This dataset contains replication files for "A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples" by Raj Chetty and John Friedman. For more information, see https://opportunityinsights.org/paper/differential-privacy/. A summary of the related publication follows. Releasing statistics based on small samples – such as estimates of social mobility by Census tract, as in the Opportunity Atlas – is very valuable for policy but can potentially create privacy risks by unintentionally disclosing information about specific individuals. To mitigate such risks, we worked with researchers at the Harvard Privacy Tools Project and Census Bureau staff to develop practical methods of reducing the risks of privacy loss when releasing such data. This paper describes the methods that we developed, which can be applied to disclose any statistic of interest that is estimated using a sample with a small number of observations. We focus on the case where the dataset can be broken into many groups (“cells”) and one is interested in releasing statistics for one or more of these cells. Building on ideas from the differential privacy literature, we add noise to the statistic of interest in proportion to the statistic’s maximum observed sensitivity, defined as the maximum change in the statistic from adding or removing a single observation across all the cells in the data. Intuitively, our approach permits the release of statistics in arbitrarily small samples by adding sufficient noise to the estimates to protect privacy. Although our method does not offer a formal privacy guarantee, it generally outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias. We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by Census tract in the Opportunity Atlas. We also provide a step-by-step guide and illustrative Stata code to implement our approach.

The FAOSTAT PopSTAT module contains timeseries on population and economically active population. The series consist of both estimates and projections for different periods as available from the original sources, namely: Population data from the UN Population Division and the data refers to the UN Revision 2012. Long term series estimates and projects from 1961 to 2050. Economically active population from the ILO and the data refers to the 5th edition, revision 2009. Long term series estimates and projects from 1980 to 2020.

The State Revenue Office (SRO) is now publishing a range of First Home Owner Grant statistics. Find out how many Grants, Bonuses and Boost payments have been received in each postcode in Victoria. The SRO have developed an online search tool which will enable users to select a postcode and receive data by number and type of benefit for various years, and months within each year. Graphs representing the data extracted are also available.

This data contains general government sector operating expenses, sourced from the Australian Bureau of Statistics historical data and the Department of Treasury and Finance, categorised by ‘government purpose classification’ (GPC) and ‘classification of the functions of government’ (COFOG).\r \r The Australian system of Government Finance Statistics (GFS) was revised by the Australian Bureau of Statistics, with the release of the Australian System of Government Finance Statistics: Concepts, Sources and Methods 2015 Cat. No. 5514.0.\r \r Implementation of the updated GFS manual has resulted in the COFOG framework replacing the former GPC framework, with effect from the 2018-19 financial year for financial reporting under AASB 1049.\r \r The underlying data from 1961-62 to 1997-98 represents a conversion from the original cash series to an accruals basis by estimating depreciation and superannuation expenses based on statistical modelling.\r \r Although the conversion provides a basis for comparison with total expenses in the current series of accrual GFS information from 1998 (in the attached table), the estimated accrued expense items have not been apportioned to individual purpose classifications.\r \r The absence of these splits between functional classifications in the attached table data therefore represents a break in the series and it is not possible to compare individual purpose categories with those in other tables.\r \r Similarly, the transition from GPC to COFOG represents an additional break in the series and comparability between the two frameworks will not be possible.\r \r The key reporting changes from GPC to COFOG are as follows:\r \r - the number of categories has reduced from 12 under GPC to 10 under COFOG; \r - the fuel and energy, agriculture, forestry, fishing and hunting categories have been abolished and are now part of the new economic affairs category. The majority of the outputs in other economic affairs are also included in this new category;\r - public debt transactions have moved from the other purposes category (i.e. primarily interest expense on borrowings) to general public services category;\r - a new environmental protection category was created to include functions such as waste management, water waste management, pollution and production of biodiversity and landscape, which were previously classified under housing and community amenities category, as well as national and state parks functions from the recreation and culture category; and\r - housing functions such as housing assistance and housing concessions are now part of the social protection category

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

Abstract

Within the frame of PCBS' efforts in providing official Palestinian statistics in the different life aspects of Palestinian society and because the wide spread of Computer, Internet and Mobile Phone among the Palestinian people, and the important role they may play in spreading knowledge and culture and contribution in formulating the public opinion, PCBS conducted the Household Survey on Information and Communications Technology, 2014.

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestine in addition to providing data on the following: -

· Prevalence of computers and access to the Internet. · Study the penetration and purpose of Technology use.

Geographic coverage

Palestine (West Bank and Gaza Strip) , type of locality (Urban, Rural, Refugee Camps) and governorate

Analysis unit

Household. Person 10 years and over .

Universe

All Palestinian households and individuals whose usual place of residence in Palestine with focus on persons aged 10 years and over in year 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of a list of enumeration areas adopted in the Population, Housing and Establishments Census of 2007. Each enumeration area has an average size of about 124 households. These were used in the first phase as Preliminary Sampling Units in the process of selecting the survey sample.

Sample Size The total sample size of the survey was 7,268 households, of which 6,000 responded.

Sample Design The sample is a stratified clustered systematic random sample. The design comprised three phases:

Phase I: Random sample of 240 enumeration areas. Phase II: Selection of 25 households from each enumeration area selected in phase one using systematic random selection. Phase III: Selection of an individual (10 years or more) in the field from the selected households; KISH TABLES were used to ensure indiscriminate selection.

Sample Strata Distribution of the sample was stratified by: 1- Governorate (16 governorates, J1). 2- Type of locality (urban, rural and camps).

Sampling deviation

-

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on persons (aged 10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Preparation of Data Entry Program: This stage included preparation of the data entry programs using an ACCESS package and defining data entry control rules to avoid errors, plus validation inquiries to examine the data after it had been captured electronically.

Data Entry: The data entry process started on 8 May 2014 and ended on 23 June 2014. The data entry took place at the main PCBS office and in field offices using 28 data clerks.

Editing and Cleaning procedures: Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

Response rate

Response Rates= 79%

Sampling error estimates

There are many aspects of the concept of data quality; this includes the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are three components to the quality of statistics: accuracy, comparability, and quality control procedures.

Checks on data accuracy cover many aspects of the survey and include statistical errors due to the use of a sample, non-statistical errors resulting from field workers or survey tools, and response rates and their effect on estimations. This section includes:

Statistical Errors Data of this survey may be affected by statistical errors due to the use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators.

Variance calculations revealed that there is no problem in disseminating results nationally or regionally (the West Bank, Gaza Strip), but some indicators show high variance by governorate, as noted in the tables of the main report.

Non-Statistical Errors Non-statistical errors are possible at all stages of the project, during data collection or processing. These are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, and practical and theoretical training took place during the training course. Training manuals were provided for each section of the questionnaire, along with practical exercises in class and instructions on how to approach respondents to reduce refused cases. Data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

The sources of non-statistical errors can be summarized as: 1. Some of the households were not at home and could not be interviewed, and some households refused to be interviewed. 2. In unique cases, errors occurred due to the way the questions were asked by interviewers and respondents misunderstood some of the questions.

Diagnosis data of patients and patients in hospitals.

The hospital diagnosis statistics are part of the hospital statistics and have been collected annually from all hospitals since 1993. The statistics include information on the main diagnosis (coded according to ICD-10), length of stay, department and selected sociodemographic characteristics such as age, gender and place of residence, among others.

Basic data of hospitals and preventive care or rehabilitation facilities.

The basic data statistics are part of the hospital statistics. The material and personnel resources of hospitals and preventive or rehabilitation facilities and their specialist departments have been reported annually since 1990.

The aggregated data are freely accessible.

The datasets containing simulation performance results during the current study, in addition to the code to replicate the simulation study in its entirety, are available here. See the README file for a description the Stata do-files, R-script files, tips to run the code, and the performance result dataset dictionaries.

This statistical press release provides statistics for writs and originating summonses issued, cases disposed and orders made in respect of mortgages in the Chancery Division of the Northern Ireland High Court.

Source agency: Northern Ireland Statistics and Research Agency

Designation: National Statistics

Language: English

Alternative title: Mortgage Press Release

Production and reserves statistics for coal seam gas, condensate, crude oil, liquefied petroleum gas and natural gas.

Please note: Due to changes in the data collection and reporting standards the Department has altered the published reports format. The Petroleum and gas production reports are published in the new format from the period Dec 2014 till Dec 2021 (current) and the Petroleum and gas reserves reports are published in the new format for the calendar year 2020 and 2021 (current)

The reports released for data periods mentioned above have been updated to reflect the current reporting measure of units and data standards.

More information about these changes can be found here: Link

Historical data (pre-30 June 2016 Production and Pre 2020 Reserves) can be found here: Link

Combined Statistical Areas (CSAs) consist of two or more adjacent CBSAs that have substantial employment interchange. The CBSAs that combine to create a CSA retain separate identities within the larger CSA. Because CSAs represent groupings of metropolitan and/or micropolitan statistical areas, they should not be ranked or compared with individual metropolitan and micropolitan statistical areas.Download: https://www2.census.gov/geo/tiger/TGRGDB24/tlgdb_2024_a_us_nationgeo.gdb.zip Layer: Combined_Statistical_AreaMetadata: https://meta.geo.census.gov/data/existing/decennial/GEO/GPMB/TIGERline/Current_19115/series_tl_2023_csa.shp.iso.xml

This is a monthly report on publicly funded community services for children, young people and adults using data from the Community Services Data Set (CSDS) reported in England for January 2018. The CSDS is a patient-level dataset providing information relating to publicly funded community services for children, young people and adults. These services can include health centres, schools, mental health trusts, and health visiting services. The data collected includes personal and demographic information, diagnoses including long-term conditions and disabilities and care events plus screening activities. It has been developed to help achieve better outcomes for children, young people and adults. It provides data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. Prior to October 2017, the predecessor Children and Young People's Health Services (CYPHS) Data Set collected data for children and young people aged 0-18. The CSDS superseded the CYPHS data set to allow adult community data to be submitted, expanding the scope of the existing data set by removing the 0-18 age restriction. The structure and content of the CSDS remains the same as the previous CYPHS data set. Further information about the CYPHS and related statistical reports is available in the related links below. References to children and young people covers records submitted for 0-18 year olds and references to adults covers records submitted for those aged over 18. Where analysis for both groups have been combined, this is referred to as all patients. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. They are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. More information about experimental statistics can be found on the UK Statistics Authority website. We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use the survey in the related links to provide us with any feedback or suggestions for improving the report.

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

National statistical systems are facing significant challenges. These challenges arise from increasing demands for high quality and trustworthy data to guide decision making, coupled with the rapidly changing landscape of the data revolution. To help create a mechanism for learning amongst national statistical systems, the World Bank has developed improved Statistical Performance Indicators (SPI) to monitor the statistical performance of countries. The SPI focuses on five key dimensions of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. This will replace the Statistical Capacity Index (SCI) that the World Bank has regularly published since 2004.

The SPI focus on five key pillars of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. The SPI are composed of more than 50 indicators and contain data for 186 countries. This set of countries covers 99 percent of the world population. The data extend from 2016-2023, with some indicators going back to 2004.

For more information, consult the academic article published in the journal Scientific Data. https://www.nature.com/articles/s41597-023-01971-0.

The global number of households with a computer in was forecast to continuously increase between 2024 and 2029 by in total 88.6 million households (+8.6 percent). After the fifteenth consecutive increasing year, the computer households is estimated to reach 1.1 billion households and therefore a new peak in 2029. Notably, the number of households with a computer of was continuously increasing over the past years.Computer households are defined as households possessing at least one computer.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of households with a computer in countries like Caribbean and Africa.

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

Crime isn't a topic most people want to use mental energy to think about. We want to avoid harm, protect our loved ones, and hold on to what we claim is ours. So how do we remain vigilant without digging too deep into the filth that is crime? Data, of course. The focus of our study is to explore possible trends between crime and communities in the city of Calgary. Our purpose is visualize Calgary criminal behaviour in order to help increase awareness for both citizens and law enforcement. Through the use of our visuals, individuals can make more informed decisions to improve the overall safety of their lives. Some of the main concerns of the study include: how crime rates increase with population, which areas in Calgary have the most crime, and if crime adheres to time-sensative patterns.