Sensing spatiotemporal patterns in urban areas: analytics and visualizations using the integrated multimedia city data platform
Sensing spatiotemporal patterns in urban areas: analytics and visualizations using the integrated multimedia city data platform
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DOI：
10.2148/benv.42.3.415
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发表时间：
2016-10
影响因子：
--
通讯作者：
P. Thakuriah;Katarzyna Siła-Nowicka;Jorge David Gonzalez Paule
中科院分区：
文献类型：
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作者：
P. Thakuriah;Katarzyna Siła-Nowicka;Jorge David Gonzalez Paule
有能力发现城市中正在出现的模式对于有效管理城市资源至关重要。有助于确定和处理未来资源消耗需求的模式包括城市形态和结构的空间变化以及一天中人类集中度和活动模式的时间变化。其他感兴趣的模式是当地人口在动态变化的社区和社会功能空间中的特征。在本文中，我们使用集成多媒体城市数据(IMCD)平台，该平台汇集了多条结构化和非结构化数据，以考察大格拉斯哥地区的这种趋势。我们提出了一种方法，首先，理解空间和时间依赖的变化，这些变化捕捉到满足不同时间和地点的居民和企业需求所需的资源流动，其次，产生关于城市参与、活动模式和出行行为的假设。为此，我们使用社交媒体数据、GPS轨迹和英国人口普查的背景数据。该方法确定了整个城市空间的活动模式中的“粗糙”，这表明了社会和功能活动的不同集中。当时间维度被添加到混合中时，我们能够发现在该地区的不同地区从一种类型的使用模式到另一种类型的使用模式的时变转换。这种过渡，特别是在混合用途地区，可以及早发现城市新陈代谢过剩的点，对交通拥堵、废物产生、能源和其他资源消耗模式产生影响。最后，检测市民在社交场合谈论什么的能力可能会提供一种方法，来了解在城市不同地区检测到的语言模式是否反映了潜在的用法和关切。评估这一想法的初步步骤是通过提取社会生成的数据的上下文感知和语义丰富。
Having the ability to detect emerging patterns in cities is crucial for efficient management of urban resources. Patterns that are useful in identifying and addressing future resource consumption needs include spatial changes in urban form and structure as well as temporal changes in human concentrations and activity patterns during the course of a day. Other patterns of interest are characteristics of local populations in dynamically changing neighborhoods and social-functional spaces. In this paper, we use the Integrated Multimedia City Data (iMCD) platform which brings together multiple strands of structured and unstructured data, to examine such trends in the Greater Glasgow region. We present an approach to, first, understand spatial and time-dependent changes that capture the flow of resources needed to meet demands of residents and businesses at different times and locations, and second, generate hypotheses regarding urban engagement, activity patterns and travel behaviour. We use social media data, GPS trajectories, and background data from the UK Population Census for this purpose. The approach identifies the “roughness” in activity patterns across the urban space that are indicative of different concentrations of social and functional activities. When the time dimension is added to the mix, we are able to uncover time-varying transitions from one type of use pattern into another in different parts of the region. Such transitions, particularly in mixed-use areas, allow early detection of points of excess urban metabolism, with implications for traffic congestion, waste production, energy and other resource consumption patterns. Finally, the ability to detect what citizens talk about socially may provide a way to understand whether or not the language patterns detected in different parts of the city reflect underlying uses and concerns. A preliminary step to evaluate this idea is explored by extracting context-awareness and semantic enrichment to socially-generated data.