Human settlements in low lying coastal zones and rugged terrain: data and methodologies

This document describes the assessment of global terrain data and a procedure to combine terrain data with newly available human settlement data. The aim is to quantify settlements in low-lying coastal zones and in topographically rugged terrain. For terrain data we use the Shuttle Radar Topographic Mission Digital Elevation Model made available at 90m (3 arc sec), for settlement data we use the Global Human Settlement Layer (GHSL) data set released in 2016 composed of built-up area (GHS-BU), population (GHS-POP) and settlement model (GHS-SMOD) grids and available for 4 epochs, 1975, 1990, 2000 and 2015. We show that SRTM at 90m and GHSL can be combined in a meaningful way. However, we could not generate accuracy assessment on the resulting figures as both datasets do not come with accuracy assessment. In addition, as the data extend only up to 60degrees north, the analysis is not completely global even if it covers the large part of the populated land masses. Preliminary results show that it is possible to derive quantitative measures related to the increase of population in coastal zones, and in steep terrain that may be considered prone to natural hazards. Preliminary analysis indicates that the rate of population growth for the four epochs in the low-lying coastal areas is higher than the global population growth rate. In addition, we show that we are able to measure the spatial expansion of settlements over steep slopes especially in the large cities in developing countries (i.e. Lima), but also in coastal settlements of developed countries (e.g., Italy and France).JRC.E.1-Disaster Risk Managemen

DUG User Guide. Version 2.1

This user guide accompanies the DUG tool which is a public tool for applying the “Degree of urbanisation” (DEGURBA) model at one kilometer grid. DUG stands for Degree of Urbanisation Grid. It has been developed in the frame of the “Global Human Settlement Layer” (GHSL) project of the European Commission’s Joint Research Centre, with the overall objective to support the DEGURBA activities. The tool builds on the GHS SMOD model that implements settlement model classifier at 1 km grid. The tool uses population and built-up grids as input data, and optionally a water mask. It has been developed and tested using GHS P2016 datasets ; however other grids can be used on user responsibility. This user guide is a comprehensive guide to all aspects of using the DUG tool. It includes instructions for the set-up of the software, the use of the tool and the manipulation of the data. It presents briefly the basic principles and background information on the methodology and its implementation. Some guidelines on the parametrization are also provided.JRC.E.1-Disaster Risk Managemen

Search improvement within the geospatial web in the context of spatial data infrastructures

El trabajo desarrollado en esta tesis doctoral demuestra que es posible mejorar la búsqueda en el contexto de las Infraestructuras de Datos Espaciales mediante la aplicación de técnicas y buenas prácticas de otras comunidades científicas, especialmente de las comunidades de la Web y de la Web Semántica (por ejemplo, Linked Data). El uso de las descripciones semánticas y las aproximaciones basadas en el contenido publicado por la comunidad geoespacial pueden ayudar en la búsqueda de información sobre los fenómenos geográficos, y en la búsqueda de recursos geoespaciales en general. El trabajo comienza con un análisis de una aproximación para mejorar la búsqueda de las entidades geoespaciales desde la perspectiva de geocodificación tradicional. La arquitectura de geocodificación compuesta propuesta en este trabajo asegura una mejora de los resultados de geocodificación gracias a la utilización de diferentes proveedores de información geográfica. En este enfoque, el uso de patrones estructurales de diseño y ontologías en esta aproximación permite una arquitectura avanzada en términos de extensibilidad, flexibilidad y adaptabilidad. Además, una arquitectura basada en la selección de servicio de geocodificación permite el desarrollo de una metodología de la georreferenciación de diversos tipos de información geográfica (por ejemplo, direcciones o puntos de interés). A continuación, se presentan dos aplicaciones representativas que requieren una caracterización semántica adicional de los recursos geoespaciales. El enfoque propuesto en este trabajo utiliza contenidos basados en heurísticas para el muestreo de un conjunto de recursos geopesaciales. La primera parte se dedica a la idea de la abstracción de un fenómeno geográfico de su definición espacial. La investigación muestra que las buenas prácticas de la Web Semántica se puede reutilizar en el ámbito de una Infraestructura de Datos Espaciales para describir los servicios geoespaciales estandarizados por Open Geospatial Consortium por medio de geoidentificadores (es decir, por medio de las entidades de una ontología geográfica). La segunda parte de este capítulo desglosa la aquitectura y componentes de un servicio de geoprocesamiento para la identificación automática de ortoimágenes ofrecidas a través de un servicio estándar de publicación de mapas (es decir, los servicios que siguen la especificación OGC Web Map Service). Como resultado de este trabajo se ha propuesto un método para la identificación de los mapas ofrecidos por un Web Map Service que son ortoimágenes. A continuación, el trabajo se dedica al análisis de cuestiones relacionadas con la creación de los metadatos de recursos de la Web en el contexto del dominio geográfico. Este trabajo propone una arquitectura para la generación automática de conocimiento geográfico de los recursos Web. Ha sido necesario desarrollar un método para la estimación de la cobertura geográfica de las páginas Web. Las heurísticas propuestas están basadas en el contenido publicado por os proveedores de información geográfica. El prototipo desarrollado es capaz de generar metadatos. El modelo generado contiene el conjunto mínimo recomendado de elementos requeridos por un catálogo que sigue especificación OGC Catalogue Service for the Web, el estandar recomendado por deiferentes Infraestructuras de Datos Espaciales (por ejemplo, the Infrastructure for Spatial Information in the European Community (INSPIRE)). Además, este estudio determina algunas características de la Web Geoespacial actual. En primer lugar, ofrece algunas características del mercado de los proveedores de los recursos Web de la información geográfica. Este estudio revela algunas prácticas de la comunidad geoespacial en la producción de metadatos de las páginas Web, en particular, la falta de metadatos geográficos. Todo lo anterior es la base del estudio de la cuestión del apoyo a los usuarios no expertos en la búsqueda de recursos de la Web Geoespacial. El motor de búsqueda dedicado a la Web Geoespacial propuesto en este trabajo es capaz de usar como base un motor de búsqueda existente. Por otro lado, da soporte a la búsqueda exploratoria de los recursos geoespaciales descubiertos en la Web. El experimento sobre la precisión y la recuperación ha demostrado que el prototipo desarrollado en este trabajo es al menos tan bueno como el motor de búsqueda remoto. Un estudio dedicado a la utilidad del sistema indica que incluso los no expertos pueden realizar una tarea de búsqueda con resultados satisfactorios

Megacities Spatiotemporal Dynamics Monitored with the Global Human Settlement Layer

Megacities are urban agglomerations hosting at least 10 million inhabitants. The rise in number, population size, and spatial extent of megacities are among the most prominent manifestations of the process of urbanisation taking place in the contemporary urban age. Until recently, urban growth has been quantified with data derived from satellites mainly for single megacities or for a limited subset of them. With the current advances in Remote Sensing and data processing, the integration of satellite data with other datasets could become a key contributor to the data revolution and support more complete urban studies and better informed policymaking. Although many remote sensing-derived products exist, few are open and free and possess the adequate resolution, information and contents to monitor the process of urban expansion. This research article builds on the premier open and free geospatial information contained in the Global Human Settlements Layer (GHSL) data package (produced at the European Commission - Joint Research Centre). This research takes advantage of existing GHSL data to identify megacities and to analyse their spatial and demographic change over the last 25 years (between 1990 and 2015). This paper quantifies how much and how fast megacities have expanded in spatial and demographic terms, and we provide graphical examples of the different manifestations of growth across megacities. The main findings of our research reveal an average demographic growth in megacities exceeding 2% a year between 1990 and 2000, and of 1.9% a year between 2000 and 2015. In the first period (1990 to 2000), megacities have expanded faster than the global average and more than the average of other urban centres. In the second period, global urban population increase has been greater than that of megacities. The comparative analysis of megacities however, reveals swift population growth in several cases: in seven cities population more than doubled between 1990 and 2015, and in six the average annual population growth exceeded 4% a year. Spatial expansion of megacities tends to occur at rates slower than that of population. In 27 cities built-up per capita has decreased over 25 years, by more than 10% in 17 cities. Megacities also differ in population density (in 2015), which in five is above 10,000 inhabitants per square kilometre, while in others, especially the ones in high-income countries, density remains around half this figure. Results highlight the value of new remote sensing-based data and methods for mapping and characterizing global urbanisation processes, in a consistent and comparable manner across space and time. The provision of open and free data ensures methods and findings can be audited and analyses extended to other cities, while the temporal dimension enables monitoring urbanisation and intergovernmental policies on sustainable urban development

Atlas of the Human Planet 2017: Global Exposure to Natural Hazards

The Atlas of the Human Planet 2017. Global Exposure to Natural Hazards summarizes the global multi-temporal analysis of exposure to six major natural hazards: earthquakes, volcanoes, tsunamis, floods, tropical cyclone winds, and sea level surge. The exposure focuses on human settlements assessed through two variables: the global built-up and the global resident population. The two datasets are generated within the Global Human Settlement Project of the Joint Research Centre. They represent the core dataset of the Atlas of the Human Planet 2016 which provides empirical evidence on urbanization trends and dynamics. The figures presented in the Atlas 2017 show that exposure to natural hazards doubled in the last 40 years, both for built-up area and population. Earthquake is the hazard that accounts for the highest number of people potentially exposed. Flood, the most frequent natural disaster, potentially affects more people in Asia (76.9% of the global population exposed) and Africa (12.2%) than in other regions. Tropical cyclone winds threaten 89 countries in the world and the population exposed to cyclones increased from 1 billion in 1975 up to 1.6 billion in 2015. The country most at risk to tsunamis is Japan, whose population is 4 times more exposed than China, the second country on the ranking. Sea level surge affects the countries across the tropical region and China has one of the largest increase of population over the last four decades (plus 200 million people from 1990 to 2015). The figures presented in the Atlas are aggregate estimates at country level. The value of the GHSL layers used to generate the figures in this Atlas is that the data are available at fine scale and exposure and the rate of change in exposure can be computed for any area of the world. Researchers and policy makers are now allowed to aggregate exposure information at all geographical scale of analysis from the country level to the region, continent and global.JRC.E.1-Disaster Risk Managemen

Global Human Settlement Analysis for Disaster Risk Reduction

The Global Human Settlement Layer (GHSL) is supported by the European Commission, Joint Research Center (JRC) in the frame of his institutional research activities. Scope of GHSL is developing, testing and applying the technologies and analysis methods integrated in the JRC Global Human Settlement analysis platform for applications in support to global disaster risk reduction initiatives (DRR) and regional analysis in the frame of the European Cohesion policy. GHSL analysis platform uses geo-spatial data, primarily remotely sensed and population. GHSL also cooperates with the Group on Earth Observation on SB-04-Global Urban Observation and Information, and various international partners andWorld Bank and United Nations agencies. Some preliminary results integrating global human settlement information extracted from Landsat data records of the last 40 years and population data are presented.JRC.G.2-Global security and crisis managemen

Automatic Generation of Geospatial Metadata for Web Resources

Web resources that are not part of any Spatial Data Infrastructure can be an important source of information. However, the incorporation of Web resources within a Spatial Data Infrastructure requires a significant effort to create metadata. This work presents an extensible architecture for an automatic characterisation of Web resources and a strategy for assignation of their geographic scope. The implemented prototype generates automatically geospatial metadata for Web pages. The metadata model conforms to the Common Element Set, a set of core properties, which is encouraged by the OGC Catalogue Service Specification to permit the minimal implementation of a catalogue service independent of an application profile. The performed experiments consisted in the creation of metadata for Web pages of providers of Geospatial Web resources. The Web pages have been gathered by a Web crawler focused on OGC Web Services. The manual revision of the results has shown that the coverage estimation method applied produces acceptable results for more than 80% of tested Web resources

Multitemporal Grid Based Analysis of the Global Human Settlement Layers

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 Managemen

Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014

A new global information baseline describing the spatial evolution of the human settlements in the past 40 years is presented. It is the most spatially global detailed data available today dedicated to human settlements, and it shows the greatest temporal depth. The core processing methodology relies on a new supervised classification paradigm based on symbolic machine learning. The information is extracted from Landsat image records organized in four collections corresponding to the epochs 1975, 1990, 2000, and 2014. The experiment reported here is the first known attempt to exploit global Multispectral Scanner data for historical land cover assessment. As primary goal, the Landsat-made Global Human Settlement Layer (GHSL) reports about the presence of built-up areas in the different epochs at the spatial resolution allowed by the Landsat sensor. Preliminary tests confirm that the quality of the information on built-up areas delivered by GHSL is better than other available global information layers extracted by automatic processing from Earth Observation data. An experimental multiple-class land-cover product is also produced from the epoch 2014 collection using low-resolution space-derived products as training set. The classification schema of the settlement distinguishes built-up areas based on vegetation contents and volume of buildings, the latter estimated from integration of SRTM and ASTER-GDEM data. On the overall, the experiment demonstrated a step forward in production of land cover information from global fine-scale satellite data using automatic and reproducible methodology.JRC.G.2-Global security and crisis managemen

Demographic Factors of Change in Urbanisation Processes

Information, theory and policy response to the process of urbanisation are growing as scientific evidences and global statistics on urban population refine. The Global Human Settlement Layer (GHSL) provides baseline data to determine the share of population living in urban areas using the GHSL Settlement Model Grid (GHS-SMOD). The SMOD ports the Degree of Urbanisation (Dijkstra and Poelman 2014) in the GHSL environment and applies it globally. The degree of urbanisation refers to the share of the total population living in urban areas. The information on population distribution contained in the GHSL population layer (GHS-POP) and settlement typology from GHS-SMOD are available for four epochs: 1975-1990-2000-2015. GHS-POP and GHS-SMOD applied to urbanisation analysis are mainly used to estimate the shares of urban population per country in the different epochs, and to calculate the changes in the degree of urbanisation over time. This information is particularly relevant in support to policymaking as it quantifies the patterns of urbanisation, rural-urban transitions and population shifts. The sole relative change of the degree of urbanisation per spatial unit, is not a comprehensive indication of the demographic and spatial transformations taking place in that spatial unit (e.g. a country). The classification schema is also useful to develop and apply analytical methods and tools for better understanding of current and future urbanisation trends to inform development and cooperation actions. In this technical report, we present a formalised application of the “Demographic Factors of Change in Urbanisation Processes” model to monitor variations in the degree of urbanisation at country level, analysing its demographic determinants (urban, rural and total population). The report proposes a formalised abstract classification of the cases of degree of urbanisation variations. The classification is then applied to the countries in the Region “Europe” as per the 2018 Revision of World Urbanization Prospects published by the United Nations Department for Economic and Social Affairs.JRC.E.1-Disaster Risk Managemen