Assessing Disaster Risk of Building Stock

This work describes a methodology to assess ¿risk to disaster¿ due to natural hazards, particularly in data poor communities. It is to be used by (1) international organizations and donors to size development programs aiming to reduce risk to disasters and (2) by local authorities as a disaster management tool for implementing risk reduction, mitigation and preparedness programs. The methodology provides the guidelines to assemble a disaster risk information system that incorporates knowledge on natural hazards, construction science and disaster dynamics and is aimed for use by decision makers with the support of technical staff. The methodology is based on Geographical Information System (GIS) technology for the development of a database of disaster related information including built-up infrastructure, population, vulnerability and the occurrence of natural hazards. It integrates Earth Observation (EO) and information collected in situ for generating essential information such as building stock and indirectly population distribution in hazard affected areas. The database can also be used for generating damage assessment in the immediate aftermath of a disaster based on information on the hazard location and its intensity. Damage information can in turn improve the information content of the database to support more accurate risk assessments in the future. The information layers could then become important information that supports the development and urban planning projects.JRC.G.2-Global security and crisis managemen

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

Global spatial and temporal analysis of human settlements from Optical Earth Observation: Concepts, procedures, and preliminary results

This report provides an overview on the concepts, processing procedures and examples used to quantify changes in built-up land from optical satellite imagery. This is part of the larger work of the Global Human Settlement (GHS) team from the Joint Research Centre (JRC) that aims to measure the spatial extent of global human settlements, to monitor its changes over time and characterize the morphology of settlements. This built-up change analysis addresses the quantification of urbanization including some socio-economic and physical processes associated with urbanization. This includes the quantification of the building stock for modeling physical exposure in disaster risk modeling, as background layer for emergency response when a disaster unfolds and as background building stock layer for normalizing physical loss data. Based on the application of three of the most used change detection methods, Principal Component Analysis, Image Differencing Comparison, and Post-Classification Comparison, we present and discuss preliminary results, and try to identify future research directions for developing an appropriate approach for GHSL result images. The case studies were carried on Alger and Dublin city areas.JRC.G.2-Global security and crisis managemen

A European Framework for Recording and Sharing Disaster Damage and Loss Data

The recently adopted ‘Sendai Framework for Action on Disaster Risk Reduction 2015-2030’ sets the goals to reduce loss of life, livelihood and critical infrastructure through enhanced national planning and international cooperation. The new Framework is expected to enhance global, regional and national efforts for building resilience to disasters, across the entire disaster management cycle (prevention, preparedness, response and early recovery). Improved monitoring and accountability frameworks, relying on harmonized disaster loss data will be required for meeting the targets and for capturing the levels of progress across different scales of governance. To overcome the problems of heterogeneous disaster data and terminologies, guidelines for reporting disaster damage and losses in a structured manner will be necessary to help national and regional bodies compile this information. In the European Union, the Member States and the European Commission worked together on the establishment of guidelines for recording and sharing disaster damage and loss data as a first step towards the development of operational indicators to translate the Sendai Framework into action. This paper describes the progress to date in setting a common framework for recording disaster damage and loss data in the European Union and identifies the challenges ahead.JRC.G.2-Global security and crisis managemen

Assessing Temporal Changes in Global Population Exposure and Impacts from Earthquakes

ABSTRACT It is frequently conveyed, especially in the media, an idea of "increasing impact of natural hazards" typically attributed to their rising frequency and/or growing vulnerability of populations. However, for certain hazard types, this may be mostly a result of increasing population exposure due to phenomenal global population growth, especially in the most hazardous areas. We investigate temporal changes in potential global population exposure and impacts from earthquakes in the XXth century. Spatial analysis is used to combine historical population distributions with a seismic intensity map. Changes in number of victims were also analyzed, while controlling for the progress in frequency and magnitude of hazard events. There is also a focus on mega-cities and implications of fast urbanization for exposure and risk. Results illustrate the relevance of population growth and exposure for risk assessment and disaster outcome, and underline the need for conducting detailed global mapping of settlements and population distribution

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

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

Recording Disaster Losses: Recommendations for a European approach

In a study commissioned by Directorate General Humanitarian Aid and Civil Protection of the European Commission, the Joint Research Centre formulates technical recommendations for a European approach to standardize loss databases. Loss data are useful for the implementation of disaster risk reduction strategies in Europe (from local to national scales) and to help understand disaster loss trends at global level. Taking stock of existing work, the study defines a conceptual framework for the utility of loss data which allows a cost-benefit analysis of implementation scenarios. The framework considered loss accounting, disaster forensics and risk modelling. Depending on the scale (detail of recording) and scope (geographic coverage), technical requirements will be more or less stringent, and costs of implementation will vary accordingly. The technical requirements proposed in this study rely as much as possible on existing standards, best practices and approaches found in literature, international and national organisations and academic institutions. The requirements cover very detailed recording (at asset level) as well as coarse scale recording. Limitations and opportunities of existing EU legislation are considered as the EU context.JRC.G.2-Global security and crisis managemen

Detecting spatial pattern of inequality from remote sensing

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 Managemen

Sensing global patterns of inequality from space

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 Managemen