Improving probabilistic wind speed forecasting using M-Rice distribution and spatial data integration

We consider the problem of short-term forecasting of surface wind speed probability distribution. Our approach simply consists in predicting the parameters of a probability density function by training a neural network model whose loss function is the log-likelihood provided by this distribution. We compare different possibilities among a set of laws that have been previously considered in the context of modeling wind fluctuations. Our results rely on two different hourly wind speed datasets: the first one has been recorded by M\'et\'eo-France in Corsica (South France), a very mountainous Mediterranean island while the other one relies on KNMI database that provides records of various stations over the Netherlands, a very flat country in Northwestern Europe. A first part of our work globally unveils the superiority of the so-called "Multifractal Rice" (M-Rice) distribution over alternative parametric models, showcasing its potential as a reliable tool for wind speed forecasting. This family of distributions has been proposed in the context of modeling wind speed fluctuations as a random cascade model. For all stations in both regions, it consistently provides better results regardless the considered probabilistic scoring rule or forecasting horizon. Our second findings demonstrate significant enhancements in forecasting accuracy when one incorporates wind speed data from proximate weather stations, in full agreement with the results obtained formerly for point-wise wind speed prediction. Moreover, we reveal that the incorporation of ERA5 reanalysis of 10 m wind data from neighboring grid points contributes to a substantial improvement at time horizon $h=6$ hours. We also find out that accounting for more explanatory factors mainly increases the resolution performances while it does not change the reliability contribution to the prediction performance metric considered (CRPS).Comment: 31 pages, 8 figures, 11 table

The Potential for Spatial Data Integration: A Case Study of Geographical Knowledge In a Small Business

In many large businesses integration of information is advocated via expensive enterprise resource planning or re-engineering initiatives. Such approaches are beyond the resources of many small businesses. This paper investigates the ways in which geography can be used in a small business to integrate information across business functions. Action research within Bayford Thrust was undertaken to show that data looked at through a geographic lens provided a focus across business activities. Applications that were too expensive for stand-alone uses were justifiable on the basis of sharing data between traditional functional business domains. Exploiting the spatial dimension of the data proved to be the unifying theme to enable data to be shared

Multi-Source Spatial Entity Linkage

Besides the traditional cartographic data sources, spatial information can also be derived from location-based sources. However, even though different location-based sources refer to the same physical world, each one has only partial coverage of the spatial entities, describe them with different attributes, and sometimes provide contradicting information. Hence, we introduce the spatial entity linkage problem, which finds which pairs of spatial entities belong to the same physical spatial entity. Our proposed solution (QuadSky) starts with a time-efficient spatial blocking technique (QuadFlex), compares pairwise the spatial entities in the same block, ranks the pairs using Pareto optimality with the SkyRank algorithm, and finally, classifies the pairs with our novel SkyEx-* family of algorithms that yield 0.85 precision and 0.85 recall for a manually labeled dataset of 1,500 pairs and 0.87 precision and 0.6 recall for a semi-manually labeled dataset of 777,452 pairs. Moreover, we provide a theoretical guarantee and formalize the SkyEx-FES algorithm that explores only 27% of the skylines without any loss in F-measure. Furthermore, our fully unsupervised algorithm SkyEx-D approximates the optimal result with an F-measure loss of just 0.01. Finally, QuadSky provides the best trade-off between precision and recall, and the best F-measure compared to the existing baselines and clustering techniques, and approximates the results of supervised learning solutions

Gaining ground for future preparedness : spatial data integration for the Maltese Islands

Preparing for the potential changes wrought by climate change can only be grounded in real-data that is based on a common integrated core. Efforts by various countries to mitigate such potentialities have resulted in a stepped-approach to data management and integration. Small island states experience an add-on burden through data dearths, disparate datasets and data hoarding. This paper reviews the processes employed in Malta that target spatio-temporal analysis of current and future scenarios aimed at integrating environmental, spatial planning and social data in line with the transposition of the Aarhus Convention, the INSPIRE Directive and the SEIS initiative. Concurrently, a series of case-studies are being employed to test for scenario- building in immersive environments through the use of gaming technology that allows for social interactionism. Scenarios include passive climatic effects or short-sharp-shock post-disaster alternatives resultant from extreme weather events that impact the higher coast-to-hinterland ratio pertaining to small islands.peer-reviewe

Data Base Management Systems Panel Workshop: Executive summary

Data base management systems (DBMS) for space acquired and associated data are discussed. The full range of DBMS needs is covered including acquiring, managing, storing, archiving, accessing and dissemination of data for an application. Existing bottlenecks in DBMS operations, expected developments in the field of remote sensing, communications, and computer science are discussed, and an overview of existing conditions and expected problems is presented. The requirements for a proposed spatial information system and characteristics of a comprehensive browse facility for earth observations applications are included

TRADEOFFS BETWEEN RURAL DEVELOPMENT POLICIES AND FOREST PROTECTION: SPATIALLY-EXPLICIT MODELING IN THE CENTRAL HIGHLANDS OF VIETNAM

Alleviating rural poverty remains an important objective of development policy in many areas of the world. However, traditional means of increasing rural livelihoods such as increased investments in agricultural intensification measures can have disastrous impacts on natural resources such as forests by greatly increasing incentives for clearing. This paper contains a spatially-explicit model of land use in the Dak Lak province in the Central Highlands of Vietnam. Land use is modeled using a reduced-form multinomial logit model, and policy simulations are conducted. These simulations demonstrate that the adoption of yield-increasing inputs requires concomitant forest protection policies, both in terms of forest area and spatial configuration.International Development,

Emergent realities for social wellbeing : environmental, spatial and social pathways

Opening a window into the future is not an easy task. Attempting to open one in a generation after the initial launching step might seemed either idealistic, naïve or with hindsight plain driven. An idea that started off in 1995 by Formosa as paralleled by a similar but unconnected effort by Perit Vincent Cassar took two decades to take a semblance of form and structure within an achievable framework. These two steams of though came together in 2013 at the then MEPA which effort sought to push all spatial information within an integrated core that allows Malta to jump from a Data Phase to an Information Phase and over the next years to a Knowledge, Action and Wisdom phased approach. The two proponents converged and the effort took national significance through the SIntegraM concept: a concept that aimed to create a functionality targeting the Spatial Data Integration for the Maltese Islands: Developing Integrated National Spatial Information Capacity – hence the acronym SIntegraM.peer-reviewe

Estimating hurricane hazards using a GIS system

Abstract. This paper develops a GIS-based integrated approach to the Multi-Hazard model method, with reference to hurricanes. This approach has three components: data integration, hazard assessment and score calculation to estimate elements at risk such as affected area and affected population. First, spatial data integration issues within a GIS environment, such as geographical scales and data models, are addressed. Particularly, the integration of physical parameters and population data is achieved linking remotely sensed data with a high resolution population distribution in GIS. In order to assess the number of affected people, involving heterogeneous data sources, the selection of spatial analysis units is basic. Second, specific multi-hazard tasks, such as hazard behaviour simulation and elements at risk assessment, are composed in order to understand complex hazard and provide support for decision making. Finally, the paper concludes that the integrated approach herein presented can be used to assist emergency management of hurricane consequences, in theory and in practice.</p