Experience of geographical typology of secularization processes in the modern world

In the modern world, seven geographical types of development of secularization processes have been identified; the level of secularization is determined not only by the maturity of social space but also by the structure of geospace. The Latin type includes some countries of Southern and Western Europe, most of the population of which confess Catholicism. The countries of the Anglo-Scandinavian type are characterized by Protestant pluralism. The polarized type combines countries and territories in which Protestants and Catholics form stable, influential and competing blocs. The countries in which the secularization processes were unable to seriously shake the influence of religion in society, because it is the institute of church that was resisting the political and cultural infringement by neighboring states over centuries, refer to the type of religious infringement. The resettlement type is characteristic for the countries whose population was being formed as a result of migration of various confessional (primarily Protestant) groups. The post-socialist type includes the countries in which an active policy of not infrequently forced “political” secularization was pursued. The Confucianistic type combines the states, most of the population of which was pursuing various religious-cultural traditions, with Confucianism predominating in general, and with a widespread occurrence of polyconfessionalism. This geographical typology of secularization embraces the countries, the communities of which have gone through the stages of secular development. The spatial boundedness of the secularization processes in the world is determined by the fact that secularization itself is the product primarily of the entire Christian European civilization.     

Influence of melioration in natural ecological processes of a small river valley (Poland)

On the basis of the topographic maps from 1933, 1951 and 1962 (sc 1:10 000, 1:25 000 and 1:100 000, respectively) and the satellite image from 2014 (sc 1:10 000), we determined changes in the channel of the small Brynica river (Southern Poland) as well as in the land-use pattern along this river valley after its melioration. Terrain regulation and drainage brought about an intensification of the overland runoff, and a decrease in irrigation of the territory. Due to soil overdrying, some tracts of the valley are experiencing a moisture deficiency. Peat drying can lead to negative consequences, because shallow peats mostly occur on the study territory. Disappearance of peaty soils acts to decrease the degree of suitability of the riverside territories for economic purposes as well as their capacity for recovery. The schematic map of vegetation distribution along the river valley prior to and after melioration as compiled from results of field observations displays differences between mosaic quasi-natural vegetation and areas of typical agrocenoses. The preservation of the natural values along the Brynica valley is possible through balanced economic management, while the differentiation of topography contributes to creating favorable conditions for the formation of biodiversity.     

A Comparative Analysis of Weights of Evidence,Evidential Belief Functions,and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation

Large amounts of digital data must be analyzed and integrated to generate mineral potential maps, which can be used for exploration targeting. The quality of the mineral potential maps is dependent on the quality of the data used as inputs, with higher quality inputs producing higher quality outputs. In mineral exploration, particularly in regions with little to no exploration history, datasets are often incomplete at the scale of investigation with data missing due to incomplete mapping or the unavailability of data over certain areas. It is not always clear that datasets are incomplete, and this study examines how mineral potential mapping results may differ in this context. Different methods of mineral potential mapping provide different ways of dealing with analyzing and integrating incomplete data. This study examines the weights of evidence (WofE), evidential belief function and fuzzy logic methods of mineral potential mapping using incomplete data from the Carajás mineral province, Brazil to target for orogenic gold mineralization. Results demonstrate that WofE is the best one able to predict the location of known mineralization within the study area when either complete or unacknowledged incomplete data are used. It is suggested that this is due to the use of Bayes’ rule, which can account for “missing data.” The results indicate the effectiveness of WofE for mineral potential mapping with incomplete data.     

Metallic Mineral Resources in the Twenty-First Century. I. Historical Extraction Trends and Expected Demand

Industrial, technological, and economic developments depend on the availability of metallic raw materials. As a greater fraction of the Earth’s population has become part of developed economies and as developed societies have become more affluent, the demand on metallic mineral resources has increased. Yet metallic minerals are non-renewable natural resources, the supply of which, even if unknown and potentially large, is finite. An analysis of historical extraction trends for eighteen metals, going back to the year 1900, demonstrates that demand of metallic raw materials has increased as a result of both increase in world population and increase in per-capita consumption. These eighteen metals can be arranged into four distinct groups, for each of which it is possible to identify a consistent pattern of per-capita demand as a function of time. These patterns can, in turn, be explained in terms of the industrial and technological applications, and in some cases conventional uses as well, of the metals in each group. Under the assumption that these patterns will continue into the future, and that world population will grow by no more than about 50% by the year 2100, one can estimate the amount of metallic raw materials that will be required to sustain the world’s economy throughout the twenty-first century. From the present until the year 2100, the world can be expected to require about one order of magnitude more metal than the total amount of metal that fueled technological and economic growth between the age of steam and the present day. For most of the metals considered here, this corresponds to 5–10 times the amount of metal contained in proven ore reserves. The two chief driving factors of this expected demand are growth in per-capita consumption and present-day absolute population numbers. World population is already so large that additional population growth makes only a small contribution to the expected future demand of metallic raw materials. It is not known whether or not the amount of metal required to sustain the world’s economy throughout this century exists in exploitable mineral resources. In the accompanying paper, I show that it is nevertheless possible to make statistical inferences about the size distribution of the mineral deposits that will need to be discovered and developed in order to satisfy the expected demand. Those results neither prove nor disprove that the needed resources exist but can be used to improve our understanding of the challenges facing future supply of metallic raw materials.     

Practical Incorporation of Multivariate Parameter Uncertainty in Geostatistical Resource Modeling

There is a need to bridge theory and practice for incorporating parameter uncertainty in geostatistical simulation modeling workflows. Simulation workflows are a standard practice in natural resource and recovery modeling, but the incorporation of multivariate parameter uncertainty into those workflows is challenging. However, the objectives can be met without considerable extra effort and programming. The sampling distributions of statistics comprise the core theoretical notion with the addition of the spatial degrees of freedom to account for the redundancy in the spatially correlated data. Prior parameter uncertainty is estimated from multivariate spatial resampling. Simulation-based transfer of prior parameter uncertainty results in posterior distributions which are updated by data conditioning and the model domain extents and configuration. The results are theoretically tractable and practical to achieve, providing realistic assessments of uncertainty by accounting for large-scale parameter uncertainty, which is often the most important component impacting a project. A simulation-based multivariate workflow demonstrates joint modeling of intrinsic shale properties and uncertainty in estimated ultimate recovery in a shale gas project. The multivariate workflow accounts for joint prior parameter uncertainty given the current well locations and results in posterior estimates on global distributions of all modeled properties. This is achieved by transferring the joint prior parameter uncertainty through conditional simulations.     

Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines)

The Random Forests (RF) algorithm is a machine learning method that has recently been demonstrated as a viable technique for data-driven predictive modeling of mineral prospectivity, and thus, it is instructive to further examine its usefulness in this particular field. A case study was carried out using data from Catanduanes Island (Philippines) to investigate further (a) if RF modeling can be used for data-driven modeling of mineral prospectivity in areas with few (i.e., <20) mineral occurrences and (b) if RF modeling can handle predictor variables with missing values. We found that RF modeling outperforms evidential belief (EB) modeling of prospectivity for hydrothermal Au–Cu deposits in Catanduanes Island, where 17 hydrothermal Au–Cu prospects are known to exist. Moreover, just like EB modeling, RF modeling allows analysis of the spatial relationships between known prospects and individual layers of predictor data. Furthermore, RF modeling can handle missing values in predictor data through an RF-based imputation technique whereas in EB modeling, missing values are simply represented by maximum uncertainty. Therefore, the RF algorithm is a potentially useful method for data-driven predictive modeling of mineral prospectivity in regions with few (i.e., <20) occurrences of mineral deposits of the type sought. However, further testing of the method in other regions with few mineral occurrences is warranted to fully determine its usefulness in data-driven predictive modeling of mineral prospectivity.     

Spatial Modeling of Geometallurgical Properties: Techniques and a Case Study

High-resolution spatial numerical models of metallurgical properties constrained by geological controls and more extensively by measured grade and geomechanical properties constitute an important part of geometallurgy. Geostatistical and other numerical techniques are adapted and developed to construct these high-resolution models accounting for all available data. Important issues that must be addressed include unequal sampling of the metallurgical properties versus grade assays, measurements at different scale, and complex nonlinear averaging of many metallurgical parameters. This paper establishes techniques to address each of these issues with the required implementation details and also demonstrates geometallurgical mineral deposit characterization for a copper–molybdenum deposit in South America. High-resolution models of grades and comminution indices are constructed, checked, and are rigorously validated. The workflow demonstrated in this case study is applicable to many other deposit types.     

Mid and late Holocene forest fires and deforestation in the subalpine belt of the Iberian range,northern Spain

The conversion of subalpine forests into grasslands for pastoral use is a well-knownphenomenon, although for most mountain areas the timing of deforestation has not been determined. The presence of charcoal fragments in soil profiles affected by shallow landsliding enabled us to date the occurrence of fires and the periods of conversion ofsubalpine forest into grasslands in the Urbión Mountains, Iberian Range, Spain. We found that the treeline in the highest parts of the northwestern massifs of the Iberian Range(the Urbión, Demanda, Neila, and Cebollera massifs) is currently between 1500 and 1600 m a.s.l., probably because of pastoral use of the subalpine belt, whereas in the past it would have reached almost the highest divides(at approximately 2100–2200 m a.s.l.). The radiocarbon dates obtained indicate that the transformation of the subalpine belt occurred during the Late Neolithic, Chalcolithic, Bronze Age, Iron Age, and Middle Ages. Forest clearing was probably moderate during fires prior to the Middle Ages, as the small size of the sheep herds and the local character of the markets only required small clearings, and therefore more limited fires. Thus, it is likely that the forest recovered burnt areas in a few decades; this suggests the management of the forest and grasslands following a slash-andburn system. During the Middle and Modern Ages deforestation and grassland expansion affected most of the subalpine belt and coincided with the increasing prevalence of transhumance, as occurred in other mountains in the Iberian Peninsula(particularly the Pyrenees). Although the occurrence of shallow landslides following deforestation between the Neolithic and the Roman Period cannot be ruled out, the most extensive shallow landsliding processes would have occurred from the Middle Ages until recent times.     

Hydro-hypsometric analysis of tropical river basins,Southwest Coast of India using geospatial technology

The key aspect in planning and management of water resources is to analyze the runoff potential and erosion status of the river basin. For the detailed investigation of hydrological response, freely available Cartosat-1 (IRS-P5) data was used for the preparation of digital elevation model (DEM). The runoff potential and type of erosive process of 22 river basins originating in the global biodiversity hotspot of Western Ghats, was inferred through hypsometric analysis. Several parameters like Hypsometric integral (HI), maximum concavity (Eh), coordinates of slope inflection point (I) given by a* and h* and normalized height of hypsometric curve (h) were extracted from the hypsometric curves and used for understanding the hydrological responses. From the hypsometric curves, the landform evolution processes were inferred. Contribution of diffusive and fluvial processes in slope degradation of the river basins was understood. Basins with lesser area (<100 km²) were found to have a positive correlation between hypsometric integral and basin area, whereas for large basins no such correlation exists. Based on the study, river basins can be prioritized for the appropriate conservation measures.