This paper examines the application of spatial disaggregation model in forecasting long-term land use change. It discusses an approach for disaggregating the predicted results on the basis of zonal data. The zonal data are disaggregated for each cell and the aggregated cell data, obtained based on the building units in the base year, are then employed for land use forecasting. The requirements of the proposed model are addressed by conducting empirical analyses to answer two questions: ‘Which cells will be redeveloped before the target year?’, and ‘How much will these cells be redeveloped?’ To answer the first question, the proposed model calculates the probability of redevelopment of a cell using a binary logistic regression model. This approach is proven to be capable of identifying the cells to be redeveloped (with a success rate of 80.4%) based on the rank of the accessibility and density values for each cell. To answer the second question, the proposed model estimates the expected redeveloped floor space in the cell in terms of the location utility. The R^2 value is found to be approximately 69.5%, which is sufficiently high for a forecasting model. The results of this study are expected to be used to develop a more detailed disaggregation model to forecast changes in land use in urban regions.

中文翻译：

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基于区域数据的运输模型改善土地长期预测的空间分解模型

本文探讨了空间分解模型在预测长期土地利用变化中的应用。它讨论了一种基于区域数据分解预测结果的方法。将每个单元的区域数据分解，然后将基于基年的建筑单位获得的汇总单元数据用于土地利用预测。通过进行经验分析来回答以下两个问题，从而解决了所提出模型的要求：“哪些细胞将在目标年份之前被重建？”，以及“这些细胞将被重建多少？”。为了回答第一个问题，提出的模型使用二进制逻辑回归模型计算细胞的再发育概率。事实证明，这种方法能够识别待重建的细胞（成功率为80。4％）基于每个单元的可访问性和密度值的等级。为了回答第二个问题，建议的模型根据位置实用程序估算了单元中预期的重新开发的地面空间。发现R ^ 2值约为69.5％，对于预测模型而言足够高。预期该研究的结果将用于建立更详细的分类模型，以预测城市地区土地使用的变化。