Definition of an earthquake includes parameters with respect to region of interest. Each of those parameters has different weights on the earthquake ground motion and effect. This study examines the weight of common parameters that have an influence on the effects of earthquakes. The Analytic Hierarchy Process (AHP) is used for factor weighting of each parameter and Geographic Information Systems (GIS) are used for simulating the results of the AHP on a spatial environment. In this study, it is aimed to generate a hierarchical structure of the model for the simulation of an earthquake hazard map (EHM). The parameters of the EHM, which are selected by the criterion of non-correlated factors, are: topography, distance to epicenter, soil classification, liquefaction, and fault/focal mechanism. As a result of the study, weights of the parameters that affect the earthquake ground motion at the study area are determined and compared with a selected attenuation relation map.

The two important earthquakes that occurred in Turkey in 1999 have increased concerns on what would happen if a major earthquake affects Istanbul again. Alarming estimates reveal that Istanbul will probably be exposed to a devastating earthquake, with a magnitude between 6.5 and 7.7, in the next 25–30 years. The necessity of comprehensive plans for seismic mitigation, and response and recovery tasks after catastrophic events, has encouraged researchers to develop scientifically advanced tools to conduct loss assessment analyses. After the 1999 earthquakes hit Kocaeli and Düzce, several seismic loss assessment studies were performed for the city of Istanbul; many of them were carried out by the most important academicians in Turkey. However, due to the rapid changes in the exposed inventory of the city, those studies were losing accuracy. This article presents a seismic loss assessment system that can easily reanalyze earthquake scenarios including updates in the exposed inventory. This software can be used by government decision makers and disaster management agencies. In the analysis described in this article, the inventory data for the Zeytinburnu District is updated and visualized by using the rapid visual screening of buildings. Results from a loss assessment for the Zeytinburnu District are presented and compared with results from previous studies. The loss assessment analysis provide estimates of building damage, retrofit cost, economic loss, fiscal impact, and cost-benefit analysis.

The development of a general purpose disaster impact assessment software package is a daunting task, complicated by a number of technical, political, and administrative challenges. For example, geospatial information management should account for the administrative system of the country subject to the assessment, while the hazard, fragility, and inventory each have features that require implementation and display decisions that are consistent with the region. To accommodate the requirement of Istanbul, as possibly the largest megacity in the world with an acute earthquake risk problem, a new software package was developed, based on the Mid-America Earthquake Center platform MAEviz. MAEviz-Istanbul, the first stage of HAZTURK, is described in this article with emphasis on the regional significance and consistency of the development from an earthquake engineering viewpoint. Sample analyses are also provided at the end of the article to demonstrate the potential use of such a tool in assessing the impact of earthquakes on a densely populated cosmopolitan region.

Social media (SM) can be an invaluable resource in terms of understanding and managing the effects of catastrophic disasters. In order to use SM platforms for public participatory (PP) mapping of emergency management activities, a bias investigation should be undertaken with regard to the data related to the study area (urban, regional or national, etc.) to determine the spatial data dynamics. Thus, such determinations can be made on how SM can be used and interpreted in terms of PP. In this study, the city of Istanbul was chosen for social media data research area, as it is one of the most crowded cities in the world and expecting a major earthquake. The methodology for the data investigation is: 1. Obtain data and engage sampling, 2. Identify the representation and temporal biases in the data and normalize it in response to representation bias, 3. Identify general anomalies and spatial anomalies, 4. Manipulate the trend of the dataset with the discretization of anomalies and 5. Examine the spatiotemporal bias. Using this bias investigation methodology, citizen footprint dynamics in the city were determined and reference maps (most likely regional anomaly maps, representation maps, time-space bias maps, etc.) were produced. The outcomes of the study can be summarized in four steps. First, highly active users generate the majority of the data and removing this data as a general approach within a pseudo-cleaning process means concealing a large amount of data. Second, data normalization in terms of activity levels, changes the anomaly outcome resulting from diverse representation levels of users. Third, spatiotemporally normalized data present strong spatial anomaly tendency in some parts of the central area. Fourth, trend data is dense in the central area and the spatiotemporal bias assessments show the data density varies in terms of the time of day, day of week and season of the year. The methodology proposed in this study can be used to extract the unbiased daily routines of the social media data of the regions for the normal days and this can be referred for the emergency or unexpected event cases to detect the change or impacts.

The data generated by social media such as Twitter are classified as big data and the usability of those data can provide a wide range of resources to various study areas including disaster management, tourism, political science, and health. However, apart from the acquisition of the data, the reliability and accuracy when it comes to using it concern scientists in terms of whether or not the use of social media data (SMD) can lead to incorrect and unreliable inferences. There have been many studies on the analyses of SMD in order to investigate their reliability, accuracy, or credibility, but that have not dealt with the filtering techniques applied to with the data before creating the results or after their acquisition. This study provides a methodology for detecting the accuracy and reliability of the filtering techniques for SMD and then a spatial similarity index that analyzes spatial intersections, proximity, and size, and compares them. Finally, we offer a comparison that shows the best combination of filtering techniques and similarity indices to create event maps of SMD by using the Getis-Ord Gi* technique. The steps of this study can be summarized as follows: an investigation of domain-based text filtering techniques for dealing with sentiment lexicons, machine learning-based sentiment analyses on reliability, and developing intermediate codes specific to domain-based studies; then, by using various similarity indices, the determination of the spatial reliability and accuracy of maps of the filtered social media data. The study offers the best combination of filtering, mapping, and spatial accuracy investigation methods for social media data, especially in the case of emergencies, where urgent spatial information is required. As a result, a new similarity index based on the spatial intersection, spatial size, and proximity relationships is introduced to determine the spatial accuracy of the fine-filtered SMD. The motivation for this research is to develop the ability to create an incidence map shortly after a disaster event such as a bombing. However, the proposed methodology can also be used for various domains such as concerts, elections, natural disasters, marketing, etc.