Digital Geography Lab, Department of Geosciences & Geography, University of Helsinki, Helsinki, Finland.
Department of Geography, University College London, London, United Kingdom.
Sci Data. 2020 Mar 4;7(1):77. doi: 10.1038/s41597-020-0413-y.
Comparable data on spatial accessibility by different travel modes are frequently needed to understand how city regions function. Here, we present a spatial dataset called the Helsinki Region Travel Time Matrix that has been calculated for 2013, 2015 and 2018. This longitudinal dataset contains travel time and distance information between all 250 metres statistical grid cell centroids in the Capital Region of Helsinki, Finland. The dataset is multimodal and multitemporal by nature: all typical transport modes (walking, cycling, public transport, and private car) are included and calculated separately for the morning rush hour and midday for an average working day. We followed a so-called door-to-door principle, making the information between travel modes comparable. The analyses were based primarily on open data sources, and all the tools that were used to produce the data are openly available. The matrices form a time-series that can reveal the accessibility conditions within the city and allow comparisons of the changes in accessibility in the region, which support spatial planning and decision-making.
为了了解城市区域的运作方式,经常需要比较不同交通方式的空间可达性数据。在这里,我们介绍了一个名为“赫尔辛基区域出行时间矩阵”的空间数据集,该数据集是针对 2013 年、2015 年和 2018 年计算的。该纵向数据集包含芬兰首都地区赫尔辛基所有 250 米统计网格单元格质心之间的出行时间和距离信息。该数据集本质上是多模式和多时相的:包含所有典型的交通方式(步行、骑自行车、公共交通和私人汽车),并分别为工作日的早高峰和中午时段进行计算。我们遵循了所谓的“门到门”原则,使不同交通方式之间的信息具有可比性。分析主要基于开放数据源,并且用于生成数据的所有工具均公开可用。这些矩阵形成了一个时间序列,可以揭示城市内部的可达性条件,并允许比较该区域可达性的变化,从而支持空间规划和决策制定。