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This paper aims to explore the impact of a recent earthquake activity on house prices and their spatial distribution in the Istanbul housing market.

The paper uses a multi-level approach within an event study framework to model changes in the pattern of house prices in Istanbul. The model allows the isolation of the effects of earthquake risk and explores the differential impact in different submarkets in two study periods – one before (2007) and one after (2012) recent earthquake activity in the Van region, which although in Eastern Turkey served to alter the perceptions of risk through the wider geographic region.

The analysis shows that there are variations in the size of price discounts in submarkets resulting from the differential influence of a recent earthquake activity on perceived risk of damage. The model results show that the spatial impacts of these changes are not transmitted evenly across the study area. Rather it is clear that submarkets at the cheaper end of the market have proportionately larger negative impacts on real estate values.

The robustness of the models would be enhanced by the addition of further spatial levels and larger data sets.

The methods introduced in this study can be used by real estate agents, valuers and insurance companies to help them more accurately assess the likely impacts of changes in the perceived risk of earthquake activity (or other environmental events such as flooding) on the formation of house prices in different market segments.

The application of these methods is intended to inform a fairer approach to setting insurance premiums and a better basis for determining policy interventions and public investment designed to mitigate potential earthquake risk.

The paper represents an attempt to develop a novel extension of the standard use of hedonic models in event studies to investigate the impact of natural disasters on real estate values. The value of the approach is that it is able to better capture the granularity of the spatial effects of environmental events than the standard approach.

In Turkey, the process of squatterisation can best be traced to the increase in its urban population from 24 percent in 1950 to 59 percent in 2000. In the periods up to the present, the prevention, improvement and renewal of squatter settlements were not achieved within the existing legal framework and planning structure; and their urban quality has been degraded.

The aim of this article is to discuss the upgrading of squatter settlements through a mitigation process considering the possibility of an earthquake in Istanbul. The target groups of this upgrading study are the squatter dwellers and their settlements.

In getting prepared for the predicted big Istanbul earthquake, the improvement of squatter housing is extremely important for the existing urban housing stock. With this aim, the undesirable consequences of a possible natural disaster in various squatter settlements in Istanbul were scrutinised. Also, earthquake-forecasting reports were analysed in conjunction with squatter maps to extract data for the purpose of upgrading squatter settlements through rehabilitation, reconstruction and reinforcement at the urban and architectural levels with amelioration of damage after an earthquake. In the article, a model is proposed which includes measures to transform squatter zones into healthy areas by means of simple reinforcement and contemporary solutions.

This article is based on a research project requested and sponsored by the Istanbul Metropolitan Municipality during 2003-2004.

This paper aims to propose a machine learning-based automatic labeling methodology for chemical tanker activities that can be applied to any port with any number of active tankers and the identification of important predictors. The methodology can be applied to any type of activity tracking that is based on automatically generated geospatial data.

The proposed methodology uses three machine learning algorithms (artificial neural networks, support vector machines (SVMs) and random forest) along with information fusion (IF)-based sensitivity analysis to classify chemical tanker activities. The data set is split into training and test data based on vessels, with two vessels in the training data and one in the test data set. Important predictors were identified using a receiver operating characteristic comparative approach, and overall variable importance was calculated using IF from the top models.

Results show that an SVM model has the best balance between sensitivity and specificity, at 93.5% and 91.4%, respectively. Speed, acceleration and change in the course on the ground for the vessels are identified as the most important predictors for classifying vessel activity.

The study evaluates the vessel movements waiting between different terminals in the same port, but not their movements between different ports for their tank-cleaning activities.

The findings in this study can be used by port authorities, shipping companies, vessel operators and other stakeholders for decision support, performance tracking, as well as for automated alerts.

This analysis makes original contributions to the existing literature by defining and demonstrating a methodology that can automatically label vehicle activity based on location data and identify certain characteristics of the activity by finding important location-based predictors that effectively classify the activity status.

This study aims to develop a better understanding of radical innovation performance and proposes a comprehensive and theoretical model of the barriers impeding radical innovation from the perspective of researchers working in research institutions in China. Both quantitative and qualitative techniques were used to test the hypotheses regarding barriers to radical innovation and the model proposed in this research.

The data was collected through questionnaires and semi-structured interviews with researchers from different research institutions across several cities in China. Next, the data was analyzed by deploying the structural equation modeling technique and calculating the statistical significance of correlations, regression and path coefficients among the latent variables.

The results indicated the major barriers impeding radical innovation in Chinese research institutes. Based on these findings, suggested policies, regulations and business models are put forward that can promote radical innovation in these institutes through increasing research freedom, enhancing organizational flexibility, attracting talented researchers and expanding research collaboration.

The research proposes a comprehensive and theoretical model of the barriers impeding radical innovation from the perspective of researchers working in research institutions in China.