Modelling of human activity development in coastal sea areas


As the marine fringes of coastal zones, coastal seas are particularly affected by human activities interacting among themselves, sometimes in a conflicting way, and with their environment. In order to analyse these interactions, a modelling platform of Human Activities Dynamics (DAHU) is developed, including a module adapted to the description of marine human activities. The methodology is based on a multi-agent type modelling approach combined with GIS. The prototype is used to implement forecasting scenarios simulating the development of one or several activities throughout the year and the impact of various events (accidental sea pollution, infrastructure development…) on their dynamics. The aim of this process is to produce relevant information to assist operational space use management in coastal sea areas.


As the marine fringes of coastal zones, coastal seas are increasingly coveted by human societies (Cicin-Sain and Knetch 1998; Kay and Alder 2005). Many activities are developed therein: commercial fishing, sea transport, materials extraction, military drills, dredging disposal, infrastructure development, marine farming, leisure activities…

These space- and resource-consuming activities can have an impact on the coastal system’s functioning and quality (environment and landscape degradation, resource depletion, land and sea pollution…). The increase in pressure on coastal sea areas over the past decades moreover generates functional, sometimes conflicting, interactions between the various existing activities (CEC 1999; Filho et al. 2008; Goldberg 1994; Johnson and Pollnac 1989; Le Tissier et al. 2004). The analysis of Man/Environment interactions is thus one of the key research goals for supporting the sustainable development of coastal societies (Coelho et al. 2003; Dronkers and Vries 1999). The study of environment utilisation and exploitation methods by Man in the spatial and temporal double perspective is an essential component thereof (Holligan 1994; Vallega 1999).

In this context, the marine environment presents specific problems. In this open space, traditionally considered as “a space of freedom,” it is particularly complex to comprehend how human activities are carried out, being subject to strong natural constraints. No fixed physical limits can be established therein to assign a space to an activity, and various activities can coexist in a same zone at the same time. Considering the temporal dimension is therefore all the more important. Furthermore, there is little structured information describing marine activities in detail and providing a global view of their development. Available data are often combined and not always connected with geographical spaces and practice periods. The quantification of activity development is then often difficult to establish. The marine environment is thus a space that can be qualified as “vague” where the analysis of human activity development is particularly complex. In order to comprehend these complex phenomena, given the large number of variables interacting on various time and space scales, resorting to a modelling approach proves to be relevant.

Recognised in terms of their representation and analysis functions, GIS favour a multidisciplinary systemic approach of coastal zones (Bartlett 1999; Longhorn 2003; Vallega 2003). However, current GIS are mainly based on an atemporal view of the geographical space and are poorly adapted to the study and representation of dynamic processes. The GIS/models combination is thus one of the challenges the geomatics field is currently engaged in (Claramunt and Theriault 1995). This contribution describes the DAHU modelling platform (Dynamique des Activités Humaines—Human Activities Dynamics) (Cuq 2001; Tissot 2003; Tissot et al. 2004) and, more specifically, its module on Marine Activities (DAHU-MAM) (Le Tixerant 2004; Le Tixerant et al. 2003). It is based on multi-agent type modelling combined with GIS aiming to describe human activity development in space and time. The approach is built on three stages: the development of a conceptual model used as a basis to designing a simulator in which forecasting scenarios are tested.

Methodological approach

There are increasing attempts toward models/GIS dedicated to marine applications combinations, which can relate, for instance, to pollution control (Howlett 1997; Pelot and Plummer 2008), fishery management (Maury and Gascuel 1999) or maritime navigation aid (Fournier et al. 2003; Goralski and Gold 2007). Studies aiming to modelise activity development in coastal sea areas and to analyse interactions, whether reciprocal or with the environment, are nonetheless fairly uncommon. The DAHU-MAM platform was designed in this prospect.

Conceptual method

The conceptual method aims to develop an approach for systemic modelling of marine activities in order to build a relevant model of reality. It meets the need to formalise a consistent simulation space integrating all the socio-economic, regulatory and environmental factors affecting the development of modelised activities.

Main characteristics

This first stage aims to provide a typology of the main human activities in coastal sea areas and to identify their thematic, spatial and temporal characteristics.

The main scientific contributions referring to Man/Environment interaction modelling stress the complexity of integrating spatial and temporal dimensions in the proposed approaches (Claramunt et al. 1999; Jacquez et al. 2005; Parent et al. 1999; Snodgrass 1992). Yet the harmonisation of a large number of heterogeneous data relating to human activities in coastal zones involves replacing them into a known spatiotemporal context. To this end, the proposed approach is based on the fact that human activity development is conditioned by numerous constraints of varied nature and origin, the most significant of which are due to environmental elements, long-term climate change and short-term meteorological conditions, institutional and legal context, and socio-economic conditions. To highlight activity development, it is thus necessary to use a method that can integrate these various constraints. In our approach, a spatiotemporal filter is associated with each practice constraint impacting on the activity’s development. Activity development modelling in time and space is carried out by simultaneously taking these various filters linked to statistical data into consideration.

Spatiotemporal filters

  • “Environmental constraints” filter

    Human activities in coastal sea areas must adapt to the physical characteristics of the environment: hydrology, bathymetry, submarine geomorphology, tides and associated currents… For instance, bathymetry and hydrology have a determining impact on maritime navigation. Deep-draught boats do not have access to particular areas. In the halieutic field, working with bottom-dragging gear (deep-sea trawling or shellfish dragging) is unlikely to occur on rocky seabeds. High tide coefficients can generate currents that are too strong to carry out activities such as netting.

    In the case of living resource exploitation, it is also essential to synthesise acquired knowledge on its biology (e.g.: reproduction periods, habitat…) to better understand exploitation methods.

    In marine environments, meteorology is a major constraint acting directly on activity development. Main parameters are wind (strength and direction), swell (height, period, direction) and sea state (resulting from swell and wind). In violent storms some vessels may therefore be forced to reach sheltered mooring areas or to stay in the harbour.

  • “Regulatory constraints” filter

    In marine environments, it is relevant to analyse the effects of regulations on human activity development in space and time as they direct activities, particularly in terms of geographic expansion, period and intensity. In most cases, there are indeed no fixed physical limits facilitating the assignment of a space to an activity. They are replaced by regulatory-type virtual limits. In marine environments, regulations thus take on particular importance and are a relevant filter to identify zones and periods in which an activity is developed.

  • “Socio-economic constraints” filter

    Human activity development at sea also depends on the socio-economic context in which activities evolve. This parameter is particularly complex to integrate insofar as it often greatly exceeds the area of study. Economically non cost-effective practice zones and/or periods can however be identified. For instance, in the case of the exploitation of a biological or mineral resource, there can be zones where the resource is indeed present yet where practice is not cost-effective due to the zone’s remoteness or poor accessibility. There will thus be no or very little practice in the area considered.

    Taking into account these various spatiotemporal filters simultaneously should thus facilitate delimiting activity development within potential practice periods and zones.

    In order to refine information and provided data are available, it is also possible to integrate the “probable practice” periods and areas identified as actually used for human activities. Within potential practice areas, there are indeed spaces where activity likelihood is more significant. This “probable practice” filter thus provides the final information to describe activity development in coastal sea areas. To have access thereto, close cooperation with professionals is thus essential (De Clers 2004; Prigent et al. 2008), especially when small-scale activities are involved such as inshore fishing for which there is little descriptive information available and there are many informal codes of practice organising the access to the resource.

Potential Practice Territory

A Potential Practice Territory (PPT) is established for each activity. An activity’s PPT is the outcome of the spatial projection of practice constraints and the superimposition of the various spatial filters thereby obtained.

  • Spatial projection of constraints

The main constraints that have an impact on the activity’s spatial development are taken into account for each activity. They tally with thematic layers formatted in a Geographic Information Base (GIB) processed by GIS (Fig. 1).

Fig. 1

Potential Practice Territory (PPT) construction method

  • Simultaneous integration of spatial filters

GIS spatial analysis functions are used to superimpose the various GIB layers into a single layer taking all of the practice conditions into account. This analysis layer, composed of coded polygons specifying whether the modelised activity is likely to be developed or not, describes the activity’s Potential Practice Territory (Fig. 1).

In the case of marine activities, this first modelling stage is fundamental as, to the contrary of land activities that can be described on the basis of fixed administrative limits, there is no analogous functional space at sea. A practice territory must therefore be established for each marine activity.

Potential Practice Schedule

As a complement to this spatial approach, the Potential Practice Schedule (PPS) highlights the periods in which the activity is likely to be carried out contingent on constraints that impact on its development over time. An activity’s PPS is determined by projecting practice constraints in time and superimposing the various temporal filters thereby obtained.

  • Temporal projection of constraints

The main constraints impacting on the temporal development of an activity are considered for each activity and over a one-year cycle, every constraint constituting a temporal filter to be taken into account (Fig. 2). In practice, each filter consists of an attribute table coded according to Boolean logic (presence/absence).

Fig. 2

Potential Practice Schedule (PPS) construction methodology

  • Superimposing temporal filters (Fig. 2)

Periods in which practice is potential or improbable can be identified in this stage, over a one-year cycle.

In the theoretical case presented in Fig. 2, the temporal filters used are: a regulatory filter (prohibited practice in February and March), a climate filter (improbable practice from December to January), a tide coefficient-related filter (improbable practice if coefficients are greater than 100) and a filter related to socio-economic conditions (improbable practice from late March to late April). The superimposition of the various filters thus facilitates identifying potential practice periods and improbable practice periods. In early June, practice is potential. All of the practice conditions are indeed favourable to the activity’s development. On the other hand, practice is improbable from December to February due to unfavourable climatic conditions. It is also improbable in late March due to economic conditions. In mid-May, tide coefficients make the activity’s operation improbable.


Once the potential practice areas and periods are identified, it is relevant to associate them with statistical data to quantify the activities in order, for instance, to highlight flows (navigation flow) or to estimate a level of pressure on the environment (number of fishing vessels present in an area over a given period). This information can most generally be obtained from sea-competent administrations, professional or producer organisations, scientific organisations studying activities at sea and through regulations that can restrict the number of vessels authorised to operate in a given area, for specific activities (number of licences, quotas…).

At this stage of the methodology, the range of data produced (PPT, PPS, statistics) are not interconnected. The next stage thus consists in linking them in the DAHU-MAM simulator.


In terms of design, the model’s architecture is based on a Multi-Agent System (MAS) in which each activity is represented by an autonomous agentFootnote 1 that reacts to the environment’s conditions (Weiss 1999; Wooldridge 2002). The simulation phase complies with a standard operation pattern: input data preparation, simulation and results processing. The system is interfaced with GIS which it relies on to format initialisation data (preprocessor) and to process results (postprocessor).

Input data preparation (preprocessor)

The simulator’s input data are first harmonised and formatted with a preprocessor. There are two stages in this operation. Spatial (PPT), temporal (PPS) and statistical information is integrated into a Relational Database (RDB). The user then selects simulation parameters, namely the activities and simulation period, thereby resulting in the creation of the corresponding agents. For each agent, a query provides the data required for the simulation (PPT, PPS, statistics, meteorological conditions over the period). At this stage, each activity is virtually represented as an initialised autonomous agent.

Stimulation/response of agents (processor)

In the processor, the autonomous agent reacts (presence/absence) by means of daily iterationFootnote 2 over the simulation period chosen on the basis of a stimuli/response principle (Fig. 3). In other words, the agents are first stimulated by the “perception” of the environment’s conditions then they react according to a predefined reflex behaviour before running a response, which is the simulation’s outcome.

Fig. 3

Theory of operation of the simulator

The agents’ response consists of attribute tables including the variables used to quantify and qualify the development of simulated activities. These tables are integrated into the Relational Database (RDB) and are structured with reference to the geographic information layer relating to the potential practice territories. They are thus directly processable by GIS.

Processing results in GIS (postprocessor)

GIS operates in the archiving, the representation and potentially the spatial analysis of simulation results. Obtained results consist of interactive dynamic maps synthesising the information that can highlight activity development, i.e. the potential practice territory which meteorological conditions, regulatory constraints and statistical data are associated with.


Forecasting scenarios are sequences of hypothetical events developed to highlight causal processes and decision stakes (Chermak et al. 2001). They are based on a reference state and consider projections contingent on hypotheses for the variables at stake. Model-based scenarios are increasingly implemented in the field of future environmental trends (Martelli 2001).

DAHU-MAM can be used in two types of scenarios:

  • Trend scenarios: e.g. the development of a single activity or of several marine activities simultaneously on different dates;

  • Factual scenarios: e.g. temporary impossible access following an event (accident at sea, operation on leaking wrecks, military drills, establishment of a prohibited access area…) or sustainable infrastructure development (wind turbines, platform, artificial reefs…). In both cases, the impact of these events on activity development is assessed.

An illustration of these types of scenarios is provided in examples from the Iroise Sea (Finistère, France), which is a relevant area of study due to the numerous potentially conflicting activities developed therein.

Trend scenario example

Following the development of a typology of activities recorded in the Iroise Sea, commercial fishing appeared as a prevailing activity (Alban and Boncoeur 2005).

The first scenario example refers to scallop fishing in the Iroise Sea. The constraints impacting on the operation of mobile bottom gear are numerous: physical (fishing impossible on rocky seabeds), regulatory (classified layers instituting fishing areas and periods with restricted or prohibited access), water quality and meteorology (small size of vessels). Contingent on these constraints, map results show the variability of the activity’s development according to the dates selected during fishing season (Fig. 4).

Fig. 4

Scallop-dragging simulation on different dates

This type of simulation can provide assessments (on a daily, monthly or annual basis) on the number of vessels that operated in a specific zone over a given period, and thereby contribute to estimating an activity’s impact on a resource. It can apply to several activities with distinct developments in space and time, and highlight positive (complementary activities) or negative (generating potential space use conflicts) interactions. In the western Iroise Sea (Fig. 5), seabeds (gravel and sand) are favourable to trawling while the area also provides potentialities to netting boats, which must however take fairly strong currents into consideration (especially during spring tides). The outcome of this simulation shows that netting, trawling and materials extraction activities can be developed at the same time in identical zones. Given the competition for space occupation and/or access to the resource that can sometimes exist between these activities, this situation could thus generate potential space use conflicts.

Fig. 5

Simulation of bottom trawl/large mesh net/small mesh net/materials extraction/commercial navigation on the same date (Thursday 10/02/2000)

Factual scenario examples

Maritime accident simulation

Due to intense maritime navigation off the area of study (Ouessant Traffic Separation Scheme—TSS), the Iroise Sea is particularly vulnerable to maritime traffic hazards. Many accidents have already occurred in the area: Amoco Cadiz (16/03/1978), Gino (28/05/1979), Melbridge Bilbao (12/11/2001)… The current organisation of the fight against accidental sea pollution in France is implemented by the POLMAR Instruction of 4 March 2002, applicable not only to hydrocarbons but also to the disposal of any substances likely to endanger marine environments. When accidental pollution occurs, the competent authorities (Maritime Prefecture in France) can decide to ban fishing activities in a given area for a specific period. It can then be useful to assess the potential impact on activity development and the economic effects this temporary measure could lead to:

  • in real-time, for the authorities in charge of making the decision to ban fishing (term of prohibition, support in defining priority protection areas);

  • and subsequently, as a compensation process intended to fishermen is implemented.

The simulation results produced by DAHU-MAM provide access to this type of information by highlighting the activities directly affected by this measure, the number of fishing days lost and the number of vessels involved (Fig. 6).

Fig. 6

Shipwreck scenario on 05/10/2000, Molène Island

Sustainable development simulation

The commitments of France as regards greenhouse gas emissionsFootnote 3 impose a voluntarist policy for the development of renewable energies (Bhuyan et al. 2008). As it has the second potential of electricity production by wind power stations at sea in Europe, this industry is thus likely to develop. In the case of a project aiming to establish wind turbines at sea, impact studies tackle technical (wind measurement, site power, maintenance plan, network connection), environmental (site impacts on the environment) and socioeconomic constraints (Kannen et al. 2004). Regarding this last point, it is particularly necessary to assess the sites’ potential impact on existing human activities, which can be divided into two groups according to the level of disturbance (CA-OWEE 2001).

  • Activities likely to be highly affected by the development of wind turbines at sea are maritime navigation and mobile-gear fishing. Due to collision hazards, the main waterways and harbour approach channels must obviously be banned. Mobile-gear operations (trawls, drag-nets, lines…) are also likely to be disturbed by the development of wind turbines at sea. The same can also apply to activities using highly limited spaces where the establishment of wind turbines could virtually make activity development impossible.

  • Activities likely to be less affected by the development of wind turbines at sea are fixed-gear fishing (nets, pot gear)Footnote 4 and recreational fishing.

In order to highlight an “optimal development area” for a wind farm project in the Iroise Sea, territories for potential activity practice are superimposed according to the level of disturbance generated. In a multi-use context, simulation leads to a potential conflict risk assessment depending on the development sites (Fig. 7).

Fig. 7

Optimal development area for a wind farm in the Iroise Sea in terms of space use conflict risks

Discussion- Conclusion

The proposed methodology aims to analyse human activity development in coastal sea areas, in space and time. Our conceptual approach facilitates preliminary data formatting and provides the grounds for a likely model of reality of activity development. Contingent on the various practice constraints, spatiotemporal modelling of the activity’s development is achieved in the form of potential practice schedules and territories. The assessment of impacts on the environment is considered by taking into account the statistical data quantifying the activity. The DAHU-MAM simulator interconnects the data provided by the conceptual approach to reach a daily description of activity development throughout the year.

On the basis of the proposed methodology, multi-source and multi-scale data from varied fields and collected among a range of actors can be integrated into a common repository. The originality of this methodology lies in favouring a territorial approach and providing the opportunity to integrate natural and anthropogenic constraints evolving on scales and at a time pace that are sometimes very different. It provides a global cross-industry view of human activity development in coastal sea areas and constitutes a platform that is sufficiently developed and structured to facilitate the integration of additional data in order to respond to specific applications.

Its transposition to an operational context however implies validation of the model. Yet the confrontation of simulation results with field observations is particularly complex. This would indeed require a system that can localise and identify all the activities operated in the area of study over a given period; and this does not currently exist. Validation of obtained results is thus limited due to the unavailability of this information that would be required for the calibration and validation of the model. Nonetheless, solutions providing partial validation of the model’s outputs can be considered by confronting the results produced by the model with the observations made by the semaphores located along the coast (traffic monitoring and daily vessel circulation data gathering) and/or the processing of location data (GPS) supplied by the beacons on board the vessels (taking into account that the great majority of coastal fishing boats are currently non-equipped). Generally speaking, working as close as possible to field reality in close cooperation with local actors, professional organisations and competent authorities in charge of management at sea would be essential.

A research promotion projectFootnote 5 is currently underway in order to design and develop a simulator for the dynamics of human activities in coastal sea areas with operational goals. It will provide innovative services in the field of support to the management of space use in coastal sea areas, based on a product that is sufficiently flexible to respond to various applications:

  • standard management of marine space by providing a global view of daily activity development (management and monitoring of fishing fleets, daily management of a Marine Protected Area…);

  • management of environmental hazards by contributing to assessing human activity pressure on the environment;

  • crisis management by providing fast access to appropriate information (determining a period and/or area of forbidden presence at sea following an accident, assistance to the implementation of compensation processes…);

  • combined management between the numerous actors of the coastal zone.

Integrated Coastal Zone Management implies a systemic basis and the appropriation of the stakes and knowledge by civil society actors (Kay and Alder 2005; UNESCO 2001). In this regard, the question concerning the conditions of transfer of knowledge and information from scientists to local actors arises (Hastings and Fischer 2001; Jude 2008), and conversely (Prigent et al. 2008; Rockloff and Lockie 2004). Innovative approaches are currently being tested in the coastal zone in order to support collaborative processes through virtual scenario simulation (Jude et al. 2003, 2007; Jude 2008). They are based on geographic information technology (Virtual Reality GIS, visualization packages) promoting visualisation and perception of the territory. The aim is to optimise strategies for management, mediation and participation of actors. The approach proposed by DAHU-MAM and developments underway intend to contribute thereto.


  1. 1.

    An agent is a physical or computerised entity that is able to sense and act upon its environment, that only has a partial representation of its environment (sometimes none), that can communicate with other agents, whose target is individual, that is skilled and that can potentially reproduce. Its final behaviour is the outcome of its targets, its perception, its skills and the communication it may establish with other agents (Weiss 1999).

  2. 2.

    Iteration is a sequence of instructions (calculational loop) designed to be run several times (at a daily time pace in our case).

  3. 3.

    EU directive of 27 September 2001 relating to the promotion of electricity produced from renewable energy sources, aiming to achieve, for France, a goal indicative of the consumption of electricity produced from renewable energies amounting to 21% by 2010, as against 15% in 1997.

  4. 4.

    Wind turbine foundations can be designed to become artificial reefs and thus potentially have a beneficial impact on the resource.

  5. 5.

    SIMARIS project funded by Brittany’s Regional Council based on a company (Terra Maris) and research laboratory (Géomer / UMR 6554 CNRS) partnership.


  1. Alban F, Boncoeur J (2005) Commercial fishing, recreational fishing and tourism: investigating the potential for developing a pluri-activity. The case of the Iroise sea, Western Brittany, France. Working Paper Series, Online publications, p. 19 p

  2. Bartlett D (1999) Working on the frontier of Science: applying GIS to the Coastal Zone. In: Wright D, Bartlett D (eds) Marine and coastal GIS. Taylor & Francis, London, pp 11–24

    Google Scholar 

  3. Bhuyan G, Bard J, Huckerby J, Teresa P, Melo AB (2008) International collaboration and role of the Implementing Agreement on Ocean Energy Systems (IEA-OES). Second International Conference on Ocean Energy (ICOE), Brest (France)

  4. CA-OWEE (2001) Offshore Wind Energy. Ready to power a sustainable Europe. Duwind 2001.006, Delft University of Technology, The Netherlands, Delft University Wind Energy Research Institute (Duwind)

  5. CEC (1999) Lessons from the European Commission’s demonstration programme on integrated coastal zone management. Commission of the European Communities, Luxembourg

    Google Scholar 

  6. Chermak TJ, Lynham SA, Ruona WEA (2001) A review of scenario planning literature. Future Res Q 17(2):7–31

    Google Scholar 

  7. Cicin-Sain B, Knetch RW (1998) Integrated coastal and ocean management, concepts and practices. Island, Washington, 517 p

    Google Scholar 

  8. Claramunt C, Theriault M (1995) Managing time in GIS: an event-oriented approach. In: Clifford J, Tuzhilin A (eds) Recent advances in temporal databases. Springer, Berlin, pp 23–42

    Google Scholar 

  9. Claramunt C, Parent C, Spaccapietra S, Thériault M (1999) Database modelling for environmental and land use changes. In: Geertman S, Openshaw S, Stillwell J (eds) Geographical information and planning: European perspectives. Springer, Berlin

    Google Scholar 

  10. Coelho C, Scott M, Istemil G, Williams AT (2003) Environmental impacts of coastal developments and activities. In: Ozhan E (ed) Medcoast 03. Middle East Technical University, Ankara, pp 121–132

    Google Scholar 

  11. Cuq F (2001) Analyse des interactions entre les actions humaines et le milieu littoral: une approche discrète de l’impact des activités anthropiques. CoastGIS’01, Halifax (Canada)

  12. De Clers S (2004) Connecting with fisheries: coastal fishermen’s maps and ecosystem description. Littoral 2004, Aberdeen, Scotland, UK, pp 377–320

  13. Dronkers J, Vries I (1999) Integrated coastal management: the challenge of transdisciplinarity. J Coast Conserv 5:97–102. doi:10.1007/BF02802745

    Article  Google Scholar 

  14. Filho W, Brandt N, Krahn D, Wennertsen R (2008) Conflict resolution in Coastal Zone management. Peter Lang Pub Inc, Frankfurt am Main, 246 p

    Google Scholar 

  15. Fournier S, Devogele T, Claramunt C (2003) A role-based multi-agent model for concurrent navigation systems. 6th AGILE Conference on Geographic Information Science. Presse Polythechniques et Universitaires Romandes, pp 623–632

  16. Goldberg ED (1994) Coastal zone space - prelude to conflict? IOC Ocean Forum I. Unesco Publishing, Paris, 138 p

    Google Scholar 

  17. Goralski RI, Gold CM (2007) The development of a dynamic GIS for maritime navigation safety. ISPRS Workshop on Updating Geo-spatial Database with imagery & The 5th ISPRS Workshop on DMGISs, pp 47–50

  18. Hastings RM, Fischer DW (2001) Management priorities for Magdalena Bay, Baja California, Mexico. J Coast Conserv 7:193–202. doi:10.1007/BF02742481

    Article  Google Scholar 

  19. Holligan PM (1994) Land Ocean Interaction in the Coastal Zone (LOICZ): implementation plan. IGBP, Stockholm, 215 p

    Google Scholar 

  20. Howlett E (1997) Environmental and geographical data management tools for oil spill modelling applications. 20th Artic and Marine Oilspill Program (AMOP) technical seminar, vol. 2. Environment Canada, Vancouver, pp 893–908

    Google Scholar 

  21. Jacquez GM, Goovaerts P, Rogerson P (2005) Space-time intelligence systems: technology, applications and methods. J Geogr Syst 7(1):1–5. doi:10.1007/s10109-005-0146-7

    Article  Google Scholar 

  22. Johnson JC, Pollnac RB (1989) Introduction to managing marine conflicts. Ocean Shorel Manag 12:191–198. doi:10.1016/0951-8312(89)90002-7

    Article  Google Scholar 

  23. Jude SR (2008) Investing the potential role of visualization techniques in participatory coastal management. Coast Manage 36:331–349. doi:10.1080/08920750802266346

    Article  Google Scholar 

  24. Jude SR, Jones AP, Bateman IJ (2003) Investigating the potential design and application issues associated with using visualisation techniques in coastal decision-making. CoastaGIS’03, Genova (Italia)

  25. Jude S, Jones AP, Watkinson AR, Brown I, Gill JA (2007) The development of a visualization methodology for integrated coastal management. Coast Manage 35:525–544. doi:10.1080/08920750701593378

    Article  Google Scholar 

  26. Kannen A, Gee K, Glaeser B (2004) Offshore Wind Farms, spatial planning and the German ICZM strategy. Littoral 2004, Aberdeen (Scotland), pp 450–455

  27. Kay R, Alder J (2005) Coastal planning and management. Taylor & Francis, London, 380 p

    Google Scholar 

  28. Le Tissier MDA, Hills JM, McGregor JA, Ireland M (2004) A training framework for understanding conflict in the coastal zone. Coast Manage 32(1):77–88. doi:10.1080/08920750490247517

    Article  Google Scholar 

  29. Le Tixerant M (2004) Dynamique des activités humaines en mer côtière; application à la mer d’Iroise. Thèse de géographie, Université de Bretagne Occidentale, 210 p

  30. Le Tixerant M, Rouan M, Cuq F, Gourmelon F (2003) Simulation of Human Activities Dynamics (DAHU) in marine environment. CoastGIS’03, Genova, Italy

  31. Longhorn RA (2003) Coastal/Marine GI/GIS—a Pan European perspective. Coastal and Marine Geo-Information systems. Springer, Berlin, pp 35–39

    Google Scholar 

  32. Martelli A (2001) Scenario building and scenario planning: state of the art and prospects of evolution. Future Res Q 17(2):57–74

    Google Scholar 

  33. Maury O, Gascuel D (1999) SHADYS (Simulateur HAlieutique de Dynamiques Spatiales), a GIS based numerical model of fisheries. Example application: the study of a marine protected area. Aquat Living Resour 12:77–88. doi:10.1016/S0990-7440(99)80018-7

    Article  Google Scholar 

  34. Parent C, Spaccapietra S, Zimanyi E (1999) Spatio-temporal conceptual models: data structures + space + time. Advance in GIS, Kansas City (USA)

  35. Pelot R, Plummer L (2008) Spatial analysis of traffic and risks in the coastal zone. J Coast Conserv 12:201–207. doi:10.1007/s11852-008-0026-7

    Article  Google Scholar 

  36. Prigent M, Fontenelle G, Rochet MJ, Trenkel VM (2008) Using cognitive maps to investigate fishers’ ecosystem objectives and knowledge. Ocean Coast Manag 51:450–462. doi:10.1016/j.ocecoaman.2008.04.005

    Article  Google Scholar 

  37. Rockloff SF, Lockie S (2004) Participatory tools for coastal zone management: use of stakeholder analysis and social mapping in Australia. J Coast Conserv 10:81–92. doi:10.1652/1400-0350(2004) 010[0081:PTFCZM]2.0.CO;2

    Article  Google Scholar 

  38. Snodgrass RT (1992) Theories and methods of spatio-temporal reasoning in geographic space. In: Formentini U, Campari I, Frank AU (eds) Temporal databases. Springer, Berlin, pp 22–64

    Google Scholar 

  39. Tissot C (2003) Evaluation de la variabilité des activités humaines dans l’espace et dans le temps. Application à l’étude des pratiques agricoles intensives dans le département du Finistère. Thèse de géographie, Université de Bretagne Occidentale, 234 p

  40. Tissot C, Le Tixerant M, Gourmelon F, Le Berre I (2004) Modeling interactions between human activities and costal zone environment. Littoral 2004, Aberdeen (Scotland)

  41. UNESCO (2001) A methodological guide to integrated coastal zone management (n° 42)

  42. Vallega A (1999) Fundamentals of integrated coastal management. Kluwer Academic, Dordrecht, p 288

    Google Scholar 

  43. Vallega A (2003) From Rio to Johannesburg: the role of coastal GIS. CoastGIS’03, Genova (Italia)

  44. Weiss G (1999) Multiagent systems. A modern approach to distributed artificial intelligence. MIT, Cambridge, 619 p

    Google Scholar 

  45. Wooldridge M (2002) An introduction to MultiAgent Systems. Wiley, New York, 348 p

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Matthieu Le Tixerant.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Le Tixerant, M., Gourmelon, F., Tissot, C. et al. Modelling of human activity development in coastal sea areas. J Coast Conserv 15, 407–416 (2011).

Download citation


  • Coastal sea
  • Human marine activities
  • GIS
  • Spatiotemporal modelling
  • Simulation
  • Scenarios
  • Management support