Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement.

中文翻译：

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大数据和机器学习时代的地理空间分析在提升城市宜居性方面的机遇与挑战

城市系统涉及许多紧密交织的组成部分，由于有了新的传感器，数据收集和时空分析方法，它们比以前更容易测量。将这些数据转化为知识，以促进规划工作以应对当前城市复杂系统的挑战，需要先进的跨学科分析方法，例如城市信息学或城市数据科学。然而，通过采用纯数据驱动的方法，很容易迷失在数据的“森林”中，而错过成功的宜居城市的“树”，而这正是城市规划的最终目标。本文评估了地理空间数据和城市分析如何使用混合方法来帮助更好地理解城市动态和人类行为，以及如何帮助规划工作来改善宜居性。在回顾最新研究的基础上，本文进一步迈进了一步，还探讨了城市分析中新数据源的潜力和局限性，以便更好地概述这些新数据源的整个“森林”，并分析方法。主要讨论围绕使用社交媒体平台或传感器上的大数据的可靠性，以及如何通过新颖的分析方法（例如机器学习）从海量数据中提取信息，以针对城市居住环境改善做出更明智的决策。 。