Real-time prediction of spatial information has attracted a lot of attention. Machine learning enables us to provide real-time prediction of spatial information such as road traffic by using aggregated sensor data. The amount of mobile traffic is forecasted to increase exponentially, thereby causing serious transmission delays when traffic loads are heavy. If a part of the data used for predicting spatial information in real time does not arrive on time, the prediction accuracy degrades because the prediction is done without the missing data. A utility-based scheduling technique has been suggested as a way of prioritizing such delay-sensitive data. However, no study has not addressed the utility-based scheduling for the real-time prediction of spatial information. Therefore, this paper proposes a scheme that enables modeling the utility function for real- time prediction of spatial information. The scheme is roughly composed of two steps: the first creates training data from original time-series data and a machine learning model using the data, while the second models the utility function using the feature selection method in the learning model. Feature selection method enables extracting the importance of data in terms of how much the data contributes to the prediction accuracy. This paper assumes the road traffic prediction as a scenario and shows the utility function modeled by the proposed scheme using real spatial datasets. A numerical study demonstrates how the model of the utility function works effectively in prioritizing data for real-time prediction in terms of accuracy.