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Emergency management services, such as firefighting, rescue teams and ambulances,are all heavily reliant on road networks. However, even for highly industrialised countries such asGermany, and even for large cities, spatial planning tools are lacking for road network interruptionsof emergency services. Moreover, dependencies of emergency management expand not only onroads but on many other systemic interrelations, such as blockages of bridges. The first challenge thispaper addresses is the development of a novel assessment that captures systemic interrelations ofcritical services and their dependencies explicitly designed to the needs of the emergency services.This aligns with a second challenge: capturing system nodes and areas around road networksand their geographical interrelation. System nodes, road links and city areas are integrated into aspatial grid of tessellated hexagons (also referred to as tiles) with geographical information systems.The hexagonal grid is designed to provide a simple map visualisation for emergency planners andfire brigades. Travel time planning is then optimised for accessing city areas in need by weighingimpaired areas of past events based on operational incidents. The model is developed and testedwith official incident data for the city of Cologne, Germany, and will help emergency managers tobetter device planning of resources based on this novel identification method of critical areas.
Remote sensing applications of change detection are increasingly in demand for many areas of land use and urbanization, and disaster risk reduction. The Sendai Framework for Disaster Risk Reduction and the New Urban Agenda by the United Nations call for risk monitoring. This study maps and assesses the urban area changes of 23 Mexican-USA border cities with a remote sensing-based approach. A literature study on existing studies on hazard mapping and social vulnerability in those cities reveals a need for further studies on urban growth. Using a multi-modal combination of aerial, declassified (CORONA, GAMBIT, HEXAGON programs), and recent (Sentinel-2) satellite imagery, this study expands existing land cover change assessments by capturing urban growth back to the 1940s. A Geographic Information System and census data assessment results reveal that massive urban growth has occurred on both sides of the national border. On the Mexican side, population and area growth exceeds the US cities in many cases. In addition, flood hazard exposure has grown along with growing city sizes, despite structural river training. These findings indicate a need for more risk monitoring that includes remote sensing data. It has socio-economic implications, too, as the social vulnerability on Mexican and US sides differ. This study calls for the maintenance and expansion of open data repositories to enable such transboundary risk comparisons. Common vulnerability variable sets could be helpful to enable better comparisons as well as comparable flood zonation mapping techniques. To enable risk monitoring, basic data such as urban boundaries should be mapped per decade and provided on open data platforms in GIS formats and not just in map viewers.
Exposure is an essential component of risk models and describes elements that are endangered by a hazard and susceptible to damage. The associated vulnerability characterizes the likelihood of experiencing damage (which can translate into losses) at a certain level of hazard intensity. Frequently, the compilation of exposure information is the costliest component (in terms of time and labor) of risk assessment procedures. Existing models often describe exposure in an aggregated manner, e.g., by relying on statistical/census data for given administrative entities. Nowadays, earth observation techniques allow the collection of spatially continuous information for large geographic areas while enabling a high geometric and temporal resolution. Consequently, we exploit measurements from the earth observation missions TanDEM-X and Sentinel-2, which collect data on a global scale, to characterize the built environment in terms of constituting morphologic properties, namely built-up density and height. Subsequently, we use this information to constrain existing exposure data in a spatial disaggregation approach. Thereby, we establish dasymetric methods for disaggregation. The results are presented for the city of Santiago de Chile, which is prone to natural hazards such as earthquakes. We present loss estimations due to seismic ground shaking and corresponding sensitivity as a function of the resolution properties of the exposure data used in the model. The experimental results underline the benefits of deploying modern earth observation technologies for refined exposure mapping and related earthquake loss estimation with enhanced accuracy properties.