Select metrics by Community District/Neighborhood Tabulation Districts where available. To see the full set of indicators (including those without CD/NTD level data), please refer to “Social Indicators Report Data – Citywide” at https://data.cityofnewyork.us/Social-Services/Social-Indicators-Report-Data-Citywide/gysw-j2f3.

The Social Indicators Report is an analysis of social conditions across New York City, including geographic and demographic breakdowns, changes over time, and the Mayor's plan for responding to problems highlighted in the report. The report can be found at http://www1.nyc.gov/assets/operations/downloads/pdf/Social-Indicators-Report-April-2016.pdf. See also, the recently released Disparity Report produced by the Center for Innovation through Data Intelligence (CIDI). The report can be found at http://www1.nyc.gov/assets/operations/downloads/pdf/Social-Indicators-Report-April-2016.pdf

Database of the nation''s substance abuse and mental health research data providing public use data files, file documentation, and access to restricted-use data files to support a better understanding of this critical area of public health. The goal is to increase the use of the data to most accurately understand and assess substance abuse and mental health problems and the impact of related treatment systems. The data include the U.S. general and special populations, annual series, and designs that produce nationally representative estimates. Some of the data acquired and archived have never before been publicly distributed. Each collection includes survey instruments (when provided), a bibliography of related literature, and related Web site links. All data may be downloaded free of charge in SPSS, SAS, STATA, and ASCII formats and most studies are available for use with the online data analysis system. This system allows users to conduct analyses ranging from cross-tabulation to regression without downloading data or relying on other software. Another feature, Quick Tables, provides the ability to select variables from drop down menus to produce cross-tabulations and graphs that may be customized and cut and pasted into documents. Documentation files, such as codebooks and questionnaires, can be downloaded and viewed online.

This dataset contains model-based ZIP Code tabulation Areas (ZCTA) level estimates for the PLACES project 2020 release. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 27 measures: 5 chronic disease-related unhealthy behaviors, 13 health outcomes, and 9 on use of preventive services. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population data, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.

These ionospheric data consist of scaling notes, equipment usage logs, and ionospheric values in the form of daily work sheets, F-Plots, tabulations, and booklets. The station notes list the equipment used, power settings, frequencies used, equipment problems, and other information about the equipment. The daily work sheets, commonly referred to an 7E's, are the paper forms used for recording the scaled values values of the ionospheric parameters. Tabulations and booklets contain the published values by ionospheric parameter. If publication errors arise or are suspected, the daily work sheets are usually consulted, if they are available.These data are available from the NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) as electrostatic copies and some as imaged files. Most of the data are in the form of daily work sheets, tabulations, and booklets. The booklets and tabulations have each ionospheric parameter listed on one or two pages, depending upon the format. The daily work sheets have one day (24 hours) of scaled ionospheric parameters on each sheet.

(Link to Metadata) This layer represents the smallest voting tabulation area. In some cases, the geographic extent is a municipality, in other cases it is a section of a municipality. Many of the polygons in this layer represent PART of a state House district (the House District is the unit to which votes are tabulated to actually determine the winner of an election, and many of them are multi-town). An inherent problem in creating this layer was the lack of spatial congruence between the HOUS2012 layer and the TWNBNDS layer. Although in many cases parts of the HOUS2012 layer purport to follow town boundaries (according to the statute where they are verbally described) the data layer that was used to create the data was not the TWNBNDS layer, but likely a Census Tiger Files layer. This Voting Tabulation layer was created using aspects of the two existing layers: Town Boundaries from the TWNBNDS layer, whereas any boundaries that split towns were derived from the HOUS2012 layer. The Process Steps section below describes how this was achieved, and how district labels were assigned to new polygons.

Abstract

The presence of adequate and current statistical data in various economic sectors that are considered essential for development planning, socio-economic policy formulation and economic analysis is vital in promoting the economic development of a country. Based on this general objective, the Central Statistical Agency (CSA) has been conducting surveys of various economic activities, of which, the annual Large and Medium Scale Manufacturing Industries survey is one.

Manufacturing is defined here according to International Standard Industrial Classification (ISIC Revision-3.1) as "the physical or chemical transformation of materials or components into new products, whether the work is performed by power-driven machines or by hand, whether it is done in a factory or in the worker's home, and whether the products are sold at wholesale or retail. The assembly of the component parts of manufactured products is also considered as manufacturing activities."

CSA has been publishing results of the survey of Manufacturing and Electricity Industries on annual basis since 1968 Ethiopian Calendar to provide users with reliable, comprehensive and timely statistical data on these sectors. In this respect, this survey, which is conducted on annual basis, is the principal source of industrial statistics on large and medium scale manufacturing industries in the country.

The survey questionnaire is designed to answer questions about number of establishments, number of persons engaged, wages and salaries paid by industrial group, sex, nationality and occupation, paid-up capital, gross value of production, industrial and non-industrial costs. value added, operating surplus, quantity of production and raw materials consumed, fixed assets, investment and production capacity and etc..

The main objectives of the annual survey of Large and Medium Scale Manufacturing and Electricity Industries are to: 1.Obtain basic statistical data that are essential for policy makers, planners and researchers by major industrial group. 2.Collect basic quantitative information on employment, volume of quantitative information on employment, volume of production and raw materials, structure and performance of the country's Large and Medium Scale Manufacturing and Electricity Industries. 3.Compile statistical data which will be an input to the System of National Accounts (SNA), on Large and Medium Scale Manufacturing and Electricity establishments as a whole and by major industrial group. 4.Obtain the number of proprietors engaged in these sectors and find out the major problems that create stumbling blocks for their activities.The identification of the problems is required for planning and executing any type of government intervention program.

Geographic coverage

National

Analysis unit

Establishment/ Enterprise

Universe

The universe of the large and medium scale manufacturing survey is confined to those establishments which engaged 10 persons and above and use power-driven machines and covers both public and private industries.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey covers all large and medium manufacturing industries which engage 10 persons or more and use power-driven machines.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questinnaire contains the following sections/ items:

Section 1.1 - Address of the establishments: This section has variables that identify the questionnaire uniquely. The variables are; Killil, Zone, Wereda, Town, Higher, Kebele, House no, Year, ISIC, Establishment number, Telephone number and P.O. Box codes or numbers.

Section 1.2 - Address of Head Office if different from Factory: In this section information about the factory head office is collected (if the factory is separated from the head office). The variables used to collect the information are; Killil, Zone, Wereda, Town, Higher, Kebele, House no, Telephone no and P.O. Box.

Section 2 - Basic Information about the establishment: This section has questions related to basic information about the establishment.

Section 3.1 - Number of Persons Engaged: This section has variables (questions) that used to collect establishment's employees number by employees occupation.

Section 3.2 - Wages and Salaries and Other Employee Benefits Paid: This section has variables related to wages and other employees' benefits by employee occupation.

Section 3.3 - Number of Permanent Employees by Basic Salary Group: This section has variables related to salary groups by sex of employees

Section 4.1 - Products and By-products: This section has questions related to product produced, produced quantity and sales.

Section 4.2 - Service and Other Receipts: Contains questions related to income from different source other than selling the products.

Section 5 - Value of Stocks: Contains questions that related to information about materials in the stock.

Section 6.1 - Cost and Quantity of Raw Materials, Parts and Containers Used: This section has questions related to principal raw materials, raw material type, quantity, value and source (local or imported).

Section 6.2 - Other Industrial Costs: This section has questions related to other industrial costs including cost of energy and other expenses.

Section 6.3 - Other Non-industrial Expenses: Contains questions related to non-industrial expenses like license fee, advertising, stationary, etc.

Section 6.4 - Taxes Paid: This section has questions related to taxes like indirect tax and income tax.

Section 7.1 - Type and Value of Fixed Assets: This section has questions related to fixed assets of the establishment.

Section 7.2 - Annual Investment by Type and Source: This section has questions related to investment on fixed assets and working capitals.

Section 8.1 - Annual Production at Full Capacity: This section has questions about quantity and value of products if the establishment uses its full capacity.

Section 8.2 - Estimated Value and Quantity of Raw Materials Needed, at Full Capacity: This section has questions about the estimate of quantity and value of raw materials that needed to function at full capacity.

Section 8.3 - The percentage of the 1994 production as compared to the factory's production at full capacity

Section 8.4 - The three major problems that prevented the establishment from operating with full capacity.

Section 8.5 - Reason for lack of market if there is a problem of getting market.

Section 8.6 - About whether the factory made applied for a loan.

Section8.7 - Reason for not solving shortage of working capital if there is a shortage of working capital.

Section 8.8 - The three major problems that are facing the establishment at present.

Section 8.9 - Whether the factory faced problem during export.

Section 8.10 - Three major problems faced during export.

Cleaning operations

Editing, Coding and Verification: A number of quality control steps were taken to ensure the quality of data. The first step taken in this direction was, to revise the questionnaire, to make it easier for internal consistency checking or editing, both at field and office level. Furthermore, based on this revised questionnaire, revised instruction manual with field editing procedures were prepared in Amharic for both enumerators and supervisors (field editors). Using this manual, some editing and coding were carried out by field editors during the data collection stage. After the majority of the completed questionnaires were brought back to head office, final editing, coding and verification were performed by editors, statistical technicians and statisticians. Finally, the edited and coded questionnaires were checked and verified by other senior professionals.

Data Entry, Cleaning and Tabulation: The data were entered and verified on personal computers using CSpro (Census and Survey Processing System) Software. Twelve CSA data entry staff and one data cleaner participated in this activity for fifteen days with close supervision of the activities by two professionals. Then, the data entered were cleaned using personal computers in combination with manual cleaning for some serious errors. Finally, the tabulation of the results was processed using the IMPS (Integrated Microcomputer Processing System) software by one programmer with technical assistance from Industry, Trade and Services Statistics Department staff.

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by modified ZIP Code Tabulation Area (ZCTA) of residence. Modified ZCTA reflects the first non-missing address within NYC for each person reported with an antibody test result. This unit of geography is similar to ZIP codes but combines census blocks with smaller populations to allow more stable estimates of population size for rate calculation. It can be challenging to map data that are reported by ZIP Code. A ZIP Code doesn’t refer to an area, but rather a collection of points that make up a mail delivery route. Furthermore, there are some buildings that have their own ZIP Code, and some non-residential areas with ZIP Codes. To deal with the challenges of ZIP Codes, the Health Department uses ZCTAs which solidify ZIP codes into units of area. Often, data reported by ZIP code are actually mapped by ZCTA. The ZCTA geography was developed by the U.S. Census Bureau. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-modzcta.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level.
These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents.

In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders)

Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning.

Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020.

Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates.
For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data • https://data.cityofnewyork.us/Health/Modified-Zip-Code-Tabulation-Areas-MODZCTA-/pri4-ifjk

Abstract

The presence of adequate and current statistical data in various economic sectors that are considered essential for development planning, socio-economic policy formulation and economic analysis is vital in promoting the economic development of a country. Based on this general objective, the Central Statistical Agency (CSA) has been conducting surveys of various economic activities, of which, the annual Large and Medium Scale Manufacturing Industries survey is one.

Manufacturing is defined here according to International Standard Industrial Classification (ISIC Revision-3) as “the physical or chemical transformation of materials or components into new products, whether the work is performed by power-driven machines or by hand, whether it is done in a factory or in the worker’s home, and whether the products are sold at wholesale or retail. The assembly of the component parts of manufactured products is also considered as manufacturing activities.”

CSA has been publishing results of the survey of Manufacturing and Electricity Industries on annual basis since 1968 E.C. to provide users with reliable, comprehensive and timely statistical data on these sectors. In this respect, this survey, which is conducted on annual basis, is the principal source of industrial statistics on large and medium scale manufacturing industries in the country. In this edition value added in the national account concept at factor cost is replaced with value added in the national account concept at basic price. So as to comply with the current practice of System of National Account (SNA). As a result, the time serious data for the previous four years have also been adjusted. In addition to this the concept and data in respect of census value added is withdrawn from the report because its application is no more used in practice.

The main objectives of the annual survey of Large and Medium Scale Manufacturing and Electricity Industries are to:- 1. Obtain basic statistical data that are essential for policy makers, planners and researchers by major industrial group. 2. Collect basic quantitative information on employment, volume of production and raw materials, structure and performance of the country’s Large and Medium Scale Manufacturing and Electricity Industries. 3. Compile statistical data which will be an input to the System of National Accounts (SNA), on Large and Medium Scale Manufacturing and Electricity establishments as a whole and by major industrial group. 4. Obtain the number of proprietors engaged in these sectors and find out the major problems that create stumbling blocks for their activities.

Geographic coverage

National

Analysis unit

Establishment/ Enterprise

Universe

The universe of the large and medium scale manufacturing survey is confined to those establishments which engaged 10 persons and above and use power-driven machines and covers both public and private industries in all Regions of the country.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable - the survey enumerated all manufacturing industries/ enterprises that qualified as large and medium manufacturing industry category.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questinnaire contains the following sections/ items:

Item 1.1. Adress of the establishments: This section has varibles that identify the questionnaire uniquely. The variables are; Killil, Zone, Wereda, Town, Higher, Kebele, House no, Year, ISIC, Establishmnet no, Eelephone no and P.O.Box codes or numbers.

Item 1.2. Address of Head Office if Separated From Factory: In this section information about factory head office is collected (if the factory is separated from the head office). The varibles used to collect the information are; Killil, Zone, Wereda, Town, Higher, Kebele, House no, Telephone no and P.O.Box.

Item 2. Basic Information About The Establishment: This section has questions related to basic information about the establishment.

Item 3.1. Number of Persons Engaged: This section has variables (questions) that used to collect establishment's employees number by employees occupation.

Item 3.2. Number of Persons Engaged by Educational Status: This section has varabils (questions) that used to collect establishment's employees number by their educational status.

Item 3.3. Number of Persons Engaged by Age Group: Contains variables that used to collect information about employees number by employees age group.

Item 3.4. Wages and Salaries and Other Employee Benefits Paid: This section has variables related to wages and other employees benefits by employee occupation.

Item 3.5. Number of Permanent Employees by Basic Salary Group: This section has variables related to salary groups by sex of employees

Item 4.1. Products and By-products: This section has questions related to product produced, produced quantity and sales.

Item 4.2. Service and Other Receipts: Contains questions related to income from different source other than selling the products.

Item 5. Value of Stocks: Contains questions that related to information about materials in the stock.

Item 6.1. Cost and Quantity of Raw Materials, Parts and Containers Used: This section has questions related to principal raw materials, raw material type, quantity, value and source (local or imported).

Item 6.2. Other Industrial Costs: This sections has questions related to other industrial costs including cost of energy and other expenses.

Item 6.3. Other Non-industrial Expenses: Contains questions related to non-industrial expenses like license fee, advertising, stationary, etc.

Item 6.4. Taxes Paid: This section has questions related to taxes like indirect tax and income tax.

Item 7. Fixed Assets and Investment: This section has questions related to fixed assets and investment on fixed assests and working capital.

Item 8.1. Annual Production at Full Capacity: This section has questions about quantity and value of products if the establishment uses its full capacity.

Item 8.2. Estimated Value and Quantity of Raw Materials Needed, at Full Capacity: This section has questions about the estimate of quantity and value of raw materials that needed to function at full capacity.

Item 8.3. The three major problems that prevented the establishment from operating at full capacity.

Item 8.4. The three major problems that are facing the establishment at present.

Cleaning operations

Editing, Coding and Verification: A number of quality control steps were taken to ensure the quality of data. The first step taken in this direction was, to revise the questionnaire, to make it easier for internal consistency checking or editing, both at field and office level. Furthermore, based on this revised questionnaire, revised instruction manual with field editing procedures was prepared in Amharic for both enumerators and supervisors (field editors). Using this manual, some editing and coding were carried out by field editors during the data collection stage. After the majority of the completed questionnaires were brought back to head office, final editing, coding and verification were performed by editors, statistical technicians and statisticians. Finally, the edited and coded questionnaires were checked and verified by other senior professionals.

Data Entry, Cleaning and Tabulation: The data were entered and verified on personal computers using CSpro (Census and Survey Processing System) Software. Twelve CSA data entry staff and one data cleaner participated in this activity for fifteen days with close supervision of the activities by two professionals. Then, the data entered were cleaned using personal computers in combination with manual cleaning for some serious errors. Finally, the tabulation of the results was processed using the same software by one programmer with technical assistance from Industry, Trade and Services Statistics Department staff.

This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 36 measures: 13 for health outcomes, 9 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, and 3 for health status. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2021 or 2020 data, Census Bureau 2010 population data, and American Community Survey 2015–2019 estimates. The 2023 release uses 2021 BRFSS data for 29 measures and 2020 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

The U.S. Department of Housing and Urban Development (HUD) periodically receives "custom tabulations" of Census data from the U.S. Census Bureau that are largely not available through standard Census products. These datasets, known as "CHAS" (Comprehensive Housing Affordability Strategy) data, demonstrate the extent of housing problems and housing needs, particularly for low income households. The primary purpose of CHAS data is to demonstrate the number of households in need of housing assistance. This is estimated by the number of households that have certain housing problems and have income low enough to qualify for HUD’s programs (primarily 30, 50, and 80 percent of median income). CHAS data provides counts of the numbers of households that fit these HUD-specified characteristics in a variety of geographic areas. In addition to estimating low-income housing needs, CHAS data contributes to a more comprehensive market analysis by documenting issues like lead paint risks, "affordability mismatch," and the interaction of affordability with variables like age of homes, number of bedrooms, and type of building. This dataset is a special tabulation of the 2016-2020 American Community Survey (ACS) and reflects conditions over that time period. The dataset uses custom HUD Area Median Family Income (HAMFI) figures calculated by HUD PDR staff based on 2016-2020 ACS income data. CHAS datasets are used by Federal, State, and Local governments to plan how to spend, and distribute HUD program funds. To learn more about the Comprehensive Housing Affordability Strategy (CHAS), visit: https://www.huduser.gov/portal/datasets/cp.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs Data Dictionary: DD_ACS 5-Year CHAS Estimate Data by State Date of Coverage: 2016-2020

This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates for the PLACES 2021 release. PLACES is the expansion of the original 500 Cities Project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 29 measures: 4 chronic disease-related health risk behaviors, 13 health outcomes, 3 health status, and 9 on using preventive services. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2010 population data, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.

This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 40 measures: 12 for health outcomes, 7 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, 3 for health status, and 7 for health-related scocial needs. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 population data, and American Community Survey 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates for the PLACES 2022 release. PLACES covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 29 measures: 13 for health outcomes, 9 for preventive services use, 4 for chronic disease-related health risk behaviors, and 3 for health status. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2020 or 2019 data, Census Bureau 2010 population data, and American Community Survey 2015–2019 estimates. The 2022 release uses 2020 BRFSS data for 25 measures and 2019 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

Abstract

The 2006 Tanzania Service Provision Assessment (TSPA 2006) is a facility-based survey designed to extract information about the general performance of facilities that offer maternal, child, and reproductive health services as well as services for specific infectious diseases, including sexually transmitted infections (STIs), HIV/AIDS, tuberculosis (TB), and malaria.

The TSPA 2006 provides national- and zonal-level representative information for hospitals, health centres, dispensaries, and stand-alone facilities offering HIV/AIDS-related services. Findings can supplement household-based health information from the Tanzania Demographic and Health Survey (TDHS) conducted in 2004-05, which provides information on health and the utilisation of services by the overall population.

The objectives of the 2006 TSPA were to: • Describe how well prepared facilities are to provide good quality reproductive and child health services and services for some infectious diseases (HIV/AIDS, STIs, malaria, and TB); • Provide a comprehensive body of information on the performance of the full range of public and private health care facilities that provide reproductive, child health, and HIV/AIDS services; • Help identify strengths and weaknesses in the delivery of reproductive, child health, and HIV/AIDS services at health care facilities, producing information that can be used to better target service delivery improvement interventions and to improve on-going supervisory systems; • Describe the processes used in providing child, maternal, and reproductive health services and the extent to which accepted standards for good quality service provision are followed; • Provide information for periodically monitoring progress in improving the delivery of reproductive, child health, and HIV/AIDS services at Tanzanian health facilities; • Provide input into the evolution of a system of accreditation of health facilities in Tanzania; and • Provide baseline information on the capacity of health facilities to provide basic and advanced level HIV/AIDS care and support services, and on the recordkeeping systems in place for monitoring HIV/AIDS preventive, diagnostic, care, and support services.

Geographic coverage

National

Analysis unit

• Health service facilities
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Data were collected from a representative sample of facilities, a sample of health service providers at each facility, and a sample of sick children, family planning, ANC, and STI clients.

Sample of Facilities The sample used for the TSPA 2006 was obtained from a list of 5,663 health facilities in Tanzania. The list included hospitals, health centres, dispensaries, and stand-alone facilities, with different managing authorities, including government, private for-profit, parastatal, and faith-based organisations. A sample size of 612 facilities was selected for the survey, based on logistic considerations as well as the minimum sample size required for the desired analysis (margin of error of 10 percent). The sample allows for national and zonal estimates for key indicators for Mainland Tanzania and Zanzibar. All national referral hospitals, regional general hospitals, and district/district-designated hospitals were purposely included in the sample. The rest of the facilities (health centres, dispensaries, stand-alone facilities, and other private hospitals) were sampled in such a way as to provide national and zonal-level representation. Thus, the TSPA final sample covered approximately 10 percent of all facilities in the Mainland and approximately 36 percent in Zanzibar. This sample size is not large enough to present findings at the regional level.

Sample of Health Service Providers A health service provider is defined as one who provides consultation services, counselling, health education, or laboratory services to clients. For example, health workers were not eligible for observation or interview if they only take measurements or complete registers and never provide any type of professional client services. The sample of health service providers was selected from providers who were present in the facility on the day of the survey and who provided services that were assessed by the TSPA. The idea was to interview an average of eight providers in a facility. In facilities with fewer than eight health providers, all of the providers present on the day of the visit were interviewed. In facilities with more than eight providers, an average of eight providers was interviewed, including all providers whose work was observed. If interviewers observed fewer than eight providers, then they also interviewed a random selection of the remaining health providers to obtain an average of eight provider interviews.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four main types of data collection tools were used: 1. Using the Facility Audit Questionnaires, interviewers collected information on the availability of resources, support systems, and facility infrastructure elements necessary to provide a level of service that generally meets accepted national and international standards. The support services assessed were those that are commonly acknowledged as essential management tools for maintaining health services. The facility audit questionnaires include MCH, HIV/AIDS, laboratory, and pharmacy sections. The HIV/AIDS section assessed how clients with HIV/AIDS were handled, from counselling and testing through treatment, referral, and follow up. Interviewers also collected information on health facility policies and practises related to collecting and reporting HIV/AIDS-related records and statistics for services provided to clients through the health facility.

1. The Observation Protocol was tailored to the service being provided. For sick child, antenatal care, family planning, and STI consultations, the observer assessed the extent to which service providers adhered to standards of care, based on generally accepted practices for good quality service delivery. The observations included both the process used in conducting specific procedures and examinations, and also the content of information (including history, symptoms, and advice) exchanged between the provider and the client.

2. After clients were observed receiving a service, they were asked to participate in an Exit Interview as they left the facility. The exit interview included questions on the client’s understanding of the consultation or examination, as well as his/her recall of instructions received about treatment or preventive behaviour. The interviewer also elicited the client’s perception of the service delivery environment.

3. In the Health Worker/Provider Interview, service providers were interviewed regarding their qualifications (training, experience, and continued in-service training), the supervision they had received, and their perceptions of the service delivery environment.

Cleaning operations

Management of questionnaires in the field: After completing data collection in each facility, the interviewers reviewed the questionnaires before handing them over to the team leader who reviewed them a second time. Staff from headquarters picked up the questionnaire when visiting the teams. Sometimes team leaders posted the questionnaires to headquarters by courier services.

Data sorting and editing at headquarters: Once the questionnaires from each facility were received at headquarters, they were first sorted to ensure that they were in the correct order and none were missing. They were then edited to eliminate any mistakes that would prevent the computer from accepting information during data entry. In cases where there was a problem with the questionnaires from a facility, the data collection team was consulted so that the problem could be rectified.

Data processing:The design of the tabulation plan and the preparation of the programs for producing statistical tables were carried out from August through September 2006. Data analysis, including clarification of unclear information, was carried out from October 2006 through February 2007. During data analysis, the analysis plan was revised on the basis of feedback from the TSPA Task Force to ensure that the analysis was appropriate for the Tanzanian health system.

Abstract

The presence of adequate and current statistical data in various economic sectors that are considered essential for development planning, socio-economic policy formulation and economic analysis is vital in promoting the economic development of a country. Based on this general objective, the Central Statistical Authority (CSA) has been conducting surveys of various economic activities of which the annual Large and Medium Scale Manufacturing Industries survey is one.

Manufacturing is defined here according to International Standard Industrial Classification as "the physical or chemical transformation of materials or components into new products, whether the work is performed by power-driven machines or by hand, whether it is done in a factory or in the worker's home, and whether the products are sold at wholesale or retail. The assembly of the component parts of manufactured products is also considered as manufacturing activities."

The survey questionnaire is designed to answer questions about number of establishments, number of persons engaged, wages and salaries paid by industrial group, sex, nationality and occupation, paid-up capital, gross value of production, industrial and non-industrial costs. value added, operating surplus, quantity of production and raw materials conusmed, fixed assets, investment and production capacity and etc..

The main objectives of the annual survey of Large and Medium Scale Manufacturing and Electricity Industries are to: 1.Obtain basic statistical data that are essential for policy makers, planners and researchers by major industrial group. 2.Collect basic quantitative information on employment, volume of quantitative information on employment, volume of production and raw materials, structure and performance of the country's Large and Medium Scale Manufacturing and Electricity Industries. 3.Compile statistical data which will be an input to the System of National Accounts (SNA), on Large and Medium Scale Manufacturing and Electricity establishments as a whole and by major industrial group. 4.Obtain the number of proprietors engaged in these sectors and find out the major problems that create stumbling blocks for their activities.The identification of the problems is required for planning and executing any type of government intervention program.

Geographic coverage

National

Analysis unit

Establishment

Universe

The universe of the large and medium scale manufacturing survey is confined to those establishments which engaged 10 persons and above and use power-driven machines and covers both public and private industries.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey covers all large and medium manufacturing industries which engage 10 persons or more and use power-driven machines

Mode of data collection

Face-to-face [f2f]

Research instrument

The questinnaire contains the following sections/ items:

Section 1.1. Adress of the establishments: This section has varibles that identify the questionnaire uniquely. The variables are; Killil, Zone, Wereda, Town, Higher, Kebele, House no, Year, ISIC, Establishmnet no, Eelephone no and P.O.Box codes or numbers.

Section 1.2. Address of Head Office if Separated From Factory: In this section information about factory head office is collected (if the factory is separated from the head office). The varibles used to collect the information are; Killil, Zone, Wereda, Town, Higher, Kebele, House no, Telephone no and P.O.Box.

Section 2. Basic Information About The Establishment: This section has questions related to basic information about the establishment.

Section 3.1. Number of Persons Engaged: This section has variables (questions) that used to collect establishment's employees number by employees occupation.

Section 3.2. Wages and Salaries and Other Employee Benefits Paid: This section has variables related to wages and other employees benefits by employee occupation.

Section 3.3. Number of Permanent Employees by Basic Salary Group: This section has variables related to salary groups by sex of employees

Section 4.1. Products and By-products: This section has questions related to product produced, produced quantity and sales.

Section 4.2. Service and Other Receipts: Contains questions related to income from different source other than selling the products.

Section 5. Value of Stocks: Contains questions that related to information about materials in the stock.

Section 6.1. Cost and Quantity of Raw Materials, Parts and Containers Used: This section has questions related to principal raw materials, raw material type, quantity, value and source (local or imported).

Section 6.2. Other Industrial Costs: This sections has questions related to other industrial costs including cost of energy and other expenses.

Section 6.3. Other Non-industrial Expenses: Contains questions related to non-industrial expenses like license fee, advertising, stationary, etc.

Section 6.4. Taxes Paid: This section has questions related to taxes like indirect tax and income tax.

Section 7.1. Type and Value of Fixed Assets: This section has questions related to fixed assets of the establishment.

Section 7.2. Annual Investment by Type and Source: This section has questions related to investment on fixed assets and working capitals.

Section 8.1. Annual Production at Full Capacity: This section has questions about quantity and value of products if the establishment uses its full capacity.

Section 8.2. Estimated Value and Quantity of Raw Materials Needed, at Full Capacity: This section has questions about the estimate of quantity and value of raw materials that needed to function at full capacity.

Section 8.3. The three major problems that prevented the establishment from operating at full capacity.

Section 8.4. The three major problems that are facing the establishment at present.

Cleaning operations

Editing, Coding and Verification: A number of quality control steps were taken to ensure the quality of data. The first step taken in this direction was, to revise the questionnaire, to make it easier for internal consistency checking or editing, both at field and office level. Furthermore, based on this revised questionnaire, revised instruction manual with field editing procedures were prepared in Amharic for both enumerators and supervisors (field editors). Using this manual, some editing and coding were carried out by field editors during the data collection stage. After the majority of the completed questionnaires were brought back to head office, final editing, coding and verification were performed by 9 statisticians and statistical technicians and 10 editors. Finally, the edited and coded questionnaires were checked and verified by other senior professionals.

Data Entry, Cleaning and Tabulation: The data were entered and verified on personal computers IMPS (Integrated Microcomputer Processing System) Software. Twelve CSA data entry staff and one data cleaner participated in this activity for fifteen days with close supervision of the activities by two professionals. Then, the data entered were cleaned using personal computers in combination with manual cleaning for some serious errors. Finally, the tabulation of the results was processed using the IMPS (Integrated Microcomputer Processing System) software by one programmer with technical assistance from Industry, Trade and Services Statistics Department staff.

Abstract

The 2005 Guyana HIV/AIDS Indicator Survey (GAIS) is the first household-based, comprehensive survey on HIV/AIDS to be carried out in Guyana. The 2005 GAIS was implemented by the Guyana Responsible Parenthood Association (GRPA) for the Ministry of Health (MoH). ORC Macro of Calverton, Maryland provided technical assistance to the project through its contract with the U.S. Agency for International Development (USAID) under the MEASURE DHS program. Funding to cover technical assistance by ORC Macro and for local costs was provided in their entirety by USAID/Washington and USAID/Guyana.

The 2005 GAIS is a nationally representative sample survey of women and men age 15-49 initiated by MoH with the purpose of obtaining national baseline data for indicators on knowledge/awareness, attitudes, and behavior regarding HIV/AIDS. The survey data can be effectively used to calculate valuable indicators of the President’s Emergency Plan for AIDS Relief (PEPFAR), the Joint United Nations Program on HIV/AIDS (UNAIDS), the United Nations General Assembly Special Session (UNGASS), the United Nations Children Fund (UNICEF) Orphan and Vulnerable Children unit (OVC), and the World Health Organization (WHO), among others. The overall goal of the survey was to provide program managers and policymakers involved in HIV/AIDS programs with information needed to monitor and evaluate existing programs; and to effectively plan and implement future interventions, including resource mobilization and allocation, for combating the HIV/AIDS epidemic in Guyana.

Other objectives of the 2005 GAIS include the support of dissemination and utilization of the results in planning, managing and improving family planning and health services in the country; and enhancing the survey capabilities of the institutions involved in order to facilitate the implementation of surveys of this type in the future.

The 2005 GAIS sampled over 3,000 households and completed interviews with 2,425 eligible women and 1,875 eligible men. In addition to the data on HIV/AIDS indicators, data on the characteristics of households and its members, malaria, infant and child mortality, tuberculosis, fertility, and family planning were also collected.

Geographic coverage

National

Analysis unit

• Individuals;
• Households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the 2005 GAIS is to provide estimates with acceptable precision for important population characteristics such as HIV/AIDS related knowledge, attitudes, and behavior. The population to be covered by the 2005 GAIS was defined as the universe of all women and men age 15-49 in Guyana.

The major domains to be distinguished in the tabulation of important characteristics for the eligible population are: • Guyana as a whole • The urban area and the rural area each as a separate major domain • Georgetown and the remainder urban areas.

Administratively, Guyana is divided into 10 major regions. For census purposes, each region is further subdivided in enumeration districts (EDs). Each ED is classified as either urban or rural. There is a list of EDs that contains the number of households and population for each ED from the 2002 census. The list of EDs is grouped by administrative units as townships. The available demarcated cartographic material for each ED from the last census makes an adequate sample frame for the 2005 GAIS.

The sampling design had two stages with enumeration districts (EDs) as the primary sampling units (PSUs) and households as the secondary sampling units (SSUs). The standard design for the GAIS called for the selection of 120 EDs. Twenty-five households were selected by systematic random sampling from a full list of households from each of the selected enumeration districts for a total of 3,000 households. All women and men 15-49 years of age in the sample households were eligible to be interviewed with the individual questionnaire.

The database for the recently completed 2002 Census was used as a sampling frame to select the sampling units. In the census frame, EDs are grouped by urban-rural location within the ten administrative regions and they are also ordered in each administrative unit in serpentine fashion. Therefore, this stratification and ordering will be also reflected in the 2005 GAIS sample.

Based on response rates from other surveys in Guyana, around 3,000 interviews of women and somewhat fewer of men expected to be completed in the 3,000 households selected.

Several allocation schemes were considered for the sample of clusters for each urban-rural domain. One option was to allocate clusters to urban and rural areas proportionally to the population in the area. According to the census, the urban population represents only 29 percent of the population of the country. In this case, around 35 clusters out of the 120 would have been allocated to the urban area. Options to obtain the best allocation by region were also examined. It should be emphasized that optimality is not guaranteed at the regional level but the power for analysis is increased in the urban area of Georgetown by departing from proportionality. Upon further analysis of the different options, the selection of an equal number of clusters in each major domain (60 urban and 60 rural) was recommended for the 2005 GAIS. As a result of the nonproportionalallocation of the number of EDs for the urban-rural and regional domains, the household sample for the 2005 GAIS is not a self-weighted sample.

The 2005 GAIS sample of households was selected using a stratified two-stage cluster design consisting of 120 clusters. The first stage-units (primary sampling units or PSUs) are the enumeration areas used for the 2002 Population and Housing Census. The number of EDs (clusters) in each domain area was calculated dividing its total allocated number of households by the sample take (25 households for selection per ED). In each major domain, clusters are selected systematically with probability proportional to size.

The sampling procedures are more fully described in "Guyana HIV/AIDS Indicator Survey 2005 - Final Report" pp.135-138.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires were used in the survey, namely: the Household Questionnaire and the Individual Questionnaire. The contents of these questionnaires were based on model questionnaires developed by the MEASURE DHS program. In consultation with USAID/Guyana, MoH, GRPA, and other government agencies and local organizations, the model questionnaires were modified to reflect issues relevant to HIV/AIDS in Guyana. The questionnaires were finalized around mid-May.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. For each person listed, information was collected on sex, age, education, and relationship to the head of the household. An important purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview.

The Household Questionnaire also collected non-income proxy indicators about the household's dwelling unit, such as the source of water; type of toilet facilities; materials used for the floor, roof and walls of the house; and ownership of various durable goods and land. As part of the Malaria Module, questions were included on ownership and use of mosquito bednets.

The Individual Questionnaire was used to collect information from women and men age 15-49 years and covered the following topics: • Background characteristics (age, education, media exposure, employment, etc.) • Reproductive history (number of births and—for women—a birth history, birth registration, current pregnancy, and current family planning use) • Marriage and sexual activity • Husband’s background • Knowledge about HIV/AIDS and exposure to specific HIV-related mass media programs • Attitudes toward people living with HIV/AIDS • Knowledge and experience with HIV testing • Knowledge and symptoms of other sexually transmitted infections (STIs) • The malaria module and questions on tuberculosis

Cleaning operations

The processing of the GAIS questionnaires began in mid-July 2005, shortly after the beginning of fieldwork and during the first visit of the ORC Macro data processing specialist. Questionnaires for completed clusters (enumeration districts) were periodically submitted to GRPA offices in Georgetown, where they were edited by data processing personnel who had been trained specifically for this task. The concurrent processing of the data—standard for surveys participating in the DHS program—allowed GRPA to produce field-check tables to monitor response rates and other variables, and advise field teams of any problems that were detected during data entry. All data were entered twice, allowing 100 percent verification. Data processing, including data entry, data editing, and tabulations, was done using CSPro, a program developed by ORC Macro, the U.S. Bureau of Census, and SERPRO for processing surveys and censuses. The data entry and editing of the questionnaires was completed during a second visit by the ORC Macro specialist in mid-September. At this time, a clean data set was produced and basic tables with the basic HIV/AIDS indicators were run. The tables included in the current report were completed by the end of November 2005.

Response rate

• From a total of 3,055 households in the sample, 2,800 were occupied. Among these households, interviews were completed in 2,608, for a response rate of 93 percent. • A total of 2,776 eligible women were identified and

Context

As digital media is growing the competition between online platforms also has rapidly increased. Online platforms like Buzzfeed, Mashable, Medium, towards data science publish hundreds of articles of every day. In this report, we analyze the Mashable dataset which consists of articles data information mainly as a number of unique words, number of non-stop words, the postpositive polarity of words, negative polarity of words, etc. Here we intend to predict the number of shares that articles can be shared. This will be very helpful for Mashable to decide which articles should they publish because they can actually predict which articles will be having the maximum number of shares. Random forest regression has been used to predict the number of shares and it can achieve an accuracy of 70% with Parameter tuning. As there is the number of articles that will be collected from different ways but to classify or group these articles into separate categories for an online platform it will be a difficult job. To handle this problem, in this report we have used neural-networks to classify the articles into different categories. By doing so, the people doesn't need to do an extensive search because the Mashable can keep an interface with articles classified into different categories which in-turn will help people to choose the category and directly search their articles.

Content

With the growth of the Internet in daily life, people are in a minute away to read the news or watch any entertainment or read articles of different categories. As the growth of the internet, even the usage by the people of it has increased rapidly, it actually became their part of life. Nowadays as people using the internet more, they are studying the articles for their knowledge or news or of any sector online. As the demand is increased even online platforms rivalry has increased. Due to this, every online platform is striving to publish the articles on their site which have great value and bring most shares. In this project, we do the prediction of shares of an article based on the data produced by ‘Mashable’ where they collected data of around 39000 articles. For this prediction, we have used Random forest Regression. In this report will be discussing why the Random forest Regression has been choosing for the prediction of shares by analyzing the Data set and doing cross-tabulation, what is the variance of the dataset and how many levels of bias it is with-holding. Even discuss about the features selection and why decided to do some feature engineering and how it will be helpful in increasing the accuracy. Even in this report, we discuss how these predictions will be helpful for Mashable organization on their decision of publishing the articles.

In this paper, we will see to handle the issue of classifying articles such as entertainment, news, lifestyle, technology, etc. To obtain this classification used the neural networks. In this paper, we will discuss why did we choose the neural networks for classification and what type of feature engineering has been used. At what levels of hidden layers and neurons the model is being affected at what stages model got started getting overfitted. For classification after the output layer, we used soft-max function. In this paper, an 11 layer neural network classifier has been used and achieved around 80% of accuracy. Methods used to achieve this accuracy are constant check rate of accuracy with different layers and neurons, standardization technique and feature selection using a correlation matrix.

Related work on the study and analysis of Online News Popularity is done by the Shuo Zhang from Australian National University where they predicted the article will be popular or not and used binary neural network classification. The other related works also achieved greater accuracy of 70% but here they actually predicted the shares by applying different regression techniques. This paper was worked by He Ren and Quan Yang work in DepaDepartment of Electrical Engineering at Stanford University.

Acknowledgements

Bringing value from a heavy data set. How does this value will be helpful to Organizations. Analyzing the large volumes of data and how to bring the values from it. Correlating the features and calculating the predictability power to the target variable which we are predicting. Selection of different Machine Learning algorithms and their compatibility. Neural Networks works efficient for high dimensional data sets but Needed a very high computational time.

Inspiration

• Predicting the number of shares an article can get it • Classifying the articles into different categories? • Which category of article should be published maximum for higher number of shares? • On What week-day What type of article should Mashable post more? • For different categories of articles what should be their min and max content length?

Housing Assessment Resource Tools (HART) This dataset contains 2 tables and 5 files which draw upon data from the 2021 Census of Canada. The tables are a custom order and contain data pertaining to older adults and housing need. The 2 tables have 6 dimensions in common and 1 dimension that is unique to each table. Table 1's unique dimension is the "Ethnicity / Indigeneity status" dimension which contains data fields related to visible minority and Indigenous identity within the population in private households. Table 2's unique dimension is "Structural type of dwelling and Period of Construction" which contains data fields relating to the structural type and period of construction of the dwelling. Each of the two tables is then split into multiple files based on geography. Table 1 has two files: Table 1.1 includes Canada, Provinces and Territories (14 geographies), CDs of NWT (6), CDs of Yukon (1) and CDs of Nunavut (3); and Table 1.2 includes Canada and the CMAs of Canada (44). Table 2 has three files: Table 2.1 includes Canada, Provinces and Territories (14), CDs of NWT (6), CDs of Yukon (1) and CDs of Nunavut (3); Table 2.2 includes Canada and the CMAs of Canada excluding Ontario and Quebec (20 geographies); and Table 2.3 includes Canada and the CMAs of Canada that are in Ontario and Quebec (25 geographies). The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and data fields: Geography: - Country of Canada as a whole - All 10 Provinces (Newfoundland, Prince Edward Island (PEI), Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia) as a whole - All 3 Territories (Nunavut, Northwest Territories, Yukon), as a whole as well as all census divisions (CDs) within the 3 territories - All 43 census metropolitan areas (CMAs) in Canada Data Quality and Suppression: - The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. - Area suppression is used to replace all income characteristic data with an 'x' for geographic areas with populations and/or number of households below a specific threshold. If a tabulation contains quantitative income data (e.g., total income, wages), qualitative data based on income concepts (e.g., low income before tax status) or derived data based on quantitative income variables (e.g., indexes) for individuals, families or households, then the following rule applies: income characteristic data are replaced with an 'x' for areas where the population is less than 250 or where the number of private households is less than 40. Source: Statistics Canada - When showing count data, Statistics Canada employs random rounding in order to reduce the possibility of identifying individuals within the tabulations. Random rounding transforms all raw counts to random rounded counts. Reducing the possibility of identifying individuals within the tabulations becomes pertinent for very small (sub)populations. All counts are rounded to a base of 5, meaning they will end in either 0 or 5. The random rounding algorithm controls the results and rounds the unit value of the count according to a predetermined frequency. Counts ending in 0 or 5 are not changed. Universe: Full Universe: Population aged 55 years and over in owner and tenant households with household total income greater than zero in non-reserve non-farm private dwellings. Definition of Households examined for Core Housing Need: Private, non-farm, non-reserve, owner- or renter-households with incomes greater than zero and shelter-cost-to-income ratios less than 100% are assessed for 'Core Housing Need.' Non-family Households with at least one household maintainer aged 15 to 29 attending school are considered not to be in Core Housing Need, regardless of their housing circumstances. Data Fields: Table 1: Age / Gender (12) 1. Total – Population 55 years and over 2. Men+ 3. Women+ 4. 55 to 64 years 5. Men+ 6. Women+ 7. 65+ years 8. Men+ 9. Women+ 10. 85+ 11. Men+ 12. Women+ Housing indicators (13) 1. Total – Private Households by core housing need status 2. Households below one standard only...

The confusion matrix shows a cross-tabulation of the actual class with the model’s predicted class (based on the conventional probability threshold of 0.5).

Abstract

The design of the victimization survey in Palestine 2016 is to provide comprehensive statistical data for policy-makers and stakeholders in sociopolitical decision-making as crime and victimization statistics are valuable nationally, regionally and on the globe.

The Victimization survey was implemented during the period from 9th October 2016 to 5th January 2017. The sample included 7,603 households in Palestine, for the purpose of providing data on the status of victimization and crime in the Palestinian society. In addition, the Survey aims to examining the general features of victims and providing necessary information on the types of households and individual criminal acts, location of crimes, perpetrators, crime reporting and whether the crime reported was referred to court in addition to victims' human and material losses as a result of such acts. Furthermore, it aims create a database that fills in the gaps in administrative records related to crime and victimization statistics.

The Victimization Surveywas carried out five times, the first in the years 1996, 1999, 2004, 2008, 2012, by studying the basic indicators stipulated by international recommendations, the experiences of other countries, and the recommendations of experts who came to the agency.

Geographic coverage

Region: (West Bank and Gaza Strip). Locality Type: (urban, rural, refugee camps).

Analysis unit

person family

Universe

It consists of all Palestinian households usually residing in the state of Palestine during 2016, focuses specifically on household and individuals who were victims of criminal acts during the 12 months preceding the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

Target Population It consists of all Palestinian households usually residing in the state of Palestine during 2016, focuses specifically on household and individuals who were victims of criminal acts during the 12 months preceding the survey.

Sampling Frame The sampling frame consists of the master sample updated in 2011. The master sample consists of 596 enumeration areas lodging an average of 124 households each. 498 of these enumeration areas were used in the sampling frame of the Labor Force Survey 2016. These units were used as primary sampling units (PSU's) I the first phase of sample selection.

Sample size The estimated sample size for is 7,603 households. The number of complete households was 5,858 including 3,734 in the West Bank and 2,124 in the Gaza Strip.

Sample Design The sample of this survey is the same sample as the Labor Force Survey (LFS) in the fourth quarter (cycle 83), which has been implemented periodically by PCBS in September 1995 on quarterly basis. It is distributed over 13 weeks equally. The sample is an organized random cluster sample selected in two phases. In the first phase, an organized random stratified cluster was selected in the master sample enumeration areas, which amount to 498 areas for a full cycle. Int he second phase an organized random stratified sample was selected among the households in every selected enumeration area from the first phase. 16 households from every enumeration area were selected.

Sample strata The population was divided as follows: 1- Governorate (16 governorates in addition to the part of Jerusalem governorate (j1) as a separate stratum)
2- Type of Locality (urban, rural, refugee camps)

Domain

Publication Levels: Region: (West Bank and Gaza Strip). Locality Type: (urban, rural, refugee camps).

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire is the main survey tool to gather information. It must be conforming to the technical standards of the fieldwork and should respond to the requirements of data processing and analysis. The questionnaire is composed of three sections: The first part involves general questions about criminal offenses (theft, threat, assault, etc.) at household level; The second part has detailed questions about individuals, victim of criminal offenses and the sociodemographic characteristics of perpetrators; The third section relates to people's feeling of security and their opinion about drug phenomenon. It should be noted that the questionnaire is an annex to the Labor Force Survey Questionnaire in the fourth quarter of 2016.

Cleaning operations

Collecting data of the Labor Force Survey were started in Palestine from the beginning of the first quarter of 2013 except for Jerusalem (J1) and the Gaza Strip. As of the beginning of the second quarter of 2016, data collection using PC-tablets in the West Bank (excluding Jerusalem (J1).

The use of PC-tablets reduces the time needed for survey implementation. The fieldworker enters and encrypts data by collecting data on the handheld tablet and sending data directly to the project manager.

In order to work in parallel with Jerusalem (J1), a victimization survey program was prepared using the same PC-tablet technology, using the same hardware database; data collected on paper is entered on the same program data base.

During fieldwork, data files were withdrawn three times for purpose of cleaning errors and preparing statements of amendments prior to returning to field work, if needed. Upon completion of the entry and editing phase, in the last phase, data were prepared for tabulation and dissemination, then were inter-linked through relations. Internal checks were conducted for answers out of scope and comprehensive databases for implementation through an output program to locate statements' errors and amend the questionnaires to prepare for clean, accurate, ready to publish and ready to tabulate data.

Response rate

7,603 households, representative of the Palestinian Territory, were selected. There were 5,858 completed households, including 3,734 in the West Bank and 2,124 in the Gaza Strip. Weights were adjusted to the design strata to adjust the impact of refusal and non-response rates. Response rate in the West Bank attained 82.4% and in the Gaza Strip it was 92.2%.

Sampling error estimates

Data in this survey is affected by sampling error because of use of a sample rather than a full census of all study population units. Therefore, differences from real values are expected to appear through censuses. Variance was calculated for the key indicators. The variance calculation table is annexed to this report. There are no problems at the level of publication of the said estimates at national (State of Palestine) and regional (West Bank and Gaza Strip) levels

Data appraisal

Comparisons were made between the results of this survey with the previous surveys. Comparisons were made at household and individual levels. The tables of these comparisons are included in the introduction to the main tables of the survey.