Doctoral Dissertation Research: Parameter and Metric Space Investigation: Towards "Honesty in Modeling"

2000年在加拿大班夫举行的第四届GIS与环境建模集成国际会议上，一群科学家和研究人员发表了一份简短但有力的声明，内容涉及如何消除建模与GIS集成进一步取得进展的一些主要障碍。 在总结会议讨论主题的“班夫声明”中出现的七项建议中，有：（3）模型应该有元数据，应该评估模型中的误差传播，并将其传达给模型预测的用户;（6）“建模的诚实性”[应该考虑]预测中的不确定性和误差。 这个博士论文研究项目将寻求通过调查广泛使用的城市元胞自动机模型SLEUTH中的错误传播来源来提高地理建模的诚实性。 这位博士生认为，SLEUTH的参数空间中有一些区域，通过扩展，所有的元胞自动机模型，参数相互作用可以产生不寻常和不稳定的行为，将错误传播到模型输出中。 此外，学生希望证明在模型的参数空间内，有多组令人满意的城市系统描述参数，无论使用哪种拟合度量来比较模型与控制数据的性能。 虽然在城市自动机建模之前的工作一直集中在模型的应用，最近的研究已经开始考虑模型校准作为建模过程中的一个关键阶段。 通过检查一个模型的“螺母和螺栓”并确定参数相互作用是否会导致模型误差的传播，将进一步进行先前的研究。 这种方法是理解这些广泛使用的模型内部如何运作和行为的重要一步。 SLEUTH城市增长模型的参数空间将通过一系列实验与基本的几何，理论和现实世界的城市数据进行检查。 SLEUTH模型将通过将参数空间解析为100个单元范围内的5个单元的块来进行彻底和重复的重新校准，从而产生4，084，101个初始参数集。 这些重复校准将用于使用模型中当前使用的14个拟合度量来找到最佳拟合数据的校准。 将添加模型的新度量m，即参数空间的细胞无序。 该度量将计算整个参数空间的稳定性，并允许识别具有不一致行为的区域。 然后，将使用自组织地图分析和可视化三个数据集的详尽校准的度量结果。 这将允许确定用于拟合度量和模型行为的度量之间的联系。 使用三个不同的数据集提供了一定程度的信心，结果不是数据的残差，而是模型的行为。该项目将校准模型的模型参数值的极大组合。 在过去的十年里，这种模型引起了人们的极大兴趣，希望这种空间特征和土地利用或土地覆盖变化可以用元胞自动机模型模拟和现实复制。 许多学者对这些模型持怀疑态度，认为任何空间变化模式都可以被复制，只要模型具有足够数量的参数，其值已经用当前地理计算模型的能力进行了校准。 这个项目通过证明参数空间是否合理稳定以及参数值是否合理一致来处理这些问题，即使给定完全不同的初始状态配置。 这项工作的更广泛的影响包括：开发一种新的方法，用于研究城市自动机模型框架内发生的参数空间和相互作用。 作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立的研究生涯。

A group of scientists and researchers who met in Banff, Canada, in 2000 for the 4th International Conference on Integrating GIS and Environmental Modeling issued a brief but powerful statement regarding how to eliminate some of the major barriers to further progress in integrating modeling and GIS. Among the seven recommendations that appeared in "the Banff Statement" that summarized topics discussed at the meeting were: (3) Models should have metadata, and the propagation of error through the model should be assessed and communicated to the users of model predictions; and (6) "Honesty in Modeling" [should account] for uncertainty and error in predictions. This doctoral dissertation research project will seek to advance greater honesty in geographic modeling by investigating the sources of error propagation in a widely used urban cellular automata model, SLEUTH. The doctoral student argues that there are areas within the parameter space of SLEUTH and, by extension, all cellular automata models, where parameter interactions can create unusual and unstable behavior that propagates error into model outputs. Additionally the student expects to demonstrate that within the parameter space of a model, there are multiple sets of satisfactory urban system description parameters, no matter which measure of fit are used to compare the model's performance to control data. While prior work in urban automata modeling has been focused on the application of models, recent studies have begun to look at the model calibration as a critical stage in the modeling process. Prior research will be taken a step further by examining the "nuts and bolts" of one model and by determining if the parameter interactions can lead to the propagation of model error. The approach is a significant step forward in understanding how these widely used models internally function and behave. The parameter space of the SLEUTH urban growth model will be examined through a series of experiments with basic geometric, theoretical, and real-world urban data. The SLEUTH model will be exhaustively and repeatedly recalibrated by parsing the parameter space into blocks of 5 out of the range of 100 units, resulting in 4,084,101 initial parameter sets. These repeat calibrations will be used to find those that best fit the data using the fourteen metrics of fit that are currently used in the model. A new measure for the model, m, the cellular disorder of the parameter space will be added. This metric will calculate the stability throughout the parameter space and will allow for the recognition of areas that have inconsistent behavior. Metric results from the exhaustive calibration of the three datasets will then be analyzed and visualized using self-organizing maps. This will allow for the determination of links between the metrics used to measures of fit and model behavior. Using the three different datasets provides some degree of confidence that the results are not residuals of the data, but of the model's behavior.The project will calibrate the model for an extremely large combination of the model parameter values. Such models have captured major interest in the past ten years in the hope that such spatial characteristics and land-use or land-cover change might be simulated and reality replicated with cellular automata models. Many scholars view these models with a great deal of skepticism, believing that any spatial pattern of change can be replicated given a model with a sufficient number of parameters whose values have been calibrated with the power of current geocomputational models. This project deals with these concerns by demonstrating whether the parameter space is reasonably stable and if the parameter values are reasonably consistent even given quite different initial state configurations. The broader impacts of this work include: the development of a new method for investigating the parameter space and interactions taking place within the framework of urban automata models. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.