Space-time analytics of human physiology for urban planning.

Recent advancements in mobile sensing and wearable technologies create new opportunities to improve our understanding of how people experience their environment. This understanding can inform urban design decisions. Currently, an important urban design issue is the adaptation of infrastructure to increasing cycle and e-bike use. Using data collected from 12 cyclists on a cycle highway between two municipalities in The Netherlands, we coupled location and wearable emotion data at a high spatiotemporal resolution to model and examine relationships between cyclists' emotional arousal (operationalized as skin conductance responses) and visual stimuli from the environment (operationalized as extent of visible land cover type). We specifically took a within-participants multilevel modeling approach to determine relationships between different types of viewable land cover area and emotional arousal, while controlling for speed, direction, distance to roads, and directional change. Surprisingly, our model suggests ride segments with views of larger natural, recreational, agricultural, and forested areas were more emotionally arousing for participants. Conversely, segments with views of larger developed areas were less arousing. The presented methodological framework, spatial-emotional analyses, and findings from multilevel modeling provide new opportunities for spatial, data-driven approaches to portable sensing and urban planning research. Furthermore, our findings have implications for design of infrastructure to optimize cycling experiences

More is not better:The emotional dynamics of an excellent experience

Emotions embody the value in tourism experiences and drive essential outcomes such as intent to recommend. Current models do not explain how the ebb and flow of emotional arousal during an experience relate to outcomes, however. We analyzed 15 participants’ experiences at the Vincentre museum and guided village tour in Nuenen, the Netherlands. This Vincent van Gogh-themed experience led to a wide range of intent to recommend and emotional arousal, measured as continuous phasic skin conductance, across participants and exhibits. Mixed-effects analyses modeled emotional arousal as a function of proximity to exhibits and intent to recommend. Experiences with the best outcomes featured moments of both high and low emotional arousal, not one continuous “high,” with more emotion during the middle of the experience. Tourist experience models should account for a complex relationship between emotions experienced and outcomes such as intent to recommend. Simply put, more emotion is not always better

Downscaling of real-time coastal flooding predictions for decision support

During coastal storms, forecasters and researchers use numerical models to predict the magnitude and extent of coastal flooding. These models must represent the large regions that may be affected by a storm, and thus, they can be computationally costly and may not use the highest geospatial resolution. However, predicted flood extents can be downscaled (by increasing resolution) as a post-processing step. Existing downscaling methods use either a static extrapolation of the flooding as a flat surface, or rely on subsequent simulations with nested, full-physics models at higher resolution. This research explores a middle way, in which the downscaling includes simplified physics to improve accuracy. Using results from a state-of-the-art model, we downscale its flood predictions with three methods: (1) static, in which the water surface elevations are extrapolated horizontally until they intersect the ground surface; (2) slopes, in which the gradient of the water surface is used; and (3) head loss, which accounts for energy losses due to land cover characteristics. The downscaling methods are then evaluated for forecasts and hindcasts of Hurricane Florence (2018), which caused widespread flooding in North Carolina. The static and slopes methods tend to over-estimate the flood extents. However, the head loss method generates a downscaled flooding extent that is a close match to the predictions from a higher-resolution, full-physics model. These results are encouraging for the use of these downscaling methods to support decision-making during coastal storms

A simple and efficient GIS tool for volume calculations of submarine landslides

A numeric tool is presented for calculating volumes of topographic voids such as slump scars of landslides, canyons or craters (negative/concave morphology), or alternatively, bumps and hills (positive/convex morphology) by means of digital elevation models embedded within a geographical information system (GIS). In this study, it has been used to calculate landslide volumes. The basic idea is that a (singular) event (landslide, meteorite impact, volcanic eruption) has disturbed an intact surface such that it is still possible to distinguish between the former (undisturbed) landscape and the disturbance (crater, slide scar, debris avalanche). In such cases, it is possible to reconstruct the paleo-surface and to calculate the volume difference between both surfaces, thereby approximating the volume gain or loss caused by the event. I tested the approach using synthetically generated land surfaces that were created on the basis of Shuttle Radar Topography Mission data. Also, I show the application to two real cases, (1) the calculation of the volume of the Masaya Slide, a submarine landslide on the Pacific continental slope of Nicaragua, and (2) the calculation of the void of a segment of the Fish River Canyon, Namibia. The tool is provided as a script file for the free GIS GRASS. It performs with little effort, and offers a range of interpolation parameters. Testing with different sets of interpolation parameters results in a small range of uncertainty. This tool should prove useful in surface studies not exclusively on earth

Monthly precipitation mapping of the Iberian Peninsula using spatial interpolation tools implemented in a Geographic Information System

Premi a l'excel·lència investigadora. Àmbit de les Ciències Socials. 2008In this study, spatial interpolation techniques have been applied to develop an objective climatic cartography of precipitation in the Iberian Peninsula (583,551 km2). The resulting maps have a 200m spatial resolution and a monthly temporal resolution. Multiple regression, combined with a residual correction method, has been used to interpolate the observed data collected from the meteorological stations. This method is attractive as it takes into account geographic information (independent variables) to interpolate the climatic data (dependent variable). Several models have been developed using different independent variables, applying several interpolation techniques and grouping the observed data into different subsets (drainage basin models) or into a single set (global model). Each map is provided with its associated accuracy, which is obtained through a simple regression between independent observed data and predicted values. This validation has shown that the most accurate results are obtained when using the global model with multiple regression mixed with the splines interpolation of the residuals. In this optimum case, the average R2 (mean of all the months) is 0.85. The entire process has been implemented in a GIS (Geographic Information System) which has greatly facilitated the filtering, querying, mapping and distributing of the final cartography

Risk and clinical-outcome indicators of delirium in an emergency department intermediate care unit (EDIMCU) : an observational prospective study

We are thankful to the staff at the EDIMCU of Hospital de Braga.Background Identification of delirium in emergency departments (ED) is often underestimated; within EDs, studies on delirium assessment and relation with patient outcome in Intermediate Care Units (IMCU) appear missing in European hospital settings. Here we aimed to determine delirium prevalence in an EDIMCU (Hospital de Braga, Braga, Portugal) and assessed routine biochemical parameters that might be delirium indicators. Methods The study was prospective and observational. Sedation level was assessed via the Richmond Agitation-Sedation Scale and delirium status by the Confusion Assessment Method for the ICU. Information collected included age and gender, admission type, Charlson Comorbidity Index combined condition score (Charlson score), systemic inflammatory response syndrome criteria (SIRS), biochemical parameters (blood concentration of urea nitrogen, creatinine, hemoglobin, sodium and potassium, arterial blood gases, and other parameters as needed depending on clinical diagnosis) and EDIMCU length of stay (LOS). Statistical analyses were performed as appropriate to determine if baseline features differed between the ‘Delirium’ and ‘No Delirium’ groups. Multivariate logistic regression was performed to assess the effect of delirium on the 1-month outcome. Results Inclusion and exclusion criteria were met in 283 patients; 238 were evaluated at 1-month for outcome follow-up after EDIMCU discharge (“good” recovery without complications requiring hospitalization or institutionalization; “poor” institutionalization in permanent care-units/assisted-living or death). Delirium was diagnosed in 20.1% patients and was significantly associated with longer EDIMCU LOS. At admission, Delirium patients were significantly older and had significantly higher blood urea, creatinine and osmolarity levels and significantly lower hemoglobin levels, when compared with No Delirium patients. Delirium was an independent predictor of increased EDIMCU LOS (odds ratio 3.65, 95% CI 1.97-6.75) and poor outcome at 1-month after discharge (odds ratio 3.51, CI 1.84-6.70), adjusted for age, gender, admission type, presence of SIRS criteria, Charlson score and osmolarity at admission. Conclusions In an EDIMCU setting, delirium was associated with longer LOS and poor outcome at1-month post-discharge. Altogether, findings support the need for delirium screening and management in emergency settings.NCS is supported by the post-doctoral fellowship UMINHO/BPD/013/2011 by the European Commission (FP7) “SwitchBox” Project (Contract HEALTH-F2-2010-259772)

Timing and Trends for Municipal Wastewater, Lab-Confirmed Case, and Syndromic Case Surveillance of COVID-19 in Raleigh, North Carolina

Objectives. To compare 4 COVID-19 surveillance metrics in a major metropolitan area. Methods. We analyzed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater influent and primary solids in Raleigh, North Carolina, from April 10 through December 13, 2020. We compared wastewater results with lab-confirmed COVID-19 cases and syndromic COVID-like illness (CLI) cases to answer 3 questions: (1) Did they correlate? (2) What was the temporal alignment of the different surveillance systems? (3) Did periods of significant change (i.e., trends) align? Results. In the Raleigh sewershed, wastewater influent, wastewater primary solids, lab-confirmed cases, and CLI were strongly or moderately correlated. Trends in lab-confirmed cases and wastewater influent were observed earlier, followed by CLI and, lastly, wastewater primary solids. All 4 metrics showed sustained increases in COVID-19 in June, July, and November 2020 and sustained decreases in August and September 2020. Conclusions. In a major metropolitan area in 2020, the timing of and trends in municipal wastewater, lab-confirmed case, and syndromic case surveillance of COVID-19 were in general agreement