Geodata science and geochemical mapping Zuo, Renguang; Xiong, Yihui
Journal of geochemical exploration,
February 2020, 2020-02-00, Letnik:
209
Journal Article
Recenzirano
Geodata science (GDS) is an interdisciplinary field in which geoscience data are mined for us to well understand the origin, evolution and future of our Earth and planet with prediction and ...assessment of its resources, environments, and natural hazards. The data chain of GDS involves collecting geosciences data, mining geoinformation, discovering geo-knowledge, and making spatial decisions. There are three groups of GDS methods for exploring and mining geoscience data including data statistics, data mining, and data insight and prediction. A case study on geochemical exploration data mapping was conducted to demonstrate the powerful use of GDS. The results show that GDS is a new research paradigm for exploring the spatial association of geochemical patterns, mining elemental association, and recognizing geochemical anomalies associated with mineralization via geo-computation and geo-visualization techniques in support of mineral exploration.
•GDS is the science to studying and mining geospatial patterns•GDS is a new research paradigm in geoscience and can be used for geochemical mapping in support of mineral exploration•GDC can reveal the spatial association, identify the elemental association, and recognize geochemical anomalies.
Mobility planning in rural areas with a high number of tourists is important for creating sustainable destinations. By identifying mobility gaps in the transportation system, measures to improve the ...situation can be implemented. In order to identify such mobility gaps, decision-makers need a spatial decision support system (SDSS). The aim of this paper is to identify vital aspects of creating such an SDSS and to build a prototype. Two important aspects were identified, data and system design. The result of the analysis of available data shows a lack of data portals with disaggregated socio-economic and intra-destination travel data. Further, it shows that data on points of interest (POI) and public transit data are primarily found in company databases. The system design analysis showed that most SDSS today are relying on public data and are not designed to integrate disparate data sources. They are primarily developed to be used by experts. Based on these findings an SDSS that automatically integrates both public and private data was developed. It comprises of a self-hosted web mapping system and several geospatial tools. Our main conclusion is that both data and system design are important aspects to consider when building an SDSS for mobility planning. By using the architecture proposed in this article, new data can easily be incorporated in an SDSS. Furthermore, the system design also facilitates the involvement of stakeholders in the planning process.
The spatial arrangement of settlements constitutes a long-lasting legacy and shapes the prospects for transformations toward sustainability. Thus, understanding the drivers of changes in settlement ...patterns is essential. In this article, we present a spatially explicit, geostatistical analysis of settlement dynamics, and a qualitative investigation of its regulative, demographic, and economic drivers, using the example of Vienna, Austria between 1984 and 2018. Combining spatially explicit metrics of urban sprawl and cluster analysis, we analyzed high-resolution maps of buildings, population, and jobs to identify distinct settlement trajectories. Societal drivers of more or less sprawled settlement dynamics are analyzed with desk research and expert interviews. We distinguish five types of settlement dynamics: persistently dense areas with increasing use intensity, re-densification of dense areas, persistently sprawled areas, redensification of sprawled areas, and persistently isolated buildings. Urban renewal schemes have fostered the re-densification of dense areas in response to population growth and urban economic restructuring. The combination of urban renewal schemes and green space policies has successfully limited urban expansion. Challenges arise from the demand for single-family housing and corresponding zoning regulations. These factors solidify existing sprawled settlements, posing obstacles to the efficient re-densification of such areas crucial for sustainable urban development.
•Mixed-methods approach for linking settlement dynamics to potential drivers.•Geostatistical spatiotemporal clustering was combined with expert interviews.•Dense and sprawled areas in Vienna show high persistence between 1984 and 2018.•Urban renewal schemes and sustained green space protection halted new sprawl.•Urban renewal schemes can re-shape settlements towards increased density.
This paper introduces the concept of geodata science-based mineral prospectivity mapping (GSMPM), which is based on analyzing the spatial associations between geological prospecting big data (GPBD) ...and locations of known mineralization. Geodata science reveals the inter-correlations between GPBD and mineralization, converts GPBD into mappable criteria, and combines multiple mappable criteria into a mineral potential map. A workflow of the GSMPM is proposed and compared with the traditional workflow of mineral prospectivity mapping. More specifically, each component in such a workflow is explained in detail to demonstrate how geodata science serves mineral prospectivity mapping by deriving geoinformation from geoscience data, generating geo-knowledge from geoinformation, and allowing spatial decision-making by integrating geoinformation and geo-knowledge on the formation of mineral deposits. This review also presents several research directions for GSMPM in the future.
•A new spatiotemporal model for giant linear features in the Ob loess plateau is presented.•Geomorphological evidence points to an erosive-aeolian genesis of these impressive features.•Comparison ...with other landforms reveals the largest system of mega-yardangs world wide.
The foreland of the Russian Altai is dominated by the vast Ob loess plateau. The flat landscape exhibits striking linear features, partially more than 100 km in length and tens of km wide. The bottoms of these features are covered by forested dunes, whereas the loess ridges in between are intensively cultivated. To the north, the land cover changes due to gradual transition from the steppe towards the Siberian taiga. The genesis of these prominent features was debated within the last decades. Possible explanations cover tectonic lineaments, fluvial erosion, and landforms caused by outbursts of catastrophic floods from the Altai Mountains. Here, we present geomorphological evidence for the aeolian origin of these features based on field observations and geodata. These large lineaments do not show characteristic features of fluvial valleys, since the shape of the lineaments is too straight and does not show braided river characteristics as, e.g., the Ob or the Irtysh valley. The sheer size of these features also does not support the hypothesis of tectonic activity or a catastrophic flood since events like this would be imprinted in other environmental archives of the region. We show that these linear landforms show remarkable similarities with Pleistocene mega yardang systems throughout the world. These systems can usually be found in arid to hyper-arid environments, but were also described in, e.g., mid-latitude regions. We hypothesis that the Pleistocene glaciations of the Altai Mountains enhanced the strength and the influence of the westerlies in the Altai forelands. Therefore, we propose an erosive-aeolian origin of these remarkable landforms.
Geotechnologies play an increasingly extensive and diverse role in our society. Their implementation in educational settings is growing at all levels, especially in higher education, and in a wide ...variety of disciplines. The analysis and management of cultural heritage are two of the areas where they are most used. The multiple, heterogeneous, and unspecific supply of geoinformation opens up considerable possibilities for learning and new opportunities for analysis by future specialists in cultural heritage, despite the difficulties in the management of, and approach to, the data. In a scenario in which open-source educational resources are increasingly important, this work presents an exploratory analysis of open sources of georeferenced information that facilitate access to geodata for teaching and learning on cultural heritage. As an example of the differences and shortcomings of the availability of georeferenced sources in Spain, this work presents a case study on the city of Toledo, recognised as a world heritage site.
Open data are currently a hot topic and are associated with realising ambitions such as a more transparent and efficient government, solving societal problems, and increasing economic value. To ...describe and monitor the state of open data in countries and organisations, several open data assessment frameworks were developed. Despite high scores in these assessment frameworks, the actual (re)use of open government data (OGD) fails to live up to its expectations. Our review of existing open data assessment frameworks reveals that these only cover parts of the open data ecosystem. We have developed a framework, which assesses open data supply, open data governance, and open data user characteristics holistically. This holistic open data framework assesses the maturity of the open data ecosystem and proves to be a useful tool to indicate which aspects of the open data ecosystem are successful and which aspects require attention. Our initial assessment in the Netherlands indicates that the traditional geographical data perform significantly better than non-geographical data, such as healthcare data. Therefore, open geographical data policies in the Netherlands may provide useful cues for other OGD strategies.
•Convolutional network for predicting daily maps of the probability of a wildfire burn.•Convolutional networks demonstrate higher predictive accuracy and map quality.•Exploratory feature statistical ...importance metrics improves model transparency.
Wildfire continues to be a major environmental problem in the world. To help land and fire management agencies manage and mitigate wildfire-related risks, we need to develop tools for mapping those risks. Big geodata—in the form of remotely sensed images, ground-based sensor observations, and topographical datasets—can help us characterize the dynamics of wildfire related events. In this study, we design a deep fully convolutional network, called AllConvNet, to produce daily maps of the probability of a wildfire burn over the next 7 days. We applied it to burns in Victoria, Australia for the period of 2006–2017. Fifteen factors that were extracted from six different datasets and resulted into 29 quantitative features, were selected as input to the network. We compared it with three baseline methods: SegNet, multilayer perceptron, and logistic regression. AllConvNet outperforms the other three baseline methods in four of the six quantitative metrics considered. AllConvNet and SegNet provide smoother and more regularized predicted maps, with SegNet providing greater sensitivity in dificriminating less wildfire-prone locations. Input feature statistical importance was measured for all the networks and compared against logistic regression coefficients. Total precipitation, lightning flash density, and land surface temperature occur to be consistently highly weighted by all models while terrain aspect components, wind direction components, certain land cover classes (such as crop field and woodland), and distance from power lines are ranked on the lower end. We conclude that wild-fire burn prediction methods based on deep learning present quantitative and qualitative gains.
The patterns of material accumulation in buildings and infrastructure accompanied by rapid urbanization offer an important, yet hitherto largely missing stock perspective for facilitating urban ...system engineering and informing urban resources, waste, and climate strategies. However, our existing knowledge on the patterns of built environment stocks across and particularly within cities is limited, largely owing to the lack of sufficient high spatial resolution data. This study leveraged multi-source big geodata, machine learning, and bottom-up stock accounting to characterize the built environment stocks of 50 cities in China at 500 m fine-grained levels. The per capita built environment stock of many cities (261 tonnes per capita on average) is close to that in western cities, despite considerable disparities across cities owing to their varying socioeconomic, geomorphology, and urban form characteristics. This is mainly owing to the construction boom and the building and infrastructure-driven economy of China in the past decades. China’s urban expansion tends to be more “vertical” (with high-rise buildings) than “horizontal” (with expanded road networks). It trades skylines for space, and reflects a concentration–dispersion–concentration pathway for spatialized built environment stocks development within cities in China. These results shed light on future urbanization in developing cities, inform spatial planning, and support circular and low-carbon transitions in cities.
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