Future exposure modelling for risk-informed decision making in urban planning

Population increases and related urban expansion are projected to occur in various parts of the world over the coming decades. These future changes to the urban fabric could fundamentally alter the exposure to natural hazards and the associated vulnerability of people and the built environment with which they interact. Thus, modelling, quantifying, and reducing future urban disaster risk require forward-looking insights that capture the dynamic form of cities. This paper specifically focuses on the exposure component of dynamic natural-hazard disaster risk, by considering urban planning as the centre of future exposure characterisation in a given region. We use the information provided by urban plans and propose an integrated data structure for capturing future exposure to hazards. The proposed data structure provides the necessary detailing for both future physical and socio-demographic exposure in disaster risk modelling. More specifically, it enables users to develop a comprehensive multi-level, multi-scale exposure dataset, characterising attributes of land use, buildings, households and individuals. We showcase the proposed data schema using the virtual urban testbed Tomorrowville. In this case study, we also demonstrate how simplified algorithmic procedures and disaggregation methods can be used to populate the required data. This implementation demonstrates how the proposed exposure data structure can effectively support the development of forward-looking urban visioning scenarios to support decision-making for risk-sensitive and pro-poor urban planning and design in tomorrow’s cities

Multi‐hazard interrelationships and risk scenarios in urban areas: a case of Nairobi and Istanbul

This paper introduces a methodology for characterizing the breadth of natural hazard types, hazard interrelationships, and risk scenarios in Global South urban areas, focusing on Nairobi, Kenya, and Istanbul, Türkiye. Our approach involves (a) a comprehensive characterization of multi-hazards and their interrelationships in an urban setting, (b) collaborative development of relevant multi-hazard scenarios with local disaster risk reduction (DRR) stakeholders, and (c) analysis of the potential for integrating these scenarios into urban DRR efforts. Using a critical review of 135 sources (academic and gray literature, databases, online, and social media), we identify 19 natural hazard types that might influence Nairobi and 23 in Istanbul. We further identified in Nairobi 88 and Istanbul 105 hazard interrelationship pairs (e.g., an earthquake triggering landslides) out of a possible 576 interrelationships. These findings are cataloged in an extensive database, which informs the creation of multi-hazard risk scenario exemplars for each city. These exemplars are refined through stakeholder engagement, involving four workshops (47 participants) and nine semi-structured interviews with local DRR stakeholders. Despite the identified benefits, this engagement reveals a significant gap in integrating multi-hazards into current urban policy and practice. Governance challenges are highlighted as a key barrier, but opportunities for better integration are also identified, including evolving policies and growing awareness among urban actors. Our approach, particularly relevant in data-scarce urban areas of low- and middle-income countries, provides a framework for exploring multi-hazard issues in various urban contexts

Near Real-time Landslide Forecast In Gis Environment-rize Case

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2009Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2009Afetlerle mücadelede en önemli bölüm afetlerin önceden belirlenebilmesi ve konuyla ilgili yetkililerin ve sivillerin bu doğrultuda mümkün olduğunca hazırlıklı olmasıdır. Bu çalışma da bu temele dayanarak karar vericilerin en hazırlıklı biçimde afetle mücadele etmesini sağlayacak bir sistem olan, TÜBİTAK tarafından desteklenen ve İTÜ ile ULUSAL CAD ve GIS ÇÖZÜMLERİ AŞ. tarafından ortak yürütülen Rize İli Genelinde Afet Bilgi ve Meteorolojik Erken Uyarı Sistemi Kurulması Projesi (RABİS)’nin iyileştirilmesi amacıyla çıktılarının değerlendirilmesini ve bu doğrultuda öneriler geliştirmeyi amaçlayan bir çalışmadır. En genel tanımıyla bu çalışmanın amacı RABİS’te gerçekleştirilen heyelan tahmin sisteminin iyileştirilmesine altlık olacak analizleri yürütmektir. Çalışmada girdi olarak kullanılan veriler RABİS kapsamında üretilen verilerdir. Bunları üç kısımda incelemek mümkündür. Birincisi jeolojik veriler, ikincisi uzaktan algılama verileri, üçüncüsü de meteorolojik verilerdir. Jeolojik veriler RABİS kapsamında İTÜ bünyesinde çalışmakta olan jeoloji grubunun çalışmaları sonucunda elde edilmiştir. Bu kapsamda yürütülen çalışmalarda elde edilen ve bu çalışmada dikkate alınan veri Rize ilinde yüksek heyelan potansiyeli taşıyan alanların sunulduğu veri setidir. Uzaktan algılama çalışmalarından temin edilen veriler ise çeşitli uydu görüntülerinin işlenmesiyle elde edilen sınıflandırma verileri ve sayısallaştırma verileridir. Meteorolojik veriler de yine RABİS kapsamında ve İTÜ bünyesinde çalışmalarını yürüten meteoroloji çalışma grubunun araştırmaları sonucunda üretilen verilerdir ve Rize ili için DMİ tarafından üretilen meteorolojik tahmin verileridir. Bu çalışma ile elde edilen veriler Rize iline ait çok önemli bulguları ortaya koymuştur. İl genelinde gerçekleştirilmiş olan RABİS çalışmasının çıktılarından yararlanılarak öncelikle bu çıktıların performansı değerlendirilmiş, sonrasında da bu proje çıktılarının nasıl iyileştirilebileceğine dair öneriler sunularak karar destek sistemlerine altlık olacak bilgilere ulaşılmıştır.The most important parts in disaster management are to predict the disaster and make the responsible persons and civilians prepared as much as possible to the hazards. On this basis, this study aims at improvement of RABIS (Establishment of a GIS Based Disaster Information and Meteorological Early Warning System) project which was carried out by ITU and ULUSAL CAD and GIS Solutions Company with the sponsorship of TUBITAK. In this sense the outputs of the project are evaluated and tested, so there have been developed some suggestions. With the broadest definition, the main goal of this study is to carry out the analysis to improve the outputs of the RABIS project. The input data in this research is gathered from the RABIS project. These are geological data, remote sensing data and meteorological data. Geological data is obtained from the outputs of the geology workgroup that took place in the project. Data includes landslide potential map of Rize province. The remote sensing data is including the classification and digitization data which are based on satellite images. Meteorological data is constituted by the rainfall forecast data which is supplied by Turkish State Meteorological Service. The results include very important findings. Benefiting from the outputs project, the performance of these outputs is tested and then regarding these tests there have been generated some suggestions on how to improve the quality of the project. So there have been reached to critical results which can serve as base data for decision makers in Rize.Yüksek LisansM.Sc

Spatial clustering and hot spot analysis of the COVID-19 pandemic in Malatya province

It was revealed that what caused the disease that emerged with respiratory symptoms (fever, cough, shortness of breath) towards the end of 2019 in Wuhan city of China's Hubei province, and later named as COVID-19 by WHO was SARS-CoV-2 virus. The COVID-19 epidemic affected Turkey very quickly as it did the entire world, and the first official case in Turkey was detected in March 2020. In this study, how the COVID-19 cases are clustered in the districts of Malatya and the structure of this clustering as well as whether the cluster has changed over time was revealed by using the spatial exploratory analysis approach. For this purpose, Global and Local Moran I statistics that measure spatial interaction were used. For the hot spot analysis, Getis-Ord’s Gi* statistic was used. Moran I, which measures the spread of COVID-19 among districts, is statistically significant, and the spread effect is close to medium, although not very strong. It has been determined that Yazıhan and Akçadağ districts are the riskiest districts on average as of the period under consideration according to Lokal Moran I statistics. According to the Getis-Ord’s Gi* statistics, Yazıhan district is the one that is most suitable for the spread of the epidemic for Malatya, again being a hot spot location. It has been observed that Yazıhan district is frequently in the hot spot according to the monthly analysis of the Gi*statistics. In this context, it is important for Yazıhan district to increase the necessary measures in the coming periods and to make efforts to raise awareness of the citizens

Istanbul Impact Story: Shifting Urban Planning Paradigms for Co-produced Resilience

Since the inception of Tomorrow's Cities, many cities of the Global South have embarked on an innovative journey towards inclusive, participatory, risk informed urban planning and disaster risk management. These impact stories capture the unique pathways each city has taken—highlighting the strategies they adopted, the tangible changes they achieved, and the unforeseen opportunities that arose. Each narrative explores how local actions, driven by shared learning and collaboration, are paving the way for a safer and more inclusive urban future, and understand the potential for lasting impact in each city

Reducing disaster risk for the poor in tomorrow’s cities with computational science

Rapid urban expansion presents a major challenge to delivering the United Nations Sustainable Development Goals. Urban populations are forecast to increase by 2.2 billion by 2050, and business as usual will condemn many of these new citizens to lives dominated by disaster risk. This need not be the case. Computational science can help urban planners and decision-makers to turn this threat into a time-limited opportunity to reduce disaster risk for hundreds of millions of people

Complete Tsunami Hazard Assessment, Vulnerability and Risk Analysis for the Marmara Coast of Istanbul Metropolitan Area

According to east to west propagation of major earthquakes along the North Anatolian Fault (NAF) since seventeenth century, a large magnitude (at least M=7.2) earthquake is expected to occur in the Sea of Marmara where the megacity of Istanbul located at the northern coast of the sea. There are numerous possibilities about the tsunami generation in the Sea of Marmara. In order to assess tsunami risk properly; collection and processing of high-resolution data and tsunami numerical modeling are necessary. In this regard, a complete project on tsunami hazard assessment, vulnerability and risk analysis has been carried out in three stages. In stage 1, high resolution Digital Elevation Model (DEM) data is used and enhanced to include buildings for analyzing 17 different coastal districts of Istanbul Metropolitan Area bordering Marmara Sea. In stage 2, NAF sourced 14 different co-seismic and 3 submarine landslide areas are considered based on the results of previous marine surveys and related publications and then tsunami simulations are carried out for each scenario with the use of NAMI DANCE GPU software. For each 17 districts, most critical co-seismic and landslide sourced tsunami scenarios are selected and their hazard levels (distribution of maximum flow depth at land) are computed. In stage 3, a detailed vulnerability analysis is performed by using the MeTHuVA (METU Metropolitan Tsunami Human Vulnerability Assessment) Method (Tufekci et al., 2018) that covers human vulnerability assessment with GIS-based multi criteria decision analysis (MCDA). Using analytical hierarchy process (AHP), a hierarchical structure is established, composed of two main elements; vulnerability at location and evacuation resilience. Tsunami risk assessment for each district is calculated by integrating result of hazard and vulnerability assessments with a risk relation that includes a parameter (n), which represents the preparedness and awareness level of the community. As a result of this research; detailed tsunami hazard analysis is performed and flow depths of tsunami inundation in coasts are calculated precisely. Moreover, based on the MeTHuVA model; vulnerability level of the assets and associated risk are evaluated in detail. As supplementary calculations, heavily used critical structures such as Yenikapı and Maltepe meeting areas, entrances of Metro stations such as Marmaray Kazlıçeșme, Yenikapı, Sirkeci, Üsküdar, Ayrılık Çeșmesi, Eurasia Tunnel Haydarpașa and Kumkapı tunnel entrances, Haydarpașa, Ambarlı, Tuzla, Yenikapı ports are analyzed in more detail to produce possible precautions to prevent losses in these facilities. Overall, it is believed that the outputs of the study will help increasing preparedness of the megacity İstanbul against possible tsunamis in the Marmara Sea and enable tangible actions to foster resiliency of the city. Acknowledgement: The study was fully supported by Istanbul Metropolitan Municipality, Directorate of Earthquake and Ground Research. The authors thank Prof. Dr. Fumihiko Imamura and International Research Institute of Disaster Science (IRIDeS) of Tohoku University, Japan for providing the submarine landslide tsunami simulation code TWO-LAYER and long term close collaboration in tsunami research. Keywords: tsunami hazard, vulnerability, tsunami risk, numerical modeling, high-resolution, preparedness, mitigatio