• 1 MPA information
  • 2 Data information
    • 2.1 Metadata
  • 3 Geographical extent
    • 3.1 Coordinates
    • 3.2 MPA area
  • 4 Shipping density
    • 4.1 Fishing
    • 4.2 Service
    • 4.3 Dredging
    • 4.4 Sailing
    • 4.5 Pleasure Craft
    • 4.6 High Speed craft
    • 4.7 Tug and towing
    • 4.8 Passenger
    • 4.9 Cargo
    • 4.10 Tanker
    • 4.11 Military
    • 4.12 Other
    • 4.13 Unknown
  • 5 Statistics
    • 5.1 Distribution
    • 5.2 Global traffic
  • 6 Hierarchical clustering
    • 6.1 Yearly densities
    • 6.2 Classification by activities
    • 6.3 Maritime activities clusters
    • 6.4 Boxplot
  • 7 All data
  • 8 Open-notebook

1 MPA information

Wdpaid:
555526886
Pa_def:
1
Name:
Agriates
Orig_name:
Agriates
Desig:
Site of Community Importance (Habitats Directive)
Desig_eng:
Site of Community Importance (Habitats Directive)
Desig_type:
Regional
Iucn_cat:
Not Reported
Int_crit:
Not Applicable
Marine:
1
Rep_m_area:
0
Gis_m_area:
227.823768623465
Rep_area:
296.7
Gis_area:
296.821861248005
Not_take:
Not Reported
Not_tk_take:
0
Status:
Designated
Status_yr:
2006
Gov_type:
Federal or national ministry or agency
Own_type:
Not Reported
Mang_auth:
Parc naturel marin du cap Corse et de l’Agriate - Agence franaise pour la biodiversit
Mang_plan:
Not Reported
Verif:
State Verified
Metadata_i:
1832
Sub_loc:
Not Reported
Parent_iso:
FRA
Iso3:
FRA

2 Data information

EMODnet Vessel Density maps were created by Cogea in 2019 in the framework of EMODnet Human Activities, an initiative funded by the EU Commission. The maps are based on AIS data purchased by Collecte Localisation Satellites (CLS) and ORBCOMM. The maps show shipping density in 1km*1km cells of a grid covering all EU waters (and some neighbouring areas). Density is expressed as hours per square kilometre per month. The following ship types are available:0 Other, 1 Fishing, 2 Service, 3 Dredging or underwater ops, 4 Sailing, 5 Pleasure Craft, 6 High speed craft, 7 Tug and towing, 8 Passenger, 9 Cargo, 10 Tanker, 11 Military and Law Enforcement, 12 Unknown and All ship types. Data are available by month of year.

2.1 Metadata

Access metadata from dataset’s landing page

3 Geographical extent

3.1 Coordinates

## [1] "West-Longitude: 9.01"
## [1] "South-Latitude: 42.65"
## [1] "East-Longitude: 9.36"
## [1] "North-Latitude: 43.01"

3.2 MPA area

3.2.1 Topography

3.2.2 Geographical location

Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.

4 Shipping density

4.1 Fishing

4.2 Service

4.3 Dredging

4.4 Sailing

4.5 Pleasure Craft

4.6 High Speed craft

4.7 Tug and towing

4.8 Passenger

4.9 Cargo

4.10 Tanker

4.11 Military

4.12 Other

4.13 Unknown

5 Statistics

5.1 Distribution

5.1.1 Annual means

ABCDEFGHIJ0123456789
allgroup
<fctr>
mean2017
<dbl>
mean2018
<dbl>
mean2019
<dbl>
mean2020
<dbl>
fishing0.046729670.367492000.0012858010.000000000
service0.903189770.843503492.0871781153.228947594
dredging0.000000000.000000000.0713929700.000000000
sailing16.1949233723.1869373837.39811235455.511030328
pleasure15.1330036323.7445520748.10934247064.586197081
speedcraft0.104221530.000000000.3680440940.105843548
tug0.476730960.571179551.3683923320.520449392
passenger0.365658380.185695090.5731749491.722785374
cargo0.010608900.043368080.0058892920.004295221
tanker0.182340770.382363140.0031159660.000000000

5.1.2 Annual percentages

ABCDEFGHIJ0123456789
allgroup
<fctr>
mean2017
<dbl>
mean2018
<dbl>
mean2019
<dbl>
mean2020
<dbl>
fishing0.125750490.700113130.0013672560.000000000
service2.430501731.606967962.2193985612.482812335
dredging0.000000000.000000000.0759156360.000000000
sailing43.5808625444.1736944339.76724178042.683712529
pleasure40.7232153045.2360124451.15701658549.661817719
speedcraft0.280462230.000000000.3913592840.081385547
tug1.282892551.088160561.4550784860.400185551
passenger0.983994000.353769800.6094849541.324689440
cargo0.028548770.082621010.0062623720.003302694
tanker0.490682650.728444300.0033133590.000000000

5.2 Global traffic

ABCDEFGHIJ0123456789
year
<fctr>
value
<dbl>
201737.16063
201852.49037
201994.04251
2020130.05202

6 Hierarchical clustering

6.1 Yearly densities

Yearly Vessel densities (2017-2020) by activity type:

  • Dredging
  • Fishing
  • Sailing
  • Services
  • Transport

6.2 Classification by activities

Classification of vessel densities by activity type

6.3 Maritime activities clusters

Overall classification of maritime activities seen by AIS

6.4 Boxplot

Boxplot of the maritime activities by clusters

7 All data

Monthly means (hours/km2)

## [1] "01/ Fishing"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
10.000000000.0340209230.0000000000
20.000000000.0296332730.0000000000
30.000000000.0098709260.0000000000
40.000000000.0000000000.0000000000
50.000000000.2436378250.0000000000
60.000000000.0000000000.0012858010
70.000000000.0000000000.0000000000
80.000000000.0000000000.0000000000
90.000000000.0000000000.0000000000
100.046729670.0000000000.0000000000
## [1] "02/ Service"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
10.3936182910.1033237910.0404618310.01635046
20.0000000000.0000000000.0165016030.02260746
30.0527420990.0263977130.8162970710.33388980
40.0497611390.0313344710.0098074120.12346801
50.0907806770.0158634880.2297716580.77850186
60.0527300300.0933262200.0637548630.10489007
70.1321744140.2098383790.2041552340.24530747
80.0608529140.1720522450.0736726210.28727481
90.0351383820.0164156540.1171712320.13052521
100.0198978050.1334572070.0041225320.80051784
## [1] "03/ Dredging"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
1000.000000000
2000.000000000
3000.000000000
4000.000000000
5000.000000000
6000.000000000
7000.071392970
8000.000000000
9000.000000000
10000.000000000
## [1] "04/ Sailing"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
10.000000000.0024879150.0130487850.054928872
20.023459660.0127890520.3546220910.001279625
30.044776360.0190420680.1488458980.048742884
40.144619010.2216162380.1940966420.728327474
50.712444870.4499436690.2946221850.574645918
61.418283082.3800861185.8873197334.769138225
74.589643617.19553094011.40504802517.898380966
88.155142778.84162658117.19003147727.062618734
90.707551013.1973506621.6178355904.116465485
100.379805760.8336487410.2407607400.148829504
## [1] "05/ Pleasure Craft"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
10.0000000000.000000000.020670811.51549489
20.0013362620.000000000.040814590.12429209
30.0000000000.000000000.038342280.21168810
40.0267545100.053703400.266025700.00000000
50.4368618910.357099311.466533150.02564923
61.8398600382.087916727.664248605.12148080
75.1615088257.5827962017.8642141120.12289791
87.14617330211.0793443618.1623043830.14406462
90.4417474051.735387371.869889386.04900148
100.0787614010.783579760.439149120.53016192
## [1] "06/ High Speed Craft"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
10.0000000000.00000000.0000000
20.0000000000.00000000.0000000
30.0000000000.00000000.0000000
40.0000000000.00000000.0000000
50.0000000000.00000000.0000000
60.0000000000.00000000.0000000
70.0891305400.10305110.0000000
80.0150909900.26499300.1058435
90.0000000000.00000000.0000000
100.0000000000.00000000.0000000
## [1] "07/ Tug and Towing"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
10.1081481820.000000000.00000000000.00000000
20.1674531540.012849320.00000000000.00000000
30.0000000000.011145970.00000000000.00000000
40.0000000000.094246970.13884273300.25417153
50.0097203020.042415500.00732690970.00000000
60.0000000000.039500760.51818377950.09915377
70.1492301940.175517470.40945650960.02633603
80.0078256450.027874430.29364730450.14078807
90.0343534870.000000000.00093509580.00000000
100.0000000000.167629110.00000000000.00000000
## [1] "08/ Passenger"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
10.000000e+000.0010275770.00198994430.0000000000
22.586499e-030.0000000000.00417434420.0000000000
30.000000e+000.0000000000.00322374230.0028845501
49.139210e-030.0046040690.00590587280.0005738207
56.382371e-020.0216964660.03937685330.0000000000
61.086348e-010.0011823970.00979456590.0051864064
74.460487e-020.0030771040.24952099320.4213997313
81.296379e-010.0270084220.18942803570.8436437834
95.840032e-030.0037222580.00474849090.2841699991
101.391021e-030.1233767970.06434536770.1626339742
## [1] "09/ Cargo"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
10.00831831110.000000e+000.00000000000.0021478219
20.00117685682.164027e-040.00188519240.0007963658
30.00000000000.000000e+000.00252801100.0000000000
40.00000000003.725110e-040.00000000000.0001964336
50.00000000000.000000e+000.00000000000.0006509408
60.00020248690.000000e+000.00025480300.0000000000
70.00000000000.000000e+000.00000000000.0000000000
80.00000000000.000000e+000.00000000000.0000000000
90.00000000000.000000e+000.00000000000.0000000000
100.00091124883.421788e-020.00091407670.0000000000
## [1] "10/ Tanker"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
10.10998590.00000000.000000e+000
20.00000000.00000000.000000e+000
30.00000000.00000000.000000e+000
40.07235490.00000000.000000e+000
50.00000000.00000000.000000e+000
60.00000000.00000003.102945e-030
70.00000000.00000000.000000e+000
80.00000000.00000000.000000e+000
90.00000000.00000001.302086e-050
100.00000000.27862670.000000e+000
## [1] "11/ Military and Law Enforcement"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
10.000000000.0000000000.000000000
20.000000000.0000000000.000000000
30.000000000.0000000000.000000000
40.000000000.0000000000.000000000
50.000000000.0000000000.000000000
60.000000000.0000000000.000000000
70.000000000.0000000000.000000000
80.000000000.0000000000.149212836
90.000000000.0010013250.000000000
100.000000000.0613873380.000000000
## [1] "12/ Unknown"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
100.000000e+000.0000000000.000000000
200.000000e+000.0000000000.000000000
300.000000e+000.0000000000.000000000
400.000000e+000.0000000000.000000000
505.750959e-050.0000000000.000000000
601.168727e-040.0690040680.000000000
701.651771e-020.5276728060.000000000
801.387488e-020.0082596080.000000000
904.437478e-040.0000000000.001983343
1000.000000e+000.0000000000.000000000
## [1] "13/ Other"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
100.000000e+000.0000000000.000000000
200.000000e+000.0000000000.000000000
300.000000e+000.0000000000.000000000
400.000000e+000.0000000000.000000000
505.750959e-050.0000000000.000000000
601.168727e-040.0690040680.000000000
701.651771e-020.5276728060.000000000
801.387488e-020.0082596080.000000000
904.437478e-040.0000000000.001983343
1000.000000e+000.0000000000.000000000
## [1] "14/ Global traffic"
ABCDEFGHIJ0123456789
Month
<dbl>
Year_2017
<dbl>
Year_2018
<dbl>
Year_2019
<dbl>
Year_2020
<dbl>
10.621149930.142700240.08192951.5901200
20.197202040.058927120.41806130.1508512
30.098638800.083001281.01249520.6034708
40.302628760.406722160.61731511.1067373
51.328006711.210820562.04143921.3794479
63.553817354.6769439214.584123610.1706549
711.1759513416.0889398031.597578340.8516660
817.9017930121.1251436838.143610960.1300545
91.272957785.776500643.888210611.1006131
100.546804522.551152730.81067921.7233205

8 Open-notebook

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