Http://www.tandfonline.com/eprint/p4CaesjPNyeeSiHGCxpX/fullInternational audienceWhen it comes to characterize the distribution of 'things' observed spatially and identified by their geometries and attributes, the Shannon entropy has been widely used in different domains such as ecology, regional sciences, epidemiology and image analysis. In particular, recent research has taken into account the spatial patterns derived from topological and metric properties in order to propose extensions to the measure of entropy. Based on two different approaches using either distance-ratios or co-occurrences of observed classes, the research developed in this paper introduces several new indices and explores their extensions to the spatio-temporal domains which are derived whilst investigating further their application as global and local indices. Using a multiplicative space-time integration approach either at a macro or micro-level, the approach leads to a series of spatio-temporal entropy indices including from combining co-occurrence and distances-ratios approaches. The framework developed is complementary to the spatio-temporal clustering problem, introducing a more spatial and spatio-temporal structuring perspective using several indices characterizing the distribution of several class instances in space and time. The whole approach is first illustrated on simulated data evolutions of three classes over seven time stamps. Preliminary results are discussed for a study of conflicting maritime activities in the Bay of Brest where the objective is to explore the spatio-temporal patterns exhibited by a categorical variable with six classes, each representing a conflict between two maritime activities

High resolution estimates of bottom towed fishing gears are needed to provide relevant information for natural resource management, impact assessment and maritime spatial planning. The use of satellite-based vessel monitoring system (VMS) data is constrained by data access restrictions as well as rather coarse data resolution. This study focuses on mapping dredge gear fishing grounds using fishing effort estimates at the métier level based on automatic identification system (AIS) data. The performance of the approach was evaluated in terms of correct discrimination between fishing and non-fishing activities for known fishing positions as well as appropriate error propagation. The test was conducted in the Bay of Brest (France) in partnership with a committee of local fishers. The results identified dredge fishing grounds for great scallop (Pecten maximus) in the western part of the Bay of Brest and provided high-resolution information for scientists and local decision makers on the spatial and temporal seasonal variability of fishing effort. The proposed method is semi-automatic and generic making it suitable for other applications.

This contribution addresses both the role of geographical information in participatory research of coastal zones, and its potential to bridge the gap between research and coastal zone management. Over a one year period, heterogeneous data (spatial, temporal, qualitative and quantitative) were obtained which included the process of interviews, storing in a spatio-temporal database. The GIS (Geographic Information System) produced temporal snapshots of daily human activity patterns allowing it to map, identify and quantify potential space-time conflicts between activities. It was furthermore used to facilitate the exchange of ideas and knowledge at various levels: by mapping, simulation, GIS analysis and data collection. Results indicated that both captured data and the participatory workshop added real value to management and therefore it was deemed well managed by stakeholders. To incorporate a dynamic GIS would enhance pro-active integrated management by opening the path for better discussions whilst permitting management simulated scenarios.