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本刊2024年第4期主题为“多源数据驱动城市分析”。
城市是一个复杂的巨型系统，由自然、社会和物理环境的众多要素以及基础设施、运营和治理等多个系统组成。这个复杂的系统一直在不停地演变，而它的各个要素和系统也一直在相互影响。这就给全面了解城市情况、科学规划城市可持续发展带来了困难。在这方面，对城市的要素和系统以及它们之间的相互作用进行数据分析，如对人类对物理环境的感知、在物理环境中的行为以及与物理环境的相互作用进行数据分析，既是获得更多城市知识的途径，也是以更科学的方式制定城市规划的基础，具有重要作用。过去，由于缺乏获取足够数据的方法（如问卷调查和访谈），城市分析和城市研究只能以经验主义的方式进行，而如今，计算机、信息与通信、数据和人工智能等科学技术的飞速发展，使得从遥感、无线网络和社交媒体等多种来源获取海量数据，并通过机器学习、深度学习、语义分析等方法进行定量和定性数据分析变得更加容易。因此，城市分析这门新兴学科迅速崛起。它植根于数据科学，可以为城市实证研究提供定量分析，为城市规划决策提供定量论证支持。现在，即使是人类对特定物理环境的情绪感知和生理行为，也可以作为数据记录下来，并进行定量分析。有人甚至认为，随着多源数据的积累和人工智能技术的进步，数据和城市分析不仅可以帮助模拟物理环境的演变趋势，还可以通过机器学习代替人类创造城市规划和设计方案。然而，尽管数据和城市分析技术潜力巨大，但应该注意的是，数据本身并不能呈现科学规划的关键信息，城市分析技术也不会自动带来科学规划的结果。实际上，数据所承载的信息才是科学规划的根本，只有在人类创造性思维的分析框架内，根据人类制定的评价标准，通过城市分析才能从数据中解码这些信息。
本刊的4篇主题文章也从不同角度对这一主题进行了探讨，我们将在后期的推送中对每篇文章进行详细介绍，欢迎读者们关注。
A city is a mega-system of complexity that is composed of numerous elements of natural, societal, and physical environments, as well as multiple systems of infrastructure, operation, and governance. This complex system keeps evolving endlessly while its elements and systems keep interacting with each other all the time. This makes it difficult to get a comprehensive picture of the city and to make scientific plans for sustainable city development. In that regard, data analyses on the elements and systems of cities, as well as interactions among them, such as those on human beings’ perceptions on, behaviors in, and interactions with the physical environment, play a significant role as both an approach to getting more knowledge about the city and a foundation for making city plans in a more scientific way. In contrast to the situation in the past when urban analyses and urban studies had to be done in an empirical way due to the lack of methods to get enough data, such as questionnaires and interviews, nowadays the rapid science and technology advancements of computing, information and communication, data, and artificial intelligence make it much easier to get a huge amount of data from multiple sources, such as remote sensing, wireless networks, and social media, and to do both quantitative and qualitative data analyses by way of machine learning, deep learning, semantic analysis, and so on. In consequence, the new discipline of urban analytics has been rising quickly. Rooted into data sciences, it can provide empirical urban studies with quantitative analyses and support urban planning decisions with quantitative arguments. Even emotional perceptions and physiological actions of human beings on a specific physical environment can be now recorded as data and analyzed in a quantitative way. Some even believe that, along with the accumulation of multi-sourced data and the advancement of artificial intelligence technologies, data and urban analytics can help not only simulate evolutionary trends of the physical environment, but also create urban planning and design schemes on behalf of human beings through machine learning. However, in spite of the great potential of data and urban analytics, it should be noted that data itself does not present the information critical for scientific planning and urban analytics would not automatically lead to the outcome of scientific plans. It is actually the information carried by data that is fundamental for scientific planning, which can only be decoded from data through urban analytics within the analytical framework of creative thinking created by human beings and in line with the evaluation criteria established by human beings.
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《城市规划（英文版）》
China City Planning Review
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