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

Yongtian Shen Zhe Zeng Dan Liu Pei Du

Yongtian Shen, Zhe Zeng, Dan Liu, Pei Du. 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[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-022-2137-7
Citation: Yongtian Shen, Zhe Zeng, Dan Liu, Pei Du. 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[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-022-2137-7

doi: 10.1007/s13131-022-2137-7

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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  • Figure  1.  Study area.

    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

    NameData descriptionTimePurpose
    AHI
    images
    remote sensing images
    from the ahi sensor
    April and
    July from
    2016 to 2018
    sea fog identification
    and calculating the
    frequency of monthly
    sea fog
    AIS
    data
    dynamic information
    and static information
    of ship navigation
    April and
    July from
    2016 to 2018
    near miss collision
    risk detection and
    calculating the risk
    monthly
    下载: 导出CSV

    Table  2.   Near-miss collisions in the Bohai Sea

    YearMonthNumber of near-miss
    collisions
    Percentage
    of total
    2016fog season504 98328.7%
    non-fog season421 33824.0%
    2017fog season242 69013.8%
    non-fog season136 3487.8%
    2018fog season265 87215.1%
    non-fog season186 16510.6%
    下载: 导出CSV

    Table  3.   Global Moran’s I statistics for near-miss collision risk

    YearMonthMoran’s Iz-scorep-value
    2016fog season0.1588.1420
    non-fog season0.1477.5490
    2017fog season0.1618.3000
    non-fog season0.29114.8850
    2018fog season0.19710.1060
    non-fog season0.20810.6860
    下载: 导出CSV

    Table  4.   Global Moran’s I statistics for sea fog

    YearMonthMoran’s Iz-scorep-value
    2016April0.33412.4490
    July0.2168.0440
    2017April0.42915.9610
    July0.1334.9970
    2018April0.44316.4450
    July0.2077.7100
    下载: 导出CSV

    Table  5.   Geographically weighted regression (GWR) results from 2016 to 2018

    Time (fog or non-fog)AICcBandwidth/(°)Residual sum of squaresR2Adjusted R2
    Fog season, 201618269.5870.3952.883×10120.7930.746
    Non-fog season, 201613202.0830.3182.245×1090.7700.691
    Fog season, 201716444.0700.3622.208×10110.9620.952
    Non-fog season, 201712608.7150.3129.665×1080.8280.767
    Fog season, 201816384.1670.3591.997×10110.9130.890
    Non-fog season, 201813117.8460.3332.056×1090.7900.723
    下载: 导出CSV

    Table  6.   Residuals of global Moran’s I spatial autocorrelation

    Time (fog or non-fog)Moran’s Iz-scorep-value
    Fog season, 20160.0220.8770.380
    Non-fog season, 2016–0.021–0.7540.451
    Fog season, 2017–0.021–0.7410.459
    Non-fog season, 2017–0.013–0.4230.673
    Fog season, 2018–0.020–0.6920.489
    Non-fog season, 20180.0210.8470.397
    下载: 导出CSV
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  • 收稿日期:  2022-10-12
  • 录用日期:  2022-12-19
  • 网络出版日期:  2023-06-05

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