The Kansas Master Ground-water Well Inventory (MWI) is a central repository that imports and links together the State's primary ground-water well data sets- KDHE's WWC5, KDA-DWR's WIMAS, and KGS' WIZARD into a single, online source. The most "accurate" of the common source fields are used to represent the well sites, for example- GPS coordinates if available are used over other methods to locate a well. The MWI maintains the primary identification tags to allow specific well records to be linked back to the original data sources.This data is compiled by the Kansas Geological Survey. For more information, please see the Groundwater Master Well Inventory page.

The Kansas Master Ground-water Well Inventory (MWI) is a central repository that imports and links together the State's primary ground-water well data sets- KDHE's WWC5, KDA-DWR's WIMAS, and KGS' WIZARD into a single, online source. The most "accurate" of the common source fields are used to represent the well sites, for example- GPS coordinates if available are used over other methods to locate a well. The MWI maintains the primary identification tags to allow specific well records to be linked back to the original data sources.This mapper is managed by the Kansas Geological Survey. For more information about the data, please see the Groundwater Master Well Inventory page.

In Module 2 Lesson 1, we will take a deeper dive into Geographic Information Systems (GIS) technology. We'll explore different types of GIS data, the importance of data attributes and queries, data symbolization, and ways to access GIS technology.

This metadata record describes the acquisition and production of 1 foot contours for 5 coastal counties Hancock, Harrison, Jackson, Pearl River and Stone. The breaklines were collected from digital imagery with a 15 cmground sample distance (GSD) for the project area for the 1 foot contour area and 30 cm for the 5 foot contour area. All imagery was acquired in spring 2007 and processed during the spring & summer of 2007. The imagery is from a project tasked by Mississippi Geographic Information, LLC (MGI) with Work Orders ED-9 & ED-9A. EarthData International, Inc. was authorized to undertake this project in accordance with the terms and conditions of the professional service agreement between MGI and EarthData International, Inc., dated February 14, 2007.

This dataset contains raw GIS data sourced from the BAG (Basisregistratie Adressen en Gebouwen; Registry of Addresses and Buildings). It provides comprehensive information on buildings, including advanced height data and administrative details. It also contains geographic divisions within The Hague. Additionally, the dataset incorporates energy label data, offering insights into the energy efficiency and performance of these buildings. This combined dataset serves as the backbone of a Master's thesis in Industrial Ecology, analysing residential and office cooling and its environmental impacts in The Hague, Netherlands. The codebase of this analysis can be found in this Github repository: https://github.com/simonvanlierde/msc-thesis-ie

The dataset includes a background research spreadsheet containing supporting calculations. It also presents geopackages with results from the cooling demand model (CDM) for various scenarios: Status quo (SQ), 2030, and 2050 scenarios (Low, Medium, and High)

Background research data

The background_research_data.xlsx spreadsheet contains comprehensive background research calculations supporting the shaping of input parameters used in the model. It contains several sheets:

• Cooling Technologies: Details the various cooling technologies examined in the study, summarizing their characteristics and the market penetration mixes used in the analysis.
• LCA Results of Ventilation Systems: Provides an overview of the ecoinvent processes serving as proxies for the life-cycle impacts of cooling equipment, along with calculations of the weight of cooling systems and contribution tables from the LCA-based assessment.
• Material Scarcity: A detailed examination of the critical raw material content in the material footprint of ecoinvent processes, representing cooling equipment.
• Heat Plans per Neighbourhood: Forecasts of future heating solutions for each neighbourhood in The Hague.
• Building Stock: Analysis of the projected growth trends in residential and office building stocks in The Hague. AC Market: Market analysis covering air conditioner sales in the Netherlands from 2002 to 2022.
• Climate Change: Computations of climate-related parameters based on KNMI climate scenarios.
• Electricity Mix Analysis: Analysis of future projections for the Dutch electricity grid and calculations of life-cycle carbon intensities of the grid.

Input data

Geographic divisions

• The outline of The Hague municipality through the Municipal boundaries (Gemeenten) layer, sourced from the Administrative boundaries (Bestuurlijke Gemeenten) dataset on the PDOK WFS service.
• District (Wijken) and Neighbourhood (Buurten) layers were downloaded from the PDOK WFS service (from the CBS Wijken en Buurten 2022 data package) and clipped to the outline of The Hague.
• The 4-digit postcodes layer was downloaded from PDOK WFS service (CBS Postcode4 statistieken 2020) and clipped to The Hague's outline. The postcodes within The Hague were subsequently stored in a csv file.
• The census block layer was downloaded from the PDOK WFS service (from the CBS Vierkantstatistieken 100m 2021 data package) and also clipped to the outline of The Hague.
• These layers have been combined in the GeographicDivisions_TheHague GeoPackage.

BAG data

• BAG data was acquired through the download of a BAG GeoPackage from the BAG ATOM download page.
• In the resulting GeoPackage, the Residences (Verblijfsobject) and Building (Pand) layers were clipped to match The Hague's outline.
• The resulting residence data can be found in the BAG_buildings_TheHague GeoPackage.

3D BAG

• Due to limitations imposed by the PDOK WFS service, which restricts the number of downloadable buildings to 10,000, it was necessary to acquire 145 individual GeoPackages for tiles covering The Hague from the 3D BAG website.
• These GeoPackages were merged using the ogr2ogr append function from the GDAL library in bash.
• Roof elevation data was extracted from the LoD 1.2 2D layer from the resulting GeoPackage.
• Ground elevation data was obtained from the Pand layer.
• Both of these layers were clipped to match The Hague's outline.
• Roof and ground elevation data from the LoD 1.2 2D and Pand layers were joined to the Pand layer in the BAG dataset using the BAG ID of each building.
• The resulting data can be found in the BAG_buildings_TheHague GeoPackage.

Energy labels

• Energy labels were downloaded from the Energy label registry (EP-online) and stored in energy_labels_TheNetherlands.csv.

UHI effect data

• A bitmap with the UHI effect intensity in The Hague was retrieved from the from the Dutch Natural Capital Atlas (Atlas Natuurlijk Kapitaal) and stored in UHI_effect_TheHague.tiff.

Output data

• The residence-level data joined to the building layer is contained in the BAG_buildings_with_residence_data_full GeoPackage.
• The results for each building, according to different scenarios, are compiled in the buildings_with_CDM_results_[scenario]_full GeoPackages. The scenarios are abbreviated as follows:
  • SQ: Status Quo, covering the 2018-2022 reference period.
  • 2030: An average scenario projected for the year 2030.
  • 2050_L: A low-impact, best-case scenario for 2050.
  • 2050_M: A medium-impact, moderate scenario for 2050.
  • 2050_H: A high-impact, worst-case scenario for 2050.

Updated Continually

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty states, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of states for the purpose of data presentation.

This layer shows the Cavern Master Plan of Hong Kong. It is a subset of the geo-referenced data made available by the Civil Engineering and Development Department under the Government of Hong Kong Special Administrative Region (the “Government”) at https://DATA.GOV.HK/ (“DATA.GOV.HK”). The source data has been processed and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.

Updated Continually

In Module 1 Lesson 2 of the Green Drone AZ program, you'll be introduced to Geographic Information Systems (GIS) technology. We'll explore how we utilize this technology to improve management on the Lower Salt River Restoration Project (LSRRP). We'll also discuss varying forms of data collection and how these approaches translate to vegetation monitoring of conservation efforts. Lastly, we'll introduce how Unmanned Aircraft System (UAS) or drone technology is rewriting the possibilities of data collection and monitoring in a broad variety of disciplines.

Summary: NEW VERSION is at https://esriurl.com/agoschoolcompStorymap metadata page: URL forthcoming Possible K-12 Next Generation Science standards addressed:Grade level(s) 6-8: Standard MS-LS4-4 - Biological Evolution: Unity and Diversity - Construct an explanation based on evidence that describes how genetic variations of traits in a population increase some individuals’ probability of surviving and reproducing in a specific environmenGrade level(s) 6-8: Standard MS-LS4-6 - Biological Evolution: Unity and Diversity - Use mathematical representations to support explanations of how natural selection may lead to increases and decreases of specific traits in populations over timeGrade level(s) 6-8: Standard MS-ESS1-2 - Earth’s Place in the Universe - Develop and use a model to describe the role of gravity in the motions within galaxies and the solar systemGrade level(s) 6-8: Standard MS-ESS2-4 - Earth’s Systems - Develop a model to describe the cycling of water through Earth’s systems driven by energy from the sun and the force of gravityGrade level(s) 9-12: Standard HS-PS1-2 - Matter and Its Interactions - Construct and revise an explanation for the outcome of a simple chemical reaction based on the outermost electron states of atoms, trends in the periodic table, and knowledge of the patterns of chemical propertiesGrade level(s) 9-12: Standard HS-LS2-1 - Ecosystems: Interactions, Energy, and Dynamics - Use mathematical and/or computational representations to support explanations of factors that affect carrying capacity of ecosystems at different scalesGrade level(s) 9-12: Standard HS-LS4-2 - Biological Evolution: Unity and Diversity - Construct an explanation based on evidence that the process of evolution primarily results from four factors: (1) the potential for a species to increase in number, (2) the heritable genetic variation of individuals in a species due to mutation and sexual reproduction, (3) competition for limited resources, and (4) the proliferation of those organisms that are better able to survive and reproduce in the environment.Most frequently used words:competitionesrihsstateApproximate Flesch-Kincaid reading grade level: 10.3. The FK reading grade level should be considered carefully against the grade level(s) in the NGSS content standards above.

Current Extent\r \r The State Vegetation Type Map (SVTM) is a regional-scale map of NSW Plant Community Types. This map represents the current extent of each Plant Community Type, Vegetation Class and Vegetation Formation, across all tenures in NSW. This map is updated periodically as part of the Integrated BioNet Vegetation Data program to improve quality and alignment to the NSW vegetation classification hierarchy. \r \r An SVTM pre-clearing PCT map is available here .\r \r Further information about the mapping methods is available from the State Vegetation Type Mapping Program Page \r \r Current Release C2.0.M2.1 (November2024)\r \r This release includes revisions, using the most recent NSW PCT Classification Master list (represented by “C2.0” in the version release number). PCT spatial distributions were manually edited based on user and community feedback since the previous C2.0.M2.0 release. In addition, changes were made to the Native Vegetation Extent mask which is used to create the Native Extent map.\r \r Detailed technical information is available here .\r \r Data Access\r \r Map data may be downloaded, viewed within the SEED Map Viewer, or accessed via the underlying ArcGIS REST Services or WMS for integration in GIS or business applications. \r \r The Trees Near Me NSW app provides quick access to view the map using a mobile device or desktop. Download the app from Google Play or the App Store, or access the web site at https://treesnearme.app .\r \r Map Data Type\r \r The map is supplied as ESRI Feature Class (Quickview) and 5m GeoTiff Raster, and can be viewed and analysed in most commercial and open-source spatial software packages. If you prefer to use the download package, we supply an ArcGIS v10.4 mxd and/or a layer file for suggested symbology. The raster attributes contain PCT, Vegetation Class and Vegetation Formation.\r \r Feedback and Support\r \r We welcome your feedback to assist us in continuously improving our products. To help us track and process your feedback, please use the SEED Data Feedback tool available via the SEED map viewer. \r \r For further support, contact the BioNet Team at _ bionet@environment.nsw.gov.au. _\r \r Useful Related Data\r \r NSW BioNet Flora Survey Plots – PCT Reference Sites : full floristic plots used in the development of the quantitative Plant Community Type (PCT) classification. Currently available for eastern NSW PCTs version C2.0.\r \r NSW State Vegetation Type Map - technical notes \r \r Eastern NSW - percentage cleared calculation technical notes .

Access the file geodatabase source data in SC State Plane coordinate system

The Coconino Plateau Water Demand Web Application is an interactive tool that illustrates water demand within the Coconino Plateau. I created it as a tool for the CPWP and other stakeholders to use to find water demand (in acre-ft/year), surface water sources if applicable, and water use information (municipal, effluent, industrial, or agricultural) for a specific town or place of interest within the Coconino Plateau.Other Information:The dashboard is property of the Coconino Plateau Watershed Partnership and uses data from the ADWR Community Water System Database. I completed this as my capstone project to fulfill the requirements of the masters in GIS with Penn State.

Full Metadata This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). This web service includes layers depicting EnviroAtlas national metrics mapped at the 12-digit HUC within the conterminous United States. EnviroAtlas allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the conterminous United States as well as detailed metrics for select communities. Additional descriptive information about each attribute in this web service can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This layer shows the Invigorating Island South Conceptual Master Plan 3.0 Wong Chuk Hang Aberdeen and Ap Lei Chau.. It is a set of data made available by the Development Bureau under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and uploaded to Esri's ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.

This conflated baseline consist of two Digital Shoreline Analysis System (DSAS) Process runs.The original In 2000, the Maryland Geological Survey (MGS) was awarded a Coastal Zone Management grant to complete the acquisition of a recent (ca. 1990) digital shoreline for the coastal regions of Maryland -- the Chesapeake Bay, its tributaries, the coastal bays, and the Atlantic coast. MGS contracted the services of EarthData International, Inc. (EDI), currently of Frederick, Md., to extract shorelines from an existing wetlands delineation, which was based on photo interpretation of 3.75-minute digital orthophoto quarter quads (DOQQs). The 2000 baseline which were not created seaward includes all Maryland shoreline areas except for Anne Arundel, Baltimore, Calvert, Harford and Prince George's counties currently. The newest (2015) updated baselines were created offshore (seaward) of the shorelines utilized in DSAS analysis. The baselines were created by 1) buffering at a distance of 10m around the master shoreline feature class converting the buffer polygon to a line, and erasing the landward portion of the buffer line; and 2)manually digitizing baselines up the centerline of tributaries/rivers and other areas where baselines were needed but the buffer-created baselines did not reach. Funding for this data set was provided by two Projects of Special Merit (CZM # 14-14-1868 CZM 143 and CZM # 14-15-2005 CZM 143), funded by the National Oceanic and Atmospheric Administration (NOAA) and made available to MGS through the Department of Natural Resources (MD DNR) Chesapeake and Coastal Service (CCS). MGS wishes to thank the following project partners: 1) MD DNR CCS, Contact: Mr. Chris Cortina, Role: CCS Project Manager; 2) NOAA, Contact: Mr. Doug Graham, NOAA National Geodetic Survey, Role: Project partner & source of historical and recent shorelines; 3) MD DNR Critical Areas Commission (CAC), Contact: Ms. Lisa Hoerger, Role: Project partner & source of recent shorelines; 4) Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University, Contact: Ryan Mello, Role: Performing the critical area re-mapping for MD DNR CAC and supplying MGS with CAC shorelines; and 5) Ms. Lamere Hennesse, MGS Geologist, retired, Role: Project guidance & technical support.Previous, original Maryland Baselines, credit go to MGS, working collaboratively with Towson University’s Center for Geographic Sciences (CGIS), subsequently used the recent shorelines, along with historical ones, as input into a U.S. Geological Survey (USGS) program, the Digital Shoreline Analysis System (DSAS) (Danforth and Thieler, 1992; Thieler and others, 2001). DSAS determines linear rates of shoreline change (erosion or accretion) along closely spaced, shore-normal transects. This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Map Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/Hydrology/MD_ShorelineChanges/MapServer/1

This metadata record describes the acquisition and production of 1 foot contours for 5 coastal counties Hancock, Harrison, Jackson, Pearl River and Stone. The breaklines were collected from digital imagery with a 15 cmground sample distance (GSD) for the project area for the 1 foot contour area and 30 cm for the 5 foot contour area. All imagery was acquired in spring 2007 and processed during the spring & summer of 2007. The imagery is from a project tasked by Mississippi Geographic Information, LLC (MGI) with Work Orders ED-9 & ED-9A. EarthData International, Inc. was authorized to undertake this project in accordance with the terms and conditions of the professional service agreement between MGI and EarthData International, Inc., dated February 14, 2007.

These are the principal (or stated another way, the underlying) zoning classifications - what most people think of when they think of zoning. But the City also has layers for a variety of zoning overlay districts. The zoning applicable to a specific property is the combination of applicable regulation across all zoning layers - both the principal (underlying) zoning and all overlay zoning that apply to the site geographically.More information about the city's zoning code can be found in City Code.The data allows mapping of the following zoning districts:RL One-Family Large LotR1 One-Family ResidentialR2 One-Family ResidentialR3 One-Family ResidentialR4 One-Family ResidentialRT1 Two-Family ResidentialRT2 Townhouse ResidentialRM1 Low-Density Multiple-Family ResidentialRM2 Medium-Density Multiple-Family ResidentialRM3 High-Rise Multiple-Family ResidentialT1 Traditional NeighborhoodT2 Traditional NeighborhoodT3 Traditional NeighborhoodT3M T3 with Master PlanT4Traditional NeighborhoodT4M T4 with Master PlanOS Office ServiceB1 Local BusinessBC Community Business (Converted)B2 Community BusinessB3 General BusinessB4 Central BusinessB5 Central Business ServiceIT Transitional IndustrialITM IT with Master PlanI1 Light IndustrialI2 General IndustrialI3 Restricted IndustrialF1 FordRiver ResidentialF2 Ford Residential Mixed LowF3 Ford Residential Mixed MidF4 Ford Residential Mixed HighF5 Ford Business MixedF6 Ford GatewayVP Vehicular ParkingPD Planned DevelopmentCA Capitol Area JurisdictionAttributes (Fields) Defined:Zoning: The shorthand for the zoning district, generally a combination of two or three letters and numbers.Zoning Name: The name of the zoning district.Zoning Description: A description of the zoning district, written in HTML, intended for use in the popup in ArcGIS Online.Notes: Notes on the zoning designation.

Since 2016, the Sustainable Development Report (SDR) has provided the most up-to-date data available to track and rank the performance of all UN member states on the SDGs. Eighty years after the creation of the UN system, the report also provides improved and updated measures to track countries' efforts to support UN-based multilateralism. In total, more than 200,000 individual data points are used to produce 200+ country and regional SDG profiles. This year's edition was authored by a group of independent experts at the SDG Transformation Center, an initiative of the SDSN.This year's SDR emphasizes the following eight key message:Global commitment to the SDGs is strong: 190 out of 193 countries have presented national action plans for advancing sustainable development. A decade after the adoption of Agenda 30 and the SDGs, 190 of the 193 UN member states have participated in the Voluntary National Review (VNR) process, presenting their SDG implementation plans and sustainable development priorities to the international community. The European Union and State of Palestine have also presented VNRs. Most UN member states have presented two or more VNRs, and 39 countries volunteered to present one in 2025. Only three UN member states have not taken part in the VNR process: Haiti, Myanmar, and the United States. Additionally, a growing number of regional and local leaders have prepared Voluntary Local Reviews (VLRs) to report on SDG implementation at the subnational level. As of March 2025, 249 VLRs were listed on the dedicated UN websiteEast and South Asia has outperformed all other regions in SDG progress since 2015. This year's SDR introduces a streamlined SDG Index (SDGi), which uses 17 headline indicators to track overall SDG progress. On average, East and South Asia has shown the fastest progress on the SDGs since 2015, driven notably by rapid progress on the socioeconomic targetOther countries that have progressed more rapidly than their peers include the following: Benin (Sub-Saharan Africa), Nepal (East and South Asia), Peru (Latin America and the Caribbean), the United Arab Emirates (Middle East and North Africa), Uzbekistan (Eastern Europe and Central Asia), Costa Rica (OECD), and Saudi Arabia (G20)European countries continue to top the SDG Index. Finland ranks first this year and 19 of the top 20 countries are in Europe. Yet even these countries face significant challenges in achieving at least two goals, including those related to climate and biodiversity. In this year's SDG Index, China (#49) and India (#99) have entered the top 50 and top 100 performers respectivelyOn average globally, the SDGs are far off-track. At the global level, none of the 17 goals are currently on course to be achieved by 2030. Conflicts, structural vulnerabilities, and limited fiscal space impede SDG progress in many parts of the world. But while only 17 percent of the targets are on track to be achieved worldwide, most UN member states have made strong progress on targets related to access to basic services and infrastructure, including mobile broadband use (SDG 9), access to electricity (SDG 7), internet use (SDG 9), under-5 mortality rate (SDG 3), and neonatal mortality (SDG 3)Barbados ranks first and the United States ranks last in UN-based multilateralism. Barbados stands out as the country most committed to UN-based multilateralism, while the United States ranks last in this year's Index of countries' support for UN-based multilateralism (UN-Mi). In early 2025, the United States announced its withdrawal from the Paris Climate Agreement and the World Health Organization (WHO) and formally declared its opposition to the SDGs and the 2030 Agenda. Among G20 countries, Brazil is the most committed to UN-based multilateralism, with Chile leading among OECD countries For many developing countries, a lack of fiscal space is the major obstacle to SDG progress. Roughly half the world's population lives in countries that cannot invest adequately in sustainable development due to debt burdens and a lack of access to affordable, long-term capital. Global public goods are vastly under-financed. UN member states gathering at the 4th International Conference on Financing for Development (FfD4) in Seville, Spain (June 30 – July 3, 2025) have an enormous responsibility, not only to their own citizens but to all of humanitySustainable development offers high returns: capital should flow to the emerging and developing countries on more favourable terms. The Global Financial Architecture (GFA) is broken. Money flows readily to rich countries and not to the emerging and developing economies (EMDEs) that offer higher growth potential and rates of return. At the top of the agenda at FfD4 is the need to reform the GFA so that capital flows in far larger sums to the EMDEs. Part 1 of this report (also published online by the SDSN in May 2025) offers practical recommendations to scale up and align international financing flows to support global public goods and achieve sustainable development.About the AuthorsProf. Jeffrey Sachs, Director, SDSN; Project Director of the SDG IndexJeffrey D. Sachs is a world-renowned professor of economics, leader in sustainable development, senior UN advisor, bestselling author, and syndicated columnist whose monthly newspaper columns appear in more than 100 countries. He is the co-recipient of the 2015 Blue Planet Prize, the leading global prize for environmental leadership, and many other international awards and honors. He has twice been named among Time magazine’s 100 most influential world leaders. He was called by the New York Times, “probably the most important economist in the world,” and by Time magazine, “the world’s best known economist.” A survey by The Economist in 2011 ranked Professor Sachs as amongst the world’s three most influential living economists of the first decade of the 21st century.Professor Sachs serves as the Director of the Center for Sustainable Development at Columbia University. He is University Professor at Columbia University, the university’s highest academic rank. During 2002 to 2016 he served as the Director of the Earth Institute. Sachs is Special Advisor to United Nations Secretary-General António Guterres on the Sustainable Development Goals, and previously advised UN Secretary-General Ban Ki-moon on both the Sustainable Development Goals and Millennium Development Goals and UN Secretary-General Kofi Annan on the Millennium Development Goals.Guillaume Lafortune Director, SDSN Paris; Scientific Co-Director of the SDG IndexGuillaume Lafortune took up his duties as Director of SDSN Paris in January 2021. He joined SDSN in 2017 to coordinate the production of the Sustainable Development Report and other projects on SDG data and statistics.Previously, he has served as an economist at the Organisation for Economic Co-operation and Development (OECD) working on public governance reforms and statistics. He was one of the lead advisors for the production of the 2015 and 2017 flagship statistical report Government at a Glance. He also contributed to analytical work related to public sector efficiency, open government data and citizens’ satisfaction with public services. Earlier, Guillaume worked as an economist at the Ministry of Economic Development in the Government of Quebec (Canada). Guillaume holds a M.Sc in public administration from the National School of Public Administration (ENAP) in Montreal and a B.Sc in international economics from the University of Montreal.Contact: guillaume.lafortune@unsdsn.org Grayson Fuller Manager, SDG Index & Data team, SDSNGrayson Fuller is the lead statistician and senior manager for the SDG Index, and of the team working on SDG data and statistics at SDSN. He is co-author of the Sustainable Development Report, for which he manages the data, coding, and statistical analyses. He also coordinates the production of regional and subnational editions of the SDG Index, in addition to other statistical reports, in collaboration with national governments, NGOs and international organizations such as the WHO, UNDP and the European Commission. Grayson received his Masters degree in Economic Development at Sciences Po Paris. He holds a Bachelors in Romance Languages and Latin American Studies from Harvard University, where he graduated cum laude. Grayson has lived in several Latin American countries and speaks English, Spanish, French, Portuguese and Italian. He enjoys playing the violin, rock-climbing and taking care of his numerous plants in his free time.Contact: grayson.fuller@unsdsn.orgGuilherme Iablonovski GIS Specialist, SDG Index & Data team, SDSNGuilherme Iablonovski is a Geospatial Data Specialist at SDSN, where he conceptualizes and develops new geospatial indicators to measure important aspects of the Sustainable Development Goals. He holds a M.Sc in Urban and Environmental Planning from the Ecole d'Urbanisme de Paris, where his research focused on urban metabolism, environmental sustainability and universal scaling laws. Before joining SDSN, Guilherme worked as a solutions engineer for Esri and as geospatial data scientist for humanitarian organizations such as the World Bank, the Red Cross and UNEP. He also teaches GIS at the Peace Studies Master Programme at Université Paris-Dauphine PSL.Contact: guilherme.iablonovski@unsdsn.org---About the PublishersDublin University Press Dublin University Press is Ireland’s oldest printing and publishing house with its origins in Trinity College Dublin in 1734. The mission of Dublin University Press is to benefit society through scholarly communication, education, research and discourse. To further this goal, the Press operates as an open, innovative and inclusive channel for high quality scholarly publishing with an emphasis on equity, diversity and inclusion and with full support for author copyright retention, open access and open scholarship. As an independent, non-profit,