Crowdsourced data for bicycling research and practice
Crowdsourced data for bicycling research and practice
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DOI：
10.1080/01441647.2020.1806943
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发表时间：
2020-08-14
影响因子：
9.8
通讯作者：
Winters, Meghan
中科院分区：
文献类型：
--
作者：
Nelson, Trisalyn;Ferster, Colin;Winters, Meghan
各城市正在推广骑自行车出行，以此作为应对日益严重的交通拥堵、肥胖和相关的健康问题以及空气污染的解毒剂。然而，由于缺乏关于自行车数量、安全、基础设施和公众态度的数据，研究和实践都陷入停滞。支持GPS的智能手机、众包工具和社交媒体等新技术正在改变自行车数据的潜在来源。然而，许多发展都来自数据科学，很难评估众包数据的优势和局限性。在这篇叙述性评论中，我们对众包数据进行了概述和批评，这些数据被用来填补空白，促进自行车行为和安全知识的发展。我们评估用于绘制骑行图(健身、自行车共享和GPS/加速度计数据)、评估安全性(网络地图工具)、地图基础设施(OpenStreetMap)和跟踪态度(社交媒体)的众包数据。对于每一类数据，我们讨论了它们为研究人员和从业者提供的挑战和机会。健身应用程序数据可以用来模拟自行车骑行量的空间变化，GPS/加速度计数据为描述自行车出行的路线选择和起点-目的地提供了新的潜力；然而，使用这些数据需要在数据科学方面进行高水平的培训。新的安全和险些数据来源可用于解决报告不足和提高预测能力的问题，但需要基层推广，通常最好与官方报告结合使用。众包自行车基础设施数据可以是及时的，并有助于跨多个城市进行比较；但是，必须评估此类数据在路线类型标签中的一致性。使用社交媒体，可以跟踪人们对自行车政策和基础设施变化的反应，但将社交媒体平台上表达的态度与更广泛的人群联系起来是一个挑战。新的数据为我们提供了机会来提高我们对自行车的理解，并支持对所有人来说都是健康和安全的交通选择的决策。然而，也存在一些挑战，例如谁有权访问数据，如何为数据众包工具提供资金，保护个人隐私，数据的代表性和有偏见的数据对决策公平的影响，以及利益攸关方使用数据的能力，因为需要高级数据科学技能。如果城市要从这些新数据中受益，方法发展、工具和对终端用户的培训将需要跟踪众包数据的发展势头。
Cities are promoting bicycling for transportation as an antidote to increased traffic congestion, obesity and related health issues, and air pollution. However, both research and practice have been stalled by lack of data on bicycling volumes, safety, infrastructure, and public attitudes. New technologies such as GPS-enabled smartphones, crowdsourcing tools, and social media are changing the potential sources for bicycling data. However, many of the developments are coming from data science and it can be difficult evaluate the strengths and limitations of crowdsourced data. In this narrative review we provide an overview and critique of crowdsourced data that are being used to fill gaps and advance bicycling behaviour and safety knowledge. We assess crowdsourced data used to map ridership (fitness, bike share, and GPS/accelerometer data), assess safety (web-map tools), map infrastructure (OpenStreetMap), and track attitudes (social media). For each category of data, we discuss the challenges and opportunities they offer for researchers and practitioners. Fitness app data can be used to model spatial variation in bicycling ridership volumes, and GPS/accelerometer data offer new potential to characterise route choice and origin-destination of bicycling trips; however, working with these data requires a high level of training in data science. New sources of safety and near miss data can be used to address underreporting and increase predictive capacity but require grassroots promotion and are often best used when combined with official reports. Crowdsourced bicycling infrastructure data can be timely and facilitate comparisons across multiple cities; however, such data must be assessed for consistency in route type labels. Using social media, it is possible to track reactions to bicycle policy and infrastructure changes, yet linking attitudes expressed on social media platforms with broader populations is a challenge. New data present opportunities for improving our understanding of bicycling and supporting decision making towards transportation options that are healthy and safe for all. However, there are challenges, such as who has data access and how data crowdsourced tools are funded, protection of individual privacy, representativeness of data and impact of biased data on equity in decision making, and stakeholder capacity to use data given the requirement for advanced data science skills. If cities are to benefit from these new data, methodological developments and tools and training for end-users will need to track with the momentum of crowdsourced data.