Migration flows are derived from the relationship between the _location of current residence in the American Community Survey (ACS) sample and the responses given to the migration question "Where did you live 1 year ago?". There are flow statistics (moved in, moved out, and net moved) between county or minor civil division (MCD) of residence and county, MCD, or world region of residence 1 year ago. Estimates for MCDs are only available for the 12 strong-MCD states, where the MCDs have the same government functions as incorporated places. Migration flows between metropolitan statistical areas are available starting with the 2009-2013 5-year ACS dataset. Flow statistics are available by three or four variables for each dataset starting with the 2006-2010 5-year ACS datasets. The variables change for each dataset and do not repeat in overlapping datasets. In addition to the flow estimates, there are supplemental statistics files that contain migration/geographical mobility estimates (e.g., nonmovers, moved to a different state, moved from abroad) for each county, MCD, or metro area.

Please Note: As announced by the Minister for Immigration and Border Protection on 25 June 2017, the Department of Immigration and Border Protection (DIBP) retired the paper-based Outgoing Passenger Cards (OPC) from 1 July 2017. The information previously gathered via paper-based outgoing passenger cards is now be collated from existing government data and will continue to be provided to users. Further information can be accessed here: http://www.minister.border.gov.au/peterdutton/Pages/removal-of-the-outgoing-passenger-card-jun17.aspx.

Due to the retirement of the OPC, the Australian Bureau of Statistics (ABS) undertook a review of the OAD data based on a new methodology. Further information on this revised methodology is available at: http://www.abs.gov.au/AUSSTATS/abs@.nsf/Previousproducts/3401.0Appendix2Jul%202017?opendocument&tabname=Notes&prodno=3401.0&issue=Jul%202017&num=&view=

A sampling methodology has been applied to this dataset. This method means that data will not replicate, exactly, data released by the ABS, but the differences should be negligible.

Due to ‘Return to Source’ limitations, data supplied to ABS from non-DIPB sources are also excluded.

Overseas Arrivals and Departures (OAD) data refers to the arrival and departure of Australian residents or overseas visitors, through Australian airports and sea ports, which have been recorded on incoming or outgoing passenger cards. OAD data describes the number of movements of travellers rather than the number of travellers. That is, multiple movements of individual persons during a given reference period are all counted. OAD data will differ from data derived from other sources, such as Migration Program Outcomes, Settlement Database or Visa Grant information. Travellers granted a visa in one year may not arrive until the following year, or may not travel to Australia at all. Some visas permit multiple entries to Australia, so travellers may enter Australia more than once on a visa. Settler Arrivals includes New Zealand citizens and other non-program settlers not included on the Settlement Database. The Settlement Database includes onshore processed grants not included in Settler Arrivals.

These de-identified statistics are periodically checked for privacy and other compliance requirements. The statistics were temporarily removed in March 2024 in response to a question about privacy within the emerging technological environment. Following a thorough review and risk assessment, the Department of Home Affairs has republished the dataset.

This dashboard shows information about how the Pig movement reports service is currently performing.

This is a "beta" service. The dashboard shows number of digital transactions, total cost of transactions, cost per transaction and take-up of digital services. Performance Dashboards are likely to be used by many people, including:

government service managers and their teams journalists students and researchers members of the public interested in how public services are performing The service also provides the option of a download of the data. Attribution statement:

This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly

In New Zealand, internal migration is typically the most difficult component of net migration’s contribution to subnational population change to measure. Internal migrants are not required to register their moves with any agency. The five-yearly census of population and dwellings has included a question on “usual residence five years ago” since 1971, which has been the authoritative data source for measuring internal migration. However, the infrequency of the collection (every five years), and the ‘snapshot’ nature of a transition-based measure are significant limitations. Other measures of annual subnational population change, such as the Treasury’s Insights tool, provide estimates of internal migration flows between TAs by using linked administrative data. Their approach identifies a set of decision rules for assigning location to individuals, based on a quality assessment of a wide range of address sources in the IDI (Where we come from, where we go). The TA location transitions provide the basis for deriving statistics of annual internal migration as demonstrated by the Insights tool. The data published with this report is the first series we’ve created by estimating all internal migration flows using a movement-based approach. From individuals’ unique address notification histories in key data sources, the paired origin and destination locations defined individuals’ movements. Traditionally, we combined change of address data from a range of administrative sources with other information on international migration to produce estimates of net migration for broad subnational areas. Now, we can derive direct estimates of movements from address histories from the anonymised unit record information of address notifications in the IDI. This gives a better understanding of people’s movements within New Zealand. Internal migration information is of great interest to local and central government, businesses, and communities. Churn and turnover of populations at local area level is one of the contributors of subnational population change, in both size and characteristics. Read the full report here: https://www.stats.govt.nz/reports/internal-migration-estimates-using-linked-administrative-data-201417

Mass movement**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

National Address DatabaseThis National Geospatial Data Asset (NGDA) dataset, shared as a U.S. Department of Transportation (USDOT) feature layer, displays address data in the United States. Per USDOT, "The U.S. Department of Transportation (USDOT) and its partners from all levels of government recognize the need for a National Address Database (NAD). Accurate and up-to-date addresses are critical to transportation safety and are a vital part of Next Generation 9-1-1. They are also essential for a broad range of government services, including mail delivery, permitting, and school siting. To meet this need, USDOT partners with address programs from state, local, and tribal governments to compile their authoritative data into the NAD."District of Columbia (DC) Residential AddressesData currency: Current federal service (Address Points from National Address Database)NGDAID: 196 (National Address Database (NAD))For more information: Getting to know the National Address Database (NAD); National Address DatabaseFor feedback, please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Transportation Theme Community. Per the Federal Geospatial Data Committee (FGDC), Transportation is defined as the "means and aids for conveying persons and/or goods. The transportation system includes both physical and non-physical components related to all modes of travel that allow the movement of goods and people between locations".For other NGDA Content: Esri Federal Datasets

Hong Kong IT Fund: Year to Date: Other Cash Movements data was reported at -19,273.738 HKD mn in Sep 2018. This records a decrease from the previous number of 265.794 HKD mn for Jun 2018. Hong Kong IT Fund: Year to Date: Other Cash Movements data is updated quarterly, averaging 202.974 HKD mn from Mar 2000 (Median) to Sep 2018, with 62 observations. The data reached an all-time high of 6,351.282 HKD mn in Sep 2016 and a record low of -19,273.738 HKD mn in Sep 2018. Hong Kong IT Fund: Year to Date: Other Cash Movements data remains active status in CEIC and is reported by The Treasury. The data is categorized under Global Database’s Hong Kong SAR – Table HK.F014: Government Fund: Innovation and Technology Fund.

How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our mobility statistics program.

The "Trips by Distance" data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

These data are made available under a public domain license. Data should be attributed to the "Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland and the United States Bureau of Transportation Statistics."

Daily data for a given week will be uploaded to the BTS website within 9-10 days of the end of the week in question (e.g., data for Sunday September 17-Saturday September 23 would be updated on Tuesday, October 3). All BTS visualizations and tables that rely on these data will update at approximately 10am ET on days when new data are received, processed, and uploaded.

The methodology used to develop these data can be found at: https://rosap.ntl.bts.gov/view/dot/67520.

Data and code to replicate results reported in "The Long-Term Impact of Mobilization and Repression on Political Trust" Abstract: Authoritarian regimes respond to threatening social movements with repression and censorship. In many cases, failed movements are effectively erased from the public memory. Do such movements affect long-term attitudes? We use a survey of college graduates to measure the impact of a failed student movement. Some of our respondents began college immediately before a major protest; others started after the movement had been suppressed. Using a fuzzy regression discontinuity, we find that individuals who attended college during the movement are significantly less likely to trust the government, more than twenty-five years later, than individuals who enrolled after the protests. The effects are strongest for trust in the central government, and weakest for local government. These results are robust to a range of specifications, and show that the experience of mass mobilization and state repression can have a long-term impact on public attitudes, even if the event in question remains taboo. Acknowledgmenents: The authors gratefully acknowledge support from the NCCR Democracy Project at the University of Zurich, the "Innovations of Data Protection Regulations" Research Project for Young Scholars from the Academy of Humanities and Social Sciences of Wuhan University, and the Academic Research Foundation from the School of Journalism and Communication at Wuhan University. We thank Alex Zhao for excellent research assistance.

The Database of Government Actions on COVID-19 in Developing Countries collates and tracks national policies and actions in response to the pandemic, with a focus on developing countries.

The database provides information for 20 Global South countries – plus 6 Global North countries for reference – that Dalberg staff are either based in or know well. The database content is drawn from publicly available information combined, crucially, with on-the-ground knowledge of Dalberg staff.

The database contains a comprehensive set of 100 non-pharmaceutical interventions – organized in a framework intended to make it easy to observe common variations between countries in the scope and extent of major interventions. Interventions we are tracking include:
• Health-related: strengthening of healthcare systems, detection and isolation of actual / possible cases, quarantines
• Policy-related: government coordination and legal authorization, public communications and education, movement restrictions
• Distancing and hygiene: social distancing measures, movement restrictions, decontamination of physical spaces
• Economic measures: economic and social measures, logistics / supply chains and security.

We hope the database will be a useful resource for several groups of users: (i) governments and policymakers looking for a quick guide to actions taken by different countries—including a range of low- and middle-income countries, (ii) policy analysts and researchers studying the data to identify patterns of actions taken and compare the effectiveness of different interventions in curbing the pandemic, and (iii) media and others seeking to quickly access facts about the actions taken by governments in the countries covered in the database.

Comments on the data can be submitted to covid.database.comments@dalberg.com
Questions can be submitted to covid.database.questions@dalberg.com

www.dalberg.com

Canada Aircraft Movements: Itinerant: Domestic: Government: Military data was reported at 2,410.000 Unit in Sep 2022. This records a decrease from the previous number of 3,835.000 Unit for Aug 2022. Canada Aircraft Movements: Itinerant: Domestic: Government: Military data is updated monthly, averaging 3,218.000 Unit from Jan 1997 (Median) to Sep 2022, with 309 observations. The data reached an all-time high of 8,708.000 Unit in Jul 2019 and a record low of 830.000 Unit in Dec 2019. Canada Aircraft Movements: Itinerant: Domestic: Government: Military data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.TA013: Aircraft Movements: Monthly. [COVID-19-IMPACT]

Government Code section 65400 requires that each city, county, or city and county, including charter cities, prepare an annual progress report (APR) on the status of the housing element of its general plan and progress in its implementation. This dataset includes information reported to the Department of Housing and Community Development (HCD) by local jurisdictions on their APR form. Additional information about annual progress reports (APR), including the form, instructions, and definition can be found on HCD’s website here: https://www.hcd.ca.gov/planning-and-community-development/annual-progress-reports. 11/14/2024 UPDATE: The weekly update of this dataset has been paused due to HCD's migration to a new database system; we are aiming to resume the weekly updates sometime during 1st quarter 2025.

Hong Kong General Rev Acc: Year to Date: Other Cash Movements data was reported at 112,014.376 HKD mn in Sep 2018. This records an increase from the previous number of 38,819.483 HKD mn for Jun 2018. Hong Kong General Rev Acc: Year to Date: Other Cash Movements data is updated quarterly, averaging 11,878.021 HKD mn from Dec 1995 (Median) to Sep 2018, with 92 observations. The data reached an all-time high of 112,014.376 HKD mn in Sep 2018 and a record low of -97,126.228 HKD mn in Mar 2008. Hong Kong General Rev Acc: Year to Date: Other Cash Movements data remains active status in CEIC and is reported by The Treasury. The data is categorized under Global Database’s Hong Kong SAR – Table HK.F006: Government General Revenue Account: Receipts and Payments.

The Daily Mobility Statistics were derived from a data panel constructed from several mobile data providers, a step taken to address the reduce the risks of geographic and temporal sample bias that would result from using a single data source. In turn, the merged data panel only included data from those mobile devices whose anonymized location data met a set of data quality standards, e.g., temporal frequency and spatial accuracy of anonymized location point observations, device-level temporal coverage and representativeness, spatial distribution of data at the sample and county levels. After this filtering, final mobility estimate statistics were computed using a multi-level weighting method that employed both device- and trip-level weights, thus expanding the sample represented by the devices in the data panel to the at-large populations of each state and county in the US.

Data analysis was conducted at the aggregate national, state, and county levels. To assure confidentiality and support data quality, no data were reported for a county if it had fewer than 50 devices in the sample on any given day.

Trips were defined as movements that included a stay of longer than 10 minutes at an anonymized location away from home. A movement with multiple stays of longer than 10 minutes--before returning home--was counted as multiple trips.

The Daily Mobility Statistics data on this page, which cover the COVID and Post-COVID periods, are experimental. Experimental data products are created using novel or exploratory data sources or methodologies that benefit data users in the absence of other statistically rigorous products, and they not meet all BTS data quality standards.

This scatter chart displays net migration (people) against central government debt (% of GDP) and is filtered where the country is Georgia. The data is about countries per year.

Smart Cities, Smart Liverpool, Smart Pedestrian Project

Thousands of people walk through the Liverpool city centre every day and, through the Smart Pedestrian Project, the paths they take will help shape the city’s future.

Liverpool City Council is counting pedestrian and vehicle movements around the city centre, collecting data from smart devices and camera-counting technology. The data is stripped of any identification and relayed and collected for analysis by researchers from the University of Wollongong.

Liverpool City Council now uses this data to inform planning decisions and respond to the rising number of residents and workers making their way around the city centre every day.

Liverpool City Council secured an Australian Government Smart Cities and Suburbs grant to jointly fund the project.

Liverpool City Council worked with IT Integration Company Meshed and the University of Wollongong to deliver the technology. Meshed supplied the Low Power Wide Area Network and developed a Wi-Fi smart device counter. The university developed the people and vehicle-counting technology and data analysis. The technology makes use of the city’s existing CCTV cameras to capture images. No images are transmitted over the network, so there is no risk to privacy.

This dataset lists the API endpoint URLs to retrieve data for the smart pedestrian project. A dashboard to view the data can be found at https://pavo.its.uow.edu.au/

Please read the API documentation as you need to provide the parameters to make any API calls.

An example has been provided below that lists all the visual sensors.

The API is divided in two sections: sensors, and readings (data gathered by sensors).

Unless otherwise noted:

* all datetimes are ISO formatted

* all responses are JSON-formatted

Hong Kong Lotteries Fund: Year to Date: Other Cash Movements data was reported at 254.168 HKD mn in Sep 2018. This records a decrease from the previous number of 264.937 HKD mn for Jun 2018. Hong Kong Lotteries Fund: Year to Date: Other Cash Movements data is updated quarterly, averaging -200.549 HKD mn from Mar 2000 (Median) to Sep 2018, with 62 observations. The data reached an all-time high of 348.278 HKD mn in Jun 2006 and a record low of -10,944.794 HKD mn in Mar 2014. Hong Kong Lotteries Fund: Year to Date: Other Cash Movements data remains active status in CEIC and is reported by The Treasury. The data is categorized under Global Database’s Hong Kong SAR – Table HK.F013: Government Fund: Lotteries Fund.