Urban phenology: Toward a real-time census of the city using Wi-Fi data
Urban phenology: Toward a real-time census of the city using Wi-Fi data
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DOI：
10.1016/j.compenvurbsys.2017.01.011
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发表时间：
2017-07-01
影响因子：
6.8
通讯作者：
Johnson, Nicholas
中科院分区：
文献类型：
--
作者：
Kontokosta, Constantine E.;Johnson, Nicholas
一系列现场仪器、移动的传感和社交媒体正在产生新的数据流，这些数据流可以被整合和分析，以更好地了解城市活动和移动模式。虽然有几项研究侧重于了解整个城市的人口流动，但这些数据也可以用来创建当地人口的空间和时间粒度图，并在某些外部环境或物理条件下预测本地人口。在高空间和时间分辨率下有效地模拟人口动态将对城市运营和政策、战略性长期规划过程、应急响应和管理以及公共health.This产生重大影响，本文开发了一个使用Wi-Fi数据的城市实时普查，以探索城市物候作为本地人口动态的函数。使用Wi-Fi探头和连接数据，占2015年来自纽约市曼哈顿下城社区的20，000，000多个数据点-结合来自美国人口普查美国社区调查，纵向雇主-家庭动态调查和纽约市行政记录的相关数据，我们提出了一个模型来创建按居民分类的实时人口估计，工作人员和访客/游客，并定位到邻近Wi-Fi接入点的街区或地理位置。结果表明，该方法有优点：我们估计在5%的调查验证数据的一天内，小时工和居民人口计数。我们的建筑物级测试案例证明了类似的准确性，估计工人人数在报告的建筑物占用率的1%以内。(C)2017爱思唯尔有限公司版权所有
New streams of data are being generated by a range of in-situ instrumentation, mobile sensing, and social media that can be integrated and analyzed to better understand urban activity and mobility patterns. While several studies have focused on understanding flows of people throughout a city, these data can also be used to create a more spatially and temporally granular picture of local population, and to forecast localized population given some exogenous environmental or physical conditions. Effectively modeling population dynamics at high spatial and temporal resolutions would have significant implications for city operations and policy, strategic long-term planning processes, emergency response and management, and public health.This paper develops a real-time census of the city using Wi-Fi data to explore urban phenology as a function of localized population dynamics. Using Wi-Fi probe and connection data accounting for more than 20,000,000 data points for the year 2015 from New York City's Lower Manhattan neighborhood - combined with correlative data from the U.S. Census American Community Survey, the Longitudinal Employer-Household Dynamics survey, and New York City administrative records we present a model to create real-time population estimates classified by residents, workers, and visitors/tourists in a given neighborhood and localized to a block or geolocation proximate to a Wi-Fi access point. The results indicate that the approach has merit: we estimate intra-day, hourly worker and resident population counts within 5% of survey validation data. Our building-level test case demonstrates similar accuracy, estimating worker population to within 1% of the reported building occupancy. (C) 2017 Elsevier Ltd. All rights reserved.