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Article

Communities facing urban flood risk have access to powerful flood simulation software for use in disaster-risk-reduction (DRR) initiatives. However, recent research has shown that flood risk continues to escalate globally, despite an increase in the primary outcome of flood simulation: increased knowledge. Thus, a key issue with the utilization of urban flood models is not necessarily development of new knowledge about flooding, but rather the achievement of more socially robust and context-sensitive knowledge production capable of converting knowledge into action. There are early indications that this can be accomplished when an urban flood model is used as a tool to bring together local lay and scientific expertise around local priorities and perceptions, and to advance improved, target-oriented methods of flood risk communication. The success of urban flood models as a facilitating agent for knowledge coproduction will depend on whether they are trusted by both the scientific and local expert, and to this end, whether the model constitutes an accurate approximation of flood dynamics is a key issue. This is not a sufficient condition for knowledge coproduction, but it is a necessary one. For example, trust can easily be eroded at the local level by disagreements among scientists about what constitutes an accurate approximation. Motivated by the need for confidence in urban flood models, and the wide variety of models available to users, this article reviews progress in urban flood model development over three eras: (1) the era of theory, when the foundation of urban flood models was established using fluid mechanics principles and considerable attention focused on development of computational methods for solving the one- and two-dimensional equations governing flood flows; (2) the era of data, which took form in the 2000s, and has motivated a reexamination of urban flood model design in response to the transformation from a data-poor to a data-rich modeling environment; and (3) the era of disaster risk reduction, whereby modeling tools are put in the hands of communities facing flood risk and are used to codevelop flood risk knowledge and transform knowledge to action. The article aims to inform decision makers and policy makers regarding the match between model selection and decision points, to orient the engineering community to the varied decision-making and policy needs that arise in the context of DRR activities, to highlight the opportunities and pitfalls associated with alternative urban flood modeling techniques, and to frame areas for future research.

Article

Djamel Bouchaffra and Faycal Ykhlef

The need for environmental protection, monitoring, and security is increasing, and land cover change detection (LCCD) can aid in the valuation of burned areas, the study of shifting cultivation, the monitoring of pollution, the assessment of deforestation, and the analysis of desertification, urban growth, and climate change. Because of the imminent need and the availability of data repositories, numerous mathematical models have been devised for change detection. Given a sample of remotely sensed images from the same region acquired at different dates, the models investigate if a region has undergone change. Even if there is no substantial advantage to using pixel-based classification over object-based classification, a pixel-based change detection approach is often adopted. A pixel can encompass a large region, and it is imperative to determine whether this pixel (input) has changed or not. A changed image is compared to the available ground truth image for pixel-based performance evaluation. Some existing change detection systems do not take into account reversible changes due to seasonal weather effects. In other words, when snow falls in a region, the land cover is not considered as a change because it is seasonal (reversible). Some approaches exploit time series of Landsat images, which are based on the Normalized Difference Vegetation Index technique. Others evaluate built-up expansion to assess urban morphology changes using an unsupervised approach that relies on labels clustering. Change detection methods have also been applied to the field of disaster management using object-oriented image classification. Some methodologies are based on spectral mixture analysis. Other techniques invoke a similarity measure based on the evolution of the local statistics of the image between two dates for vegetation LCCD. Probabilistic approaches based on maximum entropy have been applied to vegetation and forest areas, such as Hustai National Park in Mongolia. Researchers in this field have proposed an LCCD scheme based on a feed-forward neural network using backpropagation for training. This paper invokes the new concept of homology theory, a subfield of algebraic topology. Homology theory is incorporated within a Structural Hidden Markov Model.

Article

Anna Bozza, Domenico Asprone, and Gaetano Manfredi

In the early 21st century, achieving the sustainability of urban environments while coping with increasingly occurring natural disasters is a very ambitious challenge for contemporary communities. In this context, urban resilience is a comprehensive objective that communities can follow to ensure future sustainable cities able to cope with the risks to which they are exposed. Researchers have developed different definitions of resilience as this concept has been applied to diverse topics and issues in recent decades. Essentially, resilience is defined as the capability of a system to withstand major unexpected events and recover in a functional and efficient manner. When dealing with urban environments, the efficiency of the recovery can be related to multiple aspects, many of which are often hard to control. Mainly it is quantified in terms of the restoration of urban economy, population, and built form (Davoudi et al., 2012). In this article, engineering resilience is defined in relation to cities’ capability to be sustainable in the phase of an extreme event occurrence while reconfiguring their physical configuration. In this view, a city is resilient if it is sustainable in the occurrence of a hazardous event. Accordingly, in an urban context, a wide range of nonhomogeneous factors and intrinsic dynamics have to be accounted for, which requires a multi-scale approach, from the single building level to the urban and, ultimately, the global environmental scale. As a consequence, cities can be understood as physical systems assessed through engineering metrics. Hence, the physical dimension represents a starting point from which to approach resilience. When shifting the focus from the single structure to the city scale, human behavior is revealed to be a critical factor because social actors behave and make choices every day in an unpredictable and unorganized manner, which affects city functioning. According to the ecosystem theory, urban complexity can be addressed through the ecosystem theory approach, which accounts for interrelations between physical and human components.

Article

Abhilash Panda and Dilanthi Amaratunga

In 1990, 43% (2.3 billion) of the world’s population lived in urban areas, and by 2014 this percentage was at 54%. The urban population exceeded the rural population for the first time in 2008, and by 2050 it is predicted that urbanization will rise to 70% (see Albrito, “Making cities resilient: Increasing resilience to disasters at the local level,” Journal of Business Continuity & Emergency Planning, 2012). However, this increase in urban population has not been evenly spread throughout the world. As the urban population increases, the land area occupied by cities has increased at an even higher rate. It has been projected that by 2030, the urban population of developing countries will double, while the area covered by cities will triple (see United Nations, Department of Economic and Social Affairs, “World Urbanization Prospects: The 2014 Revision”). This emphasizes the need for resilience in the urban environment to anticipate and respond to disasters. Realizing this need, many local and international organizations have developed tools and frameworks to assist governments to plan and implement disaster risk reduction strategies efficiently. Sendai Framework’s Priorities for Action, Making Cities Resilient: My City is Getting Ready, and UNISDR’s Disaster Resilience Scorecard for Cities are major documents that provide essential guidelines for urban resilience. Given that, the disaster governance also needs to be efficient with ground-level participation for the implementation of these frameworks. This can be reinforced by adequate financing and resources depending on the exposure and risk of disasters. In essence, the resilience of a city is the resistance, coping capacity, recovery, adaptive capacity, and responsibility of everyone.

Article

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Natural Hazard Science. Please check back later for the full article. Spatial and urban planning are acknowledged as important tools and processes that influence exposure to natural and technical hazards and risk accumulation, as well as risk and vulnerability reduction. Even though natural hazards (such as floods) and technical hazards have been discussed in spatial and urban planning for quite some time in various countries and regions, only in a very few cities and regions has there been a sufficient and systematic approach to establish risk management as part of the planning task within the field of spatial planning and urban land-use planning. Risk management strategies in spatial and urban planning have often been strengthened after major crises, such as severe fires in the middle ages in cities in Europe, or after major floods or hurricanes in North America, Asia, and Latin America, as well as Europe and Africa. In this context, risk management is understood as a cluster of concrete and practical strategies and actions on how to handle risks, and in terms of spatial and urban planning, including those risks that are of spatial importance or significant with regard to planning processes.