Gold prospectivity maps of the Red Lake greenstone belt: application of GIS technology

2006 ◽  
Vol 43 (7) ◽  
pp. 865-893 ◽  
Author(s):  
J R Harris ◽  
M Sanborn-Barrie ◽  
D A Panagapko ◽  
T Skulski ◽  
J R Parker
Keyword(s):  
Logistic Regression ◽  
Gold Mineralization ◽  
Regional Scale ◽  
Geochemical Data ◽  
Data Sets ◽  
Gis Technology ◽  
Red Lake ◽  
Mineral Prospectivity Maps ◽  
Lode Gold Deposit ◽  
Gis Software

Recent advances in the use of Geographic Information Systems (GIS) software and analysis can be used in conjunction with traditional geoscience data sets to determine effective predictors for gold mineralization, from which mineral prospectivity maps can be generated that highlight potential exploration targets on a regional scale. In this paper, key components of the Archean lode gold deposit model for the Red Lake belt are selected and modeled using weights of evidence (WofE) analysis and logistic regression, leading to the creation of gold prospectivity maps. The best predictors for past and present gold producers in the Red Lake camp, according to WofE analysis include (1) elevated trace elements, Au, As, and Sb; (2) a number of alteration indices calculated from oxide geochemical data; (3) alteration characterized by pervasive and vein-style ferroan carbonate and elevated Au, As, Sb, and S anomalies; (4) proximity to the Mackenzie Island stock and diorite phases of the Dome stock; and, (5) tholeiitic basaltic flows and associated gabbroic rocks of the Balmer assemblage. Gold prospectivity maps produced by logistic regression using binary evidence maps highlight anomalous localities within known and highly prospective areas in the district (Madsen – Red Lake corridor, Balmertown – Cochenour – East Bay). In addition, a number of localities not known to contain significant deposits were also identified as prospective.

GENE SELECTION USING LOGISTIC REGRESSIONS BASED ON AIC, BIC AND MDL CRITERIA

2005 ◽  
Vol 01 (01) ◽  
pp. 129-145 ◽  
Author(s):  
XIAOBO ZHOU ◽  
XIAODONG WANG ◽  
EDWARD R. DOUGHERTY
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Gene Selection ◽  
Information Criterion ◽  
Cancer Classification ◽  
Data Sets ◽  
Classification Methods ◽  
Gene Expressions ◽  
Experimental Conditions

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables (gene expressions) and the small number of experimental conditions. Many gene-selection and classification methods have been proposed; however most of these treat gene selection and classification separately, and not under the same model. We propose a Bayesian approach to gene selection using the logistic regression model. The Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the minimum description length (MDL) principle are used in constructing the posterior distribution of the chosen genes. The same logistic regression model is then used for cancer classification. Fast implementation issues for these methods are discussed. The proposed methods are tested on several data sets including those arising from hereditary breast cancer, small round blue-cell tumors, lymphoma, and acute leukemia. The experimental results indicate that the proposed methods show high classification accuracies on these data sets. Some robustness and sensitivity properties of the proposed methods are also discussed. Finally, mixing logistic-regression based gene selection with other classification methods and mixing logistic-regression-based classification with other gene-selection methods are considered.

Black Market Business

2020 ◽  
Author(s):  
Christina Elizabeth Firpo
Keyword(s):  
Social History ◽  
Sex Workers ◽  
Black Market ◽  
Regulation System ◽  
Data Sets ◽  
Sex Industry ◽  
Gis Technology ◽  
Cultural Changes ◽  
Dance Clubs ◽  
Demographic Shifts

This book is a grassroots social history of the clandestine market for sex in colonial Tonkin. It explores the ways in which sex workers, managers, and clients evaded the colonial regulation system in the turbulent economy of the interwar years. The book argues that the confluence of economic, demographic, and cultural changes sweeping late colonial Tonkin created spaces of tension in which the interwar black-market sex industry thrived. The clandestine sex industry flourished in sites of legal inconsistency, cultural changes, economic disparity, rural–urban division, and demographic shifts. As a nexus of the many tensions besetting late colonial Tonkin, the black-market sex industry serves as a useful lens through which to examine these tensions and the ways they affected marginalized populations. More specifically, an investigation of this black market shows how a particular population of impoverished women — a group regrettably understudied by historians — experienced the tensions. Drawing on an astonishingly diverse and multilingual source base, the book includes detailed cases of juvenile prostitution, human trafficking, and debt-bondage arrangements in sex work, as well as cases in Tonkin's bars, hotels, singing houses, and dance clubs. Using GIS technology and big data sets to track individual actors in history, it serves as a model for teaching new methodological approaches to conducting social histories of women and marginalized people.

scholarly journals The Swiss joint information platform for natural hazards

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Philipp Angehrn ◽  
Sabina Steiner ◽  
Christophe Lienert
Keyword(s):  
Natural Hazards ◽  
Spatial Data ◽  
Natural Hazard ◽  
Regional Scale ◽  
Measurement Data ◽  
User Feedback ◽  
Acceptance Testing ◽  
Spatial Density ◽  
Data Sets ◽  
Information Platform

<p><strong>Abstract.</strong> The Swiss Joint Information Platform for Natural Hazards (GIN) has been realized from 2008 to 2010 as part of the Swiss federal government’s OWARNA project, which aimed at optimizing warning and alerting procedures against natural hazard. The first online-version of the platform went productive in 2011 with the primary goal of providing measured and forecast natural hazard data in form of processed cartographic, graphic and other multimedia products to professional users &amp;ndash; before, during and after natural hazard events. In Switzerland water-, weather-, snow- and earthquake-related hazards are the most relevant ones.</p><p>In 2013, an online survey showed that the platform does not fully meet user expectations, particularly as to user experience and usability of its cartographic, web-based user interface. Revaluation and redesign of the overall platform were necessary in order to improve map legibility, caused by the complexity of data, large data amounts, and high spatial density of online, real-time measurement data locations. A new web design and user interaction concept have been developed in 2014 and eventually put online in June 2017. User acceptance testing by means of surveys and direct user feedback sessions were key factors in this perennial redesign process. The GIN platform now features important novel technical and graphical elements: The starting page is based on a dashboard containing virtual dossiers (Fig. 1), with which users configure their desired information, data, and map bundles individually, or use predefined adaptable views on various existing data sets. In addition, there is a new overall spatial search function to query data parameters. A responsive approach further improves the usability of the platform. The focus of these new features is on multi-views involving maps, diagrams, tables, text products, as well as selected geographical areas on maps, and fast data queries (Fig. 2). Current user feedback suggests that the new GIN platform design is well received, and that it is moving closer to its very goal: online monitoring and management of natural hazard events by enhanced usability, more targeted and higher personalization.</p><p>Several Swiss Cantons (i.e., the political entities in Switzerland below the federation) actively participated, and still participate, in the conceptual GIN platform development process through advisory board meetings and consultations. On the operational level, Cantons actively provide and contribute further natural hazard information and measurement data from their own natural hazard monitoring networks. These additional Cantonal regional-scale data sets help to fill spatial data gaps, where no Federal data is available. GIN thusly integrates natural hazard data from Federal and Cantonal levels (and partly even private level), which adds value to all stakeholders on various political levels involved in natural hazard management (Federal, Cantonal, Regional, Communal crisis committees). Stakeholders not only use GIN’s ample database and cartographic product portfolio to accomplish their early warning and crisis management tasks, but also benefit from seamless, secure and reliable IT-services, provided by the Swiss Federal Government. With the new GIN platform, Switzerland has a powerful, integrative, and comprehensive tool for monitoring and responding to natural hazard events.</p>

scholarly journals Day-time identification of summer hailstorm cells from MSG data

2013 ◽  
Vol 1 (5) ◽  
pp. 5453-5498 ◽  
Author(s):  
A. Merino ◽  
L. López ◽  
J. L. Sánchez ◽  
E. García-Ortega ◽  
E. Cattani ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Deep Convection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Phase Method ◽  
Data Sets ◽  
Liquid Water Path ◽  
Two Phase ◽  
Logistic Regression Models ◽  
Ebro Valley

Abstract. Identifying deep convection is of paramount importance, as it may be associated with extreme weather that has significant impact on the environment, property and the population. A new method, the Hail Detection Tool (HDT), is described for identifying hail-bearing storms using multi-spectral Meteosat Second Generation (MSG) data. HDT was conceived as a two-phase method, in which the first step is the Convective Mask (CM) algorithm devised for detection of deep convection, and the second a Hail Detection algorithm (HD) for the identification of hail-bearing clouds among cumulonimbus systems detected by CM. Both CM and HD are based on logistic regression models trained with multi-spectral MSG data-sets comprised of summer convective events in the middle Ebro Valley between 2006–2010, and detected by the RGB visualization technique (CM) or C-band weather radar system of the University of León. By means of the logistic regression approach, the probability of identifying a cumulonimbus event with CM or a hail event with HD are computed by exploiting a proper selection of MSG wavelengths or their combination. A number of cloud physical properties (liquid water path, optical thickness and effective cloud drop radius) were used to physically interpret results of statistical models from a meteorological perspective, using a method based on these "ingredients." Finally, the HDT was applied to a new validation sample consisting of events during summer 2011. The overall Probability of Detection (POD) was 76.9% and False Alarm Ratio 16.7%.

scholarly journals Applications of Bayesian Shrinkage Prior Models in Clinical Research with Categorical Responses

2021 ◽  
Author(s):  
Arinjita Bhattacharyya ◽  
Subhadip Pal ◽  
Riten Mitra ◽  
Shesh Rai
Keyword(s):  
Colon Cancer ◽  
Logistic Regression ◽  
Variable Selection ◽  
Clinical Research ◽  
Disease Risk ◽  
Brier Score ◽  
Data Sets ◽  
Pima Indians ◽  
Bayesian Shrinkage ◽  
Shrinkage Priors

Abstract Background: Prediction and classification algorithms are commonly used in clinical research for identifying patients susceptible to clinical conditions like diabetes, colon cancer, and Alzheimer’s disease. Developing accurate prediction and classification methods have implications for personalized medicine. Building an excellent predictive model involves selecting features that are most significantly associated with the response at hand. These features can include several biological and demographic characteristics, such as genomic biomarkers and health history. Such variable selection becomes challenging when the number of potential predictors is large. Bayesian shrinkage models have emerged as popular and flexible methods of variable selection in regression settings. The article discusses variable selection with three shrinkage priors and illustrates its application to clinical data sets such as Pima Indians Diabetes, Colon cancer, ADNI, and OASIS Alzheimer’s data sets. Methods: We present a unified Bayesian hierarchical framework that implements and compares shrinkage priors in binary and multinomial logistic regression models. The key feature is the representation of the likelihood by a Polya-Gamma data augmentation, which admits a natural integration with a family of shrinkage priors. We specifically focus on the Horseshoe, Dirichlet Laplace, and Double Pareto priors. Extensive simulation studies are conducted to assess the performances under different data dimensions and parameter settings. Measures of accuracy, AUC, brier score, L1 error, cross-entropy, ROC surface plots are used as evaluation criteria comparing the priors to frequentist methods like Lasso, Elastic-Net, and Ridge regression. Results: All three priors can be used for robust prediction with significant metrics, irrespective of their categorical response model choices. Simulation study could achieve the mean prediction accuracy of 91% (95% CI: 90.7, 91.2) and 74% (95% CI: 73.8,74.1) for logistic regression and multinomial logistic models, respectively. The model can identify significant variables for disease risk prediction and is computationally efficient. Conclusions: The models are robust enough to conduct both variable selection and future prediction because of their high shrinkage property and applicability to a broad range of classification problems.

scholarly journals Determination of rainfall thresholds for shallow landslides by a probabilistic and empirical method

2015 ◽  
Vol 3 (5) ◽  
pp. 3487-3508
Author(s):  
J. Huang ◽  
N. P. Ju ◽  
Y. J. Liao ◽  
D. D. Liu
Keyword(s):  
Early Warning ◽  
Regional Scale ◽  
Warning System ◽  
Rainfall Threshold ◽  
Empirical Method ◽  
Shallow Landslides ◽  
Data Sets ◽  
Threshold Values ◽  
Mountainous Regions ◽  
Hourly Rainfall

Abstract. Rainfall-induced landslides not only cause property loss, but also kill and injure large numbers of people every year in mountainous areas in China. These losses and casualties may be avoided to some extent with rainfall threshold values used in an early warning system at a regional scale for the occurrence of landslides. However, the limited availability of data always causes difficulties. In this paper we present a method to calculate rainfall threshold values with limited data sets for the two rainfall parameters: maximum hourly rainfall intensity and accumulated precipitation. The method has been applied to the Huangshan region, in Anhui Province, China. Four early warning levels (Zero, Outlook, Attention, and Warning) have been adopted and the corresponding rainfall threshold values have been defined by probability lines. A validation procedure showed that this method can significantly enhance the effectiveness of a warning system, and finally reduce the risk from shallow landslides in mountainous regions.

scholarly journals Global whole-rock geochemical database compilation

2019 ◽  
Author(s):  
Matthew Gard ◽  
Derrick Hasterok ◽  
Jacqueline Halpin
Keyword(s):  
Database Management ◽  
Temporal Trends ◽  
Physical Property ◽  
Database Management System ◽  
Geochemical Data ◽  
Data Sets ◽  
Unique Contribution ◽  
Data Set ◽  
Wide Range ◽  
Geochemical Indices

Abstract. Dissemination and collation of geochemical data are critical to promote rapid, creative and accurate research and place new results in an appropriate global context. To this end, we have assembled a global whole-rock geochemical database, with other associated sample information and properties, sourced from various existing databases and supplemented with numerous individual publications and corrections. Currently the database stands at 1,023,490 samples with varying amounts of associated information including major and trace element concentrations, isotopic ratios, and location data. The distribution both spatially and temporally is quite heterogeneous, however temporal distributions are enhanced over some previous database compilations, particularly in terms of ages older than ~ 1000 Ma. Also included are a wide range of computed geochemical indices, physical property estimates and naming schema on a major element normalized version of the geochemical data for quick reference. This compilation will be useful for geochemical studies requiring extensive data sets, in particular those wishing to investigate secular temporal trends. The addition of physical properties, estimated by sample chemistry, represents a unique contribution to otherwise similar geochemical databases. The data is published in .csv format for the purposes of simple distribution but exists in a format acceptable for database management systems (e.g. SQL). One can either manipulate this data using conventional analysis tools such as MATLAB®, Microsoft® Excel, or R, or upload to a relational database management system for easy querying and management of the data as unique keys already exist. This data set will continue to grow, and we encourage readers to contact us or other database compilations contained within about any data that is yet to be included. The data files described in this paper are available at https://doi.org/10.5281/zenodo.2592823 (Gard et al., 2019).

What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests

2016 ◽  
Vol 25 (5) ◽  
pp. 505 ◽  
Author(s):  
Futao Guo ◽  
Guangyu Wang ◽  
Zhangwen Su ◽  
Huiling Liang ◽  
Wenhui Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Regional Scale ◽  
Fire Risk ◽  
Driving Factors ◽  
Fire Season ◽  
Fire Occurrence ◽  
Climate Factors ◽  
Local Factors ◽  
Risk Zones

We applied logistic regression and Random Forest to evaluate drivers of fire occurrence on a provincial scale. Potential driving factors were divided into two groups according to scale of influence: ‘climate factors’, which operate on a regional scale, and ‘local factors’, which includes infrastructure, vegetation, topographic and socioeconomic data. The groups of factors were analysed separately and then significant factors from both groups were analysed together. Both models identified significant driving factors, which were ranked in terms of relative importance. Results show that climate factors are the main drivers of fire occurrence in the forests of Fujian, China. Particularly, sunshine hours, relative humidity (fire seasonal and daily), precipitation (fire season) and temperature (fire seasonal and daily) were seen to play a crucial role in fire ignition. Of the local factors, elevation, distance to railway and per capita GDP were found to be most significant. Random Forest demonstrated a higher predictive ability than logistic regression across all groups of factors (climate, local, and climate and local combined). Maps of the likelihood of fire occurrence in Fujian illustrate that the high fire-risk zones are distributed across administrative divisions; consequently, fire management strategies should be devised based on fire-risk zones, rather than on separate administrative divisions.

scholarly journals Linking gold mineralization to regional-scale drivers of mineral systems using in situ U–Pb geochronology and pyrite LA-ICP-MS element mapping

2019 ◽  
Vol 10 (1) ◽  
pp. 89-105 ◽  
Author(s):  
Imogen O.H. Fielding ◽  
Simon P. Johnson ◽  
Sebastien Meffre ◽  
Jianwei Zi ◽  
Stephen Sheppard ◽  
...  
Keyword(s):  
Gold Mineralization ◽  
Regional Scale ◽  
Icp Ms ◽  
Element Mapping
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