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43
ANNEXES
A - CONTENT ANALYSIS METHODOLOGIES
Authors Title Citations Methodology
Morris and Trivedi, 2008
A survey of vision-based trajectory learning and
analysis for surveillance 375
Trajectory preprocessing and clustering, path modeling. Automatic activity analysis: speed profiling, path classification, abnormaly detection.
Pallotta et al., 2013
Vessel pattern
knowledge discovery from AIS data: A framework for anomaly detection and route prediction
344
Unsupervised (clustering) model:
TREAD (Traffic Route Extraction and Anomaly Detection). Point-based traffic representation with time integration.
Lee et al., 2008
Trajectory Outlier Detection: A
Partition-and-Detect Framework 341
Trajectory partitioning and detection:
distance-based and density-based approaches. Algorithm TRAOD (TRAjectory Outlier Detection).
Ristic et al., 2008
Statistical analysis of motion patterns in AIS data: Anomaly detection and motion prediction
223
Statistical analysis of vessel motion patterns. Adaptive kernel density estimation to detect motion anomalies.
Predicting the movement of vessels using the Gaussian sum tracking filter.
Silveira et al., 2013
Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of Portugal
200
Statistical analysis of traffic. Route characterization program and ship collision models (probability of occurrence based on statistical information).
Natale et al., 2015
Mapping fishing effort
through AIS data 123
Analysis of individual vessel’s speed profiles and production of high resolution map of fishing effort.
De souza et al., 2016
Improving fishing pattern detection from satellite AIS using data mining and machine learning
110
Hidden Markov Model: vessel speed as variable; data mining inspired on animal movement; multi-layered filtering based on vessel speed.
44 Laxhammar,
2008
Anomaly detection for
sea surveillance 94
Unsupervised clustering of traffic patterns. Gaussian Mixture Model and Expectation-Maximization algorithm.
Tu et al., 2018
Exploiting AIS Data for Intelligent Maritime
Navigation: A
Comprehensive Survey
from Data to
Methodology
93
Data mining (statistical methods, neural networks and machine learning): traffic anomaly detection, route estimation, collision prediction, and path planning.
Lane et al., 2010
Maritime anomaly detection and threat
assessment 87
Bayesian network to calculate probability of anomalous ship behaviours. Gaussian mixture model (clustering) and kernel density estimator.
Table A - 1 – Top-10 manuscripts per citations
45
B – CODE FOR CONSTRUCTING SHIP TRAJECTORIES
#Loading geodata and convert to GeoDataFrame t_start = datetime.now()
gdf_orig = gpd.read_file('D:/ais/ais_ptsat_d18.shp')
print("Finished reading {} rows in {}".format(len(gdf_orig),datetime.now() - t_start))
#Convert multipoint to point
gdf_p=gdf_orig.explode(index_parts=True)
#Discard mmsi= 0
print("Original size: {} rows".format(len(gdf_p))) gdf_p1 = gdf_p[(gdf_p.mmsi!=0)]
x=len(gdf_p)-len(gdf_p1)
print("Reduced to {} rows after removing {} mmsi records".format(len(gdf_p1),x))
#Discard values less than zero and values greater than 30 print("Original size: {} rows".format(len(gdf_p1)))
gdf = gdf_p1[(gdf_p.speed_over>=0)&(gdf_p1.speed_over<=30)]
x=len(gdf_p1)-len(gdf)
print("Reduced to {} rows after removing {} speed records".format(len(gdf),x)) print("Reduced to {} rows after removing {} speed records".format(len(gdf),x))
#Calculate x and y coordinates gdf["x"] = gdf["geometry"].x gdf["y"] = gdf["geometry"].y
#Create a temporal index to convert the GeoDataFrame to Trajectories:
gdf['t'] = pd.to_datetime(gdf['instant'], format='%Y-%m-%d %H:%M:%S') gdf = gdf.set_index('t')
#Create trajectories t_start = datetime.now()
traj_collection = mpd.TrajectoryCollection(gdf,'mmsi', t='t', x='x', y='y')
print("Finished creating {} trajectories in {}".format(len(traj_collection),datetime.now() - t_start))
#Return a GeoDataFrame with one row per Trajectory within the TrajectoryCollection def to_traj_gdf(self, wkt=False):
gdfs = [traj.to_traj_gdf(wkt) for traj in self.trajectories]
gdf_t = pd.concat(gdfs)
gdf_t.reset_index(drop=True, inplace=True) return gdf_t
export_traj_gdf=to_traj_gdf(traj_collection, wkt=False)
export_traj_gdf.to_file("D:/ais/traj_gdf_d", layer='trajectories', driver="GPKG")
#The trajectories layer was stored in postgreSQL database through the postgis extension in QGIS
46
C – CODE FOR SHIP ENCOUNTERS MODEL
#create connection to GIS database conn = psycopg2.connect(
host="localhost", database="GIS", user="postgres", password="****")
#store the result of the query into GeoDataFrame import_sql = "select * from tese.trajectories"
#add the coast costa_sql = """
select * from "DADOS_BASE"."NC_Paises_LR_level0_PL"
"""
trajectories_gdf = gpd.GeoDataFrame.from_postgis(import_sql, conn) costa = gpd.GeoDataFrame.from_postgis(costa_sql, conn)
#closes the connection conn.close()
#create area to restrict map size
polygon = Polygon([(-10, 36), (-7, 36), (-7, 38), (-10, 38), (-10, 36)])
#function to select trajectories with speed equal to or less than 5 knots, and which are more than 5 NM from the coast
def low_speed_5coast(trajectories,polygon_extent,coast_polygon):
traj_5kn = trajectories[(trajectories['speed_over'] <=5)]
x = len(trajectories)-len(traj_5kn)
print("Reduced to {} rows after removing {} speed records".format(len(traj_5kn),x)) poly_gdf = gpd.GeoDataFrame ([1], geometry=[polygon_extent], crs=coast_polygon.crs) coast = coast_polygon.clip(poly_gdf)
coast_proj = coast.to_crs(3857)
coast_buf = coast_proj.buffer(9260) #5NM coast_buf = coast_buf.to_crs(4326)
coast_gdf = gpd.GeoDataFrame(geometry = gpd.GeoSeries(coast_buf)) traj_filter = gpd.overlay(traj_5kn, coast_gdf, how = 'difference')
return traj_filter, coast
#Function execution
traj_new, c = low_speed_5coast(trajectories_gdf,polygon,costa)
#Database connection to import geodataframes to PostgreSQL table
engine = create_engine('postgresql+psycopg2://postgres:****@localhost:5432/GIS') conn = engine.connect()
#Import traj_new to PostgreSQL table
traj_new.to_postgis(name='traj_filter', con = conn, schema = 'tese',if_exists = 'replace')
#Closes the connection conn.close()
#Connect to GIS database conn = psycopg2.connect(
host="localhost",
47 database="GIS",
user="postgres", password="****")
#Create a cursor object called cur cur = conn.cursor()
#Create index to improve performance
idx="""CREATE INDEX mmsi_idx_tr ON tese.traj_filter (mmsi);
CREATE INDEX inst_idx_tr ON tese.traj_filter (instant);
CREATE INDEX id_idx_tr ON tese.traj_filter (id_0, mmsi, instant);
"""
#Execute query cur.execute(idx)
#Store the result of the query into table
#Selects pairs of ships that are within 300 yards of each other, and the timestamp difference is less than 20 minutes
meet = """CREATE TABLE tese.meet AS (SELECT t1.mmsi as mmsi_1,
t2.mmsi as mmsi_2, t1.id_0 as id_0_1, t2.id_0 as id_0_2, t1.instant as instant_1, t2.instant as instant_2, t1.geom as geom_1, t2.geom as geom_2
FROM tese.traj_filter as t1, tese.traj_filter as t2 WHERE
t1.mmsi > t2.mmsi -- para navios diferentes
AND ST_DWithin(t1.geom, t2.geom, 0.0025) -- 300 jj AND t1.id_0 > t2.id_0 -- para pontos diferentes AND t1.instant > t2.instant
AND t1.instant-t2.instant < '20 minutes'::interval -- cuja diferença de tempo é pequena );"""
#Execute query cur.execute(meet)
#Import meetings table
import_meet = "select * from tese.meet"
#Store the result of the query into GeoDataFrame
meet_gdf = gpd.GeoDataFrame.from_postgis(import_meet, con=conn, geom_col="geom_1")
#Store the result of the query into table
#From the previously selected pairs of ships, filter those that have been close for at least 1 hour and join the trajectory segments by ship identification
group = """
create table tese.group AS (SELECT t1.* FROM ( SELECT mmsi_1, mmsi_2,
MAX(instant_1)-MIN(instant_1) as inter_1, MAX(instant_2)-MIN(instant_2) as inter_2, ST_Union(geom_1) as geom1,
ST_Union(geom_2) as geom2 FROM tese.meet
GROUP BY
48 mmsi_1,
mmsi_2) t1
where inter_1 > '1 hour' OR inter_2 > '1 hour');
"""
#Execute query cur.execute(group)
#Import grouped meetings table
import_group = "select * from tese.group"
#Store the result of the query into GeoDataFrame
meet1_gdf = gpd.GeoDataFrame.from_postgis(import_group, con=conn, geom_col="geom1") meet2_gdf = gpd.GeoDataFrame.from_postgis(import_group, con=conn, geom_col="geom2")
#Closes the connection conn.close()