One of the most important variables that leads to effective individual and group tours is the tourism route planning approach, which enables tourists to engage with tourism with ease, speed, and safety. However, current methods of designing tourist routes have some glitches, such as relying only on external objectives to find the best route. In this paper, a novel urban tourism path planning method based on a multiobjective genetic algorithm is proposed. The main goal of this paper is to enhance the accuracy of the genetic algorithm (GA) by adopting new parameters and selecting the optimal tourism path by combining external and internal tourist site potentials. Moreover, the GA and analytical hierarchy process (AHP) were used in our proposed approach to evaluate urban tourism route planning under multiple conflicting objectives. To visualize and execute the proposed approach, the geographic information system (GIS) environment was used. Our suggested approach has been applied to develop the tourist road network of Chengdu City in China. Compared with existing tourism path planning approaches, our proposed approach is more accurate and straightforward than other approaches used to choose routes.

The evaluation of geoscience data is a far-reaching topic which cannot be systematically covered. The purpose of inferential statistics is to harness useful information from data for making decisions. This paper conducts in-depth statistical study for the Bursa-Wolf and Molodensky Badekas models of the three-dimensional transformation parameters. We also considered the combined and observation equations scenarios of these methods for the comparative study. Four key indicators are conducted to evaluate the performance of the two transformation models according to the residual results. These include root mean square error (RMSE), paired t-test, Wilcoxon signed-rank test and the Cohen’s d effect size measure. RMSE evaluation is based on the mean difference between model estimates and observed values. The correlations in the model results is investigated based on paired t-test. Wilcoxon signed-rank test assesses the statistical significance of the model’s paired differences. To estimate the effect size of the performance differences, Cohen’s d measures are computed. Further, the residuals of the estimated parameters are plotted according to their respective control points. The inference results of these tests generally show that Badekas transformation approach is more precise than Bursa-Wolf. Specifically, Badekas combined case is the most precise, followed by its observation case, then Bursa-Wolf combined and finally its observation case is the least performing model. The application of various data analysis and statistical verifications make the task of data interpretation and best model selection easier.

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