Land-use change models that accurately replicate the complex dynamics of land development provide vital information for urban planning and policy. These models require both detailed data and advanced statistical methods. Many factors influence land-use change decisions, such as parcel characteristics, accessibility to activities, and current and historical neighborhood conditions. Therefore, spatial and temporal components must be incorporated in a model at the highest possible disaggregation level in order to achieve robust results. A spatio-temporal multinomial autologistic model, incorporating space and time and their interactions, is introduced to investigate land-use dynamics at the parcel-level, and is applied to Delaware County, Ohio. It is able to capture the impacts of the existing and historical neighborhood conditions of parcels with high accuracy. Advanced computational methods are used to deal with the computational challenges of parameter estimation. The model is validated, estimating 91.4% of all observations correctly for the period 2005–2010, and is applied to land-use forecasting.

中文翻译：

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土地利用变化的时空多项自逻辑建模：宗地级方法

准确复制土地开发复杂动态的土地利用变化模型为城市规划和政策提供了重要信息。这些模型需要详细的数据和先进的统计方法。许多因素会影响土地利用变化的决策，例如地块特征、活动的可及性以及当前和历史街区条件。因此，空间和时间组件必须以尽可能高的分解级别合并到模型中，以实现稳健的结果。引入了一个时空多项自逻辑模型，结合了空间和时间及其相互作用，以研究地块级别的土地利用动态，并应用于俄亥俄州特拉华县。它能够高精度地捕捉地块现有和历史街区条件的影响。先进的计算方法用于应对参数估计的计算挑战。该模型经过验证，正确估计了 2005-2010 年期间所有观测值的 91.4%，并应用于土地利用预测。