A novel spatiotemporal prediction method based on fuzzy Transform: Application to demographic balance dat

Many issues require the application of forecasting models applied to spatiotemporal data in Geographic Information Systems (GIS) to predict the spatial distribution and evolution of a specific feature. The use of soft computing techniques in the development of these forecasting models makes it possible to detect non-linear trends but has the disadvantage of increasing the computational complexity of the model. In this paper we present a GIS-based framework in which a fast soft computing forecasting model based on the multidimensional Fuzzy Transform (for short, MF-transform) is applied to evaluate the spatial distribution and the time evolution in a study area of a measurable entity (the feature). The study area is divided into homogeneous zones (the subzones) in which the feature was measured in each time frame. The time series of the feature are analyzed to assess the trend of the feature in subsequent time frames; furthermore, those sub-areas are detected in which the feature is higher than a maximum threshold (hot spots) or lower than a minimum threshold (cold spots) in this time range. A process of fuzzifying the values of the feature is carried out in order to facilitate the interpretation of the results by expert users. The framework was tested on a study area provided by the province of Naples (Italy) to predict and analyze the spatial distribution and temporal trend of the monthly rate of births compared to deaths. Furthermore, the thematic map of the hot and cold spots detected in the three months following the time period of measurements was built. The results show that our method provides reliable results both in terms of forecast error and similarity between the detected hot and cold spots and those who have really formed.