Spatial data from Schulp et al., 2014. Uncertainties in ecosystem service maps: A comparison on the European scale. PloS ONE 9, e109643. Safeguarding the benefits that ecosystems provide to society is increasingly included as a target in international policies. To support such policies, ecosystem service maps are made. However, there is little attention for the accuracy of these maps. We made a systematic review and quantitative comparison of ecosystem service maps on the European scale to generate insights in the uncertainty of ecosystem service maps and discuss the possibilities for quantitative validation. This data package contains maps of the ecosystem services climate regulation, erosion protection, flood regulation, pollination, and recreation. For each service, a map of the average supply according to all analyzed maps is included, as well as a map of the uncertainty of the service. The data package contains a detailed read-me.

This report aims to map poverty and inequality in Sudan and would be representative of the 18

states and 131 localities of Sudan. The poverty mapping technique is based on a small area

estimation (SAE) technique developed by the World Bank to derive estimates of geographic

poverty and inequality. It combines data from the 2014/15 National Household Budget and

Poverty Survey (NHBPS) and the 2008 Population and Housing Census data to build spatially

disaggregated poverty maps.

Although household surveys usually include measures of income and wealth, they are not

representative beyond the state level. Yet, allowing lower levels of disaggregation is important

for policy interventions, particularly for countries like Sudan that have state governments, which

manage the activities of the state while reporting to the federal government. This study uses a

model of household expenditure from a survey data set to estimate household welfare at the

lower levels and apply it to the census data set which does not provide information on household

income or expenditure. These maps illustrate the information gains provided by SAE, show there

is a substantial spatial heterogeneity within the localities, and highlight the small areas most likely

to exhibit the highest risk of poverty.

This dataset is part of both Deliverable 4.3 and 5.3 and was produced by the WP4 team of the Landmark H2020 project. It contains the following shapefile: PO1_GAEC5.shp The shapefile gives an estimation of the change in soil function performance across the EU in agricultural soils after implementation of the GAEC5 under the proposed CAP. This spatial variation is represented in change in z-scores compared to the current supply on a NUTS1 level. To implement the scenario, for each crop within each environmental zone the 20% area with the lowest values of the N Cycling indicator are selected from the current SF supply map and this indicator is increased to the lowest values in the other 80% of the same crop – environmental zone combination. In a second step, for each crop within each environmental zone the 20% area with the lowest values of the water purification indicator from the current SF supply map are selected and this indicator is increased to the lowest values in the other 80% of the crop – environmental zone combination, while maintaining the N Cycling improvements. This simulates potential improvements in both N Cycling and water purification due to the implementation of the Farm Sustainability Tool for Nutrients (GAEC 5) Z-scores are calculated from the spatial SF maps for each of the NUTS1 zones. The z-scores give the signed fractional number of standard deviations by which SF means for a NUTS1 zone are above or below the mean value and allow us indicate which areas have a higher or lower soil function performance compared to the mean value. Z-scores from the current SF maps and scenario maps were then compared to each other to calculate the change in z-scores. This change in z-scores is given in the shapefiles and describes the relative change in soil function performance. Positive values indicate an improvement in soil functioning compared to the current situation, negative values a decrease. More information regarding calculation and interpretation of both this dataset and the soil function maps used to calculate the z-scores can be found in: Vrebos D., F. Bampa, R. Creamer, A. Jones, E. Lugato, L. O’Sullivan, P. Meire, R.P.O. Schulte, J. Schröder and J. Staes (2018). Scenarios maps: visualizing optimized scenarios where supply of soil functions matches demands. LANDMARK Report 4.3. and Jones A. et al. (2019). An options document to propose future policy tools for functional soil management. LANDMARK 5.3. All available from www.landmark2020.eu.

The Gap Analysis Program (GAP) is an element of the U.S. Geological Survey (USGS). GAP helps to implement the Department of Interior?s goals of inventory, monitoring, research, and information transfer. GAP has three primary goals: 1 Identify conservation gaps that help keep common species common; 2 Provide conservation information to the public so that informed resource management decisions can be made; and 3 Facilitate the application of GAP data and analysis to specific resource management activities. To implement these goals, GAP carries out the following objectives: --Map the land cover of the United States --Map predicted distributions of vertebrate species for the U.S. --Map the location, ownership and stewardship of protected areas --Document the representation of vertebrate species and land cover types in areas managed for the long-term maintenance of biodiversity --Provide this information to the public and those entities charged with land use research, policy, planning, and management --Build institutional cooperation in the application of this information to state and regional management activities. GAP provides the following data and web services: The Protected Areas Database of the United States (PAD-US) is a geodatabase that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. The PADUS GAP Status Layer web service can be found at http://gis1.usgs.gov/arcgis/rest/services/gap/PADUS_Status/MapServer . The Land Cover Data creates a seamless data set for the contiguous United States from the four regional Gap Analysis Projects and the LANDFIRE project. The Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx . In addition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer The GAP species range data show a coarse representation of the total areal extent of a species or the geographic limits within which a species can be found (Morrison and Hall 2002). The GAP species distribution models represent the areas where species are predicted to occur based on habitat associations. A full report documenting the parameters used in each species model can be found via: http://gis1.usgs.gov/csas/gap/viewer/species/Map.aspx Web map services for species distribution models can be accessed from: http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Birds http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Mammals http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Amphibians http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Reptiles A table listing all of GAP's available web map services can be found here: http://gapanalysis.usgs.gov/species/data/web-map-services/

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.This map shows Census tracts throughout the US based on if they are considered disadvantaged or partially disadvantaged according to Justice40 Initiative criteria. This is overlaid with the most recent American Community Survey (ACS) figures from the U.S. Census Bureau to communicate the predominant race that lives within these disadvantaged or partially disadvantaged tracts. Predominance helps us understand the group of population which has the largest count within an area. Colors are more transparent if the predominant race has a similar count to another race/ethnicity group. The colors on the map help us better understand the predominant race or ethnicity:Hispanic or LatinoWhite Alone, not HispanicBlack or African American Alone, not HispanicAsian Alone, not HispanicAmerican Indian and Alaska Native Alone, not HispanicTwo or more races, not HispanicNative Hawaiian and Other Pacific Islander, not HispanicSome other race, not HispanicSearch for any region, city, or neighborhood throughout the US, DC, and Puerto Rico to learn more about the population in the disadvantaged tracts. Click on any tract to learn more. Zoom to your area, filter to your county or state, and save this web map focused on your area to share the pattern with others. You can also use this web map within an ArcGIS app such as a dashboard, instant app, or story. This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.Note: Justice40 tracts use 2010-based boundaries, while the most recent ACS figures are offered on 2020-based boundaries. When you click on an area, there will be multiple pop-ups returned due to the differences in these boundaries. From Justice40 data source:"Census tract geographical boundaries are determined by the U.S. Census Bureau once every ten years. This tool utilizes the census tract boundaries from 2010 because they match the datasets used in the tool. The U.S. Census Bureau will update these tract boundaries in 2020.Under the current formula, a census tract will be identified as disadvantaged in one or more categories of criteria:IF the tract is above the threshold for one or more environmental or climate indicators AND the tract is above the threshold for the socioeconomic indicatorsCommunities are identified as disadvantaged by the current version of the tool for the purposes of the Justice40 Initiative if they are located in census tracts that are at or above the combined thresholds in one or more of eight categories of criteria.The goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening toolPurpose"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40

The World Database on Protected Areas (WDPA) is the most comprehensive global database of marine and terrestrial protected areas, updated on a monthly basis, and is one of the key global biodiversity data sets being widely used by scientists, businesses, governments, International secretariats and others to inform planning, policy decisions and management. The WDPA is a joint project between UN Environment and the International Union for Conservation of Nature (IUCN). The compilation and management of the WDPA is carried out by UN Environment World Conservation Monitoring Centre (UNEP-WCMC), in collaboration with governments, non-governmental organisations, academia and industry. There are monthly updates of the data which are made available online through the Protected Planet website where the data is both viewable and downloadable. Data and information on the world's protected areas compiled in the WDPA are used for reporting to the Convention on Biological Diversity on progress towards reaching the Aichi Biodiversity Targets (particularly Target 11), to the UN to track progress towards the 2030 Sustainable Development Goals, to some of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) core indicators, and other international assessments and reports including the Global Biodiversity Outlook, as well as for the publication of the United Nations List of Protected Areas. Every two years, UNEP-WCMC releases the Protected Planet Report on the status of the world's protected areas and recommendations on how to meet international goals and targets. Many platforms are incorporating the WDPA to provide integrated information to diverse users, including businesses and governments, in a range of sectors including mining, oil and gas, and finance. For example, the WDPA is included in the Integrated Biodiversity Assessment Tool, an innovative decision support tool that gives users easy access to up-to-date information that allows them to identify biodiversity risks and opportunities within a project boundary. The reach of the WDPA is further enhanced in services developed by other parties, such as the Global Forest Watch and the Digital Observatory for Protected Areas, which provide decision makers with access to monitoring and alert systems that allow whole landscapes to be managed better. Together, these applications of the WDPA demonstrate the growing value and significance of the Protected Planet initiative.

The peer-reviewed publication for this dataset has been published in Data & Policy, and can be accessed here: https://arxiv.org/abs/2406.16527 Please cite this when using the dataset.

This dataset has been produced as a result of the “Systematic Review of Outcomes Contracts using Machine Learning” (SyROCCo) project. The goal of the project was to apply machine learning techniques to a systematic review process of outcomes-based contracting (OBC). The purpose of the systematic review was to gather and curate, for the first time, all of the existing evidence on OBC. We aimed to map the current state of the evidence, synthesise key findings from across the published studies, and provide accessible insights to our policymaker and practitioner audiences.

OBC is a model for the provision of public services wherein a service provider receives payment, in-part or in-full, only upon the achievement of pre-agreed outcomes.

The data used to conduct the review consists of 1,952 individual studies of OBC. They include peer reviewed journal articles, book chapters, doctoral dissertations, and assorted ‘grey literature’ - that is, reports and evaluations produced outside of traditional academic publications. Those studies were manually filtered by experts on the topic from an initial search of over 11,000 results.

The full text of the articles was obtained from their PDF versions and preprocessed. This involved text format normalisation, removing acknowledgements and bibliographic references.

The corpus was then connected to the INDIGO Impact Bond Dataset. Projects and organisations mentioned in this latter dataset were searched for in the article’s corpus to relate both datasets.

Other types of information that were identified in the texts were 1) financial mechanisms (type of outcomes-based instrument); using a list of terms related to those financial mechanisms based on prior discussions with a policy advisory group (Picker et al., 2021); 2) references to the 17 Sustainable Development Goals (SDGs) defined by the United Nations General Assembly in the 2030 Agenda; 3) country names mentioned in each article and income levels related to the countries; according to the World Classification of Income Levels 2022 by the World Bank.

Three machine learning techniques were applied to the corpus:

Policy areas identification. A query-driven topic model (QDTM) (Fang et al., 2021) was used to determine the probability of an article belonging to different policy areas (health, education, homelessness, criminal justice, employment and training, child and family welfare, and agriculture and environment), using all text of the article as input. The QDTM is a semi-supervised machine learning algorithm that allows users to specify their prior knowledge in the form of simple queries in words or phrases and return query-related topics.

Named Entity Recognition. Three named entity recognition models were applied: “en_core_web_lg” and “en_core_web_trf” models from the python package ‘spaCy’ and the “ner-ontonotes-large” English model from ‘Flair’. “en_core_web_trf” is based on the RoBERTa-base transformer model. ‘Flair’ is a bi-LSTM character-based model. All models were trained on the “OntoNotes 5” data source (Marcus et al., 2011) and are able to identify geographical locations, organisation names, and laws and regulations. An ensemble method was adopted, considering the entities that appear simultaneously in the results of any two models as the correct entities.

Semantic text similarity. We calculated the similarity score between articles. The 10,000 most frequently mentioned words were first extracted from all the articles’ titles and abstracts and the text vectorization technique TF*IDF was applied to convert each article’s abstract into an importance score vector based on these words. Using these numerical vectors, the cosine similarity between different articles was calculated.

The SyROCCo Dataset includes references to the 1952 studies of OBCs mentioned above and the results of the previous processing steps and techniques. Each entry of the dataset contains the following information.

The basic information of each document is its title, abstract, authors, published years, DOI and Article ID:

Title: Title of the document.

Abstract: Text of the abstract.

Authors: Authors of a study.

Published Years: Published Years of a study.

DOI: DOI link of a study.

Article ID: ID of the document selected during the screening process.

The probability of a study belonging to each policy area:

policy_sector_health: The probability of a study belongs to the policy sector “health”.

policy_sector_education: The probability of a study belongs to the policy sector “education”.

policy_sector_homelessness: The probability of a study belongs to the policy sector “homelessness”.

policy_sector_criminal: The probability of a study belongs to the policy sector “criminal”

policy_sector_employment: The probability of a study belongs to the policy sector “employment”

policy_sector_child: The probability of a study belongs to the policy sector “child”.

policy_sector_environment: The probability of a study belongs to the policy sector “environment”.

Other types of information such as financial mechanisms, Sustainable Development Goals, and different types of named entities:

financial_mechanisms: Financial mechanisms mentioned in a study.

top_financial_mechanisms: The financial mechanisms mentioned in a study are listed in descending order according to the number of times they are mentioned, and include the corresponding context of the mentions.

top_sgds: Sustainable Development Goals mentioned in a study are listed in descending order according to the number of times they are mentioned, and include the corresponding context of the mentions.

top_countries: Country names mentioned in a study are listed in descending order according to the number of times they are mentioned, and include the corresponding context of the mentions. This entry is also used to determine the income level of the mentioned counties.

top_Project: Indigo projects mentioned in a study are listed in descending order according to the number of times they are mentioned, and include the corresponding context of the mentions.

top_GPE: Geographical locations mentioned in a study are listed in descending order according to the number of times they are mentioned, and include the corresponding context of the mentions.

top_LAW: Relevant laws and regulations mentioned in a study are listed in descending order according to the number of times they are mentioned, and include the corresponding context of the mentions.

top_ORG: Organisations mentioned in a study are listed in descending order according to the number of times they are mentioned, and include the corresponding context of the mentions.

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.Note: A new version of this data was released November 22, 2022 and is available here. There are significant changes, see the Justice40 Initiative criteria for details.This layer assesses and identifies communities that are disadvantaged according to Justice40 Initiative criteria. Census tracts in the U.S. and its territories that meet the Version 0.1 criteria are shaded in a semi-transparent blue to work with a variety of basemaps.Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 0.1 of the source data downloaded May 30, 2022.Use this layer to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications. See this blog post for more information.From the source:"Census tract geographical boundaries are determined by the U.S. Census Bureau once every ten years. This tool utilizes the census tract boundaries from 2010 because they match the datasets used in the tool. The U.S. Census Bureau will update these tract boundaries in 2020.Under the current formula, a census tract will be identified as disadvantaged in one or more categories of criteria:IF the tract is above the threshold for one or more environmental or climate indicators AND the tract is above the threshold for the socioeconomic indicatorsCommunities are identified as disadvantaged by the current version of the tool for the purposes of the Justice40 Initiative if they are located in census tracts that are at or above the combined thresholds in one or more of eight categories of criteria.The goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening toolPurpose"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40The layer has some transparency applied to allow it to work sufficiently well on top of many basemaps. For optimum map display where streets and labels are clearly shown on top of this layer, try one of the Human Geography basemaps and set transparency to 0%, as is done in this example web map.Browse the DataView the Data tab in the top right of this page to browse the data in a table and view the metadata available for each field, including field name, field alias, and a field description explaining what the field represents.

The Historic Environment Opportunity Map for New Woodland dataset identifies areas in England that may be suitable for new woodland, based solely on available Historic Environment data. The dataset categorises land by different opportunity ratings to reflect the potential suitability of land for woodland creation while acknowledging areas of uncertainty due to data availability.

The purpose of this dataset is to guide landowners, planners, and decision-makers in considering woodland creation from a historic environment perspective. It should be noted that this dataset only considers the Historic Environment and therefore the opportunity ratings do not guarantee or preclude approval for woodland creation proposals.

As any forestry proposal could have the potential to affect the Historic Environment you should contact your local historic environment service. The local historic environment service can provide further data to support woodland creation proposals.

NHLE is the official, up to date register of all nationally protected historic buildings and sites in England.

SHINE is a single, nationally consistent dataset of non-designated historic and archaeological features from across England that could benefit from land management schemes.

The opportunity ratings are as defined:

· Favourable - Areas deemed suitable for new woodland on consideration of available Historic Environment data.

· Neutral - Areas deemed neither favourable nor unfavourable for new woodland on consideration of available Historic Environment data. Proposals in these areas will require additional consideration of the Historic Environment on a case-by-case basis.

· Unclassified - Areas, where SHINE data has been supplied, with no assigned opportunity rating. This illustrates a current absence of recorded data from a Historic Environment perspective. However, as SHINE data is included in the dataset for this area, a degree of confidence may be inferred when considering the absence of historic environment features.

· Unclassified (No SHINE supplied) - Areas, where SHINE data has not been supplied, with no assigned opportunity rating. This illustrates a current absence of recorded data from a Historic Environment perspective.

· Unsuitable - Areas deemed unsuitable for new woodland on consideration of available Historic Environment data.

Unclassified areas may be suitable or unsuitable for new woodland. To better understand these areas, contact the local historic environment service in accordance with the UKFS and Historic Environment Guidance for Forestry in England - GOV.UK

The datasets included in each opportunity rating are as follows:

Favourable

· Lost Historic Woodlands (ArchAI/Forestry Commission) – An A.I. dataset that identifies areas of woodland depicted on early 20th Century Ordnance Survey mapping which have since been lost.

Neutral

· Historic Parklands (Zulu Ecosystems) – an A.I. dataset that identifies areas of parkland depicted on early 20th Century Ordnance Survey mapping.

· World Heritage Site Core data (Historic England) – Core areas of World Heritage Sites, as designated by UNESCO.

· World Heritage Site Buffer (Historic England) – Buffer zones surrounding World Heritage Sites, as designated by UNESCO.

· Ridge and Furrow (Low) (ArchAI) – an A.I. dataset that identifies areas of less well-preserved historic ridge and furrow derived from LiDAR data.

Unclassified

· HER Boundaries (SHINE supplied) – Geographic areas covered by local historic environment services, where SHINE data has been supplied to the Forestry Commission.

· HER Boundaries (No SHINE supplied) - Geographic areas covered by local historic environment services where SHINE data has not been supplied to the Forestry Commission.

Unsuitable

· Historic Landscape Characterisation (HLC) (local historic environment services) – regional datasets that provide information on the historic character of the landscape.

· Scheduled Monuments (Historic England) – Protected archaeological sites of national importance.

· Scheduled Monuments Buffer – A 20 metre buffer surrounding Scheduled Monuments in-line with UKFS.

· Selected Heritage Inventory for Natural England (SHINE)(local historic environment services) – National dataset of non-designated heritage assets.

· Registered Parks and Gardens (Historic England) – Parks and Gardens designated as being of national significance.

· Registered Battlefields (Historic England) – Battlefields designated as being of national significance.

· Ridge and Furrow (High) (ArchAI) – an A.I. dataset that identifies areas of well-preserved historic ridge and furrow derived from LiDAR data.

The COVADIS Data Standard on Risk Prevention Plans includes all technical and organisational specifications for the digital storage of geographical data represented in the Risk Prevention Plans (RPPs). Major risks include the eight foreseeable main natural hazards in the national territory: floods, earthquakes, volcanic eruptions, field movements, coastal hazards, avalanches, forest fires, cyclones and storms, and four technological risks: nuclear risk, industrial risk, risk of transport of hazardous materials and risk of dam failure. The Risk Prevention Plans (RPPs) were established by the Law of 2 February 1995 on the strengthening of environmental protection. The PPR tool is part of the law of 22 July 1987 on the organisation of civil security, the protection of the forest against fire and the prevention of major risks. The development of a PPR falls within the competence of the State. It is decided by the Prefect. Whether natural, technological or multi-hazard, risk prevention plans have similarities. They contain three categories of information: • Regulatory mapping results in a geographical delimitation of the territory concerned by the risk. This delimitation defines areas in which specific regulations apply. These regulations have easement value and impose requirements varying according to the level of hazard to which the area is exposed. The areas are represented on a zoning plan that fully covers the study area. • The hazards causing the risk are included in hazard documents that can be inserted in the submission report or annexed to the RPP. These documents are used to map the different levels of intensity of each hazard considered in the risk prevention plan. • The issues identified during the development of the RPP can also be annexed to the approved document in the form of maps. These similarities between the different types of PPR and the desire to achieve a good level of standardisation of PPR data have led COVADIS to opt for a single data standard, sufficiently generic to address the different types of risk prevention plan (PPRN natural risk prevention plans, PPRT technological risk prevention plans) This data standard does not consist of a complete modeling of a risk prevention plan file. The scope of this document is limited to the geographic data contained in the RPPs, whether regulatory or non-regulatory. The PPR standard is also not intended to standardise the knowledge of hazards. The challenge is to have a description for a homogeneous storage of the geographical data of the PPRs because these data concern several occupations within the ministries responsible for agriculture, on the one hand, and ecology, and sustainable development, on the other. Plans for the prevention of natural or technological risks are one of the tools of the State’s risk prevention policy. The RPP is a regulatory prevention record that communicates risk areas to populations and planners and defines measures to reduce vulnerability. A PPR contains geographical data on a given territory that are very useful for crisis management, land or real estate management and urban planning. However, a PPR is not an operational crisis management document. Approved planning documents must, in particular, attach the zoning plan of the PPR as soon as it is approved.The Data Standard Risk Prevention Plan (RPP) should be used to exchange data between stakeholders in these different areas. The data standard should improve the availability of geographic data produced under PPRT or PPRN procedures. Some simple use cases have been identified: • define a PPR data exchange scenario using shared structuring rules; • standardise service practices and improve the exchange of data between actors in their different fields of application; • propose technical specifications to structure the spatial data produced at the time of the development of the RPP; • facilitate the mapping of major risk prevention plans prescribed or approved in a given territory; • disseminate to the public maps representing the regulatory areas of the RPPs and the areas subject to hazard in a homogeneous manner; • keep track of hazards and issues that have been used to develop the zoning plan and regulations for the RPP. These data are interesting, especially in the event of a revision of the RPP. If this data is required in the departmental data infrastructure, another issue of this standard is to facilitate the retrieval of PPR data towards risk-knowledge and risk prevention policy monitoring applications.

COMPLETED 2010. The data was converted from the most recent (2010) versions of the adopted plans, which can be found at https://cms3.tucsonaz.gov/planning/plans/ Supplemental Information: In March 2010, Pima Association of Governments (PAG), in cooperation with the City of Tucson (City), initiated the Planned Land Use Data Conversion Project. This 9-month effort involved evaluating mapped land use designations and selected spatially explicit policies for nearly 50 of the City's adopted neighborhood, area, and subregional plans and converting the information into a Geographic Information System (GIS) format. Further documentation for this file can be obtained from the City of Tucson Planning and Development Services Department or Pima Association of Governments Technical Services. A brief summary report was provided, as requested, to the City of Tucson which highlights some of the key issues found during the conversion process (e.g., lack of mapping and terminology consistency among plans). The feature class "Plan_boundaries" represents the boundaries of the adopted plans. The feature class "Plan_mapped_land_use" represents the land use designations as they are mapped in the adopted plans. Some information was gathered that is implicit based on the land use designation or zones (see field descriptions below). Since this information is not explicitly stated in the plans, it should only be viewed by City staff for general planning purposes. The feature class "Plan_selected_policies" represents the spatially explicit policies that were fairly straightforward to map. Since these policies are not represented in adopted maps, this feature class should only be viewed by City staff for general planning purposes only. 2010 - created by Jamison Brown, working as an independent contractor for Pima Association of Governments, created this file in 2010 by digitizing boundaries as depicted (i.e. for the mapped land use) or described in the plans (i.e. for the narrative policies). In most cases, this involved tracing based on parcel (paregion) or street center line (stnetall) feature classes. Snapping was used to provide line coincidence. For some map conversions, freehand sketches were drawn to mimick the freehand sketches in the adopted plan. Field descriptions Field descriptions for the "Plan_boundaries" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number ADOPT_DATE: Date of Plan adoption IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator Field descriptions for the "Plan_mapped_land_use" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number LU_DES: Land use designation (e.g., Low density residential) LISTED_ALLOWABLE_ZONES: Allowable zones as listed in the Plan LISTED_RAC_MIN: Minimum residences per acre (if applicable), as listed in the Plan LISTED_RAC_TARGET: Target residences per acre (if applicable), as listed in the Plan LISTED_RAC_MAX: Maximum residences per acre (if applicable), as listed in the Plan LISTED_FAR_MIN: Minimum Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_TARGET: Target Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_MAX: Maximum Floor Area Ratio (if applicable), as listed in the Plan BUILDING_HEIGHT_MAX Building height maximum (ft.) if determined by Plan policy IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator IMPLIED_ALLOWABLE_ZONES: Implied (not listed in the Plan) allowable zones IMPLIED_RAC_MIN: Implied (not listed in the Plan) minimum residences per acre (if applicable) IMPLIED_RAC_TARGET: Implied (not listed in the Plan) target residences per acre (if applicable) IMPLIED_RAC_MAX: Implied (not listed in the Plan) maximum residences per acre (if applicable) IMPLIED_FAR_MIN: Implied (not listed in the Plan) minimum Floor Area Ratio (if applicable) IMPLIED_FAR_TARGET: Implied (not listed in the Plan) target Floor Area Ratio (if applicable) IMPLIED_FAR_MAX: Implied (not listed in the Plan) maximum Floor Area Ratio (if applicable) IMPLIED_LU_CATEGORY: Implied (not listed in the Plan) general land use category. General categories used include residential, office, commercial, industrial, and other.PurposeLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Dataset ClassificationLevel 0 - OpenKnown UsesThis layer is intended to be used in the City of Tucson's Open Data portal and not for regular use in ArcGIS Online, ArcGIS Enterprise or other web applications.Known ErrorsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Data ContactJohn BeallCity of Tucson Development Services520-791-5550John.Beall@tucsonaz.govUpdate FrequencyLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

This is the lightweight data bundle to be used for the PyPSA-Eur tutorial. It excludes large bathymetry and natural protection area datasets.

While the code in PyPSA-Eur is released as free software under the GPLv3, different licenses and terms of use apply to the various input data, which are summarised and linked below:

corine/*

CORINE Land Cover (CLC) database

Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/

Extract from Terms of Use:

Access to data is based on a principle of full, open and free access as established by the Copernicus data and information policy Regulation (EU) No 1159/2013 of 12 July 2013. This regulation establishes registration and licensing conditions for GMES/Copernicus users and can be found here. Free, full and open access to this data set is made on the conditions that:

When distributing or communicating Copernicus dedicated data and Copernicus service information to the public, users shall inform the public of the source of that data and information.

Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the Union.

Where that data or information has been adapted or modified, the user shall clearly state this.

The data remain the sole property of the European Union. Any information and data produced in the framework of the action shall be the sole property of the European Union. Any communication and publication by the beneficiary shall acknowledge that the data were produced “with funding by the European Union”.

https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata

eez/*

World exclusive economic zones (EEZ)

Source: http://www.marineregions.org/sources.php#unioneezcountry

Extract from Terms of Use:

Marine Regions’ products are licensed under CC-BY-NC-SA. Please contact us for other uses of the Licensed Material beyond license terms. We kindly request our users not to make our products available for download elsewhere and to always refer to marineregions.org for the most up-to-date products and services.

http://www.marineregions.org/disclaimer.php

naturalearth/*

World country shapes

Source: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/

Extract from Terms of Use:

All versions of Natural Earth raster + vector map data found on this website are in the public domain. You may use the maps in any manner, including modifying the content and design, electronic dissemination, and offset printing. The primary authors, Tom Patterson and Nathaniel Vaughn Kelso, and all other contributors renounce all financial claim to the maps and invites you to use them for personal, educational, and commercial purposes.

No permission is needed to use Natural Earth. Crediting the authors is unnecessary.

http://www.naturalearthdata.com/about/terms-of-use/

NUTS_2013_60M_SH/*

Europe NUTS3 regions

Source: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units

Extract from Terms of Use:

In addition to the general copyright and licence policy applicable to the whole Eurostat website, the following specific provisions apply to the datasets you are downloading. The download and usage of these data is subject to the acceptance of the following clauses:

The Commission agrees to grant the non-exclusive and not transferable right to use and process the Eurostat/GISCO geographical data downloaded from this page (the "data").

The permission to use the data is granted on condition that: the data will not be used for commercial purposes; the source will be acknowledged. A copyright notice, as specified below, will have to be visible on any printed or electronic publication using the data downloaded from this page.

https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units

https://ec.europa.eu/eurostat/about/policies/copyright

ch_cantons.csv

Mapping between Swiss Cantons and NUTS3 regions

Source: https://en.wikipedia.org/wiki/Data_codes_for_Switzerland

Extract from Terms of Use:

Creative Commons Attribution-ShareAlike 3.0 Unported License

https://en.wikipedia.org/wiki/Data_codes_for_Switzerland

EIA_hydro_generation_2000_2014.csv

Hydroelectricity generation per country and year

Source: https://www.eia.gov/beta/international/data/browser/#/?pa=000000000000000000000000000000g&c=1028i008006gg6168g80a4k000e0ag00gg0004g800ho00g8&ct=0&ug=8&tl_id=2-A&vs=INTL.33-12-ALB-BKWH.A&cy=2014&vo=0&v=H&start=2000&end=2016

Extract from Terms of Use:

Public domain and use of EIA content: U.S. government publications are in the public domain and are not subject to copyright protection. You may use and/or distribute any of our data, files, databases, reports, graphs, charts, and other information products that are on our website or that you receive through our email distribution service. However, if you use or reproduce any of our information products, you should use an acknowledgment, which includes the publication date, such as: "Source: U.S. Energy Information Administration (Oct 2008)."

https://www.eia.gov/about/copyrights_reuse.php

hydro_capacities.csv

Hydroelectricity generation and storage capacities

Source:

A. Kies, K. Chattopadhyay, L. von Bremen, E. Lorenz, D. Heinemann, RESTORE 2050 Work Package Report D12: Simulation of renewable feed-in for power system studies., Tech. rep., RESTORE 2050 (2016).

B. Pfluger, F. Sensfuß, G. Schubert, J. Leisentritt, Tangible ways towards climate protection in the European Union (EU Long-term scenarios 2050), Fraunhofer ISI. https://www.isi.fraunhofer.de/content/dam/isi/dokumente/ccx/2011/Final_Report_EU-Long-term-scenarios-2050.pdf

je-e-21.03.02.xls

Population and GDP data for Swiss Cantons

Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html

Extract from Terms of Use:

Information on the websites of the Federal Authorities is accessible to the public. Downloading, copying or integrating content (texts, tables, graphics, maps, photos or any other data) does not entail any transfer of rights to the content.

Copyright and any other rights relating to content available on the websites of the Federal Authorities are the exclusive property of the Federal Authorities or of any other expressly mentioned owners.

Any reproduction requires the prior written consent of the copyright holder. The source of the content (statistical results) should always be given. Anyone who intends on using statistical results for commercial purposes or gain must obtain an authorisation pursuant to Art. 13 of the Fee Ordinance and is liable to pay an indemnity. Please contact the FSO for this purpose.

https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html

https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html

nama_10r_3gdp.tsv.gz

Gross domestic product (GDP) by NUTS3 region

Source: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3gdp&lang=

Extract from Terms of Use:

Eurostat has a policy of encouraging free re-use of its data, both for non-commercial and commercial purposes. All statistical data, metadata, content of web pages or other dissemination tools, official publications and other documents published on its website, with the exceptions listed below, can be reused without any payment or written licence provided that:

the source is indicated as Eurostat;

when re-use involves modifications to the data or text, this must be stated clearly to the end user of the information.

Exceptions

The permission granted above does not extend to any material whose copyright is identified as belonging to a third-party, such as photos or illustrations from copyright holders other than the European Union. In these circumstances, authorisation must be obtained from the relevant copyright holder(s).

Logos and trademarks are excluded from the above mentioned general permission, except if they are redistributed as an integral part of a Eurostat publication and if the publication is redistributed unchanged.

When reuse involves translations of publications or modifications to the data or text, this must be stated clearly to the end user of the information. A disclaimer regarding the non-responsibility of Eurostat shall be included.

https://ec.europa.eu/eurostat/about/policies/copyright

nama_10r_3popgdp.tsv.gz

Population by NUTS3 region

Source: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en

Extract from Terms of Use:

Eurostat has a policy of encouraging free re-use of its data, both for non-commercial and commercial purposes. All statistical data, metadata, content of web pages or other dissemination tools, official publications and other documents published on its website, with the exceptions listed below, can be reused without any payment or written licence provided

Report contains data that can be used under Open Government Licence. Main report mostly narrative. 3 technical reports contain data. This project is collaboration with Biodiversity and Ecosystem Service Sustainability (BESS) Programme to tap into the expertise and data already collected by BESS to answer several evidence needs for policy: 1. Improving our ability to map ecosystem services at different geographical scales. This work will build on work initiated by the BESS projects to record, build on and share good practice between groups developing spatially explicit maps of ecosystem services. It will also identify possible short and longer term options to address data, modelling and presentation issues to allow this approach to be robustly applied in new circumstances and new areas. This will be closely linked to customer needs with a view to informing prioritisation of action and resource targeting at the local and national level. 2. Provide evidence to assist the Natural Capital Committee, Defra and the Agencies, by exploring the contribution that Natura 2000 sites make to ecological resilience and the provision of ecosystem services, with a view to exploring the options for applying the ecosystem approach to mitigation of development impacts to reduce regulatory costs and increase ecological benefits. .

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2006/2007. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2012 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2006/2007:
In order to develop an effective poverty reduction policies and programs, Iraqi policy makers need to know how large the poverty problem is, what kind of people are poor, and what are the causes and consequences of poverty. Until recently, they had neither the data nor an official poverty line. (The last national income and expenditure survey was in 1988.)

In response to this situation, the Iraqi Ministry of Planning and Development Cooperation established the Household Survey and Policies for Poverty Reduction Project in 2006, with financial and technical support of the World Bank. The project has been led by the Iraqi Poverty Reduction Strategy High Committee, a group which includes representatives from Parliament, the prime minister's office, the Kurdistan Regional Government, and the ministries of Planning and Development Cooperation, Finance, Trade, Labor and Social Affairs, Education, Health, Women's Affairs, and Baghdad University.

The Project has consisted of three components: - Collection of data which can provide a measurable indicator of welfare, i.e. The Iraq Household Socio Economic Survey (IHSES).

• Establishment of an official poverty line (i.e. a cut off point below which people are considered poor) and analysis of poverty (how large the poverty problem is, what kind of people are poor and what are the causes and consequences of poverty).

• Development of a Poverty Reduction Strategy, based on a solid understanding of poverty in Iraq.

The survey has four main objectives. These are:

• To provide data that will help in the measurement and analysis of poverty. • To provide data required to establish a new consumer price index (CPI) since the current outdated CPI is based on 1993 data and no longer applies to the country's vastly changed circumstances. • To provide data that meet the requirements and needs of national accounts. • To provide other indicators, such as consumption expenditure, sources of income, human development, and time use.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2012 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Total sample size and stratification:

The total effective sample size of the Iraq Household Socio Economic Survey (IHSES) 2007 is 17,822 households. The survey was nominally designed to visit 18,144 households - 324 in each of 56 major strata. The strata are the rural, urban and metropolitan sections of each of Iraq's 18 governorates, with the exception of Baghdad, which has three metropolitan strata. The Iraq Household Socio Economic Survey (IHSES) 2007 and the MICS 2006 survey intended to visit the same nominal sample. Variable q0040 indicates whether this was indeed the case.

----> Sample frame:

The 1997 population census frame was applied to the 15 governorates that participated in the census (the three governorates in Kurdistan Region of Iraq were excluded). For Sulaimaniya, the population frame prepared for the compulsory education project was adopted. For Erbil and Duhouk, the enumeration frame implemented in the 2004 Iraq Living Conditions Survey was updated and used. The population covered by Iraq Household Socio Economic Survey (IHSES) included all households residing in Iraq from November 1, 2006, to October 30, 2007, meaning that every household residing within Iraq's geographical boundaries during that period potentially could be selected for the sample.

----> Primary sampling units and the listing and mapping exercise:

The 1997 population census frame provided a database for all households. The smallest enumeration unit was the village in rural areas and the majal (census enumeration area), which is a collection of 15-25 urban households. The majals were merged to form Primary Sampling Units (PSUs), containing 70-100 households each. In Kurdistan, PSUs were created based on the maps and frames updated by the statistics offices. Villages in rural areas, especially those with few inhabitants, were merged to form PSUs. Selecting a truly representative sample required that changes between 1997 and the pilot survey be accounted for. The names and addresses of the households in each sample point (that is, the selected PSU) were updated; and a map was drawn that defined the unit's borders, buildings, houses, and the streets and alleys passing through. All buildings were renumbered. A list of heads of household in each sample point was prepared from forms that were filled out and used as a frame for selecting the sample households.

----> Sampling strategy and sampling stages:

The sample was selected in two stages, with groups of majals (Census Enumeration Areas) as Primary Sampling Units (PSUs) and households as Secondary Sampling Units. In the first stage, 54 PSUs were selected with probability proportional to size (pps) within each stratum, using the number of households recorded by the 1997 Census as a measure of size. In the second stage, six households were selected by systematic equal probability sampling (seps) within each PSU. To these effects, a cartographic updating and household listing operation was conducted in 2006 in all 3,024 PSUs, without resorting to the segmentation of any large PSUs. The total sample is thus nominally composed of 6 households in each of 3,024 PSUs.

----> Sample Points Trios, teams and survey waves:

The PSUs selected in each governorate (270 in Baghdad and 162 in each of the other governorates) were sorted into groups of three neighboring PSUs called trios -- 90 trios in Baghdad and 54 per governorate elsewhere. The three PSUs in each trio do not necessarily belong to the same stratum. The 12 months of the data collection period were divided into 18 periods of 20 or 21 days called survey waves. Fieldworkers were organized into teams of three interviewers, each team being responsible for interviewing one trio during a survey wave. The survey used 56 teams in total - 5 in Baghdad and 3 per governorate elsewhere. The 18 trios assigned to each team were allocated into survey waves at random. The 'time use' module was administered to two of the six households selected in each PSU: nominally the second and fifth households selected by the seps procedure in the PSU.

----> Time-use sample:

The Iraq Household Socio Economic Survey (IHSES) questionnaire on time use covered all household members aged 10 years and older. A subsample of one-third of the households was selected (the second and fifth of the six households in each sample point). The second and fourth visits were designated for completion of the time-use sheet, which covered all activities performed by every member of the household.

A more detailed description of the allocation of sample across governorates is provided in the tabulation report document available among external resources in both English and Arabic.

Sampling deviation

----> Exceptional Measures

The design did not consider the replacement of any of the randomly selected units (PSUs or households.) However, sometimes a team could not visit a cluster during the allocated wave because of unsafe security conditions. When this happened, that cluster was then swapped with another cluster from a randomly selected future wave that was considered more secure. If none were considered secure, a sample point was randomly selected from among those that had been visited already. The team then visited a new cluster within that sample point. (That is, the team visited six households that had not been previously interviewed.) The original cluster as well as the new cluster were both selected by systematic equal probability sampling.

This explains why the survey datasets only contain data from 2,876 of the 3,024 originally selected PSUs, whereas 55 of the PSUs contain more that the six households nominally dictated by the design.

The wave number in the survey datasets is always the nominal wave number, corresponding to the random allocation considered by the design. The effective interview dates can be found in questions 35 to 39 of the survey questionnaires.

Remarkably few of the original clusters could not be visited during the fieldwork. Nationally, less than 2 percent of the original clusters (55 of 3,024) had to be replaced. Of the original clusters, 20 of 54 (37 percent) could not be visited in the stratum of “Kirkuk/other urban” and

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.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 can be found for all countries on the WHO website for Adolescent Health, here. Legal age for unmarried adolescents to provide consent for contraceptive services (except sterilization) without parental/legal guardian consentLegal age for unmarried adolescents to provide consent for emergency contraception without parental/legal guardian consentLegal age for unmarried adolescents to provide consent for HIV testing and counseling without parental/legal guardian consentLegal age for unmarried adolescents to provide consent for HIV care and treatment without parental/legal guardian consentLegal age for married adolescents to provide consent for contraceptive services (except sterilization) without spousal consentLegal age for married adolescents to provide consent for emergency contraception without spousal consentLegal age for married adolescents to provide consent for HIV testing and counseling without spousal consentLegal age for married adolescents to provide consent for HIV care and treatment without spousal consentNational policy/law to punish perpetrators of coerced sex involving adolescent girlsUser fee exemptions for HIV testing and counselling for adolescentsUser fee exemptions for contraceptives for adolescentsUser fee exemptions for testing and treatment of sexually transmitted infections for adolescentsUser fee exemptions for vaccination for HPV for adolescentsThis 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.

This Dataset contains the areas reserved as dog walking areas.All amendments / changes done on the maintained land layer re dog walking areas have been transfered in this Dog Walking Areas layer following consultations with the proper authorities / dog management officers and approval by the Council as of 10 July 2017.Email thread leading up to the 2017 version :Message Hi JuliaPlease see map changes below.Please let me know if you need any further info.Thanks Abbey MatthewsI Environmental Services Technical Officer I City of Launceston T 03 6323 3144 www.launceston.tas.gov.auFrom:Erica McCarthy Sent:Monday, 29 May 2017 4:27 PMTo:Abbey MatthewsSubject:FW: Dog Policy Map changesOne tick!!!Erica McCarthyI Regulations Officer I Environmental Services I City of LauncestonT 03 6323 3210 I www.launceston.tas.gov.auFrom:Barry Pickett Sent:Monday, 29 May 2017 4:18 PMTo:Erica McCarthy; Debbie FortuinCc:Leanne HurstSubject:RE: Dog Policy Map changesHi EricaLooks okay to me.RegardsBarry PickettI Natural Environment Manager I City of Launceston M 0418 525 897 I T 03 6323 3612 I F 03 6323 3501I www.launceston.tas.gov.auFrom:Erica McCarthy Sent:Monday, 29 May 2017 3:33 PMTo:Debbie Fortuin; Barry PickettCc:Leanne HurstSubject:Dog Policy Map changesHello all,Based on today's feedback we can modify the exiting maps as below. This will mean that some places (e.g. Map 14.4 the playing area in Coronation Park) is Prohibited Public Place where dogs are not allowed on the area when sports are being played, but can access it at other times.For other sporting venues where I suggest dogs are not allowed at all due to public safety and parks maintenance issues we can change these areas to Restricted Area (the signage on site would be no dogs 24hrs) which basically means we have used our discretion to make it a no dog area.Below is what I think should be changed to restricted area based on the volume of use and demographic. If you are happy with this I will send it to Spatial and Abbey to make the changes.3.4 (Hobler's Bridge Sports Centre) - changed from Prohibited Public Area to Restricted Area (Orange as per 6.5)4.4 (Heritage Forest) - changed from Prohibited Public Area to Restricted Area4.6 (Invermay Park) - changed from Prohibited Public Area to Restricted Area4.15 (UTAS stadium) - changed from Prohibited Public Area to Restricted Area5.11 (Nunamina Park) - change area of Prohibited Public Area to Restricted Area6.7 (Elphin Sportsground) - changed from Prohibited Public Area to Restricted Area6.13 (NTCA) - change from Prohibited Public Area to Restricted Area6.19 (Royal Park) - change area of Prohibited Public Area to Restricted Area14.10 (Transport Centre) - change area of Prohibited Public Area to Restricted Area15.4 (St Leonards Sports Centre Athletic) - change area of Prohibited Public Area to Restricted AreaErica McCarthyI Regulations Officer I Environmental Services I City of LauncestonT 03 6323 3210 I www.launceston.tas.gov.auMessage Hi Julia,Following the public consultation Erica has been working on some changes to the maps in the Dog Management Policy.If you would like any further details on anything in the attached document we can come up and clarify. Are you able to give us a timeframe for how long the changes might take to implement?Thank you Abbey MatthewsI Environmental Services Technical Officer I City of Launceston T 03 6323 3144 www.launceston.tas.gov.auFrom:Erica McCarthy Sent:Tuesday, 7 March 2017 2:38 PMTo:Abbey MatthewsCc:Debbie FortuinSubject:Dog Management Policy - map ammendmentsHi Abbey,Please find attached the map amendments for you to forward to the SAM team. The most significant change is the addition of the prohibited public place and the associated changes to the restricted public areas. If anything needs clarification please speak to me directly as it can get a bit confusing. Could you also please have the SAM team advise of an estimated completion date.CheersErica McCarthyI Regulations Officer I Environmental Services I City of LauncestonT 03 6323 3210 I www.launceston.tas.gov.auDog Management Policy - Map amendments1. Change the colour in the Legend for "off leashed fenced exercise area" so it is more visible - e.g. striated green.2. In the Legend the title "restricted area no dogs 24hrs" needs to be changed to "prohibited public area" so that it follows in-line with the Dog Control Act 2000, S28. (Refer to * below).3. In addition to point 2 an additional item needs to be added to the Legend to cover "restricted area no dogs 24hrs". These are areas that Council have declared e.g. City Park. (Refer to the Map changes below).4. Map 2.1 Change from on leash to restricted area no dogs 24hrs - park is very small and difficult to get 10m clearance from playground. - Clementina St Playpark5. Map 4.11 Change from on leash to restricted area no dogs 24hrs - park is small and difficult to get 10m clearance from playground. - Inveresk6. Map 6.5 Change from on leash to restricted area no dogs 24hrs - not desirable for dog usage as per feedback from public consultation. - City Park7. Map 9.5 Change from on leash to restricted area no dogs 24hrs - not suitable for dog usage. - St Catherine's Hall8. Map 17.4 Change the on leash areas to restricted area no dogs 24hrs. - Trevallyn Reserve* Dog Control Act 2000

See full Data Guide here. Surface Water Quality Classifications Set:

This dataset is a line and a polygon feature-based layer compiled at 1:24,000 scale that includes water quality classification information for surface waters for all areas of the State of Connecticut. The Surface Water Quality Classifications and the Ground Water Quality Classifications are usually presented together as a depiction of water quality classifications in Connecticut. Water Quality Classifications, based on the adopted Water Quality Standards, establish designated uses for surface and ground waters and identify the criteria necessary to support those uses. This edition of the Surface Water Quality Classifications is based on the Water Quality Standards adopted on February 25, 2011. Surface Water means the waters of Long Island Sound, its harbors, embayments, tidal wetlands and creeks; rivers and streams, brooks, waterways, lakes, ponds, marshes, swamps, bogs, federal jurisdictional wetlands, and other natural or artificial, public or private, vernal or intermittent bodies of water, excluding groundwater. The surface waters includes the coastal waters as defined by Section 22a-93 of the Connecticut General Statutes and means those waters of Long Island Sound and its harbors, embayments, tidal rivers, streams and creeks, which contain a salinity concentration of at least five hundred parts per million under the low flow stream conditions as established by the Commissioner of the Department of Environmental Protection. The Surface Water Quality Classes are AA, A, B, SA and SB. All surface waters not otherwise classified are considered as Class A if they are in Class GA Ground Water Quality Classifications areas. Class AA designated uses are: existing or proposed drinking water, fish and wildlife habitat, recreational use (maybe restricted), agricultural and industrial supply. Class A designated uses are: potential drinking water, fish and wildlife habitat, recreational use, agricultural and industrial supply. Class B designated uses are: fish and wildlife habitat, recreational use, agricultural and industrial supply and other legitimate uses including navigation. Class B* surface water is a subset of Class B waters and is identical in all ways to the designated uses, criteria and standards for Class B waters except for the restriction on direct discharges. Coastal water and marine classifications are SA and SB. Class SA designated uses are: marine fish, shellfish and wildlife habitat, shellfish harvesting for direct human consumption, recreation and other legitimate uses including navigation. Class SB designated uses are: marine fish, shellfish and wildlife habitat, shellfish harvesting for transfer to approved areas for purification prior to human consumption, recreation and other legitimate uses including navigation. There are three elements that make up the Water Quality Standards which is an important element in Connecticut's clean water program. The first of these is the Standards themselves. The Standards set an overall policy for management of water quality in accordance with the directive of Section 22a-426 of the Connecticut General Statutes. The policies can be simply summarized by saying that the Department of Environmental Protection shall: Protect surface and ground waters from degradation, Segregate waters used for drinking from those that play a role in waste assimilation, Restore surface waters that have been used for waste assimilation to conditions suitable for fishing and swimming, Restore degraded ground water to protect existing and designated uses, Provide a framework for establishing priorities for pollution abatement and State funding for clean up, Adopt standards that promote the State's economy in harmony with the environment. The second element is the Criteria, the descriptive and numerical standards that describe the allowable parameters and goals for the various water quality classifications. The final element is the Classification Maps which identify the relationship between designated uses and the applicable Standards and Criteria for each class of surface and ground water. Although federal law requires adoption of Water Quality Standards for surface waters, Water Quality Standards for ground waters are not subject to federal review and approval. Connecticut's Standards recognize that surface and ground waters are interrelated and address the issue of competing use of ground waters for drinking and for waste water assimilation. These Standards specifically identify ground water quality goals, designated uses and those measures necessary for protection of public and private drinking water supplies; the principal use of Connecticut ground waters. These three elements comprise the Water Quality Standards and are adopted using the public participation procedures contained in Section 22a-426 of the Connecticut General Statutes. The Standards, Criteria and Maps are reviewed and revised roughly every three years. Any change is considered a revision requiring public participation. The public participation process consists of public meetings held at various locations around the State, notification of all chief elected officials, notice in the Connecticut Law Journal and a public hearing. The Classification Maps are the subject of separate public hearings which are held for the adoption of the map covering each major drainage basin in the State. The Water Quality Standards and Criteria documents are available on the DEP website, www.ct.gov/dep. The Surface Water Quality Classifications is a line and polygon feature-based layer is based primarily on the Adopted Water Quality Classifications Map Sheets. The map sheets were hand-drawn at 1:50,000-scale in ink on Mylar which had been underprinted with a USGS topographic map base. The information collected and compiled by major drainage basin from 1986 to 1997. Ground Water Quality Classifications are defined separately in a data layer comprised of polygon features. The Ground and Surface Water Quality Classifications do not represent conditions at any one particular point in time. During the conversion from a manually maintained to a digitally maintained statewide data layer the Housatonic River and Southwest Coastal Basins information was updated. A revision to the Water Quality Standards adopted February 25, 2011. These revisions included eliminating surface water quality classes C, D, SC, SD and all the two tiered classifications. The two tiered classifications included a classification for the present condition and a second classification for the designated use. All the tiered classifications were changed to the designated use classification. For example, classes B/A and C/A were changed to class A. The geographic extent of each the classification was not changed. The publication date of the digital data reflects the official adoption date of the most recent Water Quality Classifications. Within the data layer the adoption dates are: Housatonic and Southwest Basins - March 1999, Connecticut and South Central Basins - February 1993, Thames and Southeast Basins - December 1986. Ground water quality classifications may be separately from the surface water quality classifications under specific circumstances. This data is updated.

Official Plan Overlays define the boundaries of the following Brampton Official Plan Schedule A Land Use Policy Areas: Appealed to OMB, Central Area, Corridor Protection Area, Deferral, Parkway Belt West, Greenbelt, Special Study Area, Village Residential, Special Land Use Policy Area and the L.B.P.I.A Operating Area. The Official Plan Schedule A map document displays the general land use designations for the entire City of Brampton at a high level to outline community patterns. Some designations are subject to additional policy overlays, illustrated by this dataset. Definitions of each land use and policy are explained in the companion text, found in various Official Plan Land Use and Special Policy sections. This data is part of the Official Plan mapping data series and is intended to be combined with all of the Official Plan Schedule A datasets to form a complete map.As per O.P. Section 1.1: “The purpose of the Official Plan is to give clear direction as to how physical development and land-use decisions should take place in Brampton to meet the current and future needs of its residents. It is also intended to reflect their collective aims and aspirations, as to the character of the landscape and the quality of life to be preserved and fostered within Brampton. The Plan also provides policy guidance to assist business interests in their decision to invest and grow in the City of Brampton. Finally, the Plan clarifies and assists in the delivery of municipal services and responsibilities.”

Definitions and permitted uses of each land use and respective policies are explained in the text of the Official Plan General Land Use section.Boundaries are based on environmental policies, regulation areas, parcel fabric boundaries, existing built features, topographic features, aerial photography, legal documents and OMB decisions.Official Plan MapsOfficial Plan DocumentsLast Updated: February 4, 2020

The Corine Land Cover datasets CLC2000, CLC2006 and CLC change 2000-2006 areproduced within the frame of the GMES land monitoring project. Corine Land Cover (CLC) provides consistent information on land cover and land cover changes across Europe. This inventory was initiated in 1985 (reference year 1990) and established a time series of land cover information with updates in 2000 and 2006.CLC products are based on photointerpretation of satellite images by national teams of participating countries - the EEA member and cooperating countries – following a standard methodology and nomenclature with the following base parameters: 44 classes in the hierarchical three level Corine nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres; minimum mapping unit (MMU) for Land Cover Changes (LCC) for the change layers is 5 hectares. The resulting national land cover inventories are further integrated into a seamless land cover map of Europe.Land cover and land use (LCLU) information is important not only for land change research, but also more broadly for the monitoring of environmental change, policy support, the creation of environmental indicators and reporting. CLC datasets provide important datasets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, assessing developments in agriculture and implementing the EU Water Framework Directive, among others.More about the Corine Land Cover (CLC) and Copernicus land monitoring data in general can be found at http://land.copernicus.eu/.

This feature layer provides digital tax parcels for the Organized Towns of the State of Maine. Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. "Maine Parcels Organized Towns Feature" and "Maine Parcels Organized Towns ADB" are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, which affects the currency of Maine GeoLibrary parcels data. Another resource for real property transaction data is the County Registry of Deeds, although organized town data should very closely match registry information, except in the case of in-process property conveyance transactions. In Unorganized Territories (defined as those regions of the state without a local government that assesses real property and collects property tax), the Maine Revenue Service is the authoritative source for parcel data. "Maine Parcels Unorganized Territory Feature" is the authoritative GIS data layer for the Unorganized Territories. However, it must always be used with auxiliary data obtained from the online resources of Maine Revenue Services (https://www.maine.gov/revenue/taxes/property-tax) to compile up-to-date parcel ownership information. Property maps are a fundamental base for many municipal activities. Although GIS parcel data cannot replace detailed ground surveys, the data can assist municipal officials with functions such as accurate property tax assessment, planning and zoning. Towns can link maps to an assessor's database and display local information, while town officials can show taxpayers how proposed development or changes in municipal services and regulations may affect the community. In many towns, parcel data also helps to provide public notices, plan bus routes, and carry out other municipal services.

This dataset contains municipality-submitted parcel data along with previously developed parcel data acquired through the Municipal Grants Project supported by the Maine Library of Geographic Information (Maine GeoLibrary). Grant recipient parcel data submissions were guided by standards presented to the Maine GeoLibrary Board on May 21, 2005, which are outlined in the "Standards for Digital Parcel Files" document available on the Maine GeoLibrary publications page (https://www.maine.gov/geolib/policies/standards.html). This dataset also contains municipal parcel data acquired through other sources; the data sources are identified (where available) by the field “FMSCORG”. Note: Join this feature layer with the "Maine Parcels Organized Towns ADB" table (https://maine.hub.arcgis.com/maps/maine::maine-parcels-organized-towns-feature/about?layer=1) for available ownership information. A date field, “FMUPDAT”, is attributed with the most recent update date for each individual parcel if available. The "FMUPDAT" field will not match the "Updated" value shown for the layer. "FMUPDAT" corresponds with the date of update for the individual data, while "Updated" corresponds with the date of update for the ArcGIS Online layer as a whole. Many parcels have not been updated in several years; use the "FMUPDAT" field to verify currency.