## 1 Introduction  
Electromagnetic radiation (EMR) transmits energy in the form of electromagnetic waves in space [1]. It may have effects on several systems of the human body, such as the cardiovascular, neurological, and reproductive systems, and pose a potential impact and threat to public health [2]. Therefore, accurately evaluating the EMR in the urban environment is helpful for the public to understand the quality of the electromagnetic environment and the management and planning of relevant departments. The amount of EMR in the environment can be assessed by constructing an EMR space field (EMR-SF), which requires the collection of a certain amount of EMR data. Usually, the EMR data are collected along roads in cities, which have a largely empty region overall but are distributed linearly locally. Constructing a high-precision field based on such sampling data is important for urban electromagnetic pollution monitoring and prevention. At present, the construction of the EMR-SF can be divided into two types. One is the numerical simulations of EMR based on physical models by using different propagation models [3, 4, 5, 6]. For this type of method, some parameters need to be known, such as the parameters related to the transmitting antenna and the parameters of the radio wave propagation environment in advance. However, these parameters are often not easy to obtain. Moreover, the method based on the physical model only calculates the radiation field from a single radiation source, and there are many radiation sources in the environment. Thus,this method is not very applicable. Another is to use spatial interpolation methods to construct the EMR-SF [7; 8; 9; 10; 11]. For this type of method, the EMR value collected by the actual position is used as the basic data source, and the EMR-SF is constructed according to the spatial interpolation method. Compared with the previous type of method, this method only needs to obtain some EMR sampling data to construct the EMR-SF in the whole region. The common spatial interpolation methods include inverse-distance weighting (IDW), ordinary kriging (OK), and radial basis function (RBF). Most interpolation methods can construct a space field with high accuracy when the sampled data are distributed more uniformly. However, EMR data collected along roads in the city have a largely empty region at the center of the sampling range. This results in the number of sample points being too small and the distribution of sample points being uneven, which tends to lead to a poor spatial correlation between data. The accuracy of the IDW and OK relies on the nearest sample points. When there are few sample points or the spatial correlation between data is not strong, the contribution of each nearest sample point will be similar, which leads to a low accuracy of the space field constructed by IDW and OK [12; 13; 14; 15]. In contrast, RBF only needs to define simple basis functions in the spatial domain, which can be linearly combined to construct the entire space field. It can have high interpolation accuracy even when the sampled data are unevenly distributed [16; 17; 18; 19], which is more suitable for the spatial interpolation of the data collected along the road. Nevertheless, the empty region of the EMR data center area sampled along the road is too large, and too much missing data will lead to a low accuracy of the EMR-SF constructed by RBF. To solve the problem of low accuracy in the construction of EMR-SF used by RBF because of the large empty region of the sampled data center area, the layered radial basis function (LRBF) spatial interpolation method is proposed. The LRBF is used for the construction of EMR-SF along the road collection. First, the regular grid point interval distance is obtained based on the Gaussian basis function and then used to construct the regular grid points. Second, the maximum aggregation distance is obtained by [PERSON]'s K function to separate the grid points into the layer. Finally, the EMR-SF is constructed by the LRBF method on the layered grid points.