Presence Of Spatial Patterns Research Articles

EFFECTS OF THE CLIMATE GRADIENT ONF THE WOOD DENSITY OF Cedrela odorata L. IN THE NEOTROPICAL REGION

Understanding wood properties is crucial for optimizing its technological potential, enhancing both the effectiveness of use and product quality. Among these properties, density is regarded as one of the most significant. Numerous studies have documented the relationship between wood density and climate. However, the influence of climatic factors on the density variation of native species remains largely unexplored. Cedrela odorata L., a species traditionally logged in Brazil and widely distributed across the Neotropical region, requires further analysis to understand its potential response to varying climate patterns, thereby informing its management. This study aimed to determine the relationship between the basic density of Cedrela odorata L. and the climatic conditions in the Neotropical region. A comprehensive database was compiled from published scientific literature. Statistical analyses were conducted using Generalized Linear Models (GLM), with the best models selected based on the corrected Akaike Information Criterion (AICc). The presence of spatial patterns was assessed using Moran’s Index to verify spatial autocorrelation. The study identified significant relationships between the wood density of C. odorata and variables such as mean annual temperature and annual precipitation. The findings indicate a strong interaction between the climatic variables analyzed and the basic density of Cedrela odorata L. wood, with density being more sensitive to mean annual temperature.

Purifying selection shaping the evolution of the Toll-like receptor 2 TIR domain in brown hares (Lepus europaeus) from Europe and the Middle East

Toll-like receptors (TLRs) are transmembrane proteins of the innate immune system, composed of the ectodomain involved in pathogen recognition and the intracellular Toll/interleukin-1 receptor (TIR) domain important for downstream signal transduction. Here, we analyze the genetic variability of TIR nucleotide and amino-acid sequences of the TLR2 gene in 243 brown hares from Europe and the Middle East and tested for the presence of selection signals and spatial structuring. TLR2 TIR domain sequences were PCR amplified and sequenced, while genotyping was performed by phasing. Genetic diversity indices were calculated in DnaSP and Arlequin, while presence of selection signals was tested using MEGA and the Datamonkey web server. The presence of spatial patterns in TIR sequence distribution was tested by spatial Principal Component Analysis (sPCA) in adegenet. A total of 13 haplotypes were revealed with haplotype diversity of 0.424, and nucleotide diversity (π) of 0.00138. Two spatial clusters were revealed: "Anatolia/Middle East" and "Europe". In Anatolia the two most prevalent amino-acid variants, A and B (the latter being the most ancestral) were maintained at similar frequencies; but in Europe a shift in genotype frequencies was observed as well as a higher number of nonsynonymous substitutions giving rise to novel amino-acid protein variants originating from the evolutionarily younger protein variant. Molecular diversity (haplotype and nucleotide diversity) indices were significantly higher in the "Anatolia/Middle East" cluster. A signal of purifying selection was detected acting on the TIR sequences.

SARS-coV-2 spatio - temporal analysis in Cali, Colombia

To describe the spatio-temporal distribution of the COVID-19 in the city of Cali during the first month of the epidemic. An exploratory analysis of spatial data was carried out, consisting of a kernel density analysis and the presence of spatial patterns was verified by the K-Ripley function. The spatial distribution of the cases tends to initially concentrate in the north and south of the city, with a changing dynamic towards the east and west. The identified spatial pattern may be influenced by the isolation measures taken at the local and national level, but the effect of the low access of the general population to diagnostic tests, delays and restraints to know the results cannot be ruled out and even possible biases due to difficulties in the technique of taking the sample or its conservation.

Análisis espacio-temporal del SARS-coV-2 en Cali, Colombia

Objetivo Describir la distribución espacio-temporal del COVID-19 en la ciudad de Cali durante el primer mes de epidemia.Métodos Se realizó un análisis exploratorio de datos espaciales, compuesto por un análisis de densidad de Kernel y se verificó la presencia de patrones espaciales por medio de la función K de Ripley.Resultados La distribución espacial de los casos tiende a concentrarse inicialmente en el norte y sur de la ciudad, con una dinámica cambiante hacia el oriente y occidente.Conclusiones El patrón espacial identificado puede estar influenciado por las medidas de aislamiento tomadas a nivel local y nivel nacional, además no se puede descartar el efecto del poco acceso que tiene la población general a las pruebas diagnósticas,los retrasos y represamientos para conocer los resultados de las mismas y aun los posibles sesgos por dificultades en la técnica de toma de la muestra o su conservación.

Análise espacial da mortalidade por acidentes de motocicleta nos municípios do Brasil

Analysis of the distribution of motorcycle-related mortality rates in Brazilian municipalities is fundamental to understand and seek to minimize the occurrence of this growing phenomenon. The main objective of this work is to analyze the spatial distribution of motorcycle rider mortality rates in Brazil, based on more robust and reliable estimates. An attempt was also made to identify the presence of spatial clusters in the distribution of such mortality rates in given municipalities. The rates were estimated based on the average number of motorcyclist deaths recorded in the years 2014, 2015 and 2016. These rates were then directly standardized and graduated based on the local empirical Bayesian estimator. A Local Indicator of Spatial Autocorrelation (LISA) indicated the presence of spatial patterns. The Northeast and Mid-West regions concentrated most of the municipalities with high mortality rates as well and most of the clusters of municipalities with a high-high distribution pattern. Graduated Bayesian estimation was effective to deal with the occurrence of extreme values, thereby improving the reliability of the estimates and enhancing the visualization of the rates on the map.

On the distributions of annual and seasonal daily rainfall extremes in central Arizona and their spatial variability

This study uses daily rainfall records of a dense network of 240 gauges in central Arizona to gain insights on (i) the variability of the seasonal distributions of rainfall extremes; (ii) how the seasonal distributions affect the shape of the annual distribution; and (iii) the presence of spatial patterns and orographic control for these distributions. For this aim, recent methodological advancements in peak-over-threshold analysis and application of the Generalized Pareto Distribution (GPD) were used to assess the suitability of the GPD hypothesis and improve the estimation of its parameters, while limiting the effect of short sample sizes. The distribution of daily rainfall extremes was found to be heavy-tailed (i.e., GPD shape parameter ξ > 0) during the summer season, dominated by convective monsoonal thunderstorms. The exponential distribution (a special case of GPD with ξ = 0) was instead showed to be appropriate for modeling wintertime daily rainfall extremes, mainly caused by cold fronts transported by westerly flow. The annual distribution exhibited a mixed behavior, with lighter upper tails than those found in summer. A hybrid model mixing the two seasonal distributions was demonstrated capable of reproducing the annual distribution. Organized spatial patterns, mainly controlled by elevation, were observed for the GPD scale parameter, while ξ did not show any clear control of location or orography. The quantiles returned by the GPD were found to be very similar to those provided by the National Oceanic and Atmospheric Administration (NOAA) Atlas 14, which used the Generalized Extreme Value (GEV) distribution. Results of this work are useful to improve statistical modeling of daily rainfall extremes at high spatial resolution and provide diagnostic tools for assessing the ability of climate models to simulate extreme events.

Geographic variations in cervical cancer risk in San Luis Potosí state, Mexico: A spatial statistical approach.

BackgroundWorldwide, Cervical Cancer (CC) is the fourth most common type of cancer and cause of death in women. It is a significant public health problem, especially in low and middle-income/Gross Domestic Product (GDP) countries. In the past decade, several studies of CC have been published, that identify the main modifiable and non-modifiable CC risk factors for Mexican women. However, there are no studies that attempt to explain the residual spatial variation in CC incidence In Mexico, i.e. spatial variation that cannot be ascribed to known, spatially varying risk factors.MethodsThis paper uses a spatial statistical methodology that takes into account spatial variation in socio-economic factors and accessibility to health services, whilst allowing for residual, unexplained spatial variation in risk. To describe residual spatial variations in CC risk, we used generalised linear mixed models (GLMM) with both spatially structured and unstructured random effects, using a Bayesian approach to inference.ResultsThe highest risk is concentrated in the southeast, where the Matlapa and Aquismón municipalities register excessive risk, with posterior probabilities greater than 0.8. The lack of coverage of Cervical Cancer-Screening Programme (CCSP) (RR 1.17, 95 % CI 1.12–1.22), Marginalisation Index (RR 1.05, 95 % CI 1.03–1.08), and lack of accessibility to health services (RR 1.01, 95 % CI 1.00–1.03) were significant covariates.ConclusionsThere are substantial differences between municipalities, with high-risk areas mainly in low-resource areas lacking accessibility to health services for CC. Our results clearly indicate the presence of spatial patterns, and the relevance of the spatial analysis for public health intervention. Ignoring the spatial variability means to continue a public policy that does not tackle deficiencies in its national CCSP and to keep disadvantaging and disempowering Mexican women in regard to their health care.

Investigating the Existence of Second Order Spatial Autocorrelation in Crash Frequency across Adjacent Freeway Segments

This study developed spatial Poisson model to incorporate spatial autocorrelation in crash frequency across contagious freeway segments. Spatial autocorrelation is the presence of spatial pattern in crash frequency over space due to geographic proximity. Usually crash caused congestion on a freeway segment propagates upstream and creates chance of occurring secondary crashes. This phenomenon makes the crash frequency on the contiguous freeway segments correlated. This correlation makes the distributional assumption of independence of crash frequency invalid. The existence of spatial autocorrelation is investigated by using Conditional autoregressive models (CAR models). The models are set up in a Bayesian modeling framework, to include terms which help to identify and quantify residual spatial autocorrelation for neighboring observation units. Models which recognize the presence of spatial dependence help to obtain unbiased estimates of parameters quantifying safety levels since the effects of spatial autocorrelation are accounted for in the modeling process. Based on CAR models, approximately 51% of crash frequencies across contiguous freeway segments are spatially auto-correlated. The incident rate ratios revealed that wider shoulder and weaving segments decreased crash frequency by factors of 0.84 and 0.75 respectively. The marginal impacts graphs showed that an increase in longitudinal space for segments with two lanes decreased crash frequency. However, an increase of facility width above three lanes results in more crashes, which indicates an increase in traffic flows and driving behavior leading to crashes. These results call an important step of analyzing contagious freeway segments simultaneously to account for the existence of spatial autocorrelation.

Effectiveness of the BFAST algorithm for detecting vegetation response patterns in a semi-arid region

The effective use of satellite image time series for examining vegetation response patterns across regional extents requires a method which accounts for variation at the seasonal scale while simultaneously detecting abrupt changes in any long term trends. The Breaks for Additive Seasonal and Trend (BFAST) algorithm has been developed to do this. However, its effectiveness in semi-arid regions, where vegetation response is typically aseasonal, has yet to be assessed.In this research the BFAST algorithm was assessed for a semi-arid study area in the Paroo catchment of far north-western New South Wales, Australia. Moderate Resolution Imaging Spectroradiometer (MODIS) EVI time series were decomposed using BFAST for 270 sample pixels to assess the algorithm's ability to detect abrupt changes in vegetation response caused by known fires and floods. The algorithm was also applied across the study area to examine regional patterns in the timing and magnitude of abrupt changes and the direction and magnitude of the long term trends.The timing of breaks detected by BFAST corresponded with the timing of known floods in the study region for between 68% and 79% of breaks detected across the sample pixels, depending on the parameters used in the decomposition. BFAST was not, however, able to accurately detect fires in the Paroo region, with agreement between the timing of breaks and fires occurring in only 3% of breaks detected. This most likely reflects the low EVI values present before a fire event, which would be typical of semi-arid zones. Spatial patterns in the timing of abrupt changes and greening and browning trends across the study area were a function of land cover and vegetation type. These results indicate that BFAST is able to detect abrupt changes in vegetation greening caused by known floods in semi-arid regions. The presence of spatial patterns in the results also indicates that the algorithm is sensitive to vegetation cover type. BFAST is therefore able to detect abrupt trend changes in regions where vegetation response is not expected to show strong seasonal patterns and could be used in further applications such as classification or regional vegetation modelling in semi-arid environments.

Patterns of chronic conditions in older adults: exploratory spatial findings from the ElderSmile program.

The increasing prevalence of primary care-sensitive conditions, notably diabetes and hypertension, among older adults presents a challenge to the public health community. Systems science conceptualizations of health, along with considerations of the social and environmental context in which older adults live, are needed before effective interventions can be designed and implemented. To examine whether spatial patterns exist in hemoglobin A1c and blood pressure measurements among participants in ElderSmile, a community-based oral health and primary care screening program. Two spatial statistical methods, global Moran's I and Cuzick-Edwards tests, were used to determine if there were significant spatial patterns among ElderSmile participants residing in northern Manhattan during 2010-2012. The analyses were conducted in 2013. Significant spatial patterns of hemoglobin A1c values and potential diabetes cases, and possibly blood pressure measurements, were found among ElderSmile participants residing in northern Manhattan. The presence of spatial patterns allows for the identification of subpopulations in need of additional resources, and can assist in informing advanced spatial and statistical analyses. Screening data collected from an ongoing community-based program can be used to understand broader patterns of urban health.

Determinants of land use changes: A spatial multinomial probit approach

AbstractWe propose a spatial multinomial probit model to examine the determinants of land use change, at the parcel level, in the French Département du Rhones from 1992 to 2003. It is based on an economic model that assumes that landowners have a choice between four land use categories for a given parcel at a given date: (1) agricultural, (2) forest, (3) urban, and (4) no use. We estimate a model that allows for both covariates and spatial dependence, and we use these features to explore the relative importance of factors that drive landowners to choose a specific land use category.ResumenProponemos un modelo probit multinomial espacial para examinar los determinantes del cambio en el uso del suelo, a escala de parcela, en el Département du Rhones francés desde 1992 hasta 2003. Esta basado en un modelo económico que asume que los propietarios de la tierra pueden elegir entre cuatro categorías de uso del suelo para una parcela en una fecha dada: (1) agrícola, (2) forestal, (3) urbano, y (4) sin uso. Estimamos un modelo que permite tanto covariables como dependencia espacial, y lo utilizamos para explorar la importancia relativa de los factores que impulsan a los propietarios del suelo a elegir una categoría de uso del suelo en particular.

Modelo autologístico espaço-temporal com aplicação à análise de padrões espaciais da leprose-dos-citros

O objetivo deste trabalho foi propor estratégias de modelagem aplicadas aos dados de incidência de leprose-dos-citros, por meio do uso de um modelo autologístico espaço-temporal. A adequação do modelo autologístico foi avaliada quanto à: análise de dados provenientes de avaliações feitas em diferentes momentos; detecção de padrões espaciais da doença, pela avaliação de diferentes estruturas de vizinhança; consideração do efeito defasado no tempo de covariáveis de vizinhança; e ao efeito do ácaro transmissor na probabilidade de nova infecção. O modelo autologístico espaço-temporal adotado estendeu o modelo logístico usual, em que a estrutura de vizinhança é descrita por meio da construção de covariáveis, a partir da resposta observada em plantas vizinhas à planta avaliada, na mesma avaliação, ou em avaliações anteriores. Os dados de incidência de leprose nas plantas de citros foram coletados em pontos referenciados no espaço, durante aproximadamente dois anos. Os modelos detectam o efeito da presença do vetor e os padrões espaciais na ocorrência de novas infecções, tanto para covariáveis de vizinhança da mesma avaliação, quanto para covariáveis de vizinhança da avaliação anterior. Além disso, os modelos considerados permitem quantificar as variações na probabilidade de ocorrência da doença de acordo com o estado da doença e com a incidência do ácaro transmissor.

Role of biomass spread in vegetation pattern formation within arid ecosystems

The spatial organization of biomass resulting from plant‐water feedbacks in arid ecosystems, “patterned vegetation,” provides a macroscopic signal of nonlinear plant‐water interactions and ecosystem health. Current models that reproduce such patterning assume diffusive biomass movement but do not account for realistic transport through seed dispersal. An adaptation of an existing three‐equation model that accounts for the interactions between overland flow, subsurface flow, and biomass dynamics is used to investigate the impact of representing biomass spread with realistic dispersal kernels (a “kernel‐based method”). Model results indicate that dispersion behavior changes the spatial organization of vegetation, destabilizing the regular patterns predicted by diffusion‐based models. The kernel‐based approach provides a closer match to power spectra derived from a remotely sensed image of patterned vegetation when compared to their diffusion‐based counterpart. Potential feedbacks between the presence of spatial patterns and selection of optimal seed dispersal length scales are also investigated.

Patterns of organic carbon and nitrogen isotopic compositions of latest Pliocene sapropels from six locations across the Mediterranean Sea

Inter-layered in the organic-poor sediments that comprise most of the sedimentary record of the Mediterranean Sea are organic-rich sapropels that represent periods of wetter climate, increased primary productivity, and suboxic to anoxic conditions at depth. In order to explore processes important in the spatial and temporal variability of sapropel-forming conditions, we selected sedimentary sequences containing two latest Pliocene sapropels at ODP Sites 974 and 975 in the western basin, the Vrica Plio-Pleistocene type-section, and ODP Sites 964, 967, and 969 in the eastern basin. Our main tools are nitrogen and organic carbon isotopes, which are diagnostic of changes in nutrient origins and availability and in primary productivity. Extremely low δ 15N values (to − 5.1‰) indicate high rates of nitrogen fixation during sapropel formation stimulated by nearly complete consumption of nitrate in the water column due to exceptionally high rates of denitrification. Interruptions in sapropels represent brief intervals of low productivity and diminished nitrogen fixation. The geographical distribution of the interruptions and the presence of spatial patterns in TOC contents, C org/N tot ratios, and δ 15N values suggest a central role of the eastern basin in initiating sapropel formation. The eastern Mediterranean evidently experienced higher rates of primary productivity during sapropel deposition and probably more intense dysoxic conditions in the water column. Organic carbon isotopes present an intriguing geographic trend across the eastern Mediterranean that is probably related to non-biological processes including variations in water temperature, salinity, and amounts of local fluvial runoff.

Spatial distribution of bacterial communities and their relationships with the micro-architecture of soil

Biological soil thin-sections and a combination of image analysis and geostatistical tools were used to conduct a detailed investigation into the distribution of bacteria in soil and their relationship with pores. The presence of spatial patterns in the distribution of bacteria was demonstrated at the microscale, with ranges of spatial autocorrelation of 1 mm and below. Bacterial density gradients were found within bacterial patches in topsoil samples and also in one subsoil sample. Bacterial density patches displayed a mosaic of high and low values in the remaining subsoil samples. Anisotropy was detected in the spatial structure of pores, but was not detected in relation to the distribution of bacteria. No marked trend as a function of distance to the nearest pore was observed in bacterial density values in the topsoil, but in the subsoil bacterial density was greatest close to pores and decreased thereafter. Bacterial aggregation was greatest in the cropped topsoil, though no consistent trends were found in the degree of bacterial aggregation as a function of distance to the nearest pore. The implications of the results presented for modelling and predicting bacterial activity in soil are discussed.

Does your brain use the images in it, and if so, how?

The presence of spatial patterns of activity in the brain is suggestive of image-exploiting processes in vision and mental imagery, but not conclusive. Only behavioral evidence can confirm or disconfirm hypotheses about whether, and how, the brain uses images in its information-processing, and the arguments based on such evidence are still inconclusive.