# Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models
## 6 Discussions  
In this paper, we propose an approach to select the appropriate features and deep learning model by analyzing correlations, spatial correlations, and temporal correlations for spatiotemporal data prediction. We evaluate our system with spatiotemporal air pollution data to generate the prediction model. We take the past data (\(t_{1},...,t_{n-1}\)) as input and predict the current data at \(t_{n}\) as an output. The prediction results are compared in the map visualizations. The evaluation in Section 5 is intended to perform the deep learning modeling procedure to improve the prediction results through the system. Note that we show the modeling procedure rather than the best results in this paper. The limitations of our approach are in the following. For feature selection, our system provides the Pearson correlations between variables, temporal autocorrelation with the time lag, and spatial autocorrelation with LISA visualization. However, the extension to spatial filtering and feature extraction during the data analysis can enhance the quality of feature selection. Although our approach can be useful for identifying and predicting global trends in the overall data, our system tends to neglect the local characteristics. For example, we can filter the areas by considering geographic characteristics and environmental conditions. In the case of PM\({}_{2.5}\), the frequency of occurrence may vary according to the density of factories in neighboring areas, and the diffusion of PM\({}_{2.5}\) may be changed by mountains or high-rise buildings in nearby areas [55]. We plan to add spatial filtering and apply feature extraction techniques, such as PCA, LDA, and t-SNE. From a deep learning perspective, we trained the data using LSTM, GRU, and ConvLSTM and compared the predictive performance with the spatiotemporal relationship. According to recent research [44; 45; 46; 47; 55], various network structures extended from the RNN structure were investigated as a technique for predicting spatiotemporal data. Although not included in this study, DCRNN (diffusion convolutional recurrent neural network) [56] can be used to predict spatiotemporal data, using directed graph data. This paper utilizes data obtained from irregular discrete stations, rather than grid or known topological data. Such discrete data may be distorted in the connection between features in the process of converting them into a graph structure. When converting from discrete data to the graph structure, the relationship between features determines the weight of the graph. However, it is difficult for us to find the relationship between features from discrete data. After studying the feature selection technique with the feature extraction techniques, we plan to investigate to transform the extracted feature into a directional graph form and apply it to the DCRNN in the future. The purpose of this study is to examine whether prediction performance degradation is due to feature selection or spatiotemporal correlation. Therefore, we train the data with fixed deep learning hyperparameters such as batch size, loss function, and optimizer. However, setting up appropriate hyperparameters in deep learning is a critical factor in improving predictive performance. Therefore, we need to analyze the influence of hyperparameters on spatiotemporal prediction in the future. We apply the nearest, linear, and cubic interpolation to spatially interpolate and compare the predictions of the deep learning models as postprocessing. However, these techniques do not work correctly for irregularly distributed stations. To overcome this problem, we can consider applying the RBF network, which is a kind of artificial neural network. The RBF network is calculated using the radial basis function as an activation function and is applied for functional approximations, time series prediction, and classifications [42]. The RBF network can be added ahead of the ConvLSTM neural network as additional layers.