondents were queried on whether they were registered to vote in the November 4, 2008 election, main reasons for not being registered to vote, main reasons for not voting, whether they voted in person or by mail, and method used to register to vote. Demographic variables include age, sex, race, Hispanic origin, marital status, veteran status, educational attainment, occupation, and income. There is no supplement weight associated with the November 2008 supplement. Use the basic CPS weight, PWSSWGT, for tallying the supplement items. Please refer to the User Guide for additional information concerning the creation and use of this and other weight variables. All persons in the civilian noninstitutionalized population of the United States living in households. A multistage probability sample based on results of the decennial Census was used for the housing unit. computer-assisted personal interview (CAPI), computer-assisted telephone interview (CATI)Users are strongly encouraged to refer to the User Guide (produced by the Principal Investigators), which contains the questionnaire for the supplement, as well as additional detailed technical documentation regarding the study design, sampling frame used, and response rates.The universe statements for each variable are defined in either the basic or supplement record layout, which is located in Attachment 6 and 7, respectively, of the User Guide.ICPSR removed all FILLER and PADDING variables from the data. As a result, the column locations in any ICPSR-released data product (e.g., codebook and setup files) will have column locations that are not consistent with locations described in the User Guide.

Abstract

The Census provides a unique source of detailed demographic, social and economic data relating to the entire population at a single point in time. The most fundamental questions it will answer concern the size and distribution of the entire population, the extent to which men and women participate in the economy, and the nature of housing and household facilities.

Our country is constantly changing and information collected will allow policy planners to measure the developments of our society and to make decisions about future needs of our communities. Emphasis for the CPD is on renewable energy. Government has set itself policy goals that by 2015, 50 percent of Cook Islands electricity demand will be provided by renewable energy and 100 percent in 2020. Culture has also been identified as a priority of Government.

The topic content of the 2011 Census has been driven principally by the demands and requirements of users of census statistics, the evaluation of the 2006 Census, and the priorities of the Government as stated in its manifesto, and the advice and guidance of organisations with experience of similar operations. These have been determined by extensive consultations with various Ministries of government and non government organizations (NGO).

The topics proposed for the census are those most needed by the major users of census information and questions have been devised to produce reliable and accurate data. In each case, no other comparable and accessible source of the information is available in combination with other items in the census.

In assessing which topics should be included in the census, Statistics Office has had to consider a number of factors. The criteria for evaluating the strength of users' requirements for information were that:

· there should be a clearly demonstrated and significant need · the information collected was of major national importance · users' requirements could not adequately be met by information from other sources · there should be a requirement for multivariate analysis (that is the ability to cross-analyze one variable against other), and · there should be consideration of the ability for comparison with the 2006 Census wherever possible

Geographic coverage

National coverage.

Analysis unit

-Household -Individual.

Universe

A Dwelling Questionnaire must be completed for every occupied dwelling as at midnight on Census Night. A Personal Questionnaire must be completed for each and every man, woman and child alive at midnight on Census Night within the geographical boundaries of the Cook Islands, excluding those persons on foreign vessels, yachts and aircraft flying through or stopping temporarily (transit).

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire which was published in English, can be found as External Resources.

The questionnaire of the early Cook Islands censuses was limited; they consisted oh head counts only. With the passage of time, the topics covered by the questionnaires expanded. Gradually, questions on sex, age, marital status, religion, education, employment, etc have been included. Questions on unpaid work and income earned were included for the first time in the 1996 Census. In the 2011 Census, questions on renewable energy and culture was included.

The questionnaire of the 2011 Census of Cook Islands is divided into 2 main forms: -Dwelling form: which covers topics like: Household characteristics, water supply, energy, toilet, tradition, communication & technology, waste management, involvment in agriculture, fishing, farming, equipment, transport. -Personal form: which covers topics like: Individual characteristics, disability, literacy and language, information & technology, education, economic activity, income, narcotics, cultural activities and fertility.

SELECTION OF TOPICS AND QUESTIONS: The topic content of the 2011 Census has been driven principally by the demands and requirements of users of census statistics, the evaluation of the 2006 Census, and the priority of government as stated in this Governments Manifesto, and the advice and guidance of organizations with experience of similar operations. These have been determined by extensive consultation with various Ministries of government and NGO's. Emphasis for this Census was on Renewable Energy and Cultural Statistics. Government had set itself a policy goal that 50 percent of its electricity demands to be provided by renewable energy in 2015 and 100 percent in 2020.

The 2011 Census questionnaire is designed to be machine readable that is questionnaires will be scanned.

description: The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. The first wave of results for sub-state geographic areas in New Mexico was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics were released in the summer of 2011. The data in this particular RGIS Clearinghouse table are for all blocks in Union County. The data table provides counts of the total population. This file, along with specific narrative descriptions and definitions (in Word and text formats) are available in a single zip file.; abstract: The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. The first wave of results for sub-state geographic areas in New Mexico was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics were released in the summer of 2011. The data in this particular RGIS Clearinghouse table are for all blocks in Union County. The data table provides counts of the total population. This file, along with specific narrative descriptions and definitions (in Word and text formats) are available in a single zip file.

Introduction This report presents projections of population from 2015 to 2025 by age and sex for Illinois, Chicago and Illinois counties produced for the Certificate of Need (CON) Program. As actual future population trends are unknown, the projected numbers should not be considered a precise prediction of the future population; rather, these projections, calculated under a specific set of assumptions, indicate the levels of population that would result if our assumptions about each population component (births, deaths and net migration) hold true. The assumptions used in this report, and the details presented below, generally assume a continuation of current trends. Methodology These projections were produced using a demographic cohort-component projection model. In this model, each component of population change – birth, death and net migration – is projected separately for each five-year birth cohort and sex. The cohort – component method employs the following basic demographic balancing equation: P1 = P0 + B – D + NM Where: P1 = Population at the end of the period; P0 = Population at the beginning of the period; B = Resident births during the period; D = Resident deaths during the period; and NM = Net migration (Inmigration – Outmigration) during the period. The model roughly works as follows: for every five-year projection period, the base population, disaggregated by five-year age groups and sex, is “survived” to the next five-year period by applying the appropriate survival rates for each age and sex group; next, net migrants by age and sex are added to the survived population. The population under 5 years of age is generated by applying age specific birth rates to the survived females in childbearing age (15 to 49 years). Base Population These projections began with the July 1, 2010 population estimates by age and sex produced by the U.S. Census Bureau. The most recent census population of April 1, 2010 was the base for July 1, 2010 population estimates. Special Populations In 19 counties, the college dormitory population or adult inmates in correctional facilities accounted for 5 percent or more of the total population of the county; these counties were considered as special counties. There were six college dorm counties (Champaign, Coles, DeKalb, Jackson, McDonough and McLean) and 13 correctional facilities counties (Bond, Brown, Crawford, Fayette, Fulton, Jefferson, Johnson, Lawrence, Lee, Logan, Montgomery, Perry and Randolph) that qualified as special counties. When projecting the population, these special populations were first subtracted from the base populations for each special county; then they were added back to the projected population to produce the total population projections by age and sex. The base special population by age and sex from the 2010 population census was used for this purpose with the assumption that this population will remain the same throughout each projection period. Mortality Future deaths were projected by applying age and sex specific survival rates to each age and sex specific base population. The assumptions on survival rates were developed on the basis of trends of mortality rates in the individual life tables constructed for each level of geography for 1989-1991, 1999-2001 and 2009-2011. The application of five-year survival rates provides a projection of the number of persons from the initial population expected to be alive in five years. Resident deaths data by age and sex from 1989 to 2011 were provided by the Illinois Center for Health Statistics (ICHS), Illinois Department of Public Health. Fertility Total fertility rates (TFRs) were first computed for each county. For most counties, the projected 2015 TFRs were computed as the average of the 2000 and 2010 TFRs. 2010 or 2015 rates were retained for 2020 projections, depending on the birth trend of each county. The age-specific birth rates (ASBR) were next computed for each county by multiplying the 2010 ASBR by each projected TFR. Total births were then projected for each county by applying age-specific birth rates to the projected female population of reproductive ages (15 to 49 years). The total births were broken down by sex, using an assumed sex-ratio at birth. These births were survived five years applying assumed survival ratios to get the projected population for the age group 0-4. For the special counties, special populations by age and sex were taken out before computing age-specific birth rates. The resident birth data used to compute age-specific birth rates for 1989-1991, 1999-2001 and 2009-2011 came from ICHS. Births to females younger than 15 years of age were added to those of the 15-19 age group and births to women older than 49 years of age were added to the 45-49 age group. Net Migration Migration is the major component of population change in Illinois, Chicago and Illinois counties. The state is experiencing a significant loss of population through internal (domestic migration within the U.S.) net migration. Unlike data on births and deaths, migration data based on administrative records are not available on a regular basis. Most data on migration are collected through surveys or indirectly from administrative records (IRS individual tax returns). For this report, net migration trends have been reviewed using data from different sources and methods (such as residual method) from the University of Wisconsin, Madison, Illinois Department of Public Health, individual exemptions data from the Internal Revenue Service, and survey data from the U.S. Census Bureau. On the basis of knowledge gained through this review and of levels of net migration from different sources, assumptions have been made that Illinois will have annual net migrants of -40, 000, -35,000 and -30,000 during 2010-2015, 2015-2020 and 2020-2025, respectively. These figures have been distributed among the counties, using age and sex distribution of net migrants during 1995-2000. The 2000 population census was the last decennial census, which included the question “Where did you live five years ago?” The age and sex distribution of the net migrants was derived, using answers to this question. The net migration for Chicago has been derived independently, using census survival method for 1990-2000 and 2000-2010 under the assumption that the annual net migration for Chicago will be -40,000, -30,000 and -25,000 for 2010-2015, 2015-2020 and 2020-2025, respectively. The age and sex distribution from the 2000-2010 net migration was used to distribute the net migrants for the projection periods. Conclusion These projections were prepared for use by the Certificate of Need (CON) Program; they are produced using evidence-based techniques, reasonable assumptions and the best available input data. However, as assumptions of future demographic trends may contain errors, the resulting projections are unlikely to be free of errors. In general, projections of small areas are less reliable than those for larger areas, and the farther in the future projections are made, the less reliable they may become. When possible, these projections should be regularly reviewed and updated, using more recent birth, death and migration data.