The rise of the term ‘big data’ has contributed to recent advances in computational analysis techniques, such as machine learning and more broadly, artificial intelligence, which can extract patterns from large, multi-dimensional datasets. In the field of urban planning, it is pertinent to understand both how such techniques can advance our understanding of cities, and how they can be embedded within transparent and effective digital planning tools, known as planning support systems. This research specifically focuses on two related contributions. First, it investigates the role of planning support systems in supporting a participatory data analytics approach through an iterative process of developing and evaluating a planning support system environment. Second, it investigates how specifically machine learning planning support systems can be co-designed by built environment practitioners and stakeholders in this environment to solve a real planning issue in Sydney, Australia. This paper presents the results of applied research undertaken through the design and implementation of four workshops, involving 57 participants who were involved in a co-design process. The research follows a mixed-methods approach, studying a wide array of measures related to participatory analytics, task load, perceived added value, recordings and observations. The results highlight recommendations regarding the design and evaluation of planning support system environments for co-design and their coupling with machine learning techniques. It was found that consistency and transparency are highly valued and central to the design of a planning support system in this context. General attitudes towards machine learning and artificial intelligence as techniques for planners and developers were positive, as they were seen as both potentially transformative but also as simply another technique to assist with workflows. Some conceptual challenges were encountered driven by practitioners' simultaneous need for concrete scenarios for accurate predictions, paired with a desire for predictions to drive the development of these scenarios. Insights from this work can inform future planning support system evaluation and co-design studies, in particular those aiming to support democracy enhancement, greater inclusion and more efficient resource allocation through a participatory analytics approach.

“大数据”一词的兴起推动了计算分析技术的最新发展，例如机器学习以及更广泛的人工智能，它可以从大型多维数据集中提取模式。在城市规划领域，既要了解这些技术如何增进我们对城市的了解，又要了解如何将其嵌入透明有效的数字规划工具（称为规划支持系统）中，这是相关的。这项研究专门针对两个相关的贡献。首先，它通过开发和评估计划支持系统环境的迭代过程，研究了计划支持系统在支持参与式数据分析方法中的作用。第二，它研究了在这种环境下建筑环境从业者和利益相关者如何共同设计机器学习计划支持系统，以解决澳大利亚悉尼的实际计划问题。本文介绍了通过设计和实施四个讲习班进行的应用研究的结果，共有57位与共同设计过程有关的参与者参加了此次研讨会。这项研究遵循一种混合方法的方法，研究了与参与式分析，任务负荷，感知的增值，记录和观察有关的各种度量。结果重点介绍了有关设计和评估计划支持系统环境以进行协同设计及其与机器学习技术的耦合的建议。人们发现，在这种情况下，一致性和透明性受到高度重视，对于规划支持系统的设计至关重要。作为计划人员和开发人员的技术，人们对机器学习和人工智能的普遍态度是积极的，因为它们不仅具有潜在的变革性，而且仅仅是协助工作流程的另一种技术。从业人员同时需要准确的预测的具体方案，并伴随着推动这些方案发展的预测愿望，从而遇到了一些概念上的挑战。这项工作的见识可以为将来的计划支持系统评估和联合设计研究提供参考，尤其是那些旨在支持民主发展的研究，