Fast regridding of large, complex geospatial datasets

Fast regridding of large, comple

A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1)

The CF (Climate and Forecast) metadata conventions are designed to promote the creation, processing, and sharing of climate and forecasting data using Network Common Data Form (netCDF) files and libraries. The CF conventions provide a description of the physical meaning of data and of their spatial and temporal properties, but they depend on the netCDF file encoding which can currently only be fully understood and interpreted by someone familiar with the rules and relationships specified in the conventions documentation. To aid in development of CF-compliant software and to capture with a minimal set of elements all of the information contained in the CF conventions, we propose a formal data model for CF which is independent of netCDF and describes all possible CF-compliant data. Because such data will often be analysed and visualised using software based on other data models, we compare our CF data model with the ISO 19123 coverage model, the Open Geospatial Consortium CF netCDF standard, and the Unidata Common Data Model. To demonstrate that this CF data model can in fact be implemented, we present cf-python, a Python software library that conforms to the model and can manipulate any CF-compliant dataset

Xenopus tropicalis egg extracts provide insight into scaling of the mitotic spindle

The African clawed frog Xenopus laevis has been instrumental to investigations of both development and cell biology, but the utility of this model organism for genetic and proteomic studies is limited by its long generation time and unsequenced pseudotetraploid genome. Xenopus tropicalis, which is a small, faster-breeding relative of X. laevis, has recently been adopted for research in developmental genetics and functional genomics, and has been chosen for genome sequencing. We show that X. tropicalis egg extracts reconstitute the fundamental cell cycle events of nuclear formation and bipolar spindle assembly around exogenously added sperm nuclei. Interestingly, X. tropicalis spindles were ∼30% shorter than X. laevis spindles, and mixing experiments revealed a dynamic, dose-dependent regulation of spindle size by cytoplasmic factors. Measurements of microtubule dynamics revealed that microtubules polymerized slower in X. tropicalis extracts compared to X. laevis, but that this difference is unlikely to account for differences in spindle size. Thus, in addition to expanding the range of developmental and cell biological experiments, the use of X. tropicalis provides novel insight into the complex mechanisms that govern spindle morphogenesis

Interplay between HIV/AIDS Epidemics and Demographic Structures Based on Sexual Contact Networks

In this article, we propose a network spread model for HIV epidemics, wherein each individual is represented by a node of the transmission network and the edges are the connections between individuals along which the infection may spread. The sexual activity of each individual, measured by its degree, is not homogeneous but obeys a power-law distribution. Due to the heterogeneity of activity, the infection can persistently exist at a very low prevalence, which has been observed in real data but can not be illuminated by previous models with homogeneous mixing hypothesis. Furthermore, the model displays a clear picture of hierarchical spread: In the early stage the infection is adhered to these high-risk persons, and then, diffuses toward low-risk population. The prediction results show that the development of epidemics can be roughly categorized into three patterns for different countries, and the pattern of a given country is mainly determined by the average sex-activity and transmission probability per sexual partner. In most cases, the effect of HIV epidemics on demographic structure is very small. However, for some extremely countries, like Botswana, the number of sex-active people can be depressed to nearly a half by AIDS.Comment: 23 pages, 12 figure

Real estate investment and urban density: Exploring the polycentric urban region using a topological lens

Focusing on commercial office real estate as both a manifestation of and a conduit of cross-border capital flows, this paper refers to the concepts of topology and topography in a theoretical and empirical exploration of contemporary ‘network economy’ spatial implications for the ‘polycentric urban region’ (PUR). A body of research has cast doubt on the normative European representation of the multi-centre PUR as a balanced, sustainable spatial development model. Yet, the model has continued to be propagated in European territorial strategy and has been influential internationally. Academic perspectives and qualitative evidence reviewed in the paper shed a light on mutual dependencies and recursive relations between network economy global structural processes, international office real estate investment practices mediated by city governments, and the spatial configuration of density. Commercial investment and city planning actor practices chime with urban agglomeration, spatial concentration and density. Quantitative evidence of associations between urban density and office real estate investment returns and capital flows is found. It is concluded that network economy topology, politics and the city are in a dialectical relationship with the PUR territorial governance agenda for spatially balanced regional development

Creating a proof-of-concept climate service to assess future renewable energy mixes in Europe: an overview of the C3S ECEM project

The EU Copernicus Climate Change Service (C3S) European Climatic Energy Mixes (ECEM) has produced, in close collaboration with prospective users, a proof-of-concept climate service, or Demonstrator, designed to enable the energy industry and policy makers assess how well different energy supply mixes in Europe will meet demand, over different time horizons (from seasonal to long-term decadal planning), focusing on the role climate has on the mixes. The concept of C3S ECEM, its methodology and some results are presented here. The first part focuses on the construction of reference data sets for climate variables based on the ERA-Interim reanalysis. Subsequently, energy variables were created by transforming the bias-adjusted climate variables using a combination of statistical and physically-based models. A comprehensive set of measured energy supply and demand data was also collected, in order to assess the robustness of the conversion to energy variables. Climate and energy data have been produced both for the historical period (1979–2016) and for future projections (from 1981 to 2100, to also include a past reference period, but focusing on the 30 year period 2035–2065). The skill of current seasonal forecast systems for climate and energy variables has also been assessed. The C3S ECEM project was designed to provide ample opportunities for stakeholders to convey their needs and expectations, and assist in the development of a suitable Demonstrator. This is the tool that collects the output produced by C3S ECEM and presents it in a user-friendly and interactive format, and it therefore constitutes the essence of the C3S ECEM proof-of-concept climate service

Temporal networks of face-to-face human interactions

The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented levels of details and scale. Wearable sensors are opening up a new window on human mobility and proximity at the finest resolution of face-to-face proximity. As a consequence, empirical data describing social and behavioral networks are acquiring a longitudinal dimension that brings forth new challenges for analysis and modeling. Here we review recent work on the representation and analysis of temporal networks of face-to-face human proximity, based on large-scale datasets collected in the context of the SocioPatterns collaboration. We show that the raw behavioral data can be studied at various levels of coarse-graining, which turn out to be complementary to one another, with each level exposing different features of the underlying system. We briefly review a generative model of temporal contact networks that reproduces some statistical observables. Then, we shift our focus from surface statistical features to dynamical processes on empirical temporal networks. We discuss how simple dynamical processes can be used as probes to expose important features of the interaction patterns, such as burstiness and causal constraints. We show that simulating dynamical processes on empirical temporal networks can unveil differences between datasets that would otherwise look statistically similar. Moreover, we argue that, due to the temporal heterogeneity of human dynamics, in order to investigate the temporal properties of spreading processes it may be necessary to abandon the notion of wall-clock time in favour of an intrinsic notion of time for each individual node, defined in terms of its activity level. We conclude highlighting several open research questions raised by the nature of the data at hand.Comment: Chapter of the book "Temporal Networks", Springer, 2013. Series: Understanding Complex Systems. Holme, Petter; Saram\"aki, Jari (Eds.

Supporting smart urban growth: successful investing in density

This report analyses the characteristics of ‘good density’ and begins to quantify the relation-ship between these characteristics, investor returns, and carbon emissions. We found that cities with good density – that is, dense development thoughtfully designed to promote a high quality of life – are likely to be more resilient and prosperous in the long term, and there-fore more likely to provide sustainable returns for investors, than cities without good density. Based on a quantitative analysis of 63 global cities, the report finds that cities with good density are associated with higher returns, capital values, and levels of investment flows for commercial real estate. The research provides evidence of important issues for the long-term resilience of cities both in the OECD and in fast-growing developing regions

Modelling entomological-climatic interactions of Plasmodium falciparum malaria transmission in two Colombian endemic-regions: contributions to a National Malaria Early Warning System

BACKGROUND: Malaria has recently re-emerged as a public health burden in Colombia. Although the problem seems to be climate-driven, there remain significant gaps of knowledge in the understanding of the complexity of malaria transmission, which have motivated attempts to develop a comprehensive model. METHODS: The mathematical tool was applied to represent Plasmodium falciparum malaria transmission in two endemic-areas. Entomological exogenous variables were estimated through field campaigns and laboratory experiments. Availability of breeding places was included towards representing fluctuations in vector densities. Diverse scenarios, sensitivity analyses and instabilities cases were considered during experimentation-validation process. RESULTS: Correlation coefficients and mean square errors between observed and modelled incidences reached 0.897–0.668 (P > 0.95) and 0.0002–0.0005, respectively. Temperature became the most relevant climatic parameter driving the final incidence. Accordingly, malaria outbreaks are possible during the favourable epochs following the onset of El Niño warm events. Sporogonic and gonotrophic cycles showed to be the entomological key-variables controlling the transmission potential of mosquitoes' population. Simulation results also showed that seasonality of vector density becomes an important factor towards understanding disease transmission. CONCLUSION: The model constitutes a promising tool to deepen the understanding of the multiple interactions related to malaria transmission conducive to outbreaks. In the foreseeable future it could be implemented as a tool to diagnose possible dynamical patterns of malaria incidence under several scenarios, as well as a decision-making tool for the early detection and control of outbreaks. The model will be also able to be merged with forecasts of El Niño events to provide a National Malaria Early Warning System