Geographic visualization is essential for explaining and describing spatiotemporal geographical processes in flow fields.However,due to multi-scale structures and irregular spatial distribution of vortices in complex ...Geographic visualization is essential for explaining and describing spatiotemporal geographical processes in flow fields.However,due to multi-scale structures and irregular spatial distribution of vortices in complex geographic flow fields,existing two-dimensional visualization methods are susceptible to the effects of data accuracy and sampling resolution,resulting in incomplete and inaccurate vortex information.To address this,we propose an adaptive Line Integral Convolution(LIC)based geographic flow field visualization method by means of rotation distance.Our novel framework of rotation distance and its quantification allows for the effective identification and extraction of vortex features in flow fields effectively.We then improve the LIC algorithm using rotation distance by constructing high-frequency noise from it as input to the convolution,with the integration step size adjusted.This approach allows us to effectively distinguish between vortex and non-vortex fields and adaptively represent the details of vortex features in complex geographic flow fields.Our experimental results show that the proposed method leads to more accurate and effective visualization of the geographic flow fields.展开更多
文摘Geographic visualization is essential for explaining and describing spatiotemporal geographical processes in flow fields.However,due to multi-scale structures and irregular spatial distribution of vortices in complex geographic flow fields,existing two-dimensional visualization methods are susceptible to the effects of data accuracy and sampling resolution,resulting in incomplete and inaccurate vortex information.To address this,we propose an adaptive Line Integral Convolution(LIC)based geographic flow field visualization method by means of rotation distance.Our novel framework of rotation distance and its quantification allows for the effective identification and extraction of vortex features in flow fields effectively.We then improve the LIC algorithm using rotation distance by constructing high-frequency noise from it as input to the convolution,with the integration step size adjusted.This approach allows us to effectively distinguish between vortex and non-vortex fields and adaptively represent the details of vortex features in complex geographic flow fields.Our experimental results show that the proposed method leads to more accurate and effective visualization of the geographic flow fields.
