Underwater noise pollution from shipping activities is widely recognised as a significant threat to marine life. Noise emitted by vessels can have various detrimental effects on fish and marine ecosystems. Accurately estimating and analysing vessel-generated underwater noise is therefore of critical importance for the protection and conservation of marine environments. In this paper, we present an enhanced version of our model for the spatiotemporal characterisation of vessel-generated underwater noise, with a focus on improving its scalability. The original model was limited to fishing vessels and relied on Automatic Identification System (AIS) data to reconstruct trajectories, as well as engine horsepower to estimate emitted noise. Here, we generalise the approach to include all vessel categories — including tankers, cruise ships, and recreational boats — still relying on AIS data, but estimating noise as a function of vessel length overall (LOA) and category, since horsepower information is not available for all vessels in the dataset. We broaden the study area to include the Central Adriatic Sea, in addition to the Northern part previously considered. The enlarged area and the substantially greater volume of AIS data introduce significant computational challenges, making scalability a primary concern. We address these challenges through a comprehensive analysis of optimisation strategies to improve query execution performance. In particular, we restructure the computational pipeline by implementing table partitioning and leveraging parallelisation techniques. Specifically, we employ PostgreSQL’s native parallel query execution and implement multiple partitioning strategies, including range, hash, and list partitioning. We further explore spatial partitioning through space tiling, comparing regular, adaptive, and k-d tree-based grids. Finally, we leverage the Citus extension to distribute computation across four and eight nodes. Our approach improves computational efficiency while preserving the accuracy of noise calculation, offering a scalable solution for large datasets.

Underwater noise pollution from human activities, particularly shipping, has been recognised as a serious threat to marine life. The sound generated by vessels can have various adverse effects on fish and aquatic ecosystems in general. In this setting, the estimation and analysis of the underwater noise produced by vessels is an important challenge for the preservation of the marine environment. In this paper we propose a model for the spatiotemporal characterisation of the underwater noise generated by vessels. The approach is based on the reconstruction of the vessels’ trajectories from Automatic Identification System (AIS) data and on their deployment in a spatiotemporal database. Trajectories are enriched with semantic information like the acoustic characteristics of the vessels’ engines or the activity performed by the vessels. We define a model for underwater noise propagation and use the trajectories’ information to infer how noise propagates in the area of interest. We develop our approach for the case study of the fishery activities in the Northern Adriatic Sea, an area of the Mediterranean Sea which is well known to be highly exploited. We implement our approach using MobilityDB, an open source geospatial trajectory data management and analysis platform, which offers spatiotemporal operators and indices improving the efficiency of our system. We use this platform to conduct various analyses of the underwater noise generated in the Northern Adriatic Sea, aiming at estimating the impact of fishing activities on underwater noise pollution and at demonstrating the flexibility and expressiveness of our approach.

Abstract In this paper we model spatio-temporal data describing the fishing activities in the Northern Adriatic Sea over four years. We build, implement and analyze a database based on the fusion of two complementary data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed) and fish catch reports (i.e., the quantity and type of fish caught) of the main fishing market of the area. We present all the phases of the database creation, starting from the raw data and proceeding through data exploration, data cleaning, trajectory reconstruction and semantic enrichment. We implement the database by using MobilityDB, an open source geospatial trajectory data management and analysis platform. Subsequently, we perform various analyses on the resulting spatio-temporal database, with the goal of mapping the fishing activities on some key species, highlighting all the interesting information and inferring new knowledge that will be useful for fishery management. Furthermore, we investigate the use of machine learning methods for predicting the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation in order to drive specific policy design. A variety of prediction methods, taking as input the data in the database and environmental factors such as sea temperature, waves height and Clorophill-a, are put at work in order to assess their prediction ability in this field. To the best of our knowledge, our work represents the first attempt to integrate fishing ships trajectories derived from AIS data, environmental data and catch data for spatio-temporal prediction of CPUE – a challenging task.

The vast diffusion of devices equipped with a GPS receiver has brought the possibility of collecting data related to massive amounts of moving objects on a scale never seen before. During the latest years, such diffusion instigated the development of many different techniques to deal with location prediction problems. Existing works mainly aim at predicting the next location of moving objects by focusing on information in the spatial domain. In this paper we want to take into account information in the temporal domain as well, both to improve the reliability of predictions and to answer not only where a moving object is going to move, but also when an object is expected to leave its current location.

Recent research has pointed out the needs and advantages of the semantic enrichment of movement data, a process where trajectories are partitioned into homogeneous segments that are annotated with contextual information. However, the lack of a comprehensive and well-defined framework for the enrichment makes this process difficult and error-prone. In this paper, we therefore propose a conceptual framework for the semantic enrichment of movement data, which benefits from the emerging Web of Data (or Linked Open Data) both as a unifying formalism and as the source of contextual data, which can be greatly useful for trajectories enrichment. Moreover, the semantic structure of such sources makes it easier to share and process enriched trajectories. We illustrate the enrichment process by presenting a case study in the tourism domain.

Despite recent progresses in methods for processing data about the movement of objects in the geographic space, some fundamental issues remain unresolved. One of them is how to describe movement segments (e.g., semantic trajectories, episodes like stops and moves) and diverse movement patterns (e.g., moving clusters, hotel-restaurant-shop-hotel), with formal semantic descriptions. Another issue is how to arrange descriptive data and measures in a Movement Data Warehouse (MDW) for powerful information analyses and reasonable performance. This paper introduces general definitions for movement segments, movement patterns, their categories and hierarchies. The proposed constructs are semantically enriched with references to concepts (categories) and/or instances of these concepts (objects) arranged in distinct hierarchies. Based on these constructs, we propose a semantic multidimensional model for MDW. A case study illustrates the expressiveness of the proposal for analyzing movement data collected via social media and semantically enriched with Linked Open Data (LOD).

Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in the data management world, the challenge after storing the data is the implementation of appropriate analytics for extracting useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. Thus, in this work, we demonstrate a framework that transforms the traditional data cube model into a trajectory warehouse. As a proof-of-concept, we implemented T-WAREHOUSE, a system that incorporates all the required steps for Visual Trajectory Data Warehousing, from trajectory reconstruction and ETL processing to Visual OLAP analysis on mobility data.

We propose a formalism and a programming environment in which sophisticated spatio-temporal reasoning can be performed, while keeping the capabilities of manipulating and presenting large amounts of geographical data, typical of commercial Geographical Information Systems (GISs). The spatio-temporal knowledge representation language, named MuTACLP+, is based on constraint logic programming and is integrated via a middleware of commands and translation features with a commercial GIS. The paper presents the language, the architecture of the environment, and a few examples of its use in the field of event planning.