通过综合领域专业知识和机器学习方法，预测城市规模的“人造”城市原型
Prediction of 'artificial' urban archetypes at the pedestrian-scale through a synthesis of domain expertise with machine learning methods
摘要：
城市空间的活力一直在稳步削弱，普遍采用以汽车为中心的城市发展形式，其特点是密度较低，街道网络为行人提供的连通性较差，缺乏可访问的土地使用;然而，即使这些问题已经明确了一段时间，问题仍然存在于新的规划形式中。这里假设，领域知识和机器学习方法的综合可以创建强大的工具集，可以以更严格的方式对新提出的发展进行基准测试，以加强问责制和更有证据的决策。一个工作的例子开发了一系列机器学习模型，将“人造”城镇与更适合步行和混合使用的“历史”城镇区分开来。该数据集是从网络中心，混合使用，土地利用可达性和人口密度的措施，作为代理的空间复杂性，这是在英国的931个城镇，城市计算在爱丁堡规模。以官方指定的“新城镇”为出发点，然后发展出一系列线索。首先，使用一个迭代的人在循环过程中，一个监督分类器（额外的树）的培养，从185个“人工”的位置被确定的基础上，数据汇总到各自的城镇或城市边界。然后，该信息用于针对较高分辨率数据集训练监督和半监督（M2）深度神经网络分类器。这些模型与城市规划师表达的直觉大致一致，并显示出持续发展的潜力，以应对随后的挑战，这些挑战涉及：选择策划的培训范例;进一步开发技术，以强调本地化的分析尺度;以及校准模型概率的方法，以与领域专家的直觉保持一致。
英文摘要：
The vitality of urban spaces has been steadily undermined by the pervasive adoption of car-centric forms of urban development as characterised by lower densities, street networks offering poor connectivity for pedestrians, and a lack of accessible land-uses; yet, even if these issues have been clearly framed for some time, the problem persists in new forms of planning. It is here posited that a synthesis of domain knowledge and machine learning methods allows for the creation of robust toolsets against which newly proposed developments can be benchmarked in a more rigorous manner in the interest of greater accountability and better-evidenced decision-making. A worked example develops a sequence of machine learning models that distinguishing `artificial' towns from their more walkable and mixed-use `historical' equivalents. The dataset is developed from network centrality, mixed-use, land-use accessibility, and population density measures as proxies for spatial complexity, which are computed at the pedestrian-scale for 931 towns and cities in Great Britain. Using officially designated `New Towns' as a departure point, a series of clues is then developed. First, using an iterative human-in-the-loop procedure, a supervised classifier (Extra-Trees) is cultivated from which 185 `artificial' locations are identified based on data aggregated to respective town or city boundaries. This information is then used to train supervised and semi-supervised (M2) deep neural network classifiers against the higher resolution dataset. The models broadly align with intuitions expressed by urbanists and show potential for continued development to broach ensuing challenges pertaining to: selection of curated training exemplars; further development of techniques to accentuate localised scales of analysis; and methods for the calibration of model probabilities to align with the intuitions of domain experts.
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