GWR based analytic method for relationships between ommastrephe bartrami resources and ocean environment factors

A technology of marine environment and analysis methods, applied in the direction of instrument, data processing application, calculation, etc., can solve the problem of ignoring the spatial non-stationarity of environmental factors

Pending Publication Date: 2016-02-24
SHANGHAI OCEAN UNIV
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Problems solved by technology

[0004] The flaws in existing studies on the relationship between fishery resources and marine environmental factors are based on the fact that the traditional research believes that ...

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  • GWR based analytic method for relationships between ommastrephe bartrami resources and ocean environment factors
  • GWR based analytic method for relationships between ommastrephe bartrami resources and ocean environment factors
  • GWR based analytic method for relationships between ommastrephe bartrami resources and ocean environment factors

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Embodiment Construction

[0048] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0049] 1) Based on the Northwest Pacific squid fishery point and CPUE data from 2004 to 2013, from the recorded fishing data, the date, latitude and longitude, catch (CPUE), and puncture were taken as the research data. Calculate the CPUE by year of the obtained data, and draw the spatial distribution of CPUE as shown in figure 2 ;

[0050] 2) For the analysis of hot and cold spots in the fish catch (CPUE), calculate the Getis-OrdGi* statistics for each element in the data set, and calculate the z-score.

[0051] 3) Based on the z-score and SST, CHL-a data, establish a geographically weighted regression model, derive the GWR parameters C1 and C2 data related to the marine environment, and draw the contour maps of C1 and C2;

[0052] First obtain a properly specified OLS model, then run GWR with the same explanatory variables. Build the GW...

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Abstract

The invention relates to a GWR based analytic method for relationships between ommastrephe bartrami resources and ocean environment factors. The method comprises: 1) obtaining commercial catch data and ocean environment data; 2) performing spatial cold and hot spot analysis on CPUE to determine a hot spot region and a cold spot region of ommastrephe bartrami resource distribution; 3) establishing a mixed geographically weighted regression (GWR) model based on CPUE data of ommastrephe bartrami and the ocean environment data, and exporting parameters of data SST and CHL-a; 4) performing overlapping display on an ommastrephe bartrami hot and cold spot analysis graph, an SST isothermal diagram and a CHL-a contour map; and 5) making typical statistics on ommastrephe bartrami CPUE of each of the hot and cold spot regions by utilizing a GIS, judging distribution data in hot and cold spots, comparing hot and cold spot distributions of different regions, and constructing a relationship with a GWR parameter. Compared with the prior art, the method has the advantages of improving cognition on fishery resources and ocean environment response, enhancing correctness and accuracy of analysis and the like.

Description

technical field [0001] The invention relates to a method for analyzing the relationship between fishery resources and marine environment factors, in particular to an analysis method for the relationship between fishery resources and marine environment factors based on GWR (geographically weighted regression model). Background technique [0002] Many studies have explored the relationship between fishery resources and the marine environment, but scholars generally believe that the impact and contribution weight of the marine environment on fishery resources is not different in space, that is, the spatial non-stationarity of marine environmental parameters is ignored. Geographically weighted regression (GWR) is an effective technique for analyzing this spatial non-stationarity, which enables people to understand how marine environmental parameters affect fishery resources differently in different spatial locations. The GWR model is an extended ordinary linear regression model,...

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Application Information

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IPC IPC(8): G06Q50/02
CPCG06Q50/02
Inventor 冯永玖崔丽陈新军
Owner SHANGHAI OCEAN UNIV
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