This application is intended for informational purposes only and is not an operational product. The tool provides the capability to access, view and interact with satellite imagery, and shows the latest view of Earth as it appears from space.For additional imagery from NOAA's GOES East and GOES West satellites, please visit our Imagery and Data page or our cooperative institute partners at CIRA and CIMSS.This website should not be used to support operational observation, forecasting, emergency, or disaster mitigation operations, either public or private. In addition, we do not provide weather forecasts on this site — that is the mission of the National Weather Service. Please contact them for any forecast questions or issues. Using the Maps​What does the Layering Options icon mean?The Layering Options widget provides a list of operational layers and their symbols, and allows you to turn individual layers on and off. The order in which layers appear in this widget corresponds to the layer order in the map. The top layer ‘checked’ will indicate what you are viewing in the map, and you may be unable to view the layers below.Layers with expansion arrows indicate that they contain sublayers or subtypes.What does the Time Slider icon do?The Time Slider widget enables you to view temporal layers in a map, and play the animation to see how the data changes over time. Using this widget, you can control the animation of the data with buttons to play and pause, go to the previous time period, and go to the next time period.Do these maps work on mobile devices and different browsers?Yes!Why are there black stripes / missing data on the map?NOAA Satellite Maps is for informational purposes only and is not an operational product; there are times when data is not available.Why does the imagery load slowly?This map viewer does not load pre-generated web-ready graphics and animations like many satellite imagery apps you may be used to seeing. Instead, it downloads geospatial data from our data servers through a Map Service, and the app in your browser renders the imagery in real-time. Each pixel needs to be rendered and geolocated on the web map for it to load.How can I get the raw data and download the GIS World File for the images I choose?The geospatial data Map Service for the NOAA Satellite Maps GOES satellite imagery is located on our Satellite Maps ArcGIS REST Web Service ( available here ).We support open information sharing and integration through this RESTful Service, which can be used by a multitude of GIS software packages and web map applications (both open and licensed).Data is for display purposes only, and should not be used operationally.Are there any restrictions on using this imagery?NOAA supports an open data policy and we encourage publication of imagery from NOAA Satellite Maps; when doing so, please cite it as "NOAA" and also consider including a permalink (such as this one) to allow others to explore the imagery.For acknowledgment in scientific journals, please use:We acknowledge the use of imagery from the NOAA Satellite Maps application: LINKThis imagery is not copyrighted. You may use this material for educational or informational purposes, including photo collections, textbooks, public exhibits, computer graphical simulations and internet web pages. This general permission extends to personal web pages. About this satellite imageryWhat am I looking at in these maps?In this map you are seeing the past 24 hours (updated approximately every 10 minutes) of the Western Hemisphere and Pacific Ocean, as seen by the NOAA GOES East (GOES-16) and GOES West (GOES-18) satellites. In this map you can also view four different ‘layers’. The views show ‘GeoColor’, ‘infrared’, and ‘water vapor’.This maps shows the coverage area of the GOES East and GOES West satellites. GOES East, which orbits the Earth from 75.2 degrees west longitude, provides a continuous view of the Western Hemisphere, from the West Coast of Africa to North and South America. GOES West, which orbits the Earth at 137.2 degrees west longitude, sees western North and South America and the central and eastern Pacific Ocean all the way to New Zealand.What does the GOES GeoColor imagery show?The 'Merged GeoColor’ map shows the coverage area of the GOES East and GOES West satellites and includes the entire Western Hemisphere and most of the Pacific Ocean. This imagery uses a combination of visible and infrared channels and is updated approximately every 15 minutes in real time. GeoColor imagery approximates how the human eye would see Earth from space during daylight hours, and is created by combining several of the spectral channels from the Advanced Baseline Imager (ABI) – the primary instrument on the GOES satellites. The wavelengths of reflected sunlight from the red and blue portions of the spectrum are merged with a simulated green wavelength component, creating RGB (red-green-blue) imagery. At night, infrared imagery shows high clouds as white and low clouds and fog as light blue. The static city lights background basemap is derived from a single composite image from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day Night Band. For example, temporary power outages will not be visible. Learn more.What does the GOES infrared map show?The 'GOES infrared' map displays heat radiating off of clouds and the surface of the Earth and is updated every 15 minutes in near real time. Higher clouds colorized in orange often correspond to more active weather systems. This infrared band is one of 12 channels on the Advanced Baseline Imager, the primary instrument on both the GOES East and West satellites. on the GOES the multiple GOES East ABI sensor’s infrared bands, and is updated every 15 minutes in real time. Infrared satellite imagery can be "colorized" or "color-enhanced" to bring out details in cloud patterns. These color enhancements are useful to meteorologists because they signify “brightness temperatures,” which are approximately the temperature of the radiating body, whether it be a cloud or the Earth’s surface. In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are usually “clear sky,” while pale white areas typically indicate low-level clouds. During a hurricane, cloud top temperatures will be higher (and colder), and therefore appear dark red. This imagery is derived from band #13 on the GOES East and GOES West Advanced Baseline Imager.How does infrared satellite imagery work?The infrared (IR) band detects radiation that is emitted by the Earth’s surface, atmosphere and clouds, in the “infrared window” portion of the spectrum. The radiation has a wavelength near 10.3 micrometers, and the term “window” means that it passes through the atmosphere with relatively little absorption by gases such as water vapor. It is useful for estimating the emitting temperature of the Earth’s surface and cloud tops. A major advantage of the IR band is that it can sense energy at night, so this imagery is available 24 hours a day.What do the colors on the infrared map represent?In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are clear sky, while pale white areas indicate low-level clouds, or potentially frozen surfaces. Learn more about this weather imagery.What does the GOES water vapor map layer show?The GOES ‘water vapor’ map displays the concentration and location of clouds and water vapor in the atmosphere and shows data from both the GOES East and GOES West satellites. Imagery is updated approximately every 15 minutes in real time. Water vapor imagery, which is useful for determining locations of moisture and atmospheric circulations, is created using a wavelength of energy sensitive to the content of water vapor in the atmosphere. In this imagery, green-blue and white areas indicate the presence of high water vapor or moisture content, whereas dark orange and brown areas indicate little or no moisture present. This imagery is derived from band #10 on the GOES East and GOES West Advanced Baseline Imager.What do the colors on the water vapor map represent?In this imagery, green-blue and white areas indicate the presence of high water vapor or moisture content, whereas dark orange and brown areas indicate less moisture present. Learn more about this water vapor imagery.About the satellitesWhat are the GOES satellites?NOAA’s most sophisticated Geostationary Operational Environmental Satellites (GOES), known as the GOES-R Series, provide advanced imagery and atmospheric measurements of Earth’s Western Hemisphere, real-time mapping of lightning activity, and improved monitoring of solar activity and space weather.The first satellite in the series, GOES-R, now known as GOES-16, was launched in 2016 and is currently operational as NOAA’s GOES East satellite. In 2018, NOAA launched another satellite in the series, GOES-T, which joined GOES-16 in orbit as GOES-18. GOES-17 became operational as GOES West in January 2023.Together, GOES East and GOES West provide coverage of the Western Hemisphere and most of the Pacific Ocean, from the west coast of Africa all the way to New Zealand. Each satellite orbits the Earth from about 22,200 miles away.

The Commonwealth of Kentucky through the Commonwealth Office of Technology's Division of Geographic Information (DGI) in conjunction with the Kentucky GIS Community has made available a wealth of GIS-related information, data sets and maps. These resources support education and training, research, and policy development for a multitude of organizations in Kentucky and across the United States.The Commonwealth Map is a statewide digital basemap available via the Internet for interactive mapping, geographic data querying, and downloading. As a collaborative effort of local, state, and federal partners, this initiative is designed to facilitate public, non-profit, and private sector GIS development, utilization, innovation, and data sharing.This web map also includes a great set of bookmarks prepared by the Kentucky Geography Network.Kentucky Division of Geographic Information: https://gis.ky.gov/Kentucky Geography Network: https://kygeonet.ky.govYou can access the Kentucky Commonwealth Map viewer here: https://kygeonet.ky.gov/tcm/ArcMap users can also access a ready to use map document (MXD file) for Kentucky that references this service. Click to launch. Requires ArcGIS 9.3 or more recent: MXD. This map document also includes the bookmarks prepared by the Kentucky Geography Network.More details about the Commonwealth Map of Kentucky map service used in this web map can be found here.

BackgroundResearch on Neglected Tropical Diseases (NTDs) has increased in recent decades, and significant need-gaps in diagnostic and treatment tools remain. Analysing bibliometric data from published research is a powerful method for revealing research efforts, partnerships and expertise. We aim to identify and map NTD research networks in Germany and their partners abroad to enable an informed and transparent evaluation of German contributions to NTD research.Methodology/Principal FindingsA SCOPUS database search for articles with German author affiliations that were published between 2002 and 2012 was conducted for kinetoplastid and helminth diseases. Open-access tools were used for data cleaning and scientometrics (OpenRefine), geocoding (OpenStreetMaps) and to create (Table2Net), visualise and analyse co-authorship networks (Gephi). From 26,833 publications from around the world that addressed 11 diseases, we identified 1,187 (4.4%) with at least one German author affiliation, and we processed 972 publications for the five most published-about diseases. Of those, we extracted 4,007 individual authors and 863 research institutions to construct co-author networks. The majority of co-authors outside Germany were from high-income countries and Brazil. Collaborations with partners on the African continent remain scattered. NTD research within Germany was distributed among 220 research institutions. We identified strong performers on an individual level by using classic parameters (number of publications, h-index) and social network analysis parameters (betweenness centrality). The research network characteristics varied strongly between diseases.Conclusions/SignificanceThe share of NTD publications with German affiliations is approximately half of its share in other fields of medical research. This finding underlines the need to identify barriers and expand Germany’s otherwise strong research activities towards NTDs. A geospatial analysis of research collaborations with partners abroad can support decisions to strengthen research capacity, particularly in low- and middle-income countries, which were less involved in collaborations than high-income countries. Identifying knowledge hubs within individual researcher networks complements traditional scientometric indicators that are used to identify opportunities for collaboration. Using free tools to analyse research processes and output could facilitate data-driven health policies. Our findings contribute to the prioritisation of efforts in German NTD research at a time of impending local and global policy decisions.

The countries of Asia and the Pacific have developed a regional road map for implementing the 2030 Agenda for Sustainable Development to facilitate cooperation at the regional level supported by the ESCAP Secretariat and other United Nations entities. The road map was agreed on during the 4th Asia Pacific Forum on Sustainable Development and subsequently endorsed by the ESCAP Member States via Resolution 73/9 which was adopted during ESCAP’s 73th Commission Session. The road map identifies priority areas of regional cooperation for implementation of the 2030 Agenda. These priority areas underline the major challenges still faced in our region, including leaving no one behind; disaster risk reduction and resilience; climate change; management of natural resources; connectivity; and, energy. Priority actions under the means of implementation for the 2030 Agenda are also identified in the road map, including data and statistics, technology, finance, policy coherence and partnerships.

At the global level in 2015 countries set in motion the most far reaching and ambitious development agenda of our time, the 2030 Agenda for Sustainable Development. In
Asia and the Pacific, countries have already begun translating this ambitious agenda into action and many have already set up the national architecture for coordinating and promoting the implementation of the Sustainable Development Goals (SDGs). However, the policy transformations required to put countries on track to achieve the SDGs have yet to take shape across this or any other region. Business as usual policies and investments are locking countries into unsustainable pathways that will create a gap between ambition and action.

To help address this gap, cooperation and action is needed at the national, regional and international levels. Regional cooperation can support and complement the effectiveness of national mechanisms upon which the ultimate success of the global 2030 Agenda rests. Transboundary challenges such as climate change and natural disasters, energy security, ecosystem degradation, and contamination of oceans, seas and marine resources require regional actions. Transboundary infrastructure networks can help reduce vulnerability, and cooperation can help develop energy solutions and ensure environmental protection of cross-border resources and ecosystems. Regional economic cooperation and integration will facilitate better transport, energy and ICT connectivity which in turn will increase access to services such as education, health, and housing as well as electricity and markets for marginalized populations.

Recognizing these opportunities, the countries of Asia and the Pacific have developed a regional road map for implementing the 2030 Agenda for Sustainable Development
to facilitate cooperation at the regional level supported by the ESCAP Secretariat and other United Nations entities. The road map was agreed on during the 4th Asia Pacific Forum on Sustainable Development and subsequently endorsed by the ESCAP Member States via Resolution 73/9 which was adopted during ESCAP’s 73th Commission Session. The road map identifies priority areas of regional cooperation for implementation of the 2030 Agenda. These priority areas underline the major challenges still faced in our region, including leaving no one behind; disaster risk reduction and resilience; climate change; management of natural resources; connectivity; and, energy. Priority actions under the means of implementation for the 2030 Agenda are also identified in the road map, including data and statistics, technology, finance, policy coherence and partnerships.

ESCAP Member States and the Secretariat considered a number of overarching issues regarding sustainable development in the region and the achievement of the 2030 Agenda as a part of the road map drafting process.

The work of ESCAP and Member States recognized that despite achievements on reducing poverty levels with action for MDGs, poverty is still persistent in the region and limits the opportunities for well-being for a large number of people. According to the latest data, some 400 million people, or 10.3 per cent of the region’s population, were living in extreme poverty in Asia and the Pacific during the period 2010-2013. The highest income poverty rates are found among countries with special needs and lower-middle-income countries.

In addition to challenges posted by poverty, inequality is also rising in the region and threatens to disrupt efforts to achieve the 2030 Agenda. ESCAP research indicates that income inequality as well as inequality for opportunities is either on the rise or still very high without signs of improvement in most of the countries in the region. The road map responds by placing the elimination of inequality at the center of the region’s development path.

Quality of governance and the effectiveness of public institutions are also crucial for the successful implementation of the 2030 Agenda and therefore recognized in the road map. Effective governance, and especially in its normative dimension, is a basis for reinstituting the state and the society towards achieving sustainable development. It is a principle means to enable voices of people and participation in an effective, transparent and results oriented decision-making process. Effective governance allows people to be the driving force for sustainable development.

As recent ESCAP research shows, governance also affects the capacity of an economy to gain access and manage better human and natural resources, improve investment prospects and sustain innovation. Additionally, effective governance is pivotal for efforts to close development gaps across countries by supporting reforms that enable countries with special needs to benefit more from regional economic integration initiatives.

All these aspects of governance are fundamental for improving the performance and effectiveness of national and regional development systems and therefore to contribute to the achievement of SDGs.

Acknowledging that promoting sustainable development in Asia Pacific is the most pervasive and lasting solution for meeting today’s challenges and delivering durable peace and stability in our region, this road map recognizes that sustainable development must be underpinned by peaceful and inclusive societies, and also places gender equality and women’s economic empowerment as a central issue in the regional policy agenda.

The Secretariat will follow up and support member states on implementing this road map as requested. It will continue to provide annual updates and recommendations to Member States, including through the Asia-Pacific Forum on Sustainable, with its reports on implementing the 2030 Agenda in the region including, but not limited to, thematic and statistical reports as well as reports on inequality and social development. It will also strengthen support to Member States in their efforts to implement the 2030 Agenda in an integrated approach, inter alia, with analytical products, technical services and capacity-building initiatives through knowledgesharing products and platforms. The Secretariat is establishing an SDG Helpdesk and a Rapid Response Facility that will make available implementation models to support key aspects of SDG implementation that span across the different thematic areas that have been identified in the regional road map.

The Secretariat will give particular attention to effectively using the means of implementation for the 2030 Agenda, while it has already realigned its programme of work and strategic framework to make this happen. Effective use of financing for development, trade, science, technology and innovation (STI) and data and statistics will define the success of SDGs implementation. On financing for development there is an opportunity to translate the concrete policies and actions as outlined in the outcome document of the Third International Conference on Financing for Development to implement the SDGs. Trade has considerable potential to boost implementation of SDGs, but there are still challenges to fully harness this potential; the road map identifies specific areas of work that can tackle obstacles and create opportunities. In the context of STI, the success of implementing the 2030 Agenda will be depend inter alia on Member States’ ability to collaborate and create open and inclusive knowledge economies. With regard to data and statistics, without credible figures and indicators it will be impossible to review progress on SDG implementation and, consequently, improve performance. With eighty-eight of the global SDG indicators lacking methodological guidelines or standards, action on strengthening the statistical capacities of member states are urgently needed. ESCAP’s long-standing programmes in each of these areas are being strengthened to ensure that member States are well-supported as they go forward, and that the actions across means of implementation are mutually supportive.

With this regional road map, ESCAP member States have set the course for transforming the region and delivering inclusive and sustainable development in Asia and the Pacific. Their success will define the global success of the 2030 Agenda.

The Multilinks project explores how demographic changes shape intergenerational solidarity, well-being and social integration. The project examines a) multiple linkages in families (e.g. transfers up and down family lineages, interdependencies between older and younger family members); b) multiple linkages across time (measures at different points in time, at different points in the individual and family life course); c) multiple linkages between, on the one hand, national and regional contexts (e.g. policy regimes, economic circumstances, normative climate, religiosity) and, on the other hand, individual behaviour, well-being and values.



The conceptual approach builds on three key premises. First, ageing affects all age groups: the young, the middle-aged and the old. Second, there are critical interdependencies between family generations as well as between men and women. Third, we must recognize and distinguish analytical levels: the individual, the dyad (parent-child, partners), family, region, historical generation and country.



The database aims to map how the state, in form of public policies and legal norms, defines and regulates intergenerational obligations within the family. What is the contribution of public authorities to support and secure financial and care needs for the young and the elderly in the family? In what ways the state assumes that intergenerational responsibilities are a family matter? In order to answer these questions the database includes a dual intergenerational perspective: upwards generations; from children to parents; and downwards; from parents to children. It looks across a variety of social policies and also includes legal obligations to support. It entails over 70 indicators on social policy rights, legal obligations to support, and care service usage. It offers a structured access to the public support for families with children and for elderly people within 30 European countries for 2004 and 2009.





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The research project MULTILINKS (How demographic changes shape intergenerational solidarity, well-being, and social integration: A Multilinks framework) existed from 2009 to 2011. It has received funding from the European Union's Seventh Framework Programme (FP7/2007-2011) under grant agreement n° 217523.



After the end of the project the results were made available as a web application and as individual datasets together with the documentation files by the WZB (http://multilinks-database.wzb.eu). Since 2020, this website no longer exists. The single datasets and reports are available here unchanged.



However, the web application, together with the documents, is still available through the "Gender & Generations Programme (GGP)" and the French Institute for Demographic Research (INED). There you will find further information, additional descriptive variables and full possibilities to explore and navigate through the database. For more details see: https://www.ggp-i.org/data/multilinks-database/

The fifth round of the Global Reproductive, Maternal, Newborn, Child and Adolescent Health Policy Survey was conducted in 2018-2019. For this survey, the questionnaire was administered online to each member state via World Health Organization (WHO) regional offices. Each WHO country office was asked to coordinate completion of the survey with the Ministry of Health and other UN partners. Respondents from each country shared original source documents including national policies, strategies, laws, guidelines, reports that are relevant to the areas of sexual and reproductive health, maternal and newborn health, child health, adolescent health, gender-based violence and cross-cutting issues. Cross cutting issues include policies, guidelines and legislation for human right to healthcare, financial protection, and quality of care.The WHO cross-cutting issues page can be found here, and the WHO data can also be accessed on their data portal page, here. Adolescent Health Policy data, provided by the WHO, show the below data attributes for countries that have an International Confederation of Midwives (ICM) membership and have completed the required surveys. Academia typically included in the RMNCAH coordinating body Adolescents/young people typically included in the RMNCAH coordinating body Civil society typically included in the RMNCAH coordinating body Donors typically included in the RMNCAH coordinating body Engagement of civil society in review of national RMNCAH programmes H6 partnership organizations typically included in the RMNCAH coordinating body Law requiring birth registration Law requiring death registration Ministry of Health typically included in the RMNCAH coordinating body National human rights institution considers RMNCAH issues National law guarantees universal access to primary health care National policy on Quality of Care for health services exists National policy on Quality of Care includes Adolescent health National policy on Quality of Care includes Maternal health National policy on Quality of Care includes Newborn health National policy on Quality of Care includes Sexual and reproductive health National policy to ensure engagement of civil society organisation representatives in national level planning of RMNCAH programmes National RMNCAH coordinating body Non-health government sectors typically included in the RMNCAH coordinating body Private sector typically included in the RMNCAH coordinating body Professional associations typically included in the RMNCAH coordinating body This data set is just one of the many datasets 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.

On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily. This dataset contains archived community transmission and related data elements by county as originally displayed on the COVID Data Tracker. Although these data will continue to be publicly available, this dataset has not been updated since October 20, 2022. An archived dataset containing weekly community transmission data by county as originally posted can also be found here: Weekly COVID-19 County Level of Community Transmission as Originally Posted | Data | Centers for Disease Control and Prevention (cdc.gov).

Related data CDC has been providing the public with two versions of COVID-19 county-level community transmission level data: this dataset with the daily values as originally posted on the COVID Data Tracker, and an historical dataset with daily data as well as the updates and corrections from state and local health departments. Similar to this dataset, the original historical dataset is archived on 10/20/2022. It will continue to be publicly available but will no longer be updated. A new dataset containing historical community transmission data by county is now published weekly and can be found at: Weekly COVID-19 County Level of Community Transmission Historical Changes | Data | Centers for Disease Control and Prevention (cdc.gov).

This public use dataset has 7 data elements reflecting community transmission levels for all available counties and jurisdictions. It contains reported daily transmission levels at the county level with the same values used to display transmission maps on the COVID Data Tracker. Each day, the dataset is appended to contain the most recent day's data. Transmission level is set to low, moderate, substantial, or high using the calculation rules below.

Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making.

CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2

Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have a transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00).

Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests conducted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have a transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00).

If the two metrics suggest different transmission levels, the higher level is selected.

The reported transmission categories include:

Low Transmission Threshold: Counties with fewer than 10 total cases per 100,000 population in the past 7 days, and a NAAT percent test positivity in the past 7 days below 5%;

Moderate Transmission Threshold: Counties with 10-49 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 5.0-7.99%;

Substantial Transmission Threshold: Counties with 50-99 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 8.0-9.99%;

High Transmission Threshold: Counties with 100 or more total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 10.0% or greater.

Blank: total new cases in the past 7 days are not reported (county data known to be unavailable) and the percentage of positive NAATs tests during the past 7 days (blank) are not reported.

Data Suppression To prevent the release of data that could be used to identify people, data cells are suppressed for low frequency. When the case counts used to calculate the total new case rate metric ("cases_per_100K_7_day_count_change") is greater than zero and less than 10, this metric is set to "suppressed" to protect individual privacy. If the case count is 0, the total new case rate metric is still displayed.

The data in this dataset are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. This dataset is created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access.

The GCS REDD+ project builds of CIFOR efforts to understand the causes of deforestation and forest degradation as well as to elaborate what can be done to reverse those trends in tropical countries. CIFOR works with research partners and stakeholders so REDD+ policy makers and practitioner communities have access to and use the information, analysis and tools needed to design and implement REDD+. Dataset of Participatory Mapping in Katingan and Kotawaringin Timur in Central Kalimantan Province which were supported the GCS REDD+ Project. In general, these data have been through of discussing with local community, which was facilitated with spatial data by GIS specialists. The spatial data are basemap and map from satellite imagery data, that the features are settlement position, roads, rivers, lakes, mountains, important buildings, etc.

Municipalities with at least 10 000 inhabitants and most municipalities with 5,000 to 10 000 inhabitants are divided into IRIS. This division, which is the basis for the dissemination of sub-communal statistics, constitutes a partition of the territory of these communes into “neighbourhoods” with a population of about 2,000 inhabitants. By extension, in order to cover the whole territory, each of the municipalities not divided into IRIS is treated as an IRIS. This division was drawn up in partnership with local partners, in particular the municipalities, in accordance with precise rules defined in consultation with the Commission Nationale Informatique et Libertés (CNIL). It is constructed on the basis of geographical and statistical criteria and, as far as possible, each IRIS must be homogeneous in terms of habitat.The IRIS offer the most developed tool to date to describe the internal structure of nearly 1,900 municipalities with at least 5,000 inhabitants.This division, which is the basis for the dissemination of sub-communal statistics, constitutes a partition of the territory of these communes into “neighbourhoods” with a population of about 2,000 inhabitants.

By extension, in order to cover the whole territory, each of the municipalities not divided into IRIS is treated as an IRIS.

This division was drawn up in partnership with local partners, in particular the municipalities, in accordance with precise rules defined in consultation with the Commission Nationale Informatique et Libertés (CNIL). It is constructed on the basis of geographical and statistical criteria and, as far as possible, each IRIS must be homogeneous in terms of habitat. The IRIS offer the most developed tool to date to describe the internal structure of nearly 1,900 municipalities with at least 5,000 inhabitants.Municipalities with at least 10 000 inhabitants and most municipalities with 5,000 to 10 000 inhabitants are divided into IRIS. This division, which is the basis for the dissemination of sub-communal statistics, constitutes a partition of the territory of these communes into “neighbourhoods” with a population of about 2,000 inhabitants. By extension, in order to cover the whole territory, each of the municipalities not divided into IRIS is treated as an IRIS.

This division was drawn up in partnership with local partners, in particular the municipalities, in accordance with precise rules defined in consultation with the Commission Nationale Informatique et Libertés (CNIL). It is constructed on the basis of geographical and statistical criteria and, as far as possible, each IRIS must be homogeneous in terms of habitat. The IRIS offer the most developed tool to date to describe the internal structure of nearly 1,900 municipalities with at least 5,000 inhabitants.

WHY IS A FOOD SYSTEM DATA COMMONS AND MAPPING SERVICE NEEDED FOR NEW MEXICO?READ NMCDC'S FRAMEWORK FOR ACTION: http://nmcdc.maps.arcgis.com/home/item.html?id=c699add109fd4a29867bc3098b75b6d5Your suggestions welcome!MAPPING FOOD AND HEALTH RELATED ISSUES - A FRAMEWORK FOR ACTION

The need for a comprehensive collection of data describing the many facets of food, hunger and health in New Mexico is growing. The New Mexico Community Data Collaborative (NMCDC) addresses this need by compiling and sharing diverse neighborhood information covering key issue areas such as childhood obesity and school nutrition, licensed food establishments and summer meal sites, chronic disease statistics and health resources, physical activity and transportation, and farmers markets and public pantries. We also teach people to put data to action.

NMCDC is utilizing a public health organizing framework to bring these diverse issues under a single guiding principle, and a social ecological analytic perspective to demonstrate the complex relationships they share.

Public Health can be the most effective arena to bring a common ethical purpose to this essential work and involve pubic organizations in concerted action for decision making and policy change.

The New Mexico Community Data Collaborative (NMCDC) is continuing to expand this work in collaboration with the many advocates and vested partners, and maintaining a comprehensive New Mexico Food System Data Warehouse.

We build data capacity among partner organizations, help coordinate the development of evidence based strategic plans, provide technical analytic services and training, and host the online service to maintain and provide access to the inventory of databases and interactive mapping functions. NMCDC brings to the task an active network of organizations and analysts already contributing tangible work and products to the larger data warehouse at no cost to each other.

This functioning data warehouse is employed widely by partner organizations and the public who advocate for and address the different key issue areas. We hope the value of the information and its tools sharing will result in participatory decision making and a process for consensus informed by solid evidence, insight into efficient and effective program design, competitive proposals for action and funding, consistent measures for evaluation and improvement, and strong arguments for policy change.

Subsequent stages of the evolution of the food system would see data work becoming a routine and essential role of ALL system partners, as actions and interventions and policy functions become the central purposes on the coalition of actors.

Please join us.

Faced with declining donor funding for HIV, low- and middle-income countries must identify efficient and cost-effective ways to integrate HIV prevention programs into public health systems for long-term sustainability. In Zambia, donor support to the voluntary medical male circumcision (VMMC) program, which previously funded non-governmental organizations as implementing partners, is increasingly being directed through government structures instead. We developed a framework to understand how the behaviors of individual decision-makers within the government could be barriers to this transition. We interviewed key stakeholders from the national, provincial, and district levels of the Ministry of Health, and from donors and partners funding and implementing Zambia’s VMMC program, exploring the decisions required to attain a sustainable VMMC program and the behavioral dynamics involved at personal and institutional levels. Using pattern identification and theme matching to analyze the content of the responses, we derived three core decision-making phases in the transition to a sustainable VMMC program: 1) developing an alternative funding strategy, 2) developing a policy for early-infant (0-2 months) and early-adolescent (15-17 years) male circumcision, which is crucial to sustainable HIV prevention; and 3) identifying integrated and efficient implementation models. We formulated a framework showing how, in each phase, a range of behavioral dynamics can form barriers that hinder effective decision-making among stakeholders at the same level (e.g., national ministries and donors) or across levels (e.g., national, provincial and district). Our research methodology and the resulting framework offer a systematic approach for in-depth investigations into organizational decision-making in public health programs, as well as development programs beyond VMMC and HIV prevention. It provides the insights necessary to map organizational development and policy-making transition plans to sustainability, by explaining tangible factors such as organizational processes and systems, as well as intangibles such as the behaviors of policymakers and institutional actors. Methods The data was collected using a semi-structured style discussion guide. Participants were interviewed and the data was audio recorded with participants consent. The data was then captured in excel sheets and analyzed using qualitative thematic techniques.

The global Human Modification map (HM) provides a cumulative measure of human modification of terrestrial lands across the globe at a 1-km resolution. It is a continuous 0-1 metric that reflects the proportion of a landscape modified based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially-explicit global datasets with a median year of 2016. The HM map was developed by scientists at The Nature Conservancy and Conservation Science Partners. For questions or feedback on this map please contact Christina Kennedy (ckennedy@tnc.org) or James Oakleaf. We welcome the opportunity to hear from and to collaborate with researchers on projects that integrate the HM map into their work.For a description of the methods and to cite the use of HM map, see:Kennedy CM, Oakleaf JR, Theobald DM, Baruch‐Mordo S, Kiesecker J. Managing the middle: A shift in conservation priorities based on the global human modification gradient. Glob Change Biol. 2019;25:811–826. https://doi.org/10.1111/gcb.14549Interactive data viewer available at: http://s3.amazonaws.com/DevByDesign-Web/Apps/gHM/index.html

Natural neighbourhoods are neighbourhood definitions and boundaries created during a consultation with Edinburgh residents. Natural neighbourhood boundaries were created in 2004 as part of a review of ward boundaries. The city has changed much since then, the population has increased, new neighbourhoods have appeared and demolition has taken place in other areas so the 2014 consultation has updated these boundaries. The boundaries will be used by the Council and its partners to plan services, consultations and inform policy and strategy development.

Introduction The GiGL Spaces to Visit dataset provides locations and boundaries for open space sites in Greater London that are available to the public as destinations for leisure, activities and community engagement. It includes green corridors that provide opportunities for walking and cycling. The dataset has been created by Greenspace Information for Greater London CIC (GiGL). As London’s Environmental Records Centre, GiGL mobilises, curates and shares data that underpin our knowledge of London’s natural environment. We provide impartial evidence to support informed discussion and decision making in policy and practice. GiGL maps under licence from the Greater London Authority. Description This dataset is a sub-set of the GiGL Open Space dataset, the most comprehensive dataset available of open spaces in London. Sites are selected for inclusion in Spaces to Visit based on their public accessibility and likelihood that people would be interested in visiting. The dataset is a mapped Geographic Information System (GIS) polygon dataset where one polygon (or multi-polygon) represents one space. As well as site boundaries, the dataset includes information about a site’s name, size and type (e.g. park, playing field etc.). GiGL developed the Spaces to Visit dataset to support anyone who is interested in London’s open spaces - including community groups, web and app developers, policy makers and researchers - with an open licence data source. More detailed and extensive data are available under GiGL data use licences for GIGL partners, researchers and students. Information services are also available for ecological consultants, biological recorders and community volunteers – please see www.gigl.org.uk for more information. Please note that access and opening times are subject to change (particularly at the current time) so if you are planning to visit a site check on the local authority or site website that it is open. The dataset is updated on a quarterly basis. If you have questions about this dataset please contact GiGL’s GIS and Data Officer. Data sources The boundaries and information in this dataset, are a combination of data collected during the London Survey Method habitat and open space survey programme (1986 – 2008) and information provided to GiGL from other sources since. These sources include London borough surveys, land use datasets, volunteer surveys, feedback from the public, park friends’ groups, and updates made as part of GiGL’s on-going data validation and verification process. Due to data availability, some areas are more up-to-date than others. We are continually working on updating and improving this dataset. If you have any additional information or corrections for sites included in the Spaces to Visit dataset please contact GiGL’s GIS and Data Officer. NOTE: The dataset contains OS data © Crown copyright and database rights 2024. The site boundaries are based on Ordnance Survey mapping, and the data are published under Ordnance Survey's 'presumption to publish'. When using these data please acknowledge GiGL and Ordnance Survey as the source of the information using the following citation: ‘Dataset created by Greenspace Information for Greater London CIC (GiGL), 2024 – Contains Ordnance Survey and public sector information licensed under the Open Government Licence v3.0 ’

DefinitionThis indicator identifies the ability of lands and waters to function under changing climate conditions within the Midwest Landscape. It prioritizes areas based on landscape diversity and connectedness. Pixels can take the following values:1 – Least resilient2 – Less resilient3 – Slightly less resilient4 – Average resilience5 – Slightly more resilient6 – More resilient7 – Most resilient8 – Indigenous lands (Note: Climate resiliency values for Indigenous lands were included in our analysis, but we have grayed out Indigenous lands and given them a value of 8 for public-facing datasets. This is in accordance with The Nature Conservancy's policy to not share data from Indigenous lands until members of Tribal Nations can review and approve the data.)SelectionThis indicator was chosen as a targetable, important feature of the MLI goals that will be used to track conditions over time and prioritize areas for conservation. Indicators were defined through elicitation and prioritization exercises with federal and state participants. Criteria for the indicators includes 1) actionable, 2) measurable, 3) relevant to multiple groups across the region, and/or 4) representative of other social and/or environmental values.Input Data & Mapping StepsThis indicator originates from the Nature Conservancy's Resilient Land Mapping tool. To create this layer, MLI partners, members, and staff completed the following mapping steps: projected all input data to NAD83 (2011) UTM Zone 15N, and reclassified the raster into the following classes: 1 – Least resilient, 2 – Less resilient, 3 – Slightly less resilient, 4 – Average resilience, 5 – Slightly more resilient, 6 – More resilient, 7 – Most resilient. "Developed" pixels were reclassified as "Least resilient" to avoid removal of urban areas. "Sea level rise area” pixels were reclassified as “Least resilient” to avoid removal of coastal areas. Because the Nature Conservancy's data has not yet been reviewed by members of sovereign indigenous nations, we have grayed out indigenous lands from public-facing data and given them a value of 8 – Indigenous lands. Finally, highly altered areas were removed using our Highly Altered Areas Mask.For full mapping details, please refer to the Midwest Conservation Blueprint 2024 Development Process. For a complete download of all Blueprint input and output data, visit the Midwest Conservation Blueprint 2024 Data Download.