Space-time cluster analysis of American visceral leishmaniasis in Bauru, São Paulo State, Brazil

A space-time analysis of American visceral leishmaniasis (AVL) in humans in the city of Bauru, São Paulo State, Brazil was carried out based on 239 cases diagnosed between June 2003 and October 2008. Spatial analysis of the disease showed that cases occurred especially in the city's urban areas. AVL annual incidence rates were calculated, demonstrating that the highest rate occurred in 2006 (19.55/100,000 inhabitants). This finding was confirmed by the time series analysis, which also showed a positive tendency over the period analyzed. The present study allows us to conclude that the disease was clustered in the Southwest side of the city in 2006, suggesting that this area may require special attention with regard to control and prevention measures.

Bayesian space-time data fusion for real-time forecasting and map uncertainty

Environmental computer models are deterministic models devoted to predict several environmental phenomena such as air pollution or meteorological events. Numerical model output is given in terms of averages over grid cells, usually at high spatial and temporal resolution. However, these outputs are often biased with unknown calibration and not equipped with any information about the associated uncertainty. Conversely, data collected at monitoring stations is more accurate since they essentially provide the true levels. Due the leading role played by numerical models, it now important to compare model output with observations. Statistical methods developed to combine numerical model output and station data are usually referred to as data fusion. In this work, we first combine ozone monitoring data with ozone predictions from the Eta-CMAQ air quality model in order to forecast real-time current 8-hour average ozone level defined as the average of the previous four hours, current hour, and predictions for the next three hours. We propose a Bayesian downscaler model based on first differences with a flexible coefficient structure and an efficient computational strategy to fit model parameters. Model validation for the eastern United States shows consequential improvement of our fully inferential approach compared with the current real-time forecasting system. Furthermore, we consider the introduction of temperature data from a weather forecast model into the downscaler, showing improved real-time ozone predictions. Finally, we introduce a hierarchical model to obtain spatially varying uncertainty associated with numerical model output. We show how we can learn about such uncertainty through suitable stochastic data fusion modeling using some external validation data. We illustrate our Bayesian model by providing the uncertainty map associated with a temperature output over the northeastern United States.

Four-window technique for measuring optical-phase-space-time-frequency tomography

A new approach, the four-window technique, was developed to measure optical phase-space-time-frequency tomography (OPSTFT). The four-window technique is based on balanced heterodyne detection with two local oscillator (LO) fields. This technique can provide independent control of position, momentum, time and frequency resolution. The OPSTFT is a Wigner distribution function of two independent Fourier transform pairs, phase-space and time-frequency. The OPSTFT can be applied for early disease detection.

A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations

In the context of expensive numerical experiments, a promising solution for alleviating the computational costs consists of using partially converged simulations instead of exact solutions. The gain in computational time is at the price of precision in the response. This work addresses the issue of fitting a Gaussian process model to partially converged simulation data for further use in prediction. The main challenge consists of the adequate approximation of the error due to partial convergence, which is correlated in both design variables and time directions. Here, we propose fitting a Gaussian process in the joint space of design parameters and computational time. The model is constructed by building a nonstationary covariance kernel that reflects accurately the actual structure of the error. Practical solutions are proposed for solving parameter estimation issues associated with the proposed model. The method is applied to a computational fluid dynamics test case and shows significant improvement in prediction compared to a classical kriging model.

Space-time clustering of childhood cancers in Switzerland: A nationwide study.

The aetiology of childhood cancers remains largely unknown. It has been hypothesized that infections may be involved and that mini-epidemics thereof could result in space-time clustering of incident cases. Most previous studies support spatio-temporal clustering for leukaemia, while results for other diagnostic groups remain mixed. Few studies have corrected for uneven regional population shifts which can lead to spurious detection of clustering. We examined whether there is space-time clustering of childhood cancers in Switzerland identifying cases diagnosed at age <16 years between 1985 and 2010 from the Swiss Childhood Cancer Registry. Knox tests were performed on geocoded residence at birth and diagnosis separately for leukaemia, acute lymphoid leukaemia (ALL), lymphomas, tumours of the central nervous system, neuroblastomas and soft tissue sarcomas. We used Baker's Max statistic to correct for multiple testing and randomly sampled time-, sex- and age-matched controls from the resident population to correct for uneven regional population shifts. We observed space-time clustering of childhood leukaemia at birth (Baker's Max p = 0.045) but not at diagnosis (p = 0.98). Clustering was strongest for a spatial lag of <1 km and a temporal lag of <2 years (Observed/expected close pairs: 124/98; p Knox test = 0.003). A similar clustering pattern was observed for ALL though overall evidence was weaker (Baker's Max p = 0.13). Little evidence of clustering was found for other diagnostic groups (p > 0.2). Our study suggests that childhood leukaemia tends to cluster in space-time due to an etiologic factor present in early life.

Wave Statistics and Space-time Extremes via Stereo Imaging

We present an analysis of the space-time dynamics of oceanic sea states exploiting stereo imaging techniques. In particular, a novel Wave Acquisition Stereo System (WASS) has been developed and deployed at the oceanographic tower Acqua Alta in the Northern Adriatic Sea, off the Venice coast in Italy. The analysis of WASS video measurements yields accurate estimates of the oceanic sea state dynamics, the associated directional spectra and wave surface statistics that agree well with theoretical models. Finally, we show that a space-time extreme, defined as the expected largest surface wave height over an area, is considerably larger than the maximum crest observed in time at a point, in agreement with theoretical predictions.

Space-time Reconstruction of Oceanic Sea States via Variational Stereo Methods

We present a remote sensing observational method for the measurement of the spatio-temporal dynamics of ocean waves. Variational techniques are used to recover a coherent space-time reconstruction of oceanic sea states given stereo video imagery. The stereoscopic reconstruction problem is expressed in a variational optimization framework. There, we design an energy functional whose minimizer is the desired temporal sequence of wave heights. The functional combines photometric observations as well as spatial and temporal regularizers. A nested iterative scheme is devised to numerically solve, via 3-D multigrid methods, the system of partial differential equations resulting from the optimality condition of the energy functional. The output of our method is the coherent, simultaneous estimation of the wave surface height and radiance at multiple snapshots. We demonstrate our algorithm on real data collected off-shore. Statistical and spectral analysis are performed. Comparison with respect to an existing sequential method is analyzed.

Space-time Measurements of Oceanic Sea States

Stereo video techniques are effective for estimating the space–time wave dynamics over an area of the ocean. Indeed, a stereo camera view allows retrieval of both spatial and temporal data whose statistical content is richer than that of time series data retrieved from point wave probes. We present an application of the Wave Acquisition Stereo System (WASS) for the analysis of offshore video measurements of gravity waves in the Northern Adriatic Sea and near the southern seashore of the Crimean peninsula, in the Black Sea. We use classical epipolar techniques to reconstruct the sea surface from the stereo pairs sequentially in time, viz. a sequence of spatial snapshots. We also present a variational approach that exploits the entire data image set providing a global space–time imaging of the sea surface, viz. simultaneous reconstruction of several spatial snapshots of the surface in order to guarantee continuity of the sea surface both in space and time. Analysis of the WASS measurements show that the sea surface can be accurately estimated in space and time together, yielding associated directional spectra and wave statistics at a point in time that agrees well with probabilistic models. In particular, WASS stereo imaging is able to capture typical features of the wave surface, especially the crest-to-trough asymmetry due to second order nonlinearities, and the observed shape of large waves are fairly described by theoretical models based on the theory of quasi-determinism (Boccotti, 2000). Further, we investigate space–time extremes of the observed stationary sea states, viz. the largest surface wave heights expected over a given area during the sea state duration. The WASS analysis provides the first experimental proof that a space–time extreme is generally larger than that observed in time via point measurements, in agreement with the predictions based on stochastic theories for global maxima of Gaussian fields.

Space-time analysis of the urban-rural grafient in the metropolitan area of Madrid and ist surroundings -Guadalajara and Toledo.

According to UN provisions in the period from 2007 to 2050 world population will grow up to 9200 million people. In fact, for the first time in history, in the year 2008 world urban population became higher than rural population. The increase of urban areas and their transport infrastructures has influenced agricultural land use due to their irreversible change, especially when they remain as periurban vacant land, losing their character and identity. In the Europe of the nineties, the traditional urban-rural gradient, characterized by a neat contact between both land types, has become so complex that it has change to a gradient in which it is difficult to separate urban and rural land uses. [Antrop 2004]. A literature review has been made on methodologies used for the urban-rural gradient analysis. One of these methodologies was selected that integrates ecological characterization based on the use of spatial metrics and geographical characterization based on spatial components. Cartographical sources used were Corine Land Cover at 1: 100000 scale and the Spanish Land Use Information System at 1:25000 scale. Urban-rural gradient paradigm is an analysis methodology, coming from landscape ecology, which enables to investigate how urbanization provokes changes in ecological patterns and processes into landscape. [Hahs and McDonnell 2006].The present research adapt this methodology to study the urban-rural gradient in the outskirts of Madrid, Toledo and Guadalajara. Both scales (1:25000 and 1:100000) were simultaneously used to reach the next objectives: 1) Analysis of landscape pattern dynamics in relation to distance to the town centre and major infrastructures. 2) Analysis of landscape pattern dynamics in the fringe of protected areas. The paper presents a new approach to the urban-rural relationship which allows better planning and management of urban áreas.

Space-time ocean wave measurement using variational stereo vision systems

Remote sensing imaging systems for the measurement of oceanic sea states have recently attracted renovated attention. Imaging technology is economical, non-invasive and enables a better understanding of the space-time dynamics of ocean waves over an area rather than at selected point locations of previous monitoring methods (buoys, wave gauges, etc.). We present recent progress in space-time measurement of ocean waves using stereo vision systems on offshore platforms. Both traditional disparity-based systems and modern elevation-based ones are presented in a variational optimization framework: the main idea is to pose the stereoscopic reconstruction problem of the surface of the ocean in a variational setting and design an energy functional whose minimizer is the desired temporal sequence of wave heights. The functional combines photometric observations as well as spatial and temporal smoothness priors. Disparity methods estimate the disparity between images as an intermediate step toward retrieving the depth of the waves with respect to the cameras, whereas elevation methods estimate the ocean surface displacements directly in 3-D space. Both techniques are used to measure ocean waves from real data collected at offshore platforms in the Black Sea (Crimean Peninsula, Ukraine) and the Northern Adriatic Sea (Venice coast, Italy). Then, the statistical and spectral properties of the resulting observed waves are analyzed. We show the advantages and disadvantages of the presented stereo vision systems and discuss the improvement of their performance in critical issues such as the robustness of the camera calibration in spite of undesired variations of the camera parameters.

Space, time, and deity, the Gifford lectures at Glasgow, 1916-1918,

Mode of access: Internet.

Space-Time Smoothing Models for Surveillance and Complex Survey Data

Thesis (Ph.D.)--University of Washington, 2016-06