littoral2014 minelli
TRANSCRIPT
Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions
A GIS tool to evaluate marine trafficspatio-temporal evolution using semaphore data.
An application on French coastal zones
Annalisa Minelli, Iwan Le Berre, Ingrid PeuziatLETG-Brest, equipe Geomer
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions
Summary1 Context
Le projet CARTAHUThe Semaphores
2 Task 1 - Clean the DataStandardisationImplementation: Clean Data By Dictionaries
3 Task 2 - Extract RoutesLet’s spatialise!Coding: Automatical Extraction of the Routes
4 Task 3 - Temporal evolutionTemporal data treatementFirst implementation
5 Perspectives and ConclusionsOngoing work and perspectivesConclusions
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 etdynamique des activites et des flux en mer cotiere”
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions
Le projet CARTAHU
Different interests on a growingenvironment:
Exploitation of natural resources
Economic interests on the sea
Economic interests on thecoastal zones
Environmental safeguard
Aim: General spatio-temporal knowledge of all these processes inorder to represent them and focus on (present or future) issues
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 andanalyse the spatio-temporal behaviour of these activities, how theyrelate each other and how to analyse the “coastal system” atdifferent scales?
Studied zone: Iroise Sea
Surface of 3700 Kmsq
Hosts almost all the marineactivities pointed above
Hosts a “Zone Atelier” since2012: the ZABRI
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions
Le projet CARTAHU
Data: different and heterogeneous
Semaphores’ data
GPS Tracking
Acoustic submarine recordings
Surveys online and in situ
Sketch maps
The semaphore’s one represents onlya part of all this data
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions
The Semaphores
The Semaphores
Semaphores constitute a systemof sourveillance, active most ofthe time 24/24 h
Ideated by Louis Jacob underNapoleon 1st, in the 1806,taking inspiration from Chappe’stelegraph
All along the French coasts
59 semaphores in the netSchematic map of “modern” semaphores distribution.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 thesemaphores are under militarysupervision
Growing of maritime trafficimplied more sourveillancemarine, military and civil
Cooperation with CROSS(Centre Regional Operationnelde Surveillance et de Sauvetage) Schematic map of “modern” semaphores distribution.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 officer records as muchboats as he is able to identify
These data are stored in .xlsfiles, one for each day
The informations recorded are:
date/timename of the boatmatricule of the boattype of boatrouteazimuth/distance
Example of the Semaphore’s raw data.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 officer records as muchboats as he is able to identify
These data are stored in .xlsfiles, one for each day
The informations recorded are:
date/timename of the boatmatricule of the boattype of boatrouteazimuth/distance
Example of the Semaphore’s raw data.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 officer records as muchboats as he is able to identify
These data are stored in .xlsfiles, one for each day
The informations recorded are:
date/timename of the boatmatricule of the boattype of boatrouteazimuth/distance
Example of the Semaphore’s raw data.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 officer records as muchboats as he is able to identify
These data are stored in .xlsfiles, one for each day
The informations recorded are:
date/timename of the boatmatricule of the boattype of boatrouteazimuth/distance
Example of the Semaphore’s raw data.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 officer records as muchboats as he is able to identify
These data are stored in .xlsfiles, one for each day
The informations recorded are:
date/timename of the boatmatricule of the boattype of boatrouteazimuth/distance
Example of the Semaphore’s raw data.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 officer records as muchboats as he is able to identify
These data are stored in .xlsfiles, one for each day
The informations recorded are:
date/timename of the boatmatricule of the boattype of boatrouteazimuth/distance
Example of the Semaphore’s raw data.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 officer records as muchboats as he is able to identify
These data are stored in .xlsfiles, one for each day
The informations recorded are:
date/timename of the boatmatricule of the boattype of boatrouteazimuth/distance
Example of the Semaphore’s raw data.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 officer records as muchboats as he is able to identify
These data are stored in .xlsfiles, one for each day
The informations recorded are:
date/timename of the boatmatricule of the boattype of boatrouteazimuth/distance
Example of the Semaphore’s raw data.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 officer records as muchboats as he is able to identify
These data are stored in .xlsfiles, one for each day
The informations recorded are:
date/timename of the boatmatricule of the boattype of boatrouteazimuth/distance
Example of the Semaphore’s raw data.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 areno rules in the recording process:
different encoding for different officers (hours of the day):
routestypesusages
no shared rules for handling missing informations
eventual errors cannot be prevented
All these things affect negatively an objective data treatment
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions
Standardisation
First standardisation
An initial standardisation has beencreated by the IUEM-LETG,grouping boats in order to have:
16 types of boats
12 usages
106 routes (for the SaintMathieu semaphore)
too long - we need to automatisethe process! Stage Report; C.Gohn, 2013
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 scientific community
Clean language design
Object oriented, dynamicallytyped, garbage collected,bytecode compiled
Efficient
Srtrong structural controlPython’s philosophy
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 first tool created has the aim to build a primary collection ofoccurrences 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 trafic using semaphore data - Littoral 2014, Klaipeda
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 trafic using semaphore data - Littoral 2014, Klaipeda
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 toclean all the raw data.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 trafic using semaphore data - Littoral 2014, Klaipeda
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 traffic fluxes
using synthetic routes
using a geometrical grid
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 find the shortest pathbetween 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 trafic using semaphore data - Littoral 2014, Klaipeda
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 oldproject), developed by differentresearch centres all around theworld
Powerful in analysing, editingand creating maps: vector,raster, imagery and databaseprocessing
More than 300 tools withdifferent ranges of uses
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 cleansemaphore recording file and atext e file containing the gates’coordinates
Gives in Output two vectormaps of routes and gates,quantifying the traffic for thegiven semaphore
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 trafic using semaphore data - Littoral 2014, Klaipeda
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 trafic using semaphore data - Littoral 2014, Klaipeda
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 beenchosen in order to treat spatio-temporal data
The Allen (1985) theory has been chosen to represent thetemporal topology of data
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 specificduration that can be associated to the usage of the boat itself
The final idea is to have a flexible tool in order to representthe traffic situation using different temporal representations
Two different options:
visualize the traffic situation on a specific momentcalculate the traffic over a specific period with a temporalgranularity
At the present time each semaphore is treated separately
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions
First implementation
addDuration.py
Input: clean data file (from“cleandDataByDicts.py”)
Each usage is read and thecorresponding durationassociated to the boat
Output: the clean data file,reporting the duration of eachevent (boat passage)
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 file,carrying the temporalinformation
The routine which finds thepaths is the same implementedin v.createRoutes.py
Output: traffic maps in aspecific moment or over aperiod with a granularity
It is possible to create ananimation if performing the“period” calculation
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 trafic using semaphore data - Littoral 2014, Klaipeda
Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions
First implementation
t.vect.createRoutes.py
The “moment-mode” elaboration output is the same thanv.createRoutes.py output.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
Context Task 1 - Clean the Data Task 2 - Extract Routes Task 3 - Temporal evolution Perspectives and Conclusions
First implementation
t.vect.createRoutes.py
The “period-mode” elaboration output is an animation of the trafficduring the selected period, cumulating boats marine traffic inreason of the temporal granularity chosen.
Animation
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 traffic from one semaphore to the other:recognizing the same boat through different records
Decreasing computational time using multiprocessingtechniques
Empowering the data consultation using a WPS (WebProcessing Service) on Indigeo (www.indigeo.fr)
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 ofthe fluxes between different equi-probable path: how to grouppaths?
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 inreason of the tide levels and the boat’s captain can take decisionsregarding different external factors and physical constraints.Let us donate them an “intelligence” through the use of MultiAgent Systems.
The MAS are systems based on the representation of each element(boats, but navigation zones or tide constraints too) as an “agent”,which adopts a specifical behaviour in reason of the interactionbetween:
other agents;
external environment.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 referencesystems (CRS) and the creation of personalized CRS byproviding the .prj string;
supports the integration of raster and vector maps, 2 and 3dimensional;
since the calculations in MAS can be often very long, theGAMA platform supports the OpenMole integration (thecalculation processess can be splitted and sent to the mostpowerful servers all over the world).
it is possible to call GAMA from an external software usingthe “GAMA-headless” package.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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 errorsthat can be included in the representation, semaphore datastill represents an unique and complete source of informationfor the maritime traffic;
At the present time we are able to monitor marine trafficfluxes over time and a functional tool has just been created inorder to represent them;
In order to better simulate the behaviour of boats and makethe model even more realistic: Multi Agent System.
Annalisa Minelli
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda
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
Spatio-temporal monitoring of maritime trafic using semaphore data - Littoral 2014, Klaipeda