Method for determining spatial interpolation model of cancer data

Abstract

The invention relates to a method for determining a spatial interpolation model of cancer data. The method comprises the following steps: acquiring cancer data and related data of a to-be-analyzed area; the cancer data comprising cancer death rate data, the related data comprising basic geographic data, mining industry distribution data, social economic data, daily value meteorological data and PM2.5 pollution data, and the basic geographic data comprises village boundary vector data, road data, river data and topographic data; based on the cancer death rate data, the basic geographic data and the mining distribution data, determining the interpolation precision of the inverse distance weighting model, the interpolation precision of the trend surface analysis model and the interpolation precision of the common Kriging model; based on the cancer death rate data and the related data, determining the interpolation precision of the polynomial regression model and the interpolation precision of the collaborative Kriging model; and determining the model corresponding to the highest interpolation precision as an optimal spatial interpolation model. According to the scheme of the invention, the optimal cancer data spatial interpolation model can be rapidly determined, and the interpolation precision and the depicted data precision are improved.

Description

technical field [0001] The invention belongs to the interdisciplinary fields of geography, environmental science and medicine, and relates to space-time environment analysis technology, model construction and optimization screening technology, chronic disease spatial statistics technology, etc., and in particular to a method for determining a spatial interpolation model of cancer data. Background technique [0002] Cancer is the target of global major disease prevention and control. However, in practical research applications, cancer data are mostly obtained from registration point statistics, and most of the traditional statistical data do not contain spatial information and cannot achieve full coverage of the region. Therefore, accurate statistics or estimates of regional cancer mortality using limited point data will help improve the ability to monitor cancer prevalence and trends, and also provide more possibilities for etiology exploration and impact factor screening. ...

AI Technical Summary

Problems solved by technology

[0004] However, when determining the optimal spatial interpolation model, researchers need to make many attempts and comparisons before they can carry out follow-up work, which not only costs a lot of manpower and material resources, but also lasts for a long time

More importantly, some researchers who lack the background knowledge of spatial interpolation do not know which interpolation model to choose

In addition, the spatial interpolation models at the current stage are either aimed at spatial features or influencing factors, so the accuracy of the selected spatial interpolation model is not high due to insufficient consideration of factors; moreover, these spatial interpolation models study large areas ( For example, national, provincial, and municipal), the level of precision of data corresponding to this method is far from meeting the requirements of precise cancer prevention and control

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