4,814 research outputs found
High performance computation of landscape genomic models integrating local indices of spatial association
Since its introduction, landscape genomics has developed quickly with the
increasing availability of both molecular and topo-climatic data. The current
challenges of the field mainly involve processing large numbers of models and
disentangling selection from demography. Several methods address the latter,
either by estimating a neutral model from population structure or by inferring
simultaneously environmental and demographic effects. Here we present
Samada, an integrated approach to study signatures of local adaptation,
providing rapid processing of whole genome data and enabling assessment of
spatial association using molecular markers. Specifically, candidate loci to
adaptation are identified by automatically assessing genome-environment
associations. In complement, measuring the Local Indicators of Spatial
Association (LISA) for these candidate loci allows to detect whether similar
genotypes tend to gather in space, which constitutes a useful indication of the
possible kinship relationship between individuals. In this paper, we also
analyze SNP data from Ugandan cattle to detect signatures of local adaptation
with Samada, BayEnv, LFMM and an outlier method (FDIST approach in
Arlequin) and compare their results. Samada is an open source software
for Windows, Linux and MacOS X available at \url{http://lasig.epfl.ch/sambada}Comment: 1 figure in text, 1 figure in supplementary material The structure of
the article was modified and some explanations were updated. The methods and
results presented are the same as in the previous versio
Spatio-temporal modeling of traffic risk mapping on urban road networks
Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesOver the past few years, traffic collisions have been one of the serious
issues all over the world. Global status report on road safety, reveals
an increasing number of fatalities due to traffic accidents, especially on
urban roads. The present research work is conducted on five years of
accident data in an urban environment to explore and analyze spatial
and temporal variation in the incidence of road traffic accidents and
casualties.
The current study proposes a spatio-temporal model that can make
predictions regarding the number of road casualties likely on any given
road segments and can generate a risk map of the entire road network.
Bayesian methodology using Integrated Nested Laplace Approximation
(INLA) with Stochastic Partial Differential Equations (SPDE)
has been applied in the modeling process. The novelty of the proposed
model is to introduce "SPDE network triangulation" precisely on linear
networks to estimate the spatial autocorrelation of discrete events.
The result risk maps can provide geospatial baseline to identify safe
routes between source and destination points. The maps can also
have implications for accident prevention and multi-disciplinary road
safety measures through an enhanced understanding of the accident
patterns and factors. Reproducibility self-assessment : 3, 1, 1, 3,
2 (input data, preprocessing, methods, computational environment,
results)
Big Data and Regional Science: Opportunities, Challenges, and Directions for Future Research
Recent technological, social, and economic trends and transformations are contributing to the production of what is usually referred to as Big Data. Big Data, which is typically defined by four dimensions -- Volume, Velocity, Veracity, and Variety -- changes the methods and tactics for using, analyzing, and interpreting data, requiring new approaches for data provenance, data processing, data analysis and modeling, and knowledge representation. The use and analysis of Big Data involves several distinct stages from "data acquisition and recording" over "information extraction" and "data integration" to "data modeling and analysis" and "interpretation", each of which introduces challenges that need to be addressed. There also are cross-cutting challenges, which are common challenges that underlie many, sometimes all, of the stages of the data analysis pipeline. These relate to "heterogeneity", "uncertainty", "scale", "timeliness", "privacy" and "human interaction". Using the Big Data analysis pipeline as a guiding framework, this paper examines the challenges arising in the use of Big Data in regional science. The paper concludes with some suggestions for future activities to realize the possibilities and potential for Big Data in regional science.Series: Working Papers in Regional Scienc
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
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