Measuring Spatial Dynamics in Metropolitan Areas

This paper introduces a new approach to measuring neighborhood change. Instead of the traditional method of identifying “neighborhoods†a priori and then studying how resident attributes change over time, our approach looks at the neighborhood more intrinsically as a unit that has both a geographic footprint and a socioeconomic composition. Therefore, change is identified when both as- pects of a neighborhood transform from one period to the next. Our approach is based on a spatial clustering algorithm that identifies neighborhoods at two points in time for one city. We also develop indicators of spatial change at both the macro (city) level as well as local (neighborhood) scale. We illustrate these methods in an application to an extensive database of time-consistent census tracts for 359 of the largest metropolitan areas in the US for the period 1990-2000.

Heterogeneity of wheat endosperm proteolipids (CM proteins)

Proteins extracted with CHCl3-MeOH from wheat endosperm have been fractionated by Sephadex G-100 and the 15 000–20 000 MW range fraction, designated CM protein, has been examined by combined electrofocusing (pH range 5–8) and electrophoresis (pH 3.2) and the heterogeneity of the electrophoretic components has been ascertained. It has been shown by joint mapping and by sequential extraction that CM proteins are extracted by 70% EtOH but not by H2O, although they can be made water-soluble after dialysis against an acid buffer, pH 3.2, 3 M urea, without losing their solubility in CHCl3-MeOH mixtures. It is concluded that CM proteins fit the definition of a Folch—Lees proteolipid. The Triticum aestivum (genomes ABD) map can be reconstructed by mixing T. durum (AB) and Aegilops squarrosa (D). The low intragenomic variability of CM protein is confirmed

Towards a quantitative phase-field model of two-phase solidification

We construct a diffuse-interface model of two-phase solidification that quantitatively reproduces the classic free boundary problem on solid-liquid interfaces in the thin-interface limit. Convergence tests and comparisons with boundary integral simulations of eutectic growth show good accuracy for steady-state lamellae, but the results for limit cycles depend on the interface thickness through the trijunction behavior. This raises the fundamental issue of diffuse multiple-junction dynamics.Comment: 4 pages, 2 figures. Better final discussion. 1 reference adde

A multi-scale risk assessment for tephra fallout and airborne concentration from multiple Icelandic volcanoes – Part 1: Hazard assessment

This is the final version of the article. Available from EGU via the DOI in this record.In order to assist the elaboration of proactive measures for the management of future volcanic eruptions in Iceland, we developed a new scenario-based approach to assess the hazard associated with tephra dispersal and sedimentation at various scales and for multiple sources. The target volcanoes are Hekla, Katla, Eyjafjallajökull and Askja, selected either for their high probabilities of eruption and/or their high potential impact. By coupling tephrostratigraphic studies, probabilistic techniques and modelling, we developed comprehensive eruption scenarios for both short- and long-lasting eruptions and compiled hazard maps for tephra ground deposition at a national scale and air concentration at a European scale using the TEPHRA2 and FALL3D models, respectively. New algorithms for the identification of realistic sets of eruptive source parameters are investigated, which assist the generation of probability density functions of eruption source parameters for the selected scenarios. Aggregation processes were accounted for using various empirical models. Outcomes, i.e. probabilities conditioned to the occurrence of an eruption, help the assessment and comparison of hazard levels at different scales. For example, at a national scale Askja has a 5–10% probability of blanketing the easternmost half of the country with a tephra accumulation of at least 1 kg m−2. At a continental scale, Katla has a 5–10% probability of producing ash clouds with concentrations of 2 mg m−3 over the UK, Scandinavia and northern Europe with a mean arrival time of 48–72 h and a mean persistence time of 6–18 h. In a companion paper, Scaini et al. (2014) present a vulnerability assessment for Iceland to ground deposition of tephra and for the European air traffic to airborne ash which, combined with the outcomes of the present paper, constitute one of the first comprehensive multi-scale risk assessment associated with tephra dispersal and sedimentation.S. Biass is supported by SNF (#200021-129997) and ESF/MemoVolc (#5193) subsides. C. Scaini is partly supported by the Spanish Research Project ATMOST (CGL2009-10244) and by the SNF (IZK0Z2_ 142343)

An automatic procedure to forecast tephra fallout

Tephra fallout constitutes a serious threat to communities around active volcanoes. Reliable short-term forecasts represent a valuable aid for scientists and civil authorities to mitigate the effects of fallout on the surrounding areas during an episode of crisis. We present a platform-independent automatic procedure with the aim to daily forecast transport and deposition of volcanic particles. The procedure builds on a series of programs and interfaces that automate the data flow and the execution and subsequent postprocess of fallout models. Firstly, the procedure downloads regional meteorological forecasts for the area and time interval of interest, filters and converts data from its native format, and runs the CALMET diagnostic model to obtain the wind field and other micro-meteorological variables on a finer local-scale 3-D grid defined by the user. Secondly, it assesses the distribution of mass along the eruptive column, commonly by means of the radial averaged buoyant plume equations depending on the prognostic wind field and on the conditions at the vent (granulometry, mass flow rate, etc). All these data serve as input for the fallout models. The initial version of the procedure includes only two Eulerian models, HAZMAP and FALL3D, the latter available as serial and parallel implementations. However, the procedure is designed to incorporate easily other models in a near future with minor modifications on the model source code. The last step is to postprocess the outcomes of models to obtain maps written in standard file formats. These maps contain plots of relevant quantities such as predicted ground load, expected deposit thickness and, for the case of or 3-D models, concentration on air or flight safety concentration thresholds

Uncertain Uncertainty: Spatial Variation in the Quality of American Community Survey Estimates

The U.S. Census Bureau's American Community Survey (ACS) is the foundation of social science research, much federal resource allocation and the development of public policy and private sector decisions. However, the high uncertainty associated with some of the ACS's most frequently used estimates can jeopardize the accuracy of inferences based on these data. While there is high level understanding in the research community that problems exist in the data, the sources and implications of these problems have been largely overlooked. Using 2006-2010 ACS median household income at the census tract scale as the test case (where a third of small-area estimates have higher than recommend errors), we explore the patterns in the uncertainty of ACS data. We consider various potential sources of uncertainty in the data, ranging from response level to geographic location to characteristics of the place. We find that there exist systematic patterns in the uncertainty in both the spatial and attribute dimensions. Using a regression framework, we identify the factors that are most frequently correlated with the error at national, regional and metropolitan area scales, and find these correlates are not consistent across the various locations tested. The implication is that data quality varies in different places, making cross-sectional analysis both within and across regions less reliable. We also present general advice for data users and potential solutions to the challenges identified

A multi-scale risk assessment for tephra fallout and airborne concentration from multiple Icelandic volcanoes – Part 2: Vulnerability and impact

This is the final version of the article. Available from EGU via the DOI in this record.We perform a multi-scale impact assessment of tephra fallout and dispersal from explosive volcanic activity in Iceland. A companion paper (Biass et al., 2014; "A multi-scale risk assessment of tephra fallout and airborne concentration from multiple Icelandic volcanoes – Part I: hazard assessment") introduces a multi-scale probabilistic assessment of tephra hazard based on selected eruptive scenarios at four Icelandic volcanoes (Hekla, Askja, Eyjafjallajökull and Katla) and presents probabilistic hazard maps for tephra accumulation in Iceland and tephra dispersal across Europe. Here, we present the associated vulnerability and impact assessment that describes the importance of single features at national and European levels and considers several vulnerability indicators for tephra dispersal and deposition. At the national scale, we focus on physical, systemic and economic vulnerability of Iceland to tephra fallout, whereas at the European scale we focus on the systemic vulnerability of the air traffic system to tephra dispersal. This is the first vulnerability and impact assessment analysis of this type and, although it does not include all the aspects of physical and systemic vulnerability, it allows for identifying areas on which further specific analysis should be performed. Results include vulnerability maps for Iceland and European airspace and allow for the qualitative identification of the impacts at both scales in the case of an eruption occurring. Maps produced at the national scale show that tephra accumulation associated with all eruptive scenarios considered can disrupt the main electricity network, in particular in relation to an eruption of Askja. Results also show that several power plants would be affected if an eruption occurred at Hekla, Askja or Katla, causing a substantial systemic impact due to their importance for the Icelandic economy. Moreover, the Askja and Katla eruptive scenarios considered could have substantial impacts on agricultural activities (crops and pastures). At the European scale, eruptive scenarios at Askja and Katla are likely to affect European airspace, having substantial impacts, in particular, in the Keflavík and London flight information regions (FIRs), but also at FIRs above France, Germany and Scandinavia. Impacts would be particularly intense in the case of long-lasting activity at Katla. The occurrence of eruptive scenarios at Hekla is likely to produce high impacts at Keflavík FIR and London FIRs, and, in the case of higher magnitude, can also impact France's FIRs. Results could support land use and emergency planning at the national level and risk management strategies of the European air traffic system. Although we focus on Iceland, the proposed methodology could be applied to other active volcanic areas, enhancing the long-term tephra risk management. Moreover, the outcomes of this work pose the basis for quantitative analyses of expected impacts and their integration in a multi-risk framework.This work has been funded by the Spanish research project “Atmospheric transport models and massive parallelism: applications to volcanic ash clouds and dispersion of pollutants at an urban micro-scale” (ATMOST, CGL2009-10244) and the Fonds National Suisse project “Volcanic-Ash Dispersal from Selected Icelandic Volcanoes: Risk Assessment for the European Region” (IZK0Z2_142343). S. Biass is supported by SNF (#200021-129997) and ESF/MemoVolc (#5193) subsidies

Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization

Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical, sequential setting of Bayesian Optimization does not translate well into laboratory experiments, for instance battery design, where measurements may come from different sources and their evaluations may require significant waiting times. Multi-fidelity Bayesian Optimization addresses the setting with measurements from different sources. Asynchronous batch Bayesian Optimization provides a framework to select new experiments before the results of the prior experiments are revealed. This paper proposes an algorithm combining multi-fidelity and asynchronous batch methods. We empirically study the algorithm behavior, and show it can outperform single-fidelity batch methods and multi-fidelity sequential methods. As an application, we consider designing electrode materials for optimal performance in pouch cells using experiments with coin cells to approximate battery performance