The COVID Analysis and Mapping of Policies (AMP) dataset is a research effort performed by researchers at the Georgetown University Center for Global Health Science and Security. This pre-print version of the COVID AMP database is an Excel file (.xlsx) which includes a library of nearly 50,000 policies from January 2020 - June 2022, collected from U.S. states and the District of Columbia, US local governments (counties, cities) and national governments globally.

In addition to the policies themselves, the COVID-AMP policy database contains a comprehensive set of metadata describing the governing authority, the type of policy measure, the policy's intentions, and an analysis of the legal authority under which the policy is enacted. A Data Dictionary is included in the file download.

This map displays Maternal and Newborn Health Policy data, provided by the WHO, for countries that have an International Confederation of Midwives (ICM) membership and completed the required surveys. This map also drives the Maternal and Newborn Health WHO Policy Mapping Application, found here: https://directrelief.maps.arcgis.com/home/item.html?id=a48053e7a6e34cc595652dff6f4ebe6cThis map is just one of the many data visualizations on the Global Midwives Hub, a digital resource with open data, maps, and mapping applications (among other things), to support advocacy for improved maternal and newborn services, supported by the International Confederation of Midwives (ICM), UNFPA, WHO, and Direct Relief.

OVERVIEWThis site is dedicated to raising the level of spatial and data literacy used in public policy. We invite you to explore curated content, training, best practices, and datasets that can provide a baseline for your research, analysis, and policy recommendations. Learn about emerging policy questions and how GIS can be used to help come up with solutions to those questions.EXPLOREGo to your area of interest and explore hundreds of maps about various topics such as social equity, economic opportunity, public safety, and more. Browse and view the maps, or collect them and share via a simple URL. Sharing a collection of maps is an easy way to use maps as a tool for understanding. Help policymakers and stakeholders use data as a driving factor for policy decisions in your area.ISSUESBrowse different categories to find data layers, maps, and tools. Use this set of content as a driving force for your GIS workflows related to policy. RESOURCESTo maximize your experience with the Policy Maps, we’ve assembled education, training, best practices, and industry perspectives that help raise your data literacy, provide you with models, and connect you with the work of your peers.

Unlocking Data to Inform Public Health Policy and Practice: WP1 Mapping Review Supplementary Excel S1
The data extracted into Excel Tab "S1 Case studies (extracted)" represents information from 31 case studies as part of the "Unlocking Data to Inform Public Health Policy and Practice" project, Workpackage (WP) 1 Mapping Review. Details about the WP1 mapping review can be found in the "Unlocking Data to Inform Public Health Policy and Practice" project report, which can be found via this DOI link: https://doi.org/10.15131/shef.data.21221606

Overview of mapping of ecosocial policies in Excel spreadsheet. Sheet 'Mapping' shows data. Sheet 'Variables explanation' includes descriptions of all variables

This dataset comprises mapping associated with the State Planning Policy 2.4 Basic Raw Materials (BRM). The Local government areas shown are those for which Significant Geological Supplies mapping has been completed to date. Many regional local government areas will not warrant the identification of Significant Geological Supplies as they do not experience the competing land use pressures associated with high growth areas nor the associated high demand for BRM. Show full description

Map Direct focus for viewing Water Policy data. Please refer to https://floridadep.gov/water for more information. Originally created on 03/01/2007, and moved to Map Direct Lite on 06/26/2015. Please contact GIS.Librarian@floridadep.gov for more information.

This publication includes .txt file and.cvs spreadsheet of map propositions, pdf Data Flow image of a static map, and a CMap file needed for recreating the CRMA stakeholder and data flow maps produced for the Curating Risk, Mediating Access (CRMA) project reports and publications. Stated assertions of relationships between organizations and data flows are based solely on information obtained via observations of public meetings, in-depth interviews with stakeholders and non-systematic web searching. Maps were generated to visualize preliminary findings of interest to stakeholder organizations and to provide a tool for further conceptualizing community engaged inquiry within such organizations. While the information contained is relevant to how some research participants and stakeholders view data governance and health data flows in the province of Alberta, it does not necessarily portray a complete or accurate description of such processes or relationships. The information is also limited by among other things, the author's access to internal documentation, role as a postdoctoral trainee and the time in which information was collected August 2013-May 2015. The purpose of the Curating Risk, Mediating Access (CRMA) project is to investigate practices, norms, policies, and infrastructure in clinical and health research that relate to sharing research data. The objectives are two-fold: 1)to map the current legal, ethical, and normative practices across the clinical and health research lifecycle that affect access to and the sharing of confidential health data; and 2)to examine the possible application of privacy-enhanced data sharing protocols for clinical and health research, given the results of the mapping exercise.

The COVID Analysis and Mapping of Policies (AMP) dataset is a research effort performed by researchers at the Georgetown University Center for Global Health Science and Security. The COVID AMP dataset is an Excel file (.xlsx) which includes data coded from 50,000 policies from January 2020 - June 2022, collected from U.S. states and the District of Columbia, US local governments (counties, cities) and national governments globally. The COVID AMP policy dataset contains a comprehensive set of fields describing the governing authority, the type of policy measure, the policy's intentions, and an analysis of the legal authority under which the policy is enacted. A Data Dictionary is included in the Excel file download.

In addition to the coded data, the COVID AMP Policy PDF Files (.zip) folder contains the raw text of the policies from which records were coded. Please note, users may experience slower download times with this large file.

Generalized Policy from the Comprehensive Plan Amendment Act of 2010, effective April 8, 2011.This data set is part of the Comprehensive Plan of the District of Columbia. It categorizes how different parts of the District may change by 2025. It highlights areas where more detailed Comprehensive Plan policies have been provided to manage this change. These policies may generally be found in the ten Area Elements. This dataset should be used to guide land use decision-making in conjunction with the Comprehensive Plan text, the Future Land Use Map, and other Comprehensive Plan maps. Boundaries within the dataset are to be interpreted in conjunction with these other resources in addition to the information shown here.

Using child labor data from the Multiple Indicator Cluster Surveys (MICS) database and Demographic and Health Surveys (DHS) database from 2001 to 2020, combined with environmental, socio-economic, and demographic data in the study area, and used statistical methods to model. Introducing machine learning to interpret nonlinear effects significantly improves the predictive accuracy of the model, resulting in the creation of a grid map of child labor distribution with a resolution of 5 × 5 kilometers.

Contains:World HillshadeWorld Street Map (with Relief) - Base LayerLarge Scale International Boundaries (v11.3)World Street Map (with Relief) - LabelsDoS Country Labels DoS Country LabelsCountry (admin 0) labels that have been vetted for compliance with foreign policy and legal requirements. These labels are part of the US Federal Government Basemap, which contains the borders and place names that have been vetted for compliance with foreign policy and legal requirements.Source: DoS Country Labels - Overview (arcgis.com)Large Scale International BoundariesVersion 11.3Release Date: December 19, 2023DownloadFor more information on the LSIB click here: https://geodata.state.gov/ A direct link to the data is available here: https://data.geodata.state.gov/LSIB.zipAn ISO-compliant version of the LSIB metadata (in ISO 19139 format) is here: https://geodata.state.gov/geonetwork/srv/eng/catalog.search#/metadata/3bdb81a0-c1b9-439a-a0b1-85dac30c59b2 Direct inquiries to internationalboundaries@state.govOverviewThe Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.3 (published 19 December 2023). The 11.3 release contains updates to boundary lines and data refinements enabling reuse of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control.National Geospatial Data AssetThis dataset is a National Geospatial Data Asset managed by the Department of State on behalf of the Federal Geographic Data Committee's International Boundaries Theme.DetailsSources for these data include treaties, relevant maps, and data from boundary commissions and national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process involves analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground.Attribute StructureThe dataset uses thefollowing attributes:Attribute NameCC1COUNTRY1CC2COUNTRY2RANKSTATUSLABELNOTES These attributes are logically linked:Linked AttributesCC1COUNTRY1CC2COUNTRY2RANKSTATUS These attributes have external sources:Attribute NameExternal Data SourceCC1GENCCOUNTRY1DoS ListsCC2GENCCOUNTRY2DoS ListsThe eight attributes listed above describe the boundary lines contained within the LSIB dataset in both a human and machine-readable fashion. Other attributes in the release include "FID", "Shape", and "Shape_Leng" are components of the shapefile format and do not form an intrinsic part of the LSIB."CC1" and "CC2" fields are machine readable fields which contain political entity codes. These codes are derived from the Geopolitical Entities, Names, and Codes Standard (GENC) Edition 3 Update 18. The dataset uses the GENC two-character codes. The code ‘Q2’, which is not in GENC, denotes a line in the LSIB representing a boundary associated with an area not contained within the GENC standard.The "COUNTRY1" and "COUNTRY2" fields contain human-readable text corresponding to the name of the political entity. These names are names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the list of Independent States in the World and the list of Dependencies and Areas of Special Sovereignty maintained by the Department of State. To ensure the greatest compatibility, names are presented without diacritics and certain names are rendered using commonly accepted cartographic abbreviations. Names for lines associated with the code ‘Q2’ are descriptive and are not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS are names of independent states. Other names are those associated with dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user.The following fields are an intrinsic part of the LSIB dataset and do not rely on external sources:Attribute NameMandatoryContains NullsRANKYesNoSTATUSYesNoLABELNoYesNOTESNoYesNeither the "RANK" nor "STATUS" field contains null values; the "LABEL" and "NOTES" fields do.The "RANK" field is a numeric, machine-readable expression of the "STATUS" field. Collectively, these fields encode the views of the United States Government on the political status of the boundary line.Attribute NameValueRANK123STATUSInternational BoundaryOther Line of International Separation Special Line A value of "1" in the "RANK" field corresponds to an "International Boundary" value in the "STATUS" field. Values of "2" and "3" correspond to "Other Line of International Separation" and "Special Line", respectively.The "LABEL" field contains required text necessarily to describe the line segment. The "LABEL" field is used when the line segment is displayed on maps or other forms of cartographic visualizations. This includes most interactive products. The requirement to incorporate the contents of the "LABEL" field on these products is scale dependent. If a label is legible at the scale of a given static product a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field is not a line labeling field but does contain the preferred description for the three LSIB line types when lines are incorporated into a map legend. Using the "CC1", "CC2", or "RANK" fields for labeling purposes is prohibited.The "NOTES" field contains an explanation of any applicable special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, any limitations regarding the purpose of the lines, or the original source of the line. Use of the "NOTES" field for labeling purposes is prohibited.External Data SourcesGeopolitical Entities, Names, and Codes Registry: https://nsgreg.nga.mil/GENC-overview.jspU.S. Department of State List of Independent States in the World: https://www.state.gov/independent-states-in-the-world/U.S. Department of State List of Dependencies and Areas of Special Sovereignty: https://www.state.gov/dependencies-and-areas-of-special-sovereignty/The source for the U.S.—Canada international boundary (NGDAID97) is the International Boundary Commission: https://www.internationalboundarycommission.org/en/maps-coordinates/coordinates.phpThe source for the “International Boundary between the United States of America and the United States of Mexico” (NGDAID82) is the International Boundary and Water Commission: https://catalog.data.gov/dataset?q=usibwcCartographic UsageCartographic usage of the LSIB requires a visual differentiation between the three categories of boundaries. Specifically, this differentiation must be between:- International Boundaries (Rank 1);- Other Lines of International Separation (Rank 2); and- Special Lines (Rank 3).Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary.Additional cartographic information can be found in Guidance Bulletins (https://hiu.state.gov/data/cartographic_guidance_bulletins/) published by the Office of the Geographer and Global Issues.ContactDirect inquiries to internationalboundaries@state.gov.CreditsThe lines in the LSIB dataset are the product of decades of collaboration between geographers at the Department of State and the National Geospatial-Intelligence Agency with contributions from the Central Intelligence Agency and the UK Defence Geographic Centre.Attribution is welcome: U.S. Department of State, Office of the Geographer and Global Issues.Changes from Prior ReleaseThe 11.3 release is the third update in the version 11 series.This version of the LSIB contains changes and accuracy refinements for the following line segments. These changes reflect improvements in spatial accuracy derived from newly available source materials, an ongoing review process, or the publication of new treaties or agreements. Notable changes to lines include:• AFGHANISTAN / IRAN• ALBANIA / GREECE• ALBANIA / KOSOVO• ALBANIA/MONTENEGRO• ALBANIA / NORTH MACEDONIA• ALGERIA / MOROCCO• ARGENTINA / BOLIVIA• ARGENTINA / CHILE• BELARUS / POLAND• BOLIVIA / PARAGUAY• BRAZIL / GUYANA• BRAZIL / VENEZUELA• BRAZIL / French Guiana (FR.)• BRAZIL / SURINAME• CAMBODIA / LAOS• CAMBODIA / VIETNAM• CAMEROON / CHAD• CAMEROON / NIGERIA• CHINA / INDIA• CHINA / NORTH KOREA• CHINA / Aksai Chin• COLOMBIA / VENEZUELA• CONGO, DEM. REP. OF THE / UGANDA• CZECHIA / GERMANY• EGYPT / LIBYA• ESTONIA / RUSSIA• French Guiana (FR.) / SURINAME• GREECE / NORTH MACEDONIA• GUYANA / VENEZUELA• INDIA / Aksai Chin• KAZAKHSTAN / RUSSIA• KOSOVO / MONTENEGRO• KOSOVO / SERBIA• LAOS / VIETNAM• LATVIA / LITHUANIA• MEXICO / UNITED STATES• MONTENEGRO / SERBIA• MOROCCO / SPAIN• POLAND / RUSSIA• ROMANIA / UKRAINEVersions 11.0 and 11.1 were updates to boundary lines. Like this version, they also contained topology fixes, land boundary terminus refinements, and tripoint adjustments. Version 11.2 corrected a few errors in the attribute data and ensured that CC1 and CC2 attributes are in alignment with an updated version of the Geopolitical Entities, Names, and Codes (GENC) Standard, specifically Edition 3 Update 17.LayersLarge_Scale_International_BoundariesTerms of

The unprecedented travel bans introduced in response to the COVID-19 pandemic is a pertinent phenomenon of interest to scholars across the globe. Quantifying the timing and content of policy changes affecting travel and immigration is key to future research on the spread of SARS-CoV-2 and the socioeconomic impacts of these policies. The COVID Border Accountability Project (COBAP) provides a systematized dataset of >1000 policies, reflecting a timeline of new country-level restrictions on movement across international borders during the 2020 year. Using a 20-question survey, trained research assistants (RAs) sourced and documented for each new border policy: start and end dates, whether the closure constitutes a "complete closure" or "partial closure", which exceptions are made, which countries are banned, and which borders are closed, among other variables. In addition, the full text of each policy was included in the database. We maintain and update the data monthly. For public use, we visualize the data in an interactive map tool visualization: covidborderaccountability.org. For ongoing and future pandemic research, the dataset will be useful to policymakers, social and biomedical scientists, and public health experts alike.

BackgroundTo design a comprehensive approach to promote children's sleep health in Amsterdam, the Netherlands, we combined Intervention Mapping (IM) with the Health in All Policies (HiAP) perspective. We aimed to create an approach that fits local infrastructures and policy domains across sectors.MethodsFirst, a needs assessment was conducted, including a systematic review, two concept mapping studies, and one cross-sectional sleep diary study (IM step 1). Subsequently, semi-structured interviews with stakeholders from policy, practice and science provided information on potential assets from all relevant social policy sectors to take into account in the program design (HiAP and IM step 1). Next, program outcomes and objectives were specified (IM step 2), with specific objectives for policy stakeholders (HiAP). This was followed by the program design (IM step 3), where potential program actions were adapted to local policy sectors and stakeholders (HiAP). Lastly, program production (IM step 4) focused on creating a multi-sector program (HiAP). An advisory panel guided the research team by providing tailored advice during all steps throughout the project.ResultsA blueprint was created for program development to promote children's sleep health, including a logic model of the problem, a logic model of change, an overview of the existing organizational structure of local policy and practice assets, and an overview of policy sectors, and related objectives and opportunities for promoting children's sleep health across these policy sectors. Furthermore, the program production resulted in a policy brief for the local government.ConclusionsCombining IM and HiAP proved valuable for designing a blueprint for the development of an integrated multi-sector program to promote children's sleep health. Health promotion professionals focusing on other (health) behaviors can use the blueprint to develop health promotion programs that fit the local public service infrastructures, culture, and incorporate relevant policy sectors outside the public health domain.

Leiden University’s study aims to demonstrate the added value of advanced bibliometrics analyses in the context of science policy, beyond its conventional uses in research evaluation. The analyses takes into consideration the example of cancer research to illustrate the potential of bibliometric data to reveal information beyond counting publications and citations, and specifically to gain insights into the dynamics of a research field or organisation, its structure and its different ways in which research is connected to societal processes. The bibliometric analyses have been conducted using the RISIS CWTS publication Dataset. The power of advanced bibliometrics is its capacity to analyse and contextualize a research group, a university, a discipline, regions, countries or group of countries. Thus, the study encourages science policy analysists to make use of advanced scientometric techniques to support decision making. In addition, it will show how bibliometrics can support benchmarking of countries and types of cancer across a variety of properties – not only number of publications and citations, but also the extent to which these publications are mentioned in social media, are cited in patents, are produced in local hospitals, in local languages (non-English), in collaboration with industry, etc. In the area of cancer research, this multidimensional perspective could help science policy analysts to identify specific research areas which might be candidates to be funded and/or to better understand and reflect on how the cancer mission evolves across Europe.

Over the past decades, there has been a rapid expansion in the number of Food Policy Groups (FPG) (including food policy councils, strategies, networks, and informal alliances) operating at municipal and regional levels across North America. FPGs are typically established with the intent of bringing together food systems stakeholders across private (e.g., small businesses, industry associations), public (e.g., government, public health, postsecondary institutions), and community (e.g., non-profits and charitable organizations) sectors to develop participatory governance mechanisms. Recognizing that food systems challenges are too often addressed in isolation, FPGs aim to instill integrated approaches to food related policy, programs, and planning. Despite growing interest, there is little quantitative or mixed methods research about the relationships that constitute FPGs or the degree to which they achieve cross-sectoral integration. Turning to Social Network Analysis (SNA) as an approach for understanding networked organizational relationships, we explore how SNA might contribute to a better understanding of FPGs. This paper presents results from a study of the Thunder Bay and Area Food Strategy (TBAFS), a FPG established in 2007 when an informal network of diverse organizations came together around shared goals of ensuring that municipal policy and governance supported healthy, equitable and sustainable food systems in the Thunder Bay region in Ontario, Canada. Drawing on data from a survey of TBAFS organizational members, we suggest that SNA can improve our understanding of the networks formed by FPGs and enhance their goals of cross-sectoral integration.

This is a data set of built environment policy instruments relevant to the state of Victoria, Australia. The instruments are categorised against a built environment policy framework developed by Hurlimann et al (2024 - see reference details below) which consists of: a policy instrument typology (strategies, laws, regulations, guidelines, voluntary instruments and programs), and a built environment policy setting (governance level, sector, property type, life stage, timeframe). Local (City of Melbourne), national (Australia) and international built environment policy instruments are also included.This data file relates to open access journal article: Hurlimann, A., March, A., Bush, J., Moosavi., S., Browne, G., Warren-Myers, G., (2024) 'Climate change transformation in built environments - A policy instrument framework. Urban Climate; doi.org/10.1016/j.uclim.2023.101771

List of works reviewed and coding report

This deposit contains the taxonomy maps and data we used to translate data on COVID-19 government responses from 7 different datasets into taxonomy developed by the CoronaNet Research Project (CoronaNet; Cheng et al 2020). These taxonomy maps form the basis of our efforts to harmonize this data into the CoronaNet database. The following taxonomy maps are deposited in the 'Taxonomy' folder:ACAPS COVID-19 Government Measures - CoronaNet Taxonomy Map Canadian Data Set of COVID-19 Interventions from the Canadian Institute for Health Information (CIHI) - CoronaNet Taxonomy Map COVID Analysis and Maping of Policies (COVID AMP) - CoronaNet Taxonomy Map Johns Hopkins Health Intervention Tracking for COVID-19 (HIT-COVID) - CoronaNet Taxonomy Map Oxford Covid-19 Government Response Tracker (OxCGRT) - CoronaNet Taxonomy Map World Health Organisation Public Health and Safety Measures (WHO PHSM) - CoronaNet Taxonomy MapMeanwhile the 'Data' folder contains the raw and mapped data for each external dataset (i.e. ACAPS, CIHI, COVID AMP, HIT-COVID, OxCGRT and WHO PHSM) as well as the combined external data for Steps 1 and 3 of the data harmonization process described in Cheng et al (2023) 'Harmonizing Government Responses to the COVID-19 Pandemic.'

Overview of mapping of ecosocial policies in Excel spreadsheet.Sheet 'Mapping' shows data.Sheet 'Variables explanation' includes descriptions of all variables