Abstract. In this paper a local case study is presented in which detailed inundation simulations have been performed to support damage analysis and risk assessment related to the 2004 tsunami in Phang Nga and Phuket, Thailand. Besides tsunami sources, bathymetry and topography, bottom roughness induced by vegetation and built environment is considered to influence inundation characteristics, such as water depths or flow velocities and therefore attracts major attention in this work. Plenty of information available on the 2004 tsunami event, high-resolution satellite imagery and extensive field measurements to derive land cover information and forest stand parameters facilitated the generation of topographic datasets, land cover maps and site-specific Manning values for the most prominent land cover classes in the study areas. The numerical models ComMIT and Mike 21 FM were used to hindcast the observed tsunami inundation and to draw conclusions on the influence of land cover on inundation patterns. Results show a strong influence of dense vegetation on flow velocities, which were reduced by up to 50 % by mangroves, while the inundation extent is influenced only to a lesser extent. In urban areas, the disregard of buildings in the model led to a significant overestimation of the inundation extent. Hence different approaches to consider buildings were used and analyzed in the model. The case study highlights the importance and quantifies the effects of considering land cover roughness in inundation simulations used for local risk assessment.

Abstract. On 11 March 2011 the Tohoku tsunami devastated the east coast of Japan, claiming thousands of casualties and destroying coastal settlements and infrastructure. In this paper tsunami generation, propagation, and inundation are modeled to hindcast the event. Earthquake source models with heterogeneous slips are developed in order to match tsunami observations, including a best fit initial sea surface elevation with water levels up to 8 m. Tsunami simulations were compared to buoys in the Pacific, showing good agreement. In the far field the frequency dispersion provided a significant reduction even for the leading wave. Furthermore, inundation simulations were performed for ten different study areas. The results compared well with run-up measurements available and trim lines derived from satellite images, but with some overestimation of the modeled surface elevation in the northern part of the Sanriku coast. For inundation modeling this work aimed at using freely available, medium-resolution data for topography, bottom friction, and bathymetry, which are easily accessible in the framework of a rapid assessment. Although these data come along with some inaccuracies, the results of the tsunami simulations suggest that their use is feasible for application in rapid tsunami hazard assessments. A heterogeneous source model is essential to simulate the observed distribution of the run-up correctly, though.

Abstract. From a geoinformation science perspective real estate portals apply non-spatial methods to analyse and visualise rental price data. Their approach shows considerable shortcomings. Portal operators neglect real estate agents' mantra that exactly three things are important in real estates: location, location and location [16]. Although real estate portals record the spatial reference of their listed apartments, geocoded address data is used insufficiently for analyses and visualisation, and in many cases the data is just used to "pin" map the listings. To date geoinformation science, spatial statistics and geovisualization play a minor role for real estate portals in analysing and visualising their housing data. This contribution discusses the analytical and geovisual status quo of real estate portals and addresses the most serious deficits of the employed non-spatial methods. Alternative analysing approaches from geostatistics, machine learning and geovisualization demonstrate potentials to optimise real estate portals´ analysing and visualisation capacities.

From a geoinformation science perspective real estate portals apply non-spatial methods to analyse and visualise rental price data. Their approach shows considerable shortcomings. Portal operators neglect real estate agents' mantra that exactly three things are important in real estates: location, location and location (Stroisch, 2010). Although real estate portals retacord the spatial reference of their listed apartments, geocoded address data is used insufficiently for analyses and visualisation, and in many cases the data is just used to “pin” map the listings. To date geoinformation science, spatial statistics and geovisualization play a minor role for real estate portals in analysing and visualising their housing data. This contribution discusses the analytical and geovisual status quo of real estate portals and addresses the most serious deficits of the employed non-spatial methods. Alternative analysing approaches from geostatistics, machine learning and geovisualization demonstrate potentials to optimise real estate portals´ analysing and visualisation capacities.

From a geoinformation science perspective real estate portals apply non-spatial methods to analyse and visualise rental price data. Their approach shows considerable shortcomings. Portal operators neglect real estate agents' mantra that exactly three things are important in real estates: location, location and location (Stroisch, 2010). Although real estate portals retacord the spatial reference of their listed apartments, geocoded address data is used insufficiently for analyses and visualisation, and in many cases the data is just used to “pin” map the listings. To date geoinformation science, spatial statistics and geovisualization play a minor role for real estate portals in analysing and visualising their housing data. This contribution discusses the analytical and geovisual status quo of real estate portals and addresses the most serious deficits of the employed non-spatial methods. Alternative analysing approaches from geostatistics, machine learning and geovisualization demonstrate potentials to optimise real estate portals´ analysing and visualisation capacities.

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