Methods for geographic profiling of biological invasions with multiple origin sites

Geographic profiling is a method that proved to be useful also in order to investigate the point of origin of a biological invasion. K-means clustering and Voronoi diagrams can partition a data set of geographic positions of populations invading a defined area and are, therefore, useful in cases in which an invasion had more introduction events as points of origin. One critical point of the method is to identify the right number of clusters in which to divide the starting data set formed by groups of points on a map. The Silhouette method proved to be capable of identifying the best number of subsets (clusters) of the general set of observations by providing different values for different subdivisions of the set of observations in clusters. For each cluster, the corresponding Voronoi tessellation was built on the starting map. To test the method, we did a simulation of clusters of data (points) on a map and we verified whether the proposed methods worked efficiently with the simulated data set with hundred repeats and using a varying number of clusters on the same map. The used techniques revealed to be efficient in finding the highest probability area of the map that would include the starting points for each cluster. A case study consisted in a known data set, that is, the spreading pattern of Caulerpa racemosa var. cylindracea (sea grapes), that was compatible (highest probability) with an original point of introduction in southern Italy and long distance (thousands of kilometers) secondary spreads via anthropic dispersal. The proposed techniques may also be applied to other kinds of data sets of biological data distributed on a map or in general on a geometrical surface.     

空间数据智能统计方法研究与软件实现

在进行空间数据统计分析的过程中,为了提高统计工作的效率,笔者采用C#+Arc Engine和数据库融合的开发模式,利用循环与数据库方法计算空间图斑数量、位置和面积等信息实现单属性和多属性自动分类统计,图斑质心八方向方位统计法确定图斑的空间位置,chart和word API方法实现地图数据的图表化和语言化表达。研究结果表明:将属性数据和空间数据分离可以实现空间数据准确高效地统计,循环和数据库方法的应用实现了空间数据的单属性和多属性分类统计;图斑质心八方向方位统计法的应用提高了空间统计的效率; chart和word API方法实现了属性数据统计图的自动生成以及地图数据报表化的自动生成与发布。     

Genetic Modeling of GIS-Based Cell Clusters and Its Application in Mineral Resources Prediction

This paper presents a synthetic analysis method for multi-sourced geological data from geo-graphic information system (GIS). In the previous practices of mineral resources prediction, a usually adopted methodology has been statistical analysis of cells delimitated based on thoughts of random sam-pling. That might lead to insufficient utilization of local spatial information, for a cell is treated as a point without internal structure. We now take “cell dusters“, L e. , spatial associations of cells, as basic units of statistics, thus the spatial configuration information of geological variables is easier to be detected and utilized, and the accuracy and reliability of prediction are improved. We build a linear multi-discriminating model for the dusters via genetic algorithm. Both the right-judgment rates and the in-class vs. betweewclass distance ratios are considered to form the evolutional adaptive values of the population. An application of the method in gold mineral resoerces prediction in east Xinjiang, China is presented.     

Separation of a geochemical anomaly from background by fractal and U-statistic methods,a case study: Khooni district,Central Iran

Separation of geochemical anomalies from background are one of the important steps in mineral exploration. The Khooni mineral district (Central Iran) has complex geochemical surface expression due to a complex geological background. This region was chosen as a study area for recognition of the spatial distribution of geochemical elements and separating anomalies from background using stream sediment geochemical data. In the past decades, geochemical anomalies have been identified by means of various methods. Some of these separation methods include: statistical analysis methods, spatial statistical methods and fractal and multi-fractal methods. In this article, two efficient methods, i.e. U-statistics and the fractal concentration-area for separation and detection of anomalous areas of the background were used. The U spatial statistic method is a weighted mean, which considers sampling point positions and their spatial relation in the estimation of anomaly location. Also, fractal and multi-fractal models have also been applied to separate anomalies from background values. In this paper, the concentration–area model (C–A) was suggested to separate the anomaly of background. For this purpose, about 256 stream sediment samples were collected and analyzed. Then anomaly maps of elements were generated based on U spatial statistics and the C-A fractal methods for Au, As and Sb elements. According to obtained results, the U-statistics method performed better than C-A method. Because the comparisons of the known deposits and occurrences against the anomalous area created using thresholds from U-statistics and C-A method show that the spatial U-statistics method hits all of 3 known deposits and occurrences, the C-A fractal method hits 1 and fails 2. In addition, the results showed that these methods with regard to spatial distribution and variability within neighboring samples, in addition to concentration value frequency distributions and correlation coefficients, have more accurate results than the traditional approaches.     

Electoral geography and the social construction of space: The example of the Nazi party in Baden, 1924–1932

This paper makes two contributions towards making a theoretically driven electoral geography a vital component of geography. First, a theoretical framework for the empirical analysis of elections is proposed that views elections as one component of the social construction of space while simultaneously illustrating how space structures electoral behavior. Second, the concepts of a geographic theory of voter mobilization need to be operationalized in such a way that space is an integral component of statistical analysis. The statistical concepts of spatial dependence and spatial heterogeneity are used to include electoral locales and the regionalization of voter mobilization in the analysis. The theoretical framework and statistical operationalization proposed are illustrated by a statistical analysis of the growth of the Nazi party vote in Baden between 1924 and 1932.     

Research and application of cluster and association analysis in geochemical data processing

For applications of data mining techniques in geosciences, through mining spatial databases which are constructed with geophysical and geochemical data measured in fields, critical knowledge, such as the spatial distribution of geological targets, the geophysical and geochemical characteristics of geological targets, the differentiation among the geological targets, and the relationship among geophysical and geochemical data, can be discovered. Due to the complexity of geophysical and geochemical data, traditional mining methods of cluster analysis and association analysis have limitations in processing complex data. In this paper, a clustering algorithm based on density and adaptive density-reachable is presented which has the ability to handle clusters of arbitrary shapes, sizes, and densities. For association analysis, mining the continuous attributes may reveal useful and interesting insights about the data objects in geoscientific applications. An approach for distance-based quantitative association analysis is presented in this paper. Experiments and applications indicate that the algorithm and approach are effective in real-world applications.     

Assessment of physico-chemical and microbiological surface water quality using multivariate statistical techniques: a case study of the Wadi El-Bey River,Tunisia

Located in the northeastern part of Tunisia, Wadi El Bey drains the watershed through farmland, industrial, and urban areas of the region. It serves to discharge treated wastewater of different types. In this work, the variations of the water quality of Wadi El Bey were studied and evaluated, during 2 years (2012–2013), using multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA). In addition, the similarities or dissimilarities among the sampling points were as well analyzed to identify spatial and temporal variations. The results obtained based on the cluster analysis, led to identify three similar water quality zones: relatively polluted (LP), moderately polluted (MP), and highly polluted (HP). The inorganic and organic parameters, temperature, conductivity, dissolved oxygen, chemical oxygen demand, salmonella, and enterococcus, seemed to be the most significant parameters of water quality. Three factors were identified as responsible for the data structure, explaining 60.95% of the total variance. The first factor is the physical and non-organic chemical parameters explaining 23.48% of the total variance. The second and third factors are, respectively, the microbiological (21.26%) and organic-nutrient (16.2%).This study shows that multivariate statistical methods can help the water managers to understand the factors affecting the water quality.     

地下工程岩体结构面的统计与应用研究

以煤矿岩体为例, 分析了大小规模结构面的相关性, 提出了地下工程岩体结构面产状的统计方法, 阐述了多组结构面并存于-岩体时迹线间距的统计与计算方法。并用这些统计参数来确定巷道危石的最大可动区域, 选择最佳轴线方向, 为巷道的支护设计提供了科学依据。     

A spatial–temporal analysis of low birth weight prevalence in Georgia, USA

Low birth weight (LBW), defined as a live birth weighing <2,500 g, is a significant public health problem in the United States, especially a few states including Georgia. Although much work has been done to study the epidemiology of LBW in various regions, the spatial–temporal patterns of LBW prevalence in Georgia remain unclear to a large degree. This paper investigates the temporal trend of LBW rates over a time span of 11 years and the spatial clusters of LBW prevalence in the state of Georgia at the county level. Comparison is also made between race and gender groups, and between county groups of different socioeconomic statuses to uncover disparities. Results showed a steady and prevalent increase of LBW rate in the state over the study period. Three counties and two county clusters with significantly higher LBW rates than the state rate were detected for 1999–2001, while one more county and two more county clusters of high LBW rates were detected for 2007–2009. More urbanized counties were found to have a relatively lower LBW rate when compared with the less urbanized ones as groups. The findings from this paper are expected to provide valuable insights to better understanding the etiology of LBW and more effective allocating prenatal health care resources in the future.     

Parallel and scalable block system generation

Generating a realistic representation of a fractured rock mass is a first step in many different analyses. Field observations need to be translated into a 3-D model that will serve as the input for these analyses. The block systems can contain hundreds of thousands to millions of blocks of varying sizes and shapes; generating these large models is very computationally expensive and requires significant computing resources.By taking advantage of the advances made in big data analytics and Cloud Computing, we have a developed an open-source program—SparkRocks—that generates block systems in parallel. The application runs on Apache Spark which enables it to run locally, on a compute cluster or the Cloud. The block generation is based on a subdivision and linear programming optimization as introduced by Boon et al. (2015). SparkRocks automatically maintains load balance among parallel processes and can be scaled up on the Cloud without having to make any changes to the underlying implementation, enabling it to generate real-world scale block systems containing millions of blocks in minutes.     

Mapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of ‘big data’

Digital social data are now practically ubiquitous, with increasingly large and interconnected databases leading researchers, politicians, and the private sector to focus on how such ‘big data’ can allow potentially unprecedented insights into our world. This paper investigates Twitter activity in the wake of Hurricane Sandy in order to demonstrate the complex relationship between the material world and its digital representations. Through documenting the various spatial patterns of Sandy-related tweeting both within the New York metropolitan region and across the United States, we make a series of broader conceptual and methodological interventions into the nascent geographic literature on big data. Rather than focus on how these massive databases are causing necessary and irreversible shifts in the ways that knowledge is produced, we instead find it more productive to ask how small subsets of big data, especially georeferenced social media information scraped from the internet, can reveal the geographies of a range of social processes and practices. Utilizing both qualitative and quantitative methods, we can uncover broad spatial patterns within this data, as well as understand how this data reflects the lived experiences of the people creating it. We also seek to fill a conceptual lacuna in studies of user-generated geographic information, which have often avoided any explicit theorizing of sociospatial relations, by employing Jessop et al.’s TPSN framework. Through these interventions, we demonstrate that any analysis of user-generated geographic information must take into account the existence of more complex spatialities than the relatively simple spatial ontology implied by latitude and longitude coordinates.     

Modeling water,sediment and organic carbon discharges in the Hudson-Mohawk basin: Coupling to terrestrial sources

A previous modeling study (Howarth et al. 1991) showed the potential of a simple watershed hydrology model, the Generalized Watershed Loading Function model (GWLF, Haith and Shoemaker 1987) for estimating annual loads of sediment and organic carbon to the Hudson River from various land uses in the upper portion of the basin. We examined the quality and accuracy of model outputs after making two major modifications. First, we used a geographic information system (GIS) and several environmental databases to improve model estimates of several parameters. Second, we evaluated alternative methods for incorporating weather data.We examined seasonal variation of model output as well as annual averages. Annual fluxes increased over those of Howarth et al. (1991) by 10% for sediment and 20% for TOC for the Hudson-Mohawk basin, resulting in closer agreement with measurement-based estimates. The estimated fluxes exhibited a strong seasonal component, with greates sediment load occurring during a few sping events. Finally, model estimates of fluxes associated with forested and agricultural scenarios, characteristic of earlier periods of the region’s history, predicted a doubling of sediment and TOC loading in an agriculturally dominated Hudson basin but a reduction of sediment by 90% and of TOC by 60% in a completely forested basin.     

Comparison of the luminosity functions of open clusters based on USNO-A1 data

The luminosity and mass functions of a group of Galactic open clusters are constructed by applying a statistical method to photometric data from the USNO-A1 catalog. Despite some limitations, this catalog can be used for statistical analyses in Galactic astronomy. Pairwise comparisons of the derived cluster luminosity functions are performed for five age intervals. The differences between the luminosity functions of the open clusters are not statistically significant in most cases. It is concluded that the luminosity functions are approximately universal throughout a large volume in the solar neighborhood. Combined luminosity and mass functions are constructed for six age intervals. The slope of the mass spectrum may vary somewhat from cluster to cluster, and the mean slope may be somewhat higher than the Salpetervalue.     

Simpson’s Paradox in Natural Resource Evaluation

Reversals of statistical relationships, when two or more groups of data in a cross tabulation are aggregated, were first revealed more than a century ago. The reversal was later named Simpson’s paradox after his reversal examples in a seminal paper drew the attention of the statistical community. However, almost all the published cases have been in sociology and biomedical statistics. Does Simpson’s reversal occur in geosciences? Various examples from petroleum geology and reservoir modeling will be shown in this paper. Boundary conditions for such a reversal will be discussed under a broader framework of sampling analysis. Ecological inference bias, change of support problem, modifiable areal unit problem, and reference class problem will be discussed in relation to the Simpson’s paradox in the framework of spatial statistics. It will be demonstrated that the traditional interpretation of the paradox as a result of disproportional sampling based on a contingency table is not always true in the framework of spatial statistics, and the reversal while theoretically benign is inferentially treacherous. Therefore, emphasis will be on the discussion of combining statistical and scientific inferences in geologic modeling and hydrocarbon resource evaluation under various sampling schemes or support effect with or without a Simpson’s reversal.     

Landslide susceptibility zonation mapping using logistic regression and its validation in Hashtchin Region,northwest of Iran

Landslide susceptibility zonation mapping assists researchers greatly to understand the spatial distribution of slope failure probability in a region. Being extremely useful in reducing landslide hazards, such maps could simply be produced using both qualitative and quantitative methods. In the present study, a multivariate statistical method called ‘logistic regression’ was used to assess landslide susceptibility in Hashtchin region, situated in west of Alborz Mountainsnorthwest of Iran. In this study, two independent variables, categorical (predictor) and continuous, were drawn on together in the model. To identify the region’s landslides use was made of aerial photographs, field studies and topographic maps. To prepare the database of factors affecting the region’s landslides and to determine landslide zones, geographic information system (GIS) was used. Using such information, landslide susceptibility modeling was accomplished. The data related to factors causing landslides were extracted as independent variables in each cell (in 50 m×50 m cells). Then, the whole data were input into the SPSS, Version 18. The prepared database was later analyzed using logistic regression, the forward stepwise method and based on maximum likelihood estimation. Regression equation was determined using obtained constants and coefficients and the landslide susceptibility of the area in grid-cells (pixels) was computed between 0 and 0.9954. The Receiver Operating Characteristic (ROC) curve was used to assess the accuracy of the logistic regression model. The predicting ability of the model was 84.1% given the area under ROC curve. Finally, the degree of success of landslide susceptibility zonation mapping was estimated to be 79%.     

Origin of Sierra Nevadan granite: evidence from small scale composite dikes

Composite dikes at Hell Hole Meadow, in the central Sierra Nevada, contain hybrids created by small scale mixing of andesitic and rhyolitic magmas. Early rhyolitic injections had partially solidified when subsequent andesitic magmas arrived and mixed with small increments of remnant rhyolitic magma. In major element chemistry, the hybrid rocks define linear MgO-variation diagrams that closely resemble those for the Half Dome, Mt. Givens, and Eagle Peak granodiorites. The patterns suggest that mixing of mafic and felsic magmas has been important in the evolution of these plutons.Hornblendes in three Hell Hole Meadow hybrid rocks ranging from dacite to andesite display indistinguishable crystal-chemical variation patterns. The crystals apparently developed as phenocrysts of endmember andesitic magma prior to the mixing event and retained their compositional character in the hybridization event. Plutonic hornblendes (Dodge et al. 1968; Noyes et al. 1983) display crystal-chemical patterns nearly identical to those in the dike cores making it unlikely that the plutonic hornblendes represent restite of a complex lower crust.Hornblende chemical data from a suite of rocks collected across the width of the compositionally zoned Half Dome granodiorite (Yosemite National Park) define clusters, like their Hell Hole Meadow counterparts, that are very similar to one another despite the large range in host rock bulk composition. Sr isotopic data (Kistler et al. 1986) and linear MgO-variation patterns for the major elements (Reid et al. 1983) suggest that the Half Dome is a mixture of high-alumina basalt and rhyolitic magmas. We propose that while the bulk chemistry of hybrid granodiorites is determined by the proportions of the constituent mafic and felsic magmas, the hornblendes in the mixed rocks largely retain compositions created in the mafic component prior to mixing. Mixing may occur either by incremental addition of felsic magma into a relatively large volume of mafic magma (as at Hell Hole Meadow), or by the chilling and subsequent disaggregation of mafic pillows in a largely felsic host.     

水库控制流域汛期分期的有效聚类分析

受诸多因素影响的水库汛期分期其实质是一个聚类数目未确知的高维时间序列聚类问题,它要求聚类方法具有能处理高维和时序性数据,且能同时回答聚为几类最为合理的能力。鉴于目前常规聚类方法不同时具备这些能力,在模糊C-均值聚类和紧密与分离聚类有效函数的基础上,提出了能够处理高维时序聚类问题的动态模糊C-均值聚类分析方法和相应的时序聚类有效性函数,耦合二者建立了适用于汛期分期的有效模糊聚类分析方法,提出采用实码加速遗传算法优化求解,克服了模糊C-均值聚类方法常规迭代优化求解对初值敏感的困难,并给出了完备的建模步骤和模型的合理性检验。最后,将模型应用于滦河流域潘家口水库汛期分期中,得出了合理的结论。     

The Orosirian-Statherian banded iron formation-bearing sequences of the southern border of the Espinhaço Range,Southeast Brazil

The Serra da Serpentina and the Serra de São José groups are two distinct banded iron formation-bearing metasedimentary sequences along the eastern border of the southern Espinhaço Range that were deposited on the boundary between the Orosirian and Statherian periods.The Serra da Serpentina Group (SSG) has an Orosirian maximum depositional age (youngest detrital zircon grain age = 1990 ± 16 Ma) and consists of fine clastic metasediments at the base and chemical sediments, including banded iron formations (BIFs), on the top, corresponding to the Meloso and Serra do Sapo formations, respectively, and correlating with the pre-Espinhaço Costa Sena Group. The SSG represents sedimentary deposition on an epicontinental-epeiric, slow downwarping sag basin with little tectonic activity.The younger Serra de São José Group (SJG) is separated from the older SSG by an erosional unconformity and was deposited in a tectonically active continental rift-basin in the early stages of the opening of the Espinhaço Trough. The Serra do São José sediments stretch along the north-south axis of the rift and comprise a complete cycle of transgressive sedimentary deposits, which were subdivided, from base to top, into the Lapão, Itapanhoacanga, Jacém and Canjica formations. The Itapanhoacanga Formation has a maximum depositional age of 1666 ± 32 Ma (Statherian), which coincides with the maximum depositional age (i.e., 1683 ± 11 Ma) of the São João da Chapada Formation, one of the Espinhaço Supergroup's basal units. The Serra de São José Rift and the Espinhaço Rift likely represent the same system, with basal units that are facies variations of the same sequence.The supracrustal rocks have undergone two stages of deformation during the west-verging Brasiliano orogeny that affected the eastern margin of the São Francisco Craton and generated a regional-scale, foreland N–S trending fold-thrust belt, which partially involved the crystalline basement. Thrust faults have segmented the terrain into a large number of tectonic blocks, where the stratigraphic sequence was nevertheless well preserved.     

空间数据挖掘技术

近年来，伴随着空间信息获取技术的快速发展，不同类型的空间数据库以及数据库中的数据量正在不断增长，如何从这些海量数据中提取出隐含的信息和知识已成为人们研究的热点。空间数据挖掘就是为了解决这一科学问题而提出的。该文在回顾已有空间数据挖掘规则的基础上。指出了空间数据挖掘过程中存在的主要问题，进而探讨了未来空间数据挖掘的主要研究方向。     

Stratigraphical correlation of the Late Jurassic Lourinhã Formation in the Consolação Sub‐basin (Lusitanian Basin), Portugal

The c. 700 m thick succession of continental–brackish‐marine deposits forming the Lourinhã Formation, cropping out along the coast of western Portugal between Baleal and Santa Cruz, has been correlated using laterally persistent shelly marker beds. Three shelly units record the episodic establishment of relatively short‐lived, brackish‐marine embayments, transgressing from the southwest, onto a low‐lying coastal plain. The succession displays systematic changes in facies types and stacking patterns reflecting differences in fluvial style, bedload character and palaeontological content. Based on these observations, four new members for the Lourinhã Formation are proposed: the Sáo Bernardino, Porto de Barças, Areia Branca and Ferrel members. New biostratigraphical data indicate that the Lourinhã Formation is Late Kimmeridgian to earliest Early Tithonian in age. This age has also been obtained from the underlying mixed carbonate and clastic deposits of the Abadia Formation at Consolação. As a result, these latter sediments are now re‐assigned to the Alcobaça Formation, a lithostratigraphical term currently in use in other areas of the Lusitanian Basin. Improved regional mapping of the Lourinhã Formation has established a new sub‐basin within the western parts of the Lusitanian Basin. This sub‐basin, now named the Consolação Sub‐basin, is bounded to the east by the Lourinhã–Caldas de Rainha (L–C) fault zone and to the west by the Berlengas Horst. Copyright © 2013 John Wiley & Sons, Ltd.