Hu Xinyu, Zhu Wei, Shen Ximing, Bai Ruxia, Shi Yi, Li Chen, Zhao Lili
College of Landscape Architecture, Nanjing Forestry University, Nanjing, 210037, China.
School of Architecture, Southeast University, Nanjing, 210096, China.
Sci Rep. 2024 Aug 8;14(1):18453. doi: 10.1038/s41598-024-69414-3.
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.
随着可持续城市发展的推进，城市功能区研究受到了广泛关注。近年来，兴趣点（POI）因其信息量丰富且易于获取，在城市功能领域研究中得到了广泛应用。然而，目前学者们主要聚焦于城市功能区的识别，通常依赖单期数据，而功能区预测方面的研究尚未得到充分验证。因此，在本研究中，我们提出一种基于多年POI数据的城市功能区预测新方法。以江苏省南京市为例，我们首先利用POI数据确定了南京市老城区多年的功能区分布，然后设计多组实验，从模型整体精度、稳健性以及预测与实际情况对比分析等多个方面，探究CA - 马尔可夫模型预测功能区的能力。结果表明：（1）对于混合或单一功能区，该模型多年的预测结果趋于稳定，多年预测的准确性表明其在预测城市功能区方面具有稳健性。（2）对于城市中的混合功能区，模型预测很大程度上依赖于用于预测的基年分布，导致结果不准确；因此，它仍然不适用于模拟和预测混合功能区。（3）对于城市中的单一功能区或区域内的主要功能，模型预测的变化程度与实际变化程度接近，结果具有参考价值。
