ABSTRACT This article discusses the prospects for automated discovery of explanatory models directly from geospatial data. Rather than taking an approach based on machine learning, which generally leads to models that cannot be understood by humans or related to domain theory, the approach described here suggests we can instead construct models from fragments of domain understanding—such as commonly encountered equation forms, known constants and laws—resulting in discovered models that can both be understood by humans and directly compared with known theory. We then propose a conceptual model of the discovery process by which the various stages and components of discovery and explanation work together to learn models from data. The approach described weaves together ideas for describing models from Harvey’s book ‘Explanation in Geography’ with current thinking on how explanatory models might be ‘discovered’ from data from Inductive Process modeling. On the way, we also highlight: (i) why it is important to have models that explain as well as predict, (ii) how such an approach contrasts with – and goes beyond – current work in deep learning, (iii) how the task of model discovery might be tackled computationally and (iv) how computational model discovery can play a valuable role in creating geographical explanations.

中文翻译：

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第四范式地理信息科学？从数据中自动发现和解释的前景

摘要 本文讨论了直接从地理空间数据中自动发现解释模型的前景。与其采用基于机器学习的方法，这通常会导致人类无法理解或与领域理论相关的模型，这里描述的方法表明我们可以从领域理解的片段中构建模型——例如常见的方程形式，已知常数和定律——导致发现的模型既可以被人类理解，又可以直接与已知理论进行比较。然后，我们提出了一个发现过程的概念模型，通过该模型，发现和解释的各个阶段和组成部分协同工作以从数据中学习模型。所描述的方法将 Harvey 的著作《地理解释》中描述模型的想法与当前关于如何从归纳过程建模的数据中“发现”解释性模型的想法交织在一起。在此过程中，我们还强调：(i) 为什么拥有解释和预测的模型很重要，(ii) 这种方法如何与当前深度学习的工作形成对比，并超越，(iii)模型发现的任务可以通过计算来解决，以及 (iv) 计算模型发现如何在创建地理解释方面发挥重要作用。