The Architecture, Engineering and Construction (AEC) industry increasingly demands the availability of semantic and interactive digital models with the environment, capable of simulating ...decision-making during its life cycle and representing the results achieved. This motivates the need to develop models that integrate spatial information (GIS) and construction information (HBIM), favouring the achievement of the Smart City and Digital Twin concepts. GIS & HBIM platform is a useful tool, with potential applications in the world of built heritage; but it still has certain inefficiencies related to interoperability, the semantics of the formats and the geometry of the models. The objective of this contribution is to suggest a procedure for the generation of 3D visualization models of existing cities by integrating HBIM models in GIS environments. For this, three software and two types of data sources (existing plans and point cloud) are used. The methodology is tested in four locations of different dimensions, managing to identify the advantages/disadvantages of each application.
Rainstorm flooding in developed urban areas has become a global focus. This study proposes a data-driven approach to urban rainstorm flood risk assessment. In contrast to the existing research, this ...study focuses on terrain watersheds as an assessment unit. Using Changsha as the study area, an inventory of 238 historical rainstorm flood locations was produced using automatic web crawling and literature data mining. Subsequently, an assessment model was developed based on a Bayesian algorithm and 16 influencing factors, and its accuracy was verified using a receiver operating characteristic curve. Because underground infrastructure is prone to backflow at its entrances and exits during rainstorms, the developed model was used to assess the backflow risk of two typical underground structures subjected to three rainstorm return periods: 5 (scenario 1), 10 (scenario 2), and 100 years (scenario 3). The conclusions are as follows: (1) The proposed method has a prediction accuracy of 88 % for flood risk. The most influential factors were H11 (proportion of impervious surface), H4 (mean elevation), and H1 (rainfall), contributing 52 %, 14.3 %, and 11.9 %, respectively. (2) Watersheds are classified into “Very Low,” “Low,” “High,” and “Very High” based on the degree of flooding impact, accounting for 83.6 %, 11.9 %, 3.9 %, and 0.7 %, respectively. Watersheds classified as “Very High” are mainly distributed in the central region. (3) A total of 48 subway stations (7.9 % of the total) and 148 underground parking lots (6.5 % of the total) in the study area are located in “Very High” risk areas. (4) Compared to that in scenario 1, the proportion of underground entrances and exits with a “Very high” protection level in scenario 3 increased by approximately 10 %. In conclusion, this framework can assist urban planners in understanding the risks of urban flooding and mitigating potential flooding impacts.
Display omitted
•The Bayesian network model is used in urban flood risk assessment.•Flood risk assessment is performed in a watershed-based unit.•Flood risk subjected to different return periods of rainstorm was evaluated.•Anti-inundation measures for underground infrastructure entrances were discussed.
Display omitted
•A new “information system” is proposed for mineral exploration targeting.•The system is called “exploration information system” (EIS).•EIS uses “mineral system” concepts in ...conjunction with GIS.•Ore-forming processes are classified into pre-, syn-, and post-mineralization parts.•EIS aims to automated translation of ore-forming processes into spatial proxies.
The advent of modern data collection and storage technologies has brought about a huge increase in data volumes with both traditional and machine learning tools struggling to effectively handle, manage and analyse the very large data quantities that are now available. The mineral exploration industry is by no means immune to this big data issue. Exploration decision-making has become much more complex in the wake of big data, in particular with respect to questions about how to best manage and use the data to obtain information, generate knowledge and gain insight. One of the ways in which the mineral exploration industry works with big data is by using a geographic information system (GIS). For example, GIS platforms are often used for integration, interrogation and interpretation of diverse geoscience and mineral exploration data with the goal of refining and prioritising known and identifying new targets. Here we (i) briefly discuss the importance of carefully translating conceptual ore deposit models into effective exploration targeting maps, (ii) propose and describe what we term exploration information systems (EIS): a new idea for an information system designed to better integrate the conceptual mineral deposit model (i.e., the critical and constituent processes of the targeted mineral system) with data available to support exploration targeting, and (iii) discuss how best to categorise mineral systems in an EIS as scale-dependent subsystems to form mineral deposits. Our vision for the future use of EIS in exploration targeting is one whereby the mappable ingredients of a targeted mineral system are translated and combined into a set of weighted evidence (or proxy) maps automatically, resulting in an auto-generated mineral prospectivity map and a series of ranked exploration targets. We do not envisage the EIS replacing human input and ingenuity; rather we envisage the EIS as an additional tool in the exploration toolbox and as an intelligence amplifying system in which humans are making use of machines to achieve the best possible results.
With the advancements of geospatial technologies, geospatial datasets of fine particulate matter (PM
) and mortality statistics are increasingly used to examine the health effects of PM
. Choices of ...these datasets with difference geographic characteristics (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results. The objective of this study is to revisit the estimations of PM
-attributable mortality by taking advantage of recent advancements in high resolution mapping of PM
concentrations and fine scale of mortality statistics and to explore the impacts of new data sources, geographic scales, and spatial variations of input datasets on mortality estimations. We estimate the PM
-mortality for the years of 2000, 2005, 2010 and 2015 using three PM
concentration datasets Chemical Transport Model (CTM), random forests-based regression kriging (RFRK), and geographically weighted regression (GWR) at two resolutions (i.e., 10 km and 1 km) and mortality rates at two geographic scales (i.e., regional-level and county-level). The results show that the estimated PM
-mortality from the 10 km CTM-derived PM
dataset tend to be smaller than the estimations from the 1 km RFRK- and GWR-derived PM
datasets. The estimated PM
-mortalities from regional-level mortality rates are similar to the estimations from those at county level, while large deviations exist when zoomed into small geographic regions (e.g., county). In a scenario analysis to explore the possible benefits of PM
concentrations reduction, the uses of the two newly developed 1 km resolution PM
datasets (RFRK and GWR) lead to discrepant results. Furthermore, we found that the change in PM
concentration is the primary factor that leads to the PM
-attributable mortality decrease from 2000 to 2015. The above results highlight the impact of the adoption of input datasets from new sources with varied geographic characteristics on the PM
-attributable mortality estimations and demonstrate the necessity to account for these impact in future disease burden studies. CAPSULE: We revisited the estimations of PM
-attributable mortality with advancements in PM
mapping and mortality statistics, and demonstrated the impact of geographic characteristics of geospatial datasets on mortality estimations.
This paper takes stock of geographical contributions to the study of energy and energy futures. The paper is written in two parts. First, I trace the methodological and philosophical traditions that ...underpin geographical approaches to energy studies. I argue that while ‘energy geography’ is arguably a pragmatic shorthand with which to communicate to the broader energy studies community, geographical studies of energy have expanded in scope and theoretical plurality so that ‘energy geographies’ is a more appropriate label. Energy geographers are well positioned to contribute to scientific and policy debates surrounding energy due to their privileged position at the borderland between various philosophical and methodological traditions. Second, I identify some of the problems, opportunities and uncertainties that contemporary energy geographers are helping to identify, understand, and resolve. Past contributions and critical issues for future scholarship are highlighted in four themes: (1) using advanced socio-spatial theory to better understand the energy-society relationship; (2) geo-political and geo-economic assessments of (changing) global energy trade networks; (3) geographical perspectives on socio-technical (energy) transitions; and (4) advanced spatial decision-support for energy planning and technology implementation. While this discussion is by no means exhaustive, it aims to bring some clarity and specificity to the policy and academic relevance of geographical thought and practice as it relates to energy issues.
To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published ...between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named ‘Global Applications of Soil Erosion Modelling Tracker (GASEMT)’, includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions.
Display omitted
•Peer-reviewed research literature on soil-erosion modelling was reviewed.•66 soil-erosion scientists from 25 countries contributed to this study.•Overall, 8471 articles identified as potentially relevant were reviewed.•1697 articles were reviewed in a comprehensive manner extracting 42 attributes.•A free and open-source database was created.
•Regional flood risk level was evaluated using both AHP and I-AHP methods.•Flood risk level of metro system was evaluated based on flood risks within 500 m range from metro lines.•Comparative results ...between AHP and I-AHP assessment results were analyzed.•Results were validated using the observed flood hazards on May 10, 2016, in Guangzhou, China.
Display omitted
Metro system is a vital component of mass transportation infrastructure, providing crucial social and economic service in urban area. Flood events may cause functional disruptions to metro systems; therefore, a better understanding of their vulnerability would enhance their resilience. A comparative study of flood risk in metro systems is presented using the analytic hierarchy process (AHP) and the interval AHP (I-AHP) methods. The flood risk in the Guangzhou metro system is evaluated according to recorded data. Evaluated results are validated using the flood event occurred in Guangzhou on May 10, 2016 (hereinafter called “May 10th event”), which inundated several metro stations. The flood risk is assessed within a range of 500 m around the metro line. The results show that >50% of metro lines are highly exposed to flood risk, indicating that the Guangzhou metro system is vulnerable to flood events. Comparisons between results from AHP and I-AHP show that the latter yields a wider range of high flooding risk than the former.
To shift towards low-fossil carbon economies, making more out of residual biomass is increasingly promoted. Yet, it remains unclear if implementing advanced technologies to reuse these streams really ...achieves net environmental benefits compared to current management practices. By integrating spatially-explicit resource flow analysis, consequential life cycle assessment (LCA), and uncertainty analysis, we propose a single framework to quantify the residual biomass environmental baseline of a territory, and apply it to the case of France. The output is the environmental threshold that a future large-scale territorial bioeconomy strategy should overpass. For France, we estimate the residual biomass baseline to generate 18.4 ± 2.7 MtCO2-eq·y-1 (climate change), 255 ± 35 ktN-eq·y-1 (marine eutrophication), and 12,300 ± 800 disease incidences per year (particulate matter formation). The current use of crop residues and livestock effluents, being essentially a return to arable lands, was found to represent more than 90 % of total environmental impacts and uncertainties, uncovering a need for more certain data. At present, utilizing residual streams as organic fertilizers fulfills over half of France's total phosphorus (P) and potassium (K) demands. However, it only meets 6 % of the nitrogen demand, primarily because nitrogen is lost through air and water. This, coupled with the overall territorial diagnosis, led us to revisit the idea of using the current situation (based on 2018 data) as a baseline for future bioeconomy trajectories. We suggest that these should rather be compared to a projected baseline accounting for ongoing basic mitigation efforts, estimated for France at 8.5 MtCO2-eq·y-1.To shift towards low-fossil carbon economies, making more out of residual biomass is increasingly promoted. Yet, it remains unclear if implementing advanced technologies to reuse these streams really achieves net environmental benefits compared to current management practices. By integrating spatially-explicit resource flow analysis, consequential life cycle assessment (LCA), and uncertainty analysis, we propose a single framework to quantify the residual biomass environmental baseline of a territory, and apply it to the case of France. The output is the environmental threshold that a future large-scale territorial bioeconomy strategy should overpass. For France, we estimate the residual biomass baseline to generate 18.4 ± 2.7 MtCO2-eq·y-1 (climate change), 255 ± 35 ktN-eq·y-1 (marine eutrophication), and 12,300 ± 800 disease incidences per year (particulate matter formation). The current use of crop residues and livestock effluents, being essentially a return to arable lands, was found to represent more than 90 % of total environmental impacts and uncertainties, uncovering a need for more certain data. At present, utilizing residual streams as organic fertilizers fulfills over half of France's total phosphorus (P) and potassium (K) demands. However, it only meets 6 % of the nitrogen demand, primarily because nitrogen is lost through air and water. This, coupled with the overall territorial diagnosis, led us to revisit the idea of using the current situation (based on 2018 data) as a baseline for future bioeconomy trajectories. We suggest that these should rather be compared to a projected baseline accounting for ongoing basic mitigation efforts, estimated for France at 8.5 MtCO2-eq·y-1.
The aim of the current study was to produce groundwater spring potential maps using novel ensemble weights-of-evidence (WoE) with logistic regression (LR) and functional tree (FT) models. First, a ...total of 66 springs were identified by field surveys, out of which 70% of the spring locations were used for training the models and 30% of the spring locations were employed for the validation process. Second, a total of 14 affecting factors including aspect, altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), lithology, normalized difference vegetation index (NDVI), land use, soil, distance to roads, and distance to streams was used to analyze the spatial relationship between these affecting factors and spring occurrences. Multicollinearity analysis and feature selection of the correlation attribute evaluation (CAE) method were employed to optimize the affecting factors. Subsequently, the novel ensembles of the WoE, LR, and FT models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) curves, standard error, confidence interval (CI) at 95%, and significance level P were employed to validate and compare the performance of three models. Overall, all three models performed well for groundwater spring potential evaluation. The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models. The groundwater spring potential maps can be adopted for the management of water resources and land use by planners and engineers.
Display omitted
•Novel ensemble WoE with LR and FT models for groundwater potential analysis•Multicollinearity analysis and CAE method for optimization of affecting factors•ROC, standard error, CI, and P for validation and comparison of three models•FT model showed the best result in groundwater potential evaluation.
PDF
