Littoral2014 minelli

1. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions A GIS tool to evaluate marine trac spatio-temporal evolution using semaphore data. An application on French coastal zones Annalisa Minelli, Iwan Le Berre, Ingrid Peuziat LETG-Brest, equipe Geomer Annalisa Minelli Spatio-temporal monitoring of maritime tra

2. c using semaphore data - Littoral 2014, Klaipeda

3. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Summary 1 Context Le projet CARTAHU The Semaphores 2 Task 1 - Clean the Data Standardisation Implementation: Clean Data By Dictionaries 3 Task 2 - Extract Routes Let's spatialise! Coding: Automatical Extraction of the Routes 4 Task 3 - Temporal evolution Temporal data treatement First implementation 5 Perspectives and Conclusions Ongoing work and perspectives Conclusions Annalisa Minelli Spatio-temporal monitoring of maritime tra

5. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Le projet CARTAHU CARTAHU Mobiliser les savoir-faire pour l'analyse spatiale et dynamique des activites et des ux en mer c^otiere Annalisa Minelli Spatio-temporal monitoring of maritime tra

7. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Le projet CARTAHU Dierent interests on a growing environment: Exploitation of natural resources Economic interests on the sea Economic interests on the coastal zones Environmental safeguard Aim: General spatio-temporal knowledge of all these processes in order to represent them and focus on (present or future) issues Annalisa Minelli Spatio-temporal monitoring of maritime tra

9. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Le projet CARTAHU Challenge: Which are the right treatment methods to observe and analyse the spatio-temporal behaviour of these activities, how they relate each other and how to analyse the coastal system at dierent scales? Studied zone: Iroise Sea Surface of 3700 Kmsq Hosts almost all the marine activities pointed above Hosts a Zone Atelier since 2012: the ZABRI Annalisa Minelli Spatio-temporal monitoring of maritime tra

11. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Le projet CARTAHU Data: dierent and heterogeneous Semaphores' data GPS Tracking Acoustic submarine recordings Surveys online and in situ Sketch maps The semaphore's one represents only a part of all this data Annalisa Minelli Spatio-temporal monitoring of maritime tra

13. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions The Semaphores The Semaphores Semaphores constitute a system of sourveillance, active most of the time 24/24 h Ideated by Louis Jacob under Napoleon 1st, in the 1806, taking inspiration from Chappe's telegraph All along the French coasts 59 semaphores in the net Schematic map ofmodern semaphores distribution. Annalisa Minelli Spatio-temporal monitoring of maritime tra

15. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions The Semaphores Military supervision Since the beginning of 1900 the semaphores are under military supervision Growing of maritime trac implied more sourveillance marine, military and civil Cooperation with CROSS (Centre Regional Operationnel de Surveillance et de Sauvetage) Schematic map ofmodern semaphores distribution. Annalisa Minelli Spatio-temporal monitoring of maritime tra

17. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions The Semaphores The raw data semaphore data Each ocer records as much boats as he is able to identify These data are stored in .xls

18. les, one for each day The informations recorded are: date/time name of the boat matricule of the boat type of boat route azimuth/distance Example of the Semaphore's raw data. Annalisa Minelli Spatio-temporal monitoring of maritime tra

44. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Standardisation Clean the Data: Lack of shared language Since the support of recording is an empty spreadsheet, there are no rules in the recording process: dierent encoding for dierent ocers (hours of the day): routes types usages no shared rules for handling missing informations eventual errors cannot be prevented All these things aect negatively an objective data treatment Annalisa Minelli Spatio-temporal monitoring of maritime tra

46. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Standardisation First standardisation An initial standardisation has been created by the IUEM-LETG, grouping boats in order to have: 16 types of boats 12 usages 106 routes (for the Saint Mathieu semaphore) too long - we need to automatise the process! Stage Report; C.Gohn, 2013 Annalisa Minelli Spatio-temporal monitoring of maritime tra

48. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Implementation: Clean Data By Dictionaries Why Python? Open source, free Widely used and growing Active scienti

49. c community Clean language design Object oriented, dynamically typed, garbage collected, bytecode compiled Ecient Srtrong structural control Python's philosophy Annalisa Minelli Spatio-temporal monitoring of maritime tra

51. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Implementation: Clean Data By Dictionaries Tool 1: createDictionaries.py The

52. rst tool created has the aim to build a primary collection of occurrences in order to crate a database (dictionaries) for: type of boats in reason of the name usage of boats in reason of the type routes synthesis Annalisa Minelli Spatio-temporal monitoring of maritime tra

54. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Implementation: Clean Data By Dictionaries Tool 1: createDictionaries.py Annalisa Minelli Spatio-temporal monitoring of maritime tra

56. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Implementation: Clean Data By Dictionaries Tool 2: CleanDataByDicts Once the dictionaries (or a core of) are created, let's use them to clean all the raw data. Annalisa Minelli Spatio-temporal monitoring of maritime tra

58. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Implementation: Clean Data By Dictionaries Tool 2: CleanDataByDicts Annalisa Minelli Spatio-temporal monitoring of maritime tra

60. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Let's spatialise! Synthetic Routes Aim of the analysis : quantify and possibly group the trac uxes using synthetic routes using a geometrical grid Annalisa Minelli Spatio-temporal monitoring of maritime tra

62. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Let's spatialise! The Gates approach Allows the software to autonomously

63. nd the shortest path between two points, lmoreover: Each iso-distance path has the same probability to be chosen The path have a (topological) a direction Annalisa Minelli Spatio-temporal monitoring of maritime tra

65. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Coding: Automatical Extraction of the Routes Why GRASS GIS? Open source, free Really stable (33 year old project), developed by dierent research centres all around the world Powerful in analysing, editing and creating maps: vector, raster, imagery and database processing More than 300 tools with dierent ranges of uses Annalisa Minelli Spatio-temporal monitoring of maritime tra

67. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Coding: Automatical Extraction of the Routes Tool 3: v.createRoutes.py v.createRoutes.py.. It takes as input a clean semaphore recording

68. le and a text e

69. le containing the gates' coordinates Gives in Output two vector maps of routes and gates, quantifying the trac for the given semaphore Annalisa Minelli Spatio-temporal monitoring of maritime tra

71. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Coding: Automatical Extraction of the Routes Tool 3: v.createRoutes.py Annalisa Minelli Spatio-temporal monitoring of maritime tra

73. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Coding: Automatical Extraction of the Routes Tool 3: v.createRoutes.py Annalisa Minelli Spatio-temporal monitoring of maritime tra

75. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Temporal data treatement Temporal data representation Considering the representation of spatial data just implemented.. The Temporal branch of GRASS GIS (TGRASS) has been chosen in order to treat spatio-temporal data The Allen (1985) theory has been chosen to represent the temporal topology of data Annalisa Minelli Spatio-temporal monitoring of maritime tra

77. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Temporal data treatement Data representation in TGRASS Each boat passage is represented as an event with a speci

78. c duration that can be associated to the usage of the boat itself The

79. nal idea is to have a exible tool in order to represent the trac situation using dierent temporal representations Two dierent options: visualize the trac situation on a speci

80. c moment calculate the trac over a speci

81. c period with a temporal granularity At the present time each semaphore is treated separately Annalisa Minelli Spatio-temporal monitoring of maritime tra

83. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions First implementation addDuration.py Input: clean data

84. le (from cleandDataByDicts.py) Each usage is read and the corresponding duration associated to the boat Output: the clean data

85. le, reporting the duration of each event (boat passage) Annalisa Minelli Spatio-temporal monitoring of maritime tra

87. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions First implementation t.vect.createRoutes.py Input: the clean data

88. le, carrying the temporal information The routine which

89. nds the paths is the same implemented in v.createRoutes.py Output: trac maps in a speci

90. c moment or over a period with a granularity It is possible to create an animation if performing the period calculation Annalisa Minelli Spatio-temporal monitoring of maritime tra

92. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions First implementation Traitements' cycle Annalisa Minelli Spatio-temporal monitoring of maritime tra

94. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions First implementation t.vect.createRoutes.py Themoment-mode elaboration output is the same than v.createRoutes.py output. Annalisa Minelli Spatio-temporal monitoring of maritime tra

96. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions First implementation t.vect.createRoutes.py Theperiod-modeelaboration output is an animation of the trac during the selected period, cumulating boats marine trac in reason of the temporal granularity chosen. Animation Annalisa Minelli Spatio-temporal monitoring of maritime tra

98. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Ongoing work and perspectives Other Semaphores and WPS Monitoring trac from one semaphore to the other: recognizing the same boat through dierent records Decreasing computational time using multiprocessing techniques Empowering the data consultation using a WPS (Web Processing Service) on Indigeo (www.indigeo.fr) Annalisa Minelli Spatio-temporal monitoring of maritime tra

100. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Ongoing work and perspectives The use of Multi Agent Systems A limitation on the shortest path route tracking is the splitting of the uxes between dierent equi-probable path: how to group paths? Annalisa Minelli Spatio-temporal monitoring of maritime tra

102. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Ongoing work and perspectives The use of Multi Agent Systems Moreover the paths and the geometrical grid itself can change in reason of the tide levels and the boat's captain can take decisions regarding dierent external factors and physical constraints. Let us donate them an intelligence through the use of Multi Agent Systems. The MAS are systems based on the representation of each element (boats, but navigation zones or tide constraints too) as anagent, which adopts a speci

103. cal behaviour in reason of the interaction between: other agents; external environment. Annalisa Minelli Spatio-temporal monitoring of maritime tra

105. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Ongoing work and perspectives The GAMA platform The GAMA platform manages well GIS data: it is a relatively young project (2007) written in Java; supports the use of all the standards coordinate reference systems (CRS) and the creation of personalized CRS by providing the .prj string; supports the integration of raster and vector maps, 2 and 3 dimensional; since the calculations in MAS can be often very long, the GAMA platform supports the OpenMole integration (the calculation processess can be splitted and sent to the most powerful servers all over the world). it is possible to call GAMA from an external software using theGAMA-headless package. Annalisa Minelli Spatio-temporal monitoring of maritime tra

107. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Conclusions Final remarks Despite of the lack of standard language and semantic errors that can be included in the representation, semaphore data still represents an unique and complete source of information for the maritime trac; At the present time we are able to monitor marine trac uxes over time and a functional tool has just been created in order to represent them; In order to better simulate the behaviour of boats and make the model even more realistic: Multi Agent System. Annalisa Minelli Spatio-temporal monitoring of maritime tra

109. Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions Conclusions The End Thank you all for the attention Annalisa.Minelli@univ-brest.fr Annalisa Minelli Spatio-temporal monitoring of maritime tra

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