Each year,accidents involving ships result in significant loss of life,environmental pollution and economic losses.The promotion of navigation safety through risk reduction requires methods to assess the spatial distr...Each year,accidents involving ships result in significant loss of life,environmental pollution and economic losses.The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence.Yet,such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures.This paper proposes the use of the Discrete Global Grid System(DGGS)as an efficient and advantageous structure to integrate vessel traffic,metocean,bathymetric,infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings.Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach.A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002.The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures,targeted to regions with the highest risk.Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure.展开更多
The emerging field of Discrete Global Grid Systems(DGGS)provides a way to organise,store and analyse spatio-temporal data at multiple resolutions and scales(from near global scales down to microns).DGGS partition the ...The emerging field of Discrete Global Grid Systems(DGGS)provides a way to organise,store and analyse spatio-temporal data at multiple resolutions and scales(from near global scales down to microns).DGGS partition the entire planet into a discrete hierarchy of global tessellations of progressively finer resolution zones(or cells).Data integration,decomposition and aggregation are optimised by assigning a unique spatio-temporal identifier to each zone.These identifiers are encodings of both the zone’s location and its resolution.As a result,complex multi-dimensional,multi-resolution spatio-temporal operations are simplified into sets of 1D array and filter operations.展开更多
基金This work is partly funded by the University of Southampton’s Marine and Maritime Institute(SMMI)and the European Research Council under the European Union’s Horizon 2020 research and innovation program(grant agreement number:723526:SEDNA).
文摘Each year,accidents involving ships result in significant loss of life,environmental pollution and economic losses.The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence.Yet,such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures.This paper proposes the use of the Discrete Global Grid System(DGGS)as an efficient and advantageous structure to integrate vessel traffic,metocean,bathymetric,infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings.Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach.A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002.The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures,targeted to regions with the highest risk.Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure.
文摘The emerging field of Discrete Global Grid Systems(DGGS)provides a way to organise,store and analyse spatio-temporal data at multiple resolutions and scales(from near global scales down to microns).DGGS partition the entire planet into a discrete hierarchy of global tessellations of progressively finer resolution zones(or cells).Data integration,decomposition and aggregation are optimised by assigning a unique spatio-temporal identifier to each zone.These identifiers are encodings of both the zone’s location and its resolution.As a result,complex multi-dimensional,multi-resolution spatio-temporal operations are simplified into sets of 1D array and filter operations.
