Next Article in Journal / Special Issue
The Application of WebGIS Tools for Visualizing Coastal Flooding Vulnerability and Planning for Resiliency: The New Jersey Experience
Previous Article in Journal
Monitoring Geologic Hazards and Vegetation Recovery in the Wenchuan Earthquake Region Using Aerial Photography
Previous Article in Special Issue
An Operational Web-Based Indicator System for Integrated Coastal Zone Management
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

A Dynamic GIS as an Efficient Tool for Integrated Coastal Zone Management

CNRS LETG-Brest, Institut Universitaire Européen de la Mer (UBO), Technopôle Brest-Iroise, Plouzané 29280, France
Pôle Halieutique, UMR ESE, Agrocampus Ouest, European University of Brittany, Rennes 35042, France
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2014, 3(2), 391-407;
Received: 19 September 2013 / Revised: 26 February 2014 / Accepted: 5 March 2014 / Published: 26 March 2014
(This article belongs to the Special Issue Coastal GIS)


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.

Graphical Abstract

1. Introduction

Many diverse activities exist along coastal seas [1] playing an essential role in human society [2]. Yet, they quite often result in conflicting interactions [3] and therefore present an ongoing challenge for both society and research [4,5]. Pittman et al. [6] identified priority needs to describe spatio-temporal distribution of activities and to better examine existing or potential conflicts. Integration of temporal components, within a multi-activities context, consists of macro- and meso-scales (world to regional) to better assess intensity indexes for each activity [7,8,9,10]. Furthermore, spatial researches of social dimensions on coastal/marine environments have progressed significantly [11]. Yet studies considering small spatial scales relevant to local marine planning, by example conducted by Le Tixerant et al. [12] in Iroise Sea (France) or by Longdill et al. [13] in the Bay of Plenty (New Zealand), still remain scarce. The identification of various potential interactions was distinguished by superimposing the activity zones [14,15,16]. Spatial intersections were subsequently related to different variables (i.e., the cumulative number of activities, the presence/absence and degree of potential conflicts; and the density of activity per unit of surface area). However, the temporal dynamics were not considered in these approaches.
Opdam [17] and others argue that communication between science and society constitutes a relevant tool to optimize any planning and management. Thus, any incentives to enhance discussions and understanding of various opinions made by stakeholders (decision-makers, experts, general public, involved in local planning) should be promoted. Therefore, spatial planning paves the way for such an approach. It would incorporate a collaborative process whereby allowing multiple stakeholders to actively brainstorm various strategies within a given area. This process has existed for many decades although this is moderately due to the advancements of information technology, such as the GIS and GIS-based tools that illustrate different scenarios [14]. Previous studies have demonstrated the value of GIS in the participatory process of integrated land-use planning, by measures of supporting local and expert spatial knowledge [18,19,20]. In fact, several involved the combined processes of virtual scenario simulations, particularly in coastal areas [21,22]. However, these studies relied strongly on geographic information technology to optimize management strategies and public participation in integrated management stakes [23,24,25]. Yet, few studies have explored the evaluation of interactive spatial support tools [20,26]. Hence, this contribution aims to: (i) provide a GIS-based method to better understand the spatio-temporal distribution of supervised maritime activities; (ii) identify potential conflicts between these activities; (iii) develop an approach to share data, information and knowledge among stakeholders; and (iv) test the contribution of this approach for better exchanges between stakeholders toward collective actions and scenarios.

2. Study Area

The Bay of Brest, located on the most western tip of Brittany (France) (Figure 1), is a maritime basin of 180 km2 and of 8 m depth. Three types of maritime activities exist within this area: commercial fishing, maritime transportation and nautical activities (windsurfing, sailing, kayaking, rowing, and scuba-diving) (Table 1). Like many coastal zones around the world, the Bay of Brest faces potential conflicts among the increasing number of sea space users. All activities must comply with the ever growing regulations and coastal policies (Natura 2000 and ICZM). Natura 2000 is a European network of natural protected areas established under the 1992 Habitats Directive. For each Natura 2000 area, a management plan must be established after a consultation procedure with stakeholders. France is one of the European countries that conducts the decentralized and contractual approach for all activities in Natura 2000 areas [27,28]. In 2012, two Natura 2000 sites were designed in the Bay of Brest, under the responsibility of the Armorique Natural Park. The Bay of Brest is also concerned with Integrated Coastal Zone Management (ICZM) policy, issued by the European Commission. ICZM requires its state members to establish management strategies by which maritime spatial planning (Maritime policy or “Blue book”) would be encouraged to reduce conflict and promote cooperation among activities. In this particular study, the participatory process was conducted by a local agency (Pays de Brest).
Figure 1. Study site.
Figure 1. Study site.
Ijgi 03 00391 g001
Table 1. Supervised maritime activities in the Bay of Brest.
Table 1. Supervised maritime activities in the Bay of Brest.
Activities (Level 1)Sub-Activities (Level 2)Number of Sub-Activities (Level 3)
Commercial fishingActive gears4 (by example: dragged gears)
Passive gears13 (by example: nets gears)
Maritime transportationTransportation of goods1
Transportation of passengers1
Nautical activitiesSupervised nautical activities6 (by example: Sailing school)
Water sports events4 (by example: Windsurfing race)

3. Methods

At various stages throughout the study, a range of geographic information technologies had been used: (i) areas of maritime activities were directly mapped by stakeholders, using a GIS-based interview procedure; (ii) temporal data were linked with area to provide a model of interactions between activities at different dates; (iii) this dynamic GIS was employed to produce a range of maps including spatial or spatio-temporal components on various human activities; (iv) these maps helped facilitate the proposal of spatial simulation based on scenarios. Moreover, the study focused on supervised marine activities organized in a specific manner and for which a representative was identified.

3.1. Data Collection

The marine environment has limited data and is viewed as a common resource [29]. However, this study aimed to identify daily human activity patterns over a one year period. Consequently, heterogeneous data (spatial, temporal, qualitative and quantitative) were obtained and stored in a spatio-temporal database. Furthermore, with the availability of a complete database from the (AIS) Automatic Identification System used by the vessel traffic service, daily precision data could be recorded throughout 2009.
The AIS data were stored in a spatio-temporal database (step 1); daily trajectories of each boat were derived from their position monitored every 10 s (step 2). This study considered shipping lanes as polygons, which encompass a representative number of boat trajectories. The boundaries of these polygons were defined by extracting 90% isopleths (arbitrary threshold) of the Kernel density of trajectories (Kernel type = normal bivariate, scaling factor = 1,000,000; smoothing factor (h) = 100; raster resolution = 40 m) (step 3). Finally, the sum of boats was calculated daily for each shipping lane (step 4) [30]. This data retained daily temporal precision to simplify the description of activities, whilst remaining closed to reality. This, and with the usage of the GIS spatial analysis, permitted the identification, quantification and mapping of daily sea traffic, consisting of maritime transportation for both passengers and goods.
To further describe the supervised activities (Table 1), the study conducted an interview survey using the GIS as a mediation tool to collect spatial data. Thirty one interviews were conducted with the stakeholders, through a framework, which permitted the exchange between researchers and key-informants (Table 2). Semi-structured interviews were used based on the key-informants’ opinions, whom were previously identified and presumably holding knowledge about the target population [31,32]. During the face-to-face meeting, each informant could directly draw the spatial activity on a tablet PC using GIS based mapping. Simultaneously, temporal, quantitative and qualitative data were also collected. The origin of the qualitative data came from two methodologies: interviews concerning potential interactions between activities—be it positive, neutral or negative—and a retrospective analysis (regional/local newspapers over a 10 year period). Finally, all data were summarized within a stakeholders-based interaction matrix [33,34,35].
Furthermore, the temporal data, with daily resolution, could indicate whether or not a given activity was present whereas the quantitative data would specify the number of boats associated with this activity. Both data sets were collected differently: (i) “Real” data were retrieved from the database of organizations involved in the survey (i.e., maritime transportation—AIS database, sailing nautical activities—nautical center billing database…); (ii) “stakeholder-based” data were obtained from key informants who described their activity patterns (i.e., a typical year with a typical seasonality and/or with typical weeks and days).
Table 2. Semi-structured interviews carried out to collect data per activity type.
Table 2. Semi-structured interviews carried out to collect data per activity type.
Activity TypeInterviews (n)Mapping Interviews (n)
Maritime transportation43
Commercial fishing76
Nautical activities2219

3.2. Spatio-Temporal Database (STDB)

The spatio-temporal unit (STU) is an elementary unit associated with a thematic attribute and is consistent with both temporal and quantitative data (i.e., maritime transportation for each shipping lane would correspond to a STU). Therefore, these heterogeneous data were specifically structured and classified into a spatio-temporal database (STDB) [36] (Figure 2) to obtain a spatio-temporal perspective. All spatial data containing the STUs were stored in a shapefile. Each STU contains attributes of geographic information, relative to its nature and source, along with the activity and geometry identifiers. Meaning, the daily occurrences of activities associated to quantitative data (i.e., the date, boat density, data quality, and identifiers) were stored in a table. Five quality classes have been defined, from “very weak” (stakeholders based data indicating archetypal activity patterns of a typical seasonality) to “very good” (AIS data or nautical center billing databases). To use the STDB would require the application of spatio-temporal queries to associate with various geo-processing tools. Therefore, to have these tasks automated, two tools were developed by ModelBuilder in ArcGIS to (i) identify and map the daily location of activities over a one year period; (ii) calculate and map the boat density distribution (per polygon).

3.3. Spatio-Temporal Conflict Analysis

The study objectives were to identify, quantify, and qualify (through time and space) the potential negative interactions among maritime activities. The hypothesis stated that spatio-temporal interactions could be approached by computing spatio-temporal intersections; and in 2009, such intersections were calculated at daily resolution. A specific tool had been developed using an algorithm that calculated the spatial intersections between STUs. Each entity of the resulting file contained information regarding the subject of concerned activities: date, number of spatio-temporal intersections, and the sum of boat density. Since the activities within spatio-temporal intersections indicated no systematic conflict, a weighting was applied to correspond with the key informants-based interaction matrix. The index value was binary: 0 = no interaction or 1 = potential negative interaction. Ultimately, to ensure the analysis of the spatio-temporal intersections, the study performed a spatial aggregation on a uniform hexagonal lattice and an identification of spatial outliers by using the Local Index of Spatial Autocorrelation (LISA) [37].
Figure 2. Spatio-temporal database (STDB).
Figure 2. Spatio-temporal database (STDB).
Ijgi 03 00391 g002

3.4. Supporting Discussion and Building Collective Scenarios Phase

The participatory workshop consisted of representatives from local commercial fisheries and five local agencies interested in coastal management; including several whom represented the Natura 2000 and ICZM. The session structure appointed an observer and moderator whom managed and recorded the reactions and discussions of all participants. A questionnaire previously prepared by researchers addressed the relevance for different types of data and information on a three-main step ICZM process: diagnosis, planning, concertation. All participants were briefed of the objectives concerning the questionnaire survey procedure. The captured data included the perceptions of methodology, the dynamic GIS and its possible relevance to initiate simulations for the Bay of Brest. Questionnaire example: “Is a matrix a relevant representation for diagnosis, planning, concertation?” Furthermore, the three steps of the workshop were also questioned in terms of participant interest, i.e., feedback regarding research work, collective scenarios, and evaluation.

4. Results

4.1. Data Collection

Spatio-temporal analysis of the AIS database mapped seven shipping lanes in total, for maritime transportation in 2009. The display capacity for the multi-scale dynamic GIS permitted this study to create geographic data layers on the foundation of scales used by stakeholders, whilst mapping their activity zones. Among the 28 mapping interviews conducted (Table 2) for spatial data, 26 key informants successfully controlled the GIS software to map their activity zones. A total of 123 entities corresponding to the location of activities were recorded. All activities were described without considering tidal and meteorological conditions. The zones described by key informants were mapped for validation and completed in the geographic database. During the interviews, temporal data concerning the period of activities and quantitative data (number of boats) were also recorded. These heterogeneous data allowed for the mapping of all activity zones, as well as creating calendars of activities associated with quantitative data for 29 activities (Figure 3 and Figure 4).
Figure 3. An example of an activity map.
Figure 3. An example of an activity map.
Ijgi 03 00391 g003
Figure 4. An example of an activity calendar (data aggregated per month).
Figure 4. An example of an activity calendar (data aggregated per month).
Ijgi 03 00391 g004

4.2. Maritime Activities in a Spatio-Temporal Perspective

The STDB contains 149 STUs which associate to 9346 of daily occurrences describing 29 activities. Potential boat density associated with each occurrence was calculated along with estimation for daily quality indexes, density and quality data of each occurrence. These respectively relied on the quality of daily comparative percentage for each occurrence and density. For instance, if more than 50% of occurrences belong to the quality class “very weak” for any given day, then daily quality index of occurrences would be considered “very weak” for that particular day. The quality of indexes ranged from “good” to “very good” for 84% of the days in terms of occurrences, and 90% of the days for boat density. Thus, using the STDB within the study’s GIS provided temporal snapshots over 2009 and a daily time step actually associated with the data related quality indexes. The successive use of snapshots permitted this study to construct an explicit spatial representation of supervised maritime activities in the Bay of Brest (Figure 5A). Additionally, this enabled us to produce original information, much like the spatial distribution of the cumulative sum of daily boat density for several or singular activities (Figure 5B).

4.3. Spatio-Temporal Conflicts between Activities

For 2009, spatio-temporal intersections between activities (n = 820,861) were calculated at a daily time step (Figure 6). Intersections between potential conflicting activities represented a sum of 20% for spatio-temporal intersections. Spatio-temporal intersections (i) between transportation of passengers and nautical activities amounted to 87% of negative spatio-temporal intersections; (ii) 8% between passive gears and transportation of passengers; (iii) 3% between transportation of goods and nautical activities; (iv) 2% between supervised nautical activities and water sports events. The analysis of the temporal evolution for spatio-temporal intersections enabled the study to identify the presence of monthly/seasonal variations and extreme values in 2009 by considering activities of totality or pairs. For example, the annual extreme value for the daily sum of spatio-temporal intersections between passenger transportation and supervised nautical sports had been reached by 20 June. The spatial analysis of the spatio-temporal intersections led to the mapping of significant clusters for high and low values (p < 0.01) by considering any given day or whole year period.
To better identify potential conflicts between maritime activities, further analysis had been conducted to balance the information of spatial approach, against the spatio-temporal approach. Consequently, the significant LISA clusters [37] of low (LL) and high (HH) values for area spatial intersections were compared with those identified in the spatio-temporal intersections. This indicated that 70% of significant clusters failed to correspond with those identified by a single spatial analysis. These results therefore indicated that integration of spatio-temporal dynamics for the identification of potential conflicts between maritime activities provides a significant difference in pattern when compared to a single spatial consideration (Figure 7).
Figure 5. Activity zones for supervised maritime activities in the Bay of Brest (A) and boats density (B), by example on Monday, 26 October 2009.
Figure 5. Activity zones for supervised maritime activities in the Bay of Brest (A) and boats density (B), by example on Monday, 26 October 2009.
Ijgi 03 00391 g005
Figure 6. Spatio-temporal intersections among main types of supervised maritime activities (2009, Bay of Brest). Spatial intersections between the activity zones were performed at a daily time step. Negative spatio-temporal intersections are quoted in yellow.
Figure 6. Spatio-temporal intersections among main types of supervised maritime activities (2009, Bay of Brest). Spatial intersections between the activity zones were performed at a daily time step. Negative spatio-temporal intersections are quoted in yellow.
Ijgi 03 00391 g006
Figure 7. Comparison of significant clusters identified by the spatial intersections between activity zones to those identified by the spatio-temporal intersections between activity zones (2009, Bay of Brest).
Figure 7. Comparison of significant clusters identified by the spatial intersections between activity zones to those identified by the spatio-temporal intersections between activity zones (2009, Bay of Brest).
Ijgi 03 00391 g007

4.4. Contribution to Knowledge, Discussion and Building of Collective Scenarios

During the participatory workshop, modeling human activities in the Bay of Brest had been discussed among participants. They positively assimilated this dynamic GIS and its high potential in terms of spatial simulations. By example, a local agency representative in charge of ICZM stated:
The simulations bring a unique representation of the activities in the Bay of Brest”. Three other participants noticed that “planning” required simulations of the finest time step (half a day) and suggested meteorological and tidal conditions should also be considered.
Subsequently, the participants were asked to collectively suggest an overall scenario possible for implementation. Three individual proposals were presented: (i) what would be the consequences of a new transportation line across the Bay of Brest over current activities? (ii) Regarding different activity calendars, when was the foremost period for extracting the invasive macro-algal seasonal blooms (Ulva sp.) near the Brest harbor? (iii) Where should the less stressed areas for any further aquaculture be developed? The dynamic GIS provided answers. For example, concerning the extraction of macro-algal seasonal blooms (Ulva sp.) occurring every year near the Brest harbor (Figure 8). Simulation results, mostly concerning nautical activities, indicated potential interactions (88% of the spatio-temporal intersections) and maritime transportation of passengers (12%). Considering the spatio-temporal intersections and boat density, the least stressed periods for extracting the algae extended during 6–18 April, 18–22 May, and 1–19 September.
To better establish an evaluation utility and significance of scientific products, the questionnaire provided guidance for each main step of the ICZM process (Table 3). Most scientific products were considered 100% useful by the participants at the three steps: (i) matrix and plots (non-spatial data); (ii) cartographic atlas (spatial data); (iii) volunteered geographic information (origin of spatial data); (iv) cumulative boat density, and spatio-temporal intersections between activities (thematic contents of spatial data). All participants considered the workshop efficient for the construction of a collective scenario. Moreover, stakeholder discussions revealed: (i) a better understanding of both time and space on maritime activities (daytime) and the type of interactions occupying the bay; (ii) an appreciation for the workshop organized by researchers to support discussion in a “neutral arena”; (iii) the session structure could positively modify the collective perception of the ICZM stakes.
All participants asked for a second workshop to discuss the results of simulations based on the three scenarios, in addition to one workshop tailored for decision makers involved in ICZM and another tailored for fishers.

5. Discussion and Conclusions

For local marine planning, understanding activity interaction would require prior knowledge of spatial and temporal patterns of activities at a relevant scale. This study provides a methodology based on the collection and integration of data in a spatio-temporal database. The GIS enables us to describe the spatio-temporal distribution of supervised activities on a daily time step within a retrospective model, over a one-year period. To facilitate the detection of potential conflicts among maritime activities, daily spatial intersections were calculated. The analysis of these spatio-temporal intersections allow us to: quantify the occurrences of intersections among activities, to emphasize their temporal evolution, and detect significant spatial clusters of low/high intersections occurring for both space and time. Whilst considering the time taken and the complexity of the study of dynamic human activity, this approach provides great relevance and information accuracy instead of only considering the spatial component. However, if assuming ambiguity of the potential interacting activities occurring through a spatio-temporal interaction at a daily time step, this method emphasizes the reason and importance of conducting the finer time step and should consider spatial uncertainty in order to achieve the best results for stakeholders.
Figure 8. An example of scenario: removal of macro-algal blooms.
Figure 8. An example of scenario: removal of macro-algal blooms.
Ijgi 03 00391 g008
Table 3. Synthesis of stakeholders’ responses to the questionnaire.
Table 3. Synthesis of stakeholders’ responses to the questionnaire.
Non-spatial data classified according to the representation type
Network graphs********
Spatial data classified according to the representation type and associated tools
Cartographic atlas*********
Geographic Information Base *******
Temporal Geographic Information Base********
GIS geo-processing tools*******
Spatial data classified according to their origins
Reference geographic information********
Volunteered geographic information*********
Spatial data classified according to their thematic contents
Activity zones********
Activity calendars********
Cumulative boats density (per activity)********
Cumulative boats density (for all activities)*********
Spatio-temporal intersections between activities*********
Backward simulation *******
Forward simulation ********
Workshop sessions
Feedback on the research work**
Collective Scenarios construction***
Stakeholders’ evaluation**
LabelIf ≥50% of“useful” answersIf ≥75% of“useful” answersIf =100% of“useful” answers
Within current integrated and participative management approaches of coastal zones, a GIS-based framework would strongly promote the relationship between researchers and stakeholders over a given coastal area via the exchange of data, information and simulation [23,38]. The model developed in this project encompasses a multiple database along with an interactive mapping device that led to feasible spatio-temporal simulations of maritime activities. This demonstration therefore encourages the exchange of knowledge and perception of stakeholders holding various skills and backgrounds (Figure 9). Concerning the relationship between various maritime researchers and space users, this study contributes to the integration of local knowledge with the (ongoing) management process. The integration of knowledge has ignited great interest among stakeholders active in natural area management. Furthermore, the volunteered geographic information described by key informants often constitutes to being the only solution for obtaining data concerned with their activities. Yet, acquiring public involvement in gathering relevant data is one of the many existing challenges for citizen science [39,40]. Hence, for this study (and with the request from those responsible for Natura 2000 and ICZM processes), to successfully contribute to real management, it has since conveyed all collected GIS data into a Spatial Data Infrastructure managed by the researchers (
Figure 9. GIS dynamic sharing process.
Figure 9. GIS dynamic sharing process.
Ijgi 03 00391 g009
Spatial dimension introduced by maps as visual artefacts stimulate the exchange between researchers and stakeholders to better address complex issues [20]. Yet, the dynamic component of the GIS appears to be of prime importance. It also yields novel information about spatio-temporal interactions, which allow stakeholders to qualify the activities from the viewpoint of intersection occurrences. The on-going evolution of both activity area and location for low high densities (of possible conflicts) can be emphasized and subsequently discussed. Nevertheless, stakeholders do understand the possibility of using the GIS to test scenarios; yet are reluctant to use it in a public sphere [41]. Furthermore, it is evident that not only does the building of relevant collective scenarios (that really can be included) into a decision making process require further time and meeting sessions, but also the potentially useful and necessary computer scenario based simulations for stakeholders exchange does in fact hold little sufficiency.
The usage of computer models and simulation methods ignites questions concerning the emergence of socio-technical democracy [42] and their instrumentalization in public policy [23,41]. The information and knowledge, along with the complexity and duration of the process, signifies a critical issue [20]. Presently, these tools are restricted to research only. However, this study provides the basis for future development as it clearly demonstrates the successful tool usage of stakeholders, under controlled conditions [14]. The temporal component of information, supplied by this study, verifies its great significance for planning instead of only considering the spatial component. It also demonstrates the necessity to tailor spatial tools for a specific context [26]. Indubitably, both the GIS-based approach and computer simulations do, in fact, promote stakeholder involvement, whilst encouraging the exchange of knowledge and acceptance of scientific products, under the condition that they are modified to meet their specific needs.


This project was funded by the LITEAUIII program (French Ministry of Ecology) and the Bretagne Region. We would like to thank all local Stakeholders and practitioners for their willingness to share knowledge and the Naval Academy Research Institute for providing the AIS database. We also acknowledge the anonymous referees who provided valuable help to improve our manuscript.

Author Contributions

Françoise Gourmelon managed the full project. Damien Le Guyader conducted the fieldwork and prepared GIS data analysis. Guy Fontenelle supervised the scenario approach with stakeholders. All three shared the writing of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Schwartz, M.L. Encyclopedia of Coastal Science; Springer: Dordrecht, The Netherlands, 2005; p. 1242. [Google Scholar]
  2. Katsanevakis, S.; Stelzenmüller, V.; South, A.; Sørensen, T.; Jones, P.; Kerr, S.; Badalamenti, F.; Anagnostou, C.; Breen, P.; Chust, G.; et al. Ecosystem-based marine spatial management: Review of concepts, policies, tools, and critical issues. Ocean Coast. Manag. 2011, 54, 807–820. [Google Scholar] [CrossRef]
  3. Young, O.R.; Osherenko, G.; Ekstrom, J.; Crowder, L.B.; Ogden, J.; Wilson, J.A.; Day, J.C.; Douvere, F.; Ehler, C.N.; McLeod, K.L.; et al. Solving the crisis in ocean governance: Place-based management of marine ecosystems. Environment 2007, 49, 20–32. [Google Scholar]
  4. Leslie, H.M.; McLeod, K.L. Confronting the challenges of implementing marine ecosystem-based management. Front. Ecol. Environ. 2007, 5, 540–548. [Google Scholar] [CrossRef]
  5. UNEP. Taking steps toward marine and coastal ecosystem-based management: An introductory guide. UNEP Reg. Seas Rep. Stud. 2011, 189, 1–68. [Google Scholar]
  6. Pittman, S.J.; Connor, D.W.; Radke, L.; Wright, D.J. Application of Estuarine and Coastal Classifications in Marine Spatial Management. In Treatise on Estuarine and Coastal Science Features; Wolanski, E., McLusky, D.S., Eds.; Elsevier Academic Press: Waltham, MA, USA, 2012; pp. 163–205. [Google Scholar]
  7. Halpern, B.S.; Walbridge, S.; Selkoe, K.A.; Kappel, C.V.; Micheli, F.; D’Agrosa, C.; Bruno, J.F.; Casey, K.S.; Ebert, C.; Fox, H.E.; et al. Global map of human impact on marine ecosystems. Science 2008, 319, 948–952. [Google Scholar] [CrossRef]
  8. Ban, N.C.; Alidina, H.M.; Ardron, J.A. Cumulative impact mapping: Advances, relevance and limitations to marine management and conservation, using Canada’s Pacific waters as a case study. Mar. Policy 2010, 34, 876–886. [Google Scholar] [CrossRef]
  9. Stelzenmüller, V.; Lee, J.; Garnacho, E.; Rogers, S.I. Assessment of a Bayesian belief network—GIS framework as a practical tool to support marine planning. Mar. Pollut. Bull. 2010, 60, 1743–1754. [Google Scholar] [CrossRef]
  10. Kappel, C.V.; Halpern, B.S.; Napoli, N. Mapping Cumulative Impacts of Human Activities on Marine Ecosystems; Sea Plan: Boston, MA, USA, 2012. [Google Scholar]
  11. Koehn, J.Z.; Reineman, D.R.; Kittinger, J.N. Progress and promise in spatial human dimensions research for ecosystem-based ocean planning. Mar. Policy 2013, 42, 31–38. [Google Scholar] [CrossRef]
  12. Le Tixerant, M.; Gourmelon, F.; Tissot, C.; Brosset, D. Modelling of human activity development in coastal sea areas. J. Coast. Conserv. 2010, 15, 407–416. [Google Scholar]
  13. Longdill, P.C.; Healy, T.R.; Black, K.P. An integrated GIS approach for sustainable aquaculture management area site selection. Ocean Coast. Manag. 2008, 51, 612–624. [Google Scholar] [CrossRef]
  14. Stelzenmüller, V.; Lee, J.; South, A.; Foden, J.; Rogers, S.I. Practical tools to support marine spatial planning: A review and some prototype tools. Mar. Policy 2013, 38, 214–227. [Google Scholar] [CrossRef]
  15. Brody, S.D.; Highfield, W.; Arlikatti, S.; Bierling, D.H.; Ismailova, R.M.; Lee, L.; Butzler, R. Conflict on the coast: Using geographic information systems to map potential environmental disputes in Matagorda Bay, Texas. Environ. Manag. 2004, 34, 597–617. [Google Scholar]
  16. Beck, M.; Ferdania, J.; Kachmar, K.; Morrison, P.; Taylor, P. Best Practices for Marine Spatial Planning; The Nature Conservancy: Arlington, VA, USA, 2009. [Google Scholar]
  17. Opdam, P. Learning science from practice. Landsc. Ecol. 2010, 35, 821–823. [Google Scholar] [CrossRef]
  18. Brown, M.E. Assessing natural resource management challenges in Senegal using data from participatory rural appraisals and remote sensing. World Dev. 2006, 34, 751–767. [Google Scholar] [CrossRef]
  19. Hessel, R.; van den Berg, J.; Kaboré, O.; van Kekem, A.; Verdandvoort, S.; Dipama, J.M.; Diallo, B. Linking participatory and GIS-based land use planning methods: A case study from Burkina Faso. Land Use Policy 2009, 26, 1162–1172. [Google Scholar] [CrossRef]
  20. Arciniegas, G.; Janssen, R.; Rietveld, P. Effectiveness of collaborative map-based decision support tools: Results of an experiment. Environ. Model. Softw. 2013, 39, 159–175. [Google Scholar] [CrossRef]
  21. Jude, S.R. Investing the potential role of visualization techniques in participatory coastal management. Coast. Manag. 2008, 36, 331–349. [Google Scholar] [CrossRef]
  22. Jude, S.R.; Jones, A.P.; Watkinson, A.R.; Brown, I.; Gill, J.A. The development of a visualization methodology for integrated coastal management. Coast. Manag. 2007, 35, 525–544. [Google Scholar] [CrossRef]
  23. Gourmelon, F.; Chlous-Ducharme, F.; Rouan, M.; Kerbiriou, C.; Bioret, F. Role-playing game developed from a modelling process: A relevant participatory tool for sustainable development? A co-construction experiment in an insular biosphere reserve. Land Use Policy 2013, 32, 96–107. [Google Scholar] [CrossRef]
  24. Smith, G.; Brennan, R.E. Losing our way with mapping: Thinking critically about marine spatial planning in Scotland. Ocean Coast. Manag. 2012, 69, 210–216. [Google Scholar] [CrossRef]
  25. Alexander, K.A.; Janssen, R.; Arciniegas, G.; O’Higgins, T.G.; Eikelboom, T.; Wilding, T.A. Interactive marine spatial planning: Siting tidal energy arrays around the Mull of Kintyre. PLoS One 2012, 7. [Google Scholar] [CrossRef]
  26. Eikelboom, T.; Janssen, R. Interactive spatial tools for the design of regional adaptation strategies. J. Environ. Manag. 2013, 127, S6–S14. [Google Scholar] [CrossRef]
  27. Buller, H. The «espace productif», the «théâtre de la nature» and the «territoires de développement local»: The opposing rationales of contemporary French rural development policy. Int. Plan. Stud. 2004, 9, 101–119. [Google Scholar] [CrossRef]
  28. McCauley, D. Sustainable development and the “governance challenge”: The French experience with Natura 2000. Eur. Environ. 2008, 18, 152–167. [Google Scholar] [CrossRef]
  29. St. Martin, K.; Hall-Arber, M. The missing layer: Geo-technologies, communities, and implications for marine spatial planning. Mar. Policy 2008, 32, 779–786. [Google Scholar] [CrossRef]
  30. Le Guyader, D.; Brosset, D.; Gourmelon, F. Exploitation de données AIS (Automatic Identification System) pour la cartographie du transport maritime. Mappemonde 2011, 104, 1–15. [Google Scholar]
  31. Tremblay, M.-A. The key informant technique: A non-ethnographic application. Am. Anthropol. 1957, 59, 688–701. [Google Scholar]
  32. Rubin, A.; Babbie, E.R. Program Evaluation. In Research Methods for Social Work; Wadsworth Publishing Company: Belmond, IA, USA, 2005; pp. 324–325. [Google Scholar]
  33. Couper, A.D. The Times Atlas of the Oceans; Van Nostrand Reinhold: New York, NY, USA, 1983. [Google Scholar]
  34. Vallega, A. Towards the sustainable management of the Mediterranean Sea. Mar. Policy 1995, 19, 47–64. [Google Scholar] [CrossRef]
  35. Ehler, C.; Douvere, F. Marine Spatial Planning: A Step-by-Step Approach toward EBM Ecosystem-Based Management; Intergovernmental Oceanographic Commission and Man and the Biosphere—UNESCO: Paris, France, 2009. [Google Scholar]
  36. Le Guyader, D. Modélisation des Activités Humaines en mer Côtière. Ph.D. Thesis, Université de Bretagne Occidentale, Brest, France, 2012. [Google Scholar]
  37. Anselin, L.; Syabri, I.; Kho, Y. GeoDa: An introduction to spatial data analysis. Geogr. Anal. 2006, 38, 5–22. [Google Scholar] [CrossRef]
  38. Barreteau, O.; Le Page, C. Using social simulation to explore the dynamics at Stake in participatory research. J. Artif. Soc. So. Simul. 2011, 14. Available online: (accessed on 14 August 2013).
  39. Irwing, A. Citizen Science: A Study of People, Expertise and Sustainable Development; Routledge: Oxford, UK, 1995; p. 202. [Google Scholar]
  40. Goodchild, M.F. Citizens as voluntary sensors: Spatial data infrastructure in the world of Web 2.0. Int. J. Spat. Data Infrastruct. Res. 2007, 2, 24–32. [Google Scholar]
  41. Becu, N.; Neef, A.; Schreinemachers, P.; Sangkapitux, C. Participatory computer simulation to support collective decision-making: Potential and limits of stakeholder involvement. Land Use Policy 2008, 25, 498–509. [Google Scholar] [CrossRef]
  42. Steyaert, P.; Barzman, M.; Billaud, J.P.; Brives, H.; Hubert, B.; Ollivier, G.; Roche, B. The role of knowledge and research in facilitating social learning among stakeholders in natural resources management in the French Atlantic coastal wetlands. Environ. Sci. Policy 2007, 10, 537–550. [Google Scholar] [CrossRef]

Share and Cite

MDPI and ACS Style

Gourmelon, F.; Le Guyader, D.; Fontenelle, G. A Dynamic GIS as an Efficient Tool for Integrated Coastal Zone Management. ISPRS Int. J. Geo-Inf. 2014, 3, 391-407.

AMA Style

Gourmelon F, Le Guyader D, Fontenelle G. A Dynamic GIS as an Efficient Tool for Integrated Coastal Zone Management. ISPRS International Journal of Geo-Information. 2014; 3(2):391-407.

Chicago/Turabian Style

Gourmelon, Françoise, Damien Le Guyader, and Guy Fontenelle. 2014. "A Dynamic GIS as an Efficient Tool for Integrated Coastal Zone Management" ISPRS International Journal of Geo-Information 3, no. 2: 391-407.

Article Metrics

Back to TopTop