Exploring the predictive ability of the CA–Markov model for urban functional area in Nanjing old city

Abstract

Abstract With advancements in sustainable urban development, research on urban functional areas has garnered significant attention. In recent years, Point-of-Interest, with their large volume of information and ease of acquisition, have been widely applied in research on urban functional domains. However, scholars currently focus on the identification of urban functional areas, usually relying on data from a single period, whereas research on the prediction of functional areas has not yet been well validated. Therefore, in this study, we propose a new method based on several years of POI data to predict urban functional areas. Taking Nanjing City, Jiangsu Province, as an example, we first identified the functional area distribution of the old city of Nanjing over several years using POI data and then designed multiple sets of experiments to explore the CA–Markov model’s ability to predict functional areas from various aspects, including model overall accuracy, robustness, and comparison analysis between predictions and actual situations. The results show that (1) for mixed or single functional areas, the model’s predictions over several years tend to be stable, and the accuracy of the predictions over many years indicates the robustness of the model in predicting urban functional areas. (2) For mixed functional areas in cities, model predictions largely rely on the distribution of the base years used for prediction, leading to inaccurate results; thus, it is still not applicable for simulating and predicting mixed functional areas. (3) For single functional areas in cities or primary functions within an area, the model’s predicted degree of change was close to the actual degree of change, making the results referable.