Capturing Flood Risk Perception via Sketch Maps

The fact that an increasing number of people and local authorities are affected by natural hazards, especially floods, highlights the necessity of adequate mitigation and preparedness within disaster management. Many governments, though, have only insufficient monetary or technological capacities. One possible approach to tackle these issues is the acquisition of information by sketch maps complemented by questionnaires, which allows to digitally capture flood risk perception. We investigate which factors influence information collected by sketch maps and questionnaires in case studies in an area prone to pluvial flooding in Santiago de Chile. Our aim is to gain more information about the methods applied. Hereby, we focus on the spatial acquisition scale of sketch maps and personal characteristics of the participants, for example, whether they live at this very location of the survey (residents) or are pedestrians passing by. Our results show that the choice of the acquisition scale of the base map influences the amount and level of detail of information captured via sketch maps. Thus, detail base maps lead to more precise results when compared to reference data, especially in the case of residents. The results also reveal that the place of living of the respondents has an effect on the resulting information because on the neighborhood level the risk perception of residents is more detailed than the one of pedestrians. The study suggests that the integration of citizens via sketch maps can provide information about flood risk perception, and thus can influence the flood mitigation in the area.

The effect of acquisition resolution on orientation decoding from V1: comparison of 3T and 7T

Previously published results indicate that the accuracy of decoding visual orientation from 7 Tesla fMRI data of V1 peaks at spatial acquisition resolutions that are routinely accessible with more conventional 3 Tesla scanners. This study directly compares the decoding performance between a 3 Tesla and a 7 Tesla dataset that were acquired using the same stimulation paradigm by applying an identical analysis procedure. The results indicate that decoding models built on 3 Tesla data are comparatively impaired. Moreover, we found no evidence for a strong coupling of BOLD signal change magnitude or temporal signal to noise ratio (tSNR) with decoding performance. Direct enhancement of tSNR via multiband fMRI acquisition at the same resolution did not translate into improved decoding performance. Additional voxel selection can boost 3 Tesla decoding performance to the 7 Tesla level only at a 3 mm acquisition resolution. In both datasets the BOLD signal available for orientation decoding is spatially broadband, but, consistent with the size of the BOLD point-spread-function, decoding models at 3 Tesla utilize spatially coarser image components.