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Changing the coordinate system of grids storing population counts is typically a challenging task, due to the need to keep the population totals unchanged (i.e. volume preservation). The Population Warp tool (GHS-POPWARP), version 1, is an information system developed in the framework of the Global Human Settlement Layer (GHSL) project to convert geospatial raster grids of population counts between different map projections and/or resolutions, while preserving population volumes. The GHS-POPWARP v1 is a flexible tool to produce geospatial population counts grids in GeoTIFF format starting from another grid in a different coordinate system and/or resolution. The tool operationalises a stable and rigorous workflow long used in the Global Human Settlement Layer to handle gridded population data. The GHS-POPWARP v1 tool allows the creation of population counts grids via a sequence of oversampling, structure conversion and reprojection geospatial procedures. The principal purpose of the tool is to expand the interoperability of GHSL tools which require population grid data as input (e.g. GHS-DUG that strictly requires a kilometric population grid in equal area projection) or to simplify the warping of population grids for users less familiar with geospatial processing. The tool is offered as a contribution to capacity enhancement in the framework of the multi-stakeholder effort for the uptake of the Degree of Urbanisation (DEGURBA) classification method. Degree of Urbanisation refers to the people-based harmonised definition of cities and settlements recommended by the 51st Session of the United Nations Statistical Commission as the methodology to delineate cities and rural areas for international statistical comparison. The GHS-POPWARP, as all GHSL tools, is issued with an end-user licence agreement, included in the download package., JRC.E.1-Disaster Risk Management

The Population To Grid tool (POP2G) –version 1.0- is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce geospatial grids of population counts at different spatial resolutions. The POP2G v1.0 is a flexible tool to produce geospatial population grids in GeoTIFF format from census data. The tool operationalises the workflow developed for the production of the Global Human Settlement Layer Population Grid layers (GHS-POP). The POP2G v1.0 tool allows the creation of population grids at 50 m, 250 m, and 1 km spatial resolutions, handling census data stored as point or polygon vector data (in latter case requires additional covariate as input for dasymetric disaggregation). The principal purpose of the tool is the production of the population grid used as input for the Degree of Urbanisation Grid (DUG) also produced in the GHSL framework. However the potential uses of the tool and population grids go far beyond this main application. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort to develop a people-based harmonised definition of cities and settlements that helps the assessment of the feasibility of applying a global definition of cities/urban areas in support of global monitoring of SDGs and the New Urban Agenda urban targets. The POP2G, as all GHSL tools, is issued with an end-user licence agreement, included in the download package, JRC.E.1-Disaster Risk Management

The ENACT grids are a set of consistent, seamless, multi-temporal and validated population density grids for the European Union that take into account major daily and monthly population variations. These grids were produced by combining official statistical data at regional level with geo-spatial data from conventional and non-conventional data sources. The grids display maximum population density at day- and night-time for each month of the year (i.e. 24 population grids in total), at a spatial resolution of 1 x 1 km resolution for the whole EU-28. This is a major contribution to the state-of-the-art information on population density in Europe, as prior grids considered only static residential population (i.e. night-time). The newly released grids were produced in the context of the JRC ENACT exploratory research project, “ENhancing ACTivity and population mapping. This document accompanies the public release of the ENACT 2011 Population Grids and describes the contents., JRC.E.1-Disaster Risk Management

The Degree of Urbanisation Grid (GHS-DUG) Tool (– version 4) is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce geospatial grids to map settlement classes and extract related statistics. The settlement classes are derived from the “Degree of Urbanisation” method and ported to the GHSL environment through the GHSL Settlement Mode (GHSL SMOD). The GHS-DUG 4 is designed as a scalable tool allowing the application of the GHSL Settlement Model to the input data available to the user or to data made available in the GHSL Data Package 2019. This document contains the description of the GHS-DUG Tool use, the rationale of the differentiation between settlement classes and the comprehensive description of the outputs. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort for the uptake of the Degree of Urbanisation, the people-based harmonised definition of cities and settlements recommended by the 51st Session of the United Nations Statistical Commission as the method to delineate cities and rural areas for international statistical comparison. The GHS-DUG, as all GHSL Tools, is issued with an end-user licence agreement, included in the download package., JRC.E.1-Disaster Risk Management

The Degree of Urbanisation Grid (DUG) Tool (– version 3.1) is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce geospatial grids to map settlement classes and extract related statistics. The settlement classes are derived from the “Degree of Urbanisation” method and ported to the GHSL environment through the GHSL Settlement Mode (GHSL SMOD). The DUG 3.1 is designed as a scalable tool allowing the application of the GHSL Settlement Model to the input data available to the user or to data made available in the GHSL Data Package 2019. This document contains the description of the DUG Tool use, the rationale of the differentiation between settlement classes and the comprehensive description of the outputs. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort to develop a people-based harmonised definition of cities and settlements that helps the assessment of the feasibility of applying a global definition of cities/urban areas in support of global monitoring of SDGs and the New Urban Agenda urban targets. The DUG, as all GHSL Tools, is issued with an end-user licence agreement, included in the download package., JRC.E.1-Disaster Risk Management

The Degree of Urbanisation Territorial Units Classifier (GHS-DU-TUC) Tool (– version 1) is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce the classification of territorial units based on the Degree of Urbanisation and extract related statistics. The tool classifies territorial units by Degree of Urbanisation at Level 1 (3 classes) and Level 2 (7 classes) based on population majority by settlement classes derived from the “Degree of Urbanisation” method and ported to the GHSL environment through the GHSL Settlement Model (GHSL SMOD). The GHS-DU-TUC 1 is designed as an operational tool to perform the second step required to apply the Degree of Urbanisation released as standalone tool and as ArcGIS Toolbox. Once the first step produces the settlement classification grid (i.e. with the GHS-DUG Tool), the user runs the GHS-DU-TUC that requires this settlement classification grid, the population grid used to produce the settlement classification grid (i.e. produced with the GHS-POP2G Tool) and a geometry of territorial units to be classified by Degree of Urbanisation. This tool is conceptualised to be deployed after the application of the GHSL tools GHS-POP2G and GHS-DUG but it accepts in input population grids produced by means of any other procedure respecting the described constrains. This document contains the description of the GHS-DU-TUC Tool use, the rationale for the second step to apply the Degree of Urbanisation (the classification of territorial units) and the comprehensive description of the outputs. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort for the uptake of the Degree of Urbanisation, the people-based harmonised definition of cities and settlements recommended by the 51st Session of the United Nations Statistical Commission as the method to delineate cities and rural areas for international statistical comparison. The GHS-DU-TUC, as all GHSL Tools, is issued with an end-user licence agreement, included in the download package., JRC.E.1-Disaster Risk Management

The Population To Grid tool (GHS-POP2G) –version 2- is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce geospatial grids of population counts at different spatial resolutions. The GHS-POP2G v2 is a flexible tool to produce geospatial population grids in GeoTIFF format from census data. The tool operationalises the workflow developed for the production of the Global Human Settlement Layer Population Grid layers (GHS-POP). The GHS-POP2G v2 tool allows the creation of population grids at 50 m, 100 m, 250 m, and 1 km spatial resolutions, handling census data stored as point or polygon vector data (in latter case requires additional covariate as input for dasymetric disaggregation). The principal purpose of the tool is the production of the population grid used as input for the Degree of Urbanisation Grid (GHS-DUG) also produced in the GHSL framework. However the potential uses of the tool and population grids go far beyond this main application. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort to develop a people-based harmonised definition of cities and settlements that helps the assessment of the feasibility of applying a global definition of cities/urban areas in support of global monitoring of SDGs and the New Urban Agenda urban targets. The GHS-POP2G, as all GHSL tools, is issued with an end-user licence agreement, included in the download package., JRC.E.1-Disaster Risk Management

The GHS-SmartDissolve Tool– version 2.0 is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to conduct smart and flexible aggregation of adjacent and complex polygons storing quantitative data. GHS-SmartDissolve is a tool that handles minimum mapping unit, resolution mismatch between layers, or spatial uncertainty problems in GISc. This tool automatically dissolves polygons below a threshold area or a threshold attribute value, updating fields’ values to meet a minimum target area or a minimum attribute value. This flexible framework allows to select the ordering of polygon analysis, different dissolve rules, and different field updating operations. The GHS-SmartDissolve is available as toolbox for ArcGIS 10.X. This document contains the description of the GHS-SmartDissolve Tool use, with details and description of the different settings and output. The GHS-SmartDissolve, as all GHSL Tools, is issued with an end-user licence agreement, included in the download package., JRC.E.1-Disaster Risk Management

The presence of green spaces within city centres has been recognized as a valuable component of the city landscape. Vegetation provides a variety of benefits including energy saving, improved air q...

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics and knowledge describing the human presence on the planet Earth. The GHSL operates in a fully open and free data and methods access policy, building the knowledge supporting the definition, the public discussion and the implementation of European policies and the international frameworks as the 2030 Development Agenda and the related thematic agreements. The GHSL supports the GEO Human Planet Initiative (HPI) that is committed to developing a new generation of measurements and information products providing new scientific evidence and a comprehensive understanding of the human presence on the planet and that can support global policy processes with agreed, actionable and goal-driven metrics. The Human Planet Initiative relies on a core set of partners committed in coordinating the production of the global settlement spatial baseline data. One of the core partners is the European Commission, Directorate General Joint Research Centre, Global Human Settlement Layer project. The Global Human Settlement Layer project produces global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet. This document describes the public release of the GHSL Data Package 2019 (GHS P2019)., JRC.E.1-Disaster Risk Management

The Global Human Settlement Layer Urban Centres Database (GHS-UCDB) is the most complete database on cities to date, publicly released as an open and free dataset - GHS STAT UCDB2015MT GLOBE R2019A V1.0. The database represents the global status on Urban Centres in 2015 by offering cities location, their extent (surface, shape), and describing each city with a set of geographical, socio-economic and environmental attributes, many of them going back 25 or even 40 years in time. Urban Centres are defined in a consistent way across geographical locations and over time, applying the “Global Definition of Cities and Settlements” developed by the European Union to the Global Human Settlement Layer Built-up (GHS-BUILT) areas and Population (GHS-POP) grids. This report contains the description of the dimensions and the derived attributes that characterise the Urban Centres in the database. The document includes notes about methodology and sources. The GHS-UCDB contains information for more than 10,000 Urban Centres and it is the baseline data of the analytical results presented in the Atlas of the Human Planet 2018., JRC.E.1-Disaster Risk Management

The Atlas of the Human Planet 2019 presents key human settlements and urbanisation statistics for 239 countries based on the progress made towards the development of a people-based global harmonised definition of cities and rural areas. Figures and statistics presented in the Atlas 2019 are the result of massive automatic big data processing carried out at the European Commission Directorate General Joint Research Centre in the framework of the Global Human Settlement Layer (GHSL) combining satellite imagery and census information to map settlements, understand their characteristics, and report about their changes over 40 years’ time (1975 – 2015). The Atlas explains the fundamentals of the GHSL, and the service it provides to upscale to the globe the Degree of Urbanisation method (currently adopted as European Union Regulation). Based on the global application of the method the Atlas presents a global urbanisation brief, a commented series of highlights on global human settlement development trajectories, supported by 239 urbanisation briefs, which form the knowledge base for the next generation of urban and territorial policy, development and cooperation action, and global reporting on progress made towards meeting the Sustainable Development Goals and the 2030 Development Agenda as a whole., JRC.E.1-Disaster Risk Management

The combination of Earth Observation and population data produces new information that describes inequalities across the globe in an original, objective and spatially distinct way.The new information contributes to a better understanding of the spatial distribution of wealth and poverty around the globe.The approach has potential for the monitoring and detection of changes in spatial patterns of inequality., JRC.E.1-Disaster Risk Management

Function Urban Areas (FUAs), as defined by the OECD and the European Union, are sets of contiguous local (administrative) units composed of a ‘city’ and its surrounding, less densely populated local units that are part of the city’s labour market (‘commuting zone’). To be included in the commuting zone, local units should at least 15% of their working population to the city. This definition is limited to the OECD countries and it is subject to both availability of commuting flows data at local level and to the definition of administrative unit boundaries. In the context of international comparability of urban-related statistics and indicators the aim of this task is to propose a FUA definition that does not depend on arbitrary and not harmonized administrative units and scale it to the globe. To pursue this goal it is proposed an automated classification procedure of FUAs based on objective characteristics (distance from the Urban Centre, area and population of the Urban Centre, local population and GDP per capita at national level), to classify areas within and outside FUAs. The automated classification of FUA is done in collaboration with the OECD and supported by DG REGIO. This document describes the public release of the GHSL-OECD Functional Urban Areas 2019 (GHS-FUA)., JRC.E.1-Disaster Risk Management

This report is an initiative of the Joint Research Centre (JRC), the science and knowledge service of the European Commission (EC), and supported by the Commission's Directorate-General for Regional and Urban Policy (DG REGIO). It highlights drivers shaping the urban future, identifying both the key challenges cities will have to address and the strengths they can capitalise on to proactively build their desired futures. The main aim of this report is to raise open questions and steer discussions on what the future of cities can, and should be, both within the science and policymaker communities. While addressing mainly European cities, examples from other world regions are also given since many challenges and solutions have a global relevance. The report is particularly novel in two ways. First, it was developed in an inclusive manner – close collaboration with the EC’s Community of Practice on Cities (CoP-CITIES) provided insights from the broader research community and city networks, including individual municipalities, as well as Commission services and international organisations. It was also extensively reviewed by an Editorial Board. Secondly, the report is supported by an online ‘living’ platform which will host future updates, including additional analyses, discussions, case studies, comments and interactive maps that go beyond the scope of the current version of the report. Steered by the JRC, the platform will offer a permanent virtual space to the research, practice and policymaking community for sharing and accumulating knowledge on the future of cities. This report is produced in the framework of the EC Knowledge Centre for Territorial Policies and is part of a wider series of flagship Science for Policy reports by the JRC, investigating future perspectives concerning Artificial Intelligence, the Future of Road Transport, Resilience, Cybersecurity and Fairness Interactive online platform : https://urban.jrc.ec.europa.eu/thefutureofcities, JRC.B.3-Territorial Development

Urban transformation is an emerging topic in recent years and its socio-economical and environmental impacts have become increasingly evident to scientists and policymakers. The ability to analyse and understand this process was limited by the lack of comprehensive datasets and formalised methodologies. The Global Human Settlement Layer (GHSL) suite accommodates this need by providing worldwide, multi-epoch information on human settlements. In particular, the GHSL suite includes three main layers that map built-up density (GHS-BUILT), population density (GHS-POP) and human settlements (GHS-SMOD). These grid-based layers are produced in a consistent and harmonised way for four epochs (1975-1990-2000-2015) allowing the comparison between different periods. In this technical report, we present a formalised methodology (workflow) to characterise and analyse the dynamics of settlements. This workflow proposes a taxonomy of all possible GHS-SMOD classification combinations between two epochs; it quantifies and analyses changes of built-up surface and population density in each taxonomical class. We show the workflow capability by using the region of New York (United States of America) as a case study. The presented workflow has a direct application to characterise urban dynamics using the GHSL. Such approach supports the assessment of urbanization processes, by monitoring urban expansion, contraction and rural-urban transitions, and by measuring sustainable urban development metrics. The application of this workflow allows taking into account, both and separately, the net and the gross change in built-up surface and population density within a urban settlement evolution. Moreover, the application of this workflow to the GHSL suite benefits of the full ranges of GHSL features (global coverage, data consistency and open and free data format)., JRC.E.1-Disaster Risk Management

The GEO Human Planet Initiative is committed to developing a new generation of measurements and information products that provide new scientific evidence and a comprehensive understanding of the human presence on the planet and that can support global policy processes with agreed, actionable and goal-driven metrics. The Human Planet Initiative relies on a core set of partners committed in coordinating the production of the global settlement spatial baseline data, and an enlarged community of partners developing experimental activities on using the new baseline data for derived post-2015 indicators. One of the core partners is the European Commission, Directorate General Joint Research Centre, Global Human Settlement Layer project. The Global Human Settlement Layer project produces global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet. This document describes the Community pre-Release of the GHSL Data Package 2018 - GHS CR2018 - created for the members of the GEO Human Planet Initiative. The data in this data package have the purpose to gather feedbacks from the GEO Human Planet Initiative community, in preparation for the public release of these data. Disclaimer: the data included in the Community Pre-Release of the GHSL Data Package 2018 should be considered preliminary and not yet validated. It may differ substantially with the data that will be included in the public final Release 2018. The JRC data are provided "as is" and "as available" in conformity with the JRC Data Policy1 and the Commission Decision on reuse of Commission documents (2011/833/EU). Although the JRC guarantees its best effort in assuring quality when publishing these data, it provides them without any warranty of warranty of any kind, either express or implied, including, but not limited to, any implied warranty against infringement of third parties' property rights, or merchantability, integration, satisfactory quality and fitness for a particular purpose. The JRC has no obligation to provide technical support or remedies for the data. The JRC does not represent or warrant that the data will be error free or uninterrupted, or that all non-conformities can or will be corrected, or that any data are accurate or complete, or that they are of a satisfactory technical or scientific quality. The JRC or as the case may be the European Commission shall not be held liable for any direct or indirect, incidental, consequential or other damages, including but not limited to the loss of data, loss of profits, or any other financial loss arising from the use of the JRC data, or inability to use them, even if the JRC is notified of the possibility of such damages., JRC.E.1-Disaster Risk Management

Spatial inequalities across the globe are not easy to detect and satellite data have shown to be of use in this task. Earth Observation (EO) data combined with other information sources can provide complementary information to those derived from traditional methods. This research shows patterns of inequalities emerging by combining global night lights measured from Earth Observation, population density and built-up in 2015. The focus of the paper is to describe the spatial patterns that emerge by combing the three variables. This work focuses on processing EO data to derive information products, and in combining built-up- and population density with nighttime emission. The built-up surface was derived entirely from remote sensing archives using artificial intelligence and pattern recognition techniques. The built-up was combined with population census data to derive population density. Also the nighttime emission data were available from EO satellite sensors. The three layers are subsequently combined as three colour compositions based on the three primary colours (i.e. red green and blue) to display the “human settlement spatial pattern” maps. These GHSL nightlights provide insights in inequalities across the globe. Many patterns seem to be associated with countries income. Typically, high income countries are very well lit at night, low income countries are poorly lit at night. All larger cities of the world are lit at night, those in low-income countries are often less well lit than cites in high-income countries. There are also important differences in nightlights emission in conflict areas, or along borders of countries. This report provides a selected number of patterns that are described at the regional, national and local scale. However, in depth analysis would be required to assess more precisely that relation between wealth access to energy and countries GDP, for example. This work also addresses regional inequality in GHSL nightlights in Slovakia. The country was selected to address the deprivation of the Roma minority community. The work aims to relate the information from the GHSL nightlights with that collected from field survey and census information conducted at the national level. Socio-economic data available at subnational level was correlated with nightlight. The analysis shows that despite the potential of GHSL nightlights in identifying deprived areas, the measurement scale of satellite derived nightlights at 375 x 375 m and 750 x 750 m pixel size is too coarse to capture the inequalities of deprived communities that occur at finer scale. In addition, in the European context the gradient of inequality is not strong enough to produce strong evidence. Although there is a specific pattern of GHSL nightlights in settlements with high Roma presence, this cannot be used to identify such areas among the others. This work is part of the exploratory data analysis conducted within the GHSL team. The exploratory analysis will be followed by more quantitative assessments that will be available in future work., JRC.E.1-Disaster Risk Management

The Atlas of the Human Planet 2018 describes the Urban Centre Database, which was produced in the framework of the Global Human Settlement Layer (GHSL) project by applying a global definition of cities and settlements to the GHSL data. The Atlas presents the key findings of the analysis of geographic, environmental and socio-economic variables that were gathered from free and open sources for each urban centre in the world., JRC.E.1-Disaster Risk Management

SmartDissolve is a polygon aggregation tool developed in the frame of the Global Human Settlement Layer (GHSL) project and it is being used to dissolve polygons setting an areal threshold. SmartDissolve is a tool that handles minimum mapping unit, resolution mismatch between layers, or spatial uncertainty problems in GISc. This tool automatically dissolves polygons below a threshold area, updating fields’ values. The toolbox allows to select the ordering of polygon analysis (i.e. from the smallest to the largest area, vice versa, or order of IDs), different dissolve rules (i.e. with smallest, largest, or maximum-border-share adjacent polygon, minimum total perimeter or maximum compactness) and different field updating operations (i.e. sum, mean or text concatenation). The software is available as toolbox for ArcGIS 10.X, a standalone command line tool or a MATLAB function. More details about the algorithm, the method and its actual applications can be found in the paper in the Reference section. This guide provides instructions about installing and using the SmartDissolve toolbox for ArcGIS and the SmartDissolve command line tool on a Windows computer and the SmartDissolve MATLAB function in a MATLAB environment., JRC.E.1-Disaster Risk Management