Exploring spatial non-stationarity of near-miss ship collisions from AIS data under the influence of sea fog using geographically weighted regression: A case study in the Bohai Sea, China
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Abstract: Sea fog is a disastrous weather phenomenon, posing a risk to the safety of maritime transportation. Dense sea fogs reduce visibility at sea and have frequently caused ship collisions. This study used a geographically weighted regression (GWR) model to explore the spatial non-stationarity of near-miss collision risk, as detected by a vessel conflict ranking operator (VCRO) model from automatic identification system (AIS) data under the influence of sea fog in the Bohai Sea. Sea fog was identified by a machine learning method that was derived from Himawari-8 satellite data. The spatial distributions of near-miss collision risk, sea fog, and the parameters of GWR were mapped. The results showed that sea fog and near-miss collision risk have specific spatial distribution patterns in the Bohai Sea, in which near-miss collision risk in the fog season is significantly higher than that outside the fog season, especially in the northeast (the sea area near Yingkou Port and Bayuquan Port) and the southeast (the sea area near Yantai Port). GWR outputs further indicated a significant correlation between near-miss collision risk and sea fog in fog season, with higher R-squared (0.890 in fog season, 2018), than outside the fog season (0.723 in non-fog season, 2018). GWR results revealed spatial non-stationarity in the relationships between-near miss collision risk and sea fog and that the significance of these relationships varied locally. Dividing the specific navigation area made it possible to verify that sea fog has a positive impact on near-miss collision risk.
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Figure 2. Spatial distribution of near-miss collision risk calculated according to the VCRO model: a. fog season, 2016, b. non-fog season, 2016, c. fog season, 2017, d. non-fog season, 2017, e. fog season, 2018, and f. non-fog season, 2018. The blue in each grid cells indicates the low risk value and the redrepresents high risk value.
Figure 3. Spatial distribution of sea fog in the Bohai Sea: a. April 2016, b. July 2016, c. April 2017, d. July 2017, e. April 2018, and f. July 2018.
Figure 4. Local R2 of geographically weighted regression (GWR): a. fog season, 2016, b. non-fog season, 2016, c. fog season, 2017, d. non-fog season, 2017, e. fog season, 2018, and f. non-fog season, 2018.
Figure 5. Standardized residuals of geographically weighted regression (GWR): a. fog season, 2016, b. non-fog season, 2016, c. fog season, 2017, d. non-fog season, 2017, e. fog season, 2018, and f. non-fog season, 2018.
6a. Local coefficient estimation of sea fog in the geographically weighted regression (GWR): a. fog season, 2016, b. non-fog season, 2016, c. fog season, 2017, d. non-fog season, 2017, e. fog season, 2018, and f. non-fog season, 2018.
6b. Local coefficient estimation of sea fog in the geographically weighted regression (GWR): a. fog season, 2016, b. non-fog season, 2016, c. fog season, 2017, d. non-fog season, 2017, e. fog season, 2018, and f. non-fog season, 2018.
Figure 7. Channel area division process: a. simplified shipping routes and points; b. Thiessen polygon using Delaunay triangulation method with route points as the source; c. shipping route areas after merging and trimming; d. channel areas with letters.
Figure 8. Number of near-miss collisions in the six navigation areas: a. fog season and non-fog season in 2016, b. fog season and non-fog season in 2017, c. fog season and non-fog season in 2018.
Table 1. Data information
Name Data description Time Purpose AHI
imagesremote sensing images
from the ahi sensorApril and
July from
2016 to 2018sea fog identification
and calculating the
frequency of monthly
sea fogAIS
datadynamic information
and static information
of ship navigationApril and
July from
2016 to 2018near miss collision
risk detection and
calculating the risk
monthly
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Table 2. Near-miss collisions in the Bohai Sea
Year Month Number of near-miss
collisionsPercentage
of total2016 fog season 504 983 28.7% non-fog season 421 338 24.0% 2017 fog season 242 690 13.8% non-fog season 136 348 7.8% 2018 fog season 265 872 15.1% non-fog season 186 165 10.6%
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Table 3. Global Moran’s I statistics for near-miss collision risk
Year Month Moran’s I z-score p-value 2016 fog season 0.158 8.142 0 non-fog season 0.147 7.549 0 2017 fog season 0.161 8.300 0 non-fog season 0.291 14.885 0 2018 fog season 0.197 10.106 0 non-fog season 0.208 10.686 0
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Table 4. Global Moran’s I statistics for sea fog
Year Month Moran’s I z-score p-value 2016 April 0.334 12.449 0 July 0.216 8.044 0 2017 April 0.429 15.961 0 July 0.133 4.997 0 2018 April 0.443 16.445 0 July 0.207 7.710 0
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Table 5. Geographically weighted regression (GWR) results from 2016 to 2018
Time (fog or non-fog) AICc Bandwidth/(°) Residual sum of squares R2 Adjusted R2 Fog season, 2016 18269.587 0.395 2.883×1012 0.793 0.746 Non-fog season, 2016 13202.083 0.318 2.245×109 0.770 0.691 Fog season, 2017 16444.070 0.362 2.208×1011 0.962 0.952 Non-fog season, 2017 12608.715 0.312 9.665×108 0.828 0.767 Fog season, 2018 16384.167 0.359 1.997×1011 0.913 0.890 Non-fog season, 2018 13117.846 0.333 2.056×109 0.790 0.723
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Table 6. Residuals of global Moran’s I spatial autocorrelation
Time (fog or non-fog) Moran’s I z-score p-value Fog season, 2016 0.022 0.877 0.380 Non-fog season, 2016 –0.021 –0.754 0.451 Fog season, 2017 –0.021 –0.741 0.459 Non-fog season, 2017 –0.013 –0.423 0.673 Fog season, 2018 –0.020 –0.692 0.489 Non-fog season, 2018 0.021 0.847 0.397
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