Calculating earthquake damage building by building: the case of the city of Cologne, Germany

The creation of building exposure models for seismic risk assessment is frequently challenging due to the lack of availability of detailed information on building structures. Different strategies have been developed in recent years to overcome this, including the use of census data, remote sensing imagery and volunteered graphic information (VGI). This paper presents the development of a building-by-building exposure model based exclusively on openly available datasets, including both VGI and census statistics, which are defined at different levels of spatial resolution and for different moments in time. The initial model stemming purely from building-level data is enriched with statistics aggregated at the neighbourhood and city level by means of a Monte Carlo simulation that enables the generation of full realisations of damage estimates when using the exposure model in the context of an earthquake scenario calculation. Though applicable to any other region of interest where analogous datasets are available, the workflow and approach followed are explained by focusing on the case of the German city of Cologne, for which a scenario earthquake is defined and the potential damage is calculated. The resulting exposure model and damage estimates are presented, and it is shown that the latter are broadly consistent with damage data from the 1978 Albstadt earthquake, notwithstanding the differences in the scenario. Through this real-world application we demonstrate the potential of VGI and open data to be used for exposure modelling for natural risk assessment, when combined with suitable knowledge on building fragility and accounting for the inherent uncertainties.

Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running

There are tremendous opportunities to advance science, clinical care, sports performance, and societal health if we are able to develop tools for monitoring musculoskeletal loading (e.g., forces on bones or muscles) outside the lab. While wearable sensors enable non-invasive monitoring of human movement in applied situations, current commercial wearables do not estimate tissue-level loading on structures inside the body. Here we explore the feasibility of using wearable sensors to estimate tibial bone force during running. First, we used lab-based data and musculoskeletal modeling to estimate tibial force for ten participants running across a range of speeds and slopes. Next, we converted lab-based data to signals feasibly measured with wearables (inertial measurement units on the foot and shank, and a pressure-insole) and used these data to develop two multi-sensor algo rithms for estimating peak tibial force: one physics-based and one machine learning. Additionally, to reflect current running wearables that utilize foot impact metrics to infer musculoskeletal loading or injury risk, we estimated tibial force using the ground reaction force vertical average loading rate (VALR). Using VALR to estimate peak tibial force resulted in a mean absolute percent error of 9.9%, which was no more accurate than a theoretical step counter that assumed the same peak force for every running step. Our physics-based algorithm reduced error to 5.2%, and our machine learning algorithm reduced error to 2.6%. Further, to gain insights into how force estimation accuracy relates to overuse injury risk, we computed bone damage expected due to peak force. We found that modest errors in tibial force translated into large errors in bone damage estimates. For example, a 9.9% error in tibial force using VALR translated into 104% error in bone damage estimates. Encouragingly, the physics-based and machine learning algorithms reduced damage errors to 41% and 18%, respectively. This study highlights the exciting potential to combine wearables, musculoskeletal biomechanics and machine learning to develop more accurate tools for monitoring musculoskeletal loading in applied situations.

Changes in flood damage with global warming on the eastern coast of Spain

Flooding is one of the main natural hazards in the world and causes huge economic and human impacts. Assessing the flood damage in the Mediterranean region is of great importance, especially because of its large vulnerability to climate change. Most past floods affecting the region were caused by intense precipitation events; thus the analysis of the links between precipitation and flood damage is crucial. The main objective of this paper is to estimate changes in the probability of damaging flood events with global warming of 1.5, 2 and 3 ∘C above pre-industrial levels and taking into account different socioeconomic scenarios in two western Mediterranean regions, namely Catalonia and the Valencian Community. To do this, we analyse the relationship between heavy precipitation and flood-damage estimates from insurance datasets in those two regions. We consider an ensemble of seven regional climate model (RCM) simulations spanning the period 1976–2100 to evaluate precipitation changes and to drive a logistic model that links precipitation and flood-damage estimates, thus deriving statistics under present and future climates. Furthermore, we incorporate population projections based on five different socioeconomic scenarios. The results show a general increase in the probability of a damaging event for most of the cases and in both regions of study, with larger increments when higher warming is considered. Moreover, this increase is higher when both climate and population change are included. When population is considered, all the periods and models show a clearly higher increase in the probability of damaging events, which is statistically significant for most of the cases. Our findings highlight the need for limiting global warming as much as possible as well as the importance of including variables that consider change in both climate and socioeconomic conditions in the analysis of flood damage.

Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut

The daily nighttime lights (NTL) and the amount of location-service requests (NLR) data have been widely used as a proxy for measures of disaster-induced power outages and geo-tagged human activity dynamics. However, the association between the two datasets is not well understood. In this study, we investigated how the NTL signals and geo-tagged human activities changed in response to Typhoon Mangkhut. The confusion matrix is constructed to quantify the changes of the NLR in response to Typhoon Mangkhut, as well as the changes of the NTL signals at the grid level. Geographically-weighted regression and quantile regression were used to examine the associations between the changes of the NTL and the NLR at both grid and county levels. The quantile regressions were also used to quantify the relationships between the dimmed NTL signals and the change of the NLR in disaster damage estimates at the county level. Results show that the percent of the grids with anomalous human activities is significantly correlated with the nearby air pressure and wind speed. Geo-tagged human activities varied in response to the evolution of Mangkhut with significant areal differentiation. Over 69.3% of the grids with significant human activity change is also characterized by declined NTL brightness, which is closely associated with abnormal human activities. Significant log-linear and moderate positive correlations were found between the changes of the NTL and NLR at both the grid and county levels, as well as between the county-level changes of NLR/NTL and the damage estimates. This study shows the geo-tagged human activities are closely associated with the changes of the daily NTL signals in response to Typhoon Mangkhut. The two datasets are complimentary in sensing the typhoon-induced losses and damages.