## 6 Conclusions  
The EMR data collected along the road are spatially distributed in a line shape. These data are unevenly distributed, and there is an empty region in the center of the data-sampling area. The accuracy of generating the EMR-SF using the RBF interpolation method is low under such data distribution. To address this problem, the LRBF spatial interpolation method is proposed for the construction of EMR-RF data collected along roads. In this method, a certain density of grid points is constructed in advance in the empty region of the study area, which can be used to increase the number of sample points in LRBF so that LRBF can obtain stable sample points in the empty region. Then, the grid points are  
Figure 19: Comparison of LRBF with different layered interval distances. (**a**) The interval distance is 55. (**b**) The interval distance is 45. (**c**) The interval distance is 35. (**d**) The interval distance is 25. (**e**) The interval distance is 15.  
separated into multiple layers from outside to inside. Finally, the LRBF spatial interpolation method is used to generate the EMR values of the grid points in layers. This can enable the LRBF basis function to ensure a certain shape in the local range and improve the LRBF interpolation accuracy to construct a better EMR-SF.  
The electromagnetic data detected along the road in the study area are collected by the EMR instrument, and the EMR-SF within the study area is constructed according to the LRBF. The experimental results show that when the number of electromagnetic radiation data samples along the road is small, the EMR-SF constructed by LRBF is better than the classical spatial interpolation methods, such as the IDW and OK methods, in terms of precision and visualization. It obtains a more accurate and continuous EMR-SF. In addition, the construction accuracy of the EMR-SF by LRBF under different grid point interval distances and layered interval distances is also discussed. According to the maximum influence distance and the maximum aggregation distance obtained from the distribution pattern of sampling points to set the LRBF grid point and layered interval distance, the accuracy of the construction of EMR-SF by LRBF is improved.  
[PERSON] wrote the paper and did the experiment. [PERSON] proposed the method of the whole paper. [PERSON]. ([PERSON]) verified the accuracy of the experiment. [PERSON]. ([PERSON]) collected experimental data. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [41901336], the Yunnan Fundamental Research Projects [202101 AT070078], the 'Revitalizing Yunnan Talents Support Program' project [YNWR-QNBJ-2020-103], and the Yunnan Academician and Expert Workstation [2017 IC063] funding support.  
Not applicable.  
The data that support the findings of this study are openly available in 'figshare.com' at [[https://doi.org/10.6084/m9.figshare.21679379.v1](https://doi.org/10.6084/m9.figshare.21679379.v1)]([https://doi.org/10.6084/m9.figshare.21679379.v1](https://doi.org/10.6084/m9.figshare.21679379.v1)) (accessed on 6 December 2022).  
The authors declare no conflict of interest.