Spatiotemporal evolution analysis of time-series land use change using self-organizing map to examine the zoning and scale effects

Abstract

In this study, we applied a self-organizing map (SOM) neural network method to analyze the spatiotemporal evolution of land-use in Beijing using five time-period classification data from 2005 to 2013. We conducted a spatiotemporal integrated expression and a comparative analysis of the time-series of land use data at 5 km grid level. The experiments at the township level and three different grid levels (20 km, 10 km and 1 km) were simultaneously conducted as the comparison study to analysis the modifiable areal unit problem (MAUP). The land use structure data of analysis unit over 5 years were used as input data for SOM. After training the SOM network, the aggregation modes for different land use types were identified on the output plane. Then, the second-step cluster of the output neurons of the SOM was analyzed to construct a series of land use change trajectories that enabled us to get the spatiotemporal patterns of land use change. The results showed five spatial aggregation patterns and three spatiotemporal change patterns of land use 2005 to 2013. The three patterns of spatiotemporal change represent (1) the expansion of urban areas onto farmland in the southeast plains, (2) the development of forest land in the northwest mountainous areas, and (3) the development of piedmont mixed type land use structures. The results of the comparison experiments showed the zoning effect and the scale effect of MAUP, which were: the 5 km grid-based analysis could provide more precise spatiotemporal evolution patterns in the mountainous area, whereas the township level analysis was more appropriate in the plain area; the pattern of forest land development could be better revealed on 20 km and 10 km grid level, while the pattern of built-up land development could be better revealed on 5 km and 1 km grid level.