Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.

Identifying urban building function plays a critical role in understanding the complexness of urban construction and improving the effectiveness of urban planning. The emergence of user generated contents has brought access to massive semantic information which complements the traditional remote sensing data for identifying urban building functions and exploring the spatial structure in urban environment. This article proposes a stepwise identification framework for urban building functions based on remote sensing imagery and point of interests (POIs) data, which merges the spatial similarity of buildings and kernel density to improve the identification accuracy and completeness. Taking Wuhan as an example, Google earth images and POI data were obtained to identify the seven primary categories for the individual buildings in the core urban area. The results suggest that the proposed stepwise framework is feasible to identify the urban building functions as the identification results exhibit the superiority in terms of accuracy and completeness. Our results suggest that the identification of urban building function is sensitive to the bandwidth of kernel density estimation and 200 meter is the optimal size. The findings also indicate that significant spatial agglomeration exists in residential and commercial buildings at both macro and microlevels.