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Urban computing is an interdisciplinary field which pertains to the study and application of computing technology in urban areas. This involves the application of wireless networks, sensors, computational power, and data to improve the quality of densely populated areas:

Urban computing is an interdisciplinary field which pertains to the study and application of computing technology in urban areas. This involves the application of wireless networks, sensors, computational power, and data to improve the quality of densely populated areas:

城市计算是一个跨学科的领域，属于计算技术的研究和应用在城市地区。这涉及到无线网络、传感器、计算能力和数据的应用，以改善人口稠密地区的质量:


The term "urban computing" was first introduced by Eric Paulos at the 2004 UbiComp conference[1] and in his paper The Familiar Stranger[2] co-authored with Elizabeth Goodman. Although closely tied to the field of urban informatics, Marcus Foth differentiates the two in his preface to Handbook of Research on Urban Informatics by saying that urban computing, urban technology, and urban infrastructure focus more on technological dimensions whereas urban informatics focuses on the social and human implications of technology in cities.[3]

The term "urban computing" was first introduced by Eric Paulos at the 2004 UbiComp conference and in his paper The Familiar Stranger co-authored with Elizabeth Goodman. Although closely tied to the field of urban informatics, Marcus Foth differentiates the two in his preface to Handbook of Research on Urban Informatics by saying that urban computing, urban technology, and urban infrastructure focus more on technological dimensions whereas urban informatics focuses on the social and human implications of technology in cities.

“城市计算”一词最早是由埃里克 · 保罗斯在2004年 UbiComp 会议上和他与伊丽莎白 · 古德曼合著的论文《熟悉的陌生人》中提出的。尽管与城市信息学领域密切相关，Marcus Foth 在他的《城市信息学研究手册》的序言中区分了这两者，他说城市计算、城市技术和城市基础设施更多地关注技术层面，而城市信息学则关注城市技术对社会和人类的影响。


Within the domain of computer science, urban computing draws from the domains of wireless and sensor networks, information science, and human-computer interaction. Urban computing uses many of the paradigms introduced by ubiquitous computing in that collections of devices are used to gather data about the urban environment to help improve the quality of life for people affected by cities. What further differentiates urban computing from traditional remote sensing networks is the variety of devices, inputs, and human interaction involved. In traditional sensor networks, devices are often purposefully built and specifically deployed for monitoring certain phenomenon such as temperature, noise, and light.[4] As an interdisciplinary field, urban computing also has practitioners and applications in fields including civil engineering, anthropology, public history, health care, urban planning, and energy, among others.[5]

Within the domain of computer science, urban computing draws from the domains of wireless and sensor networks, information science, and human-computer interaction. Urban computing uses many of the paradigms introduced by ubiquitous computing in that collections of devices are used to gather data about the urban environment to help improve the quality of life for people affected by cities. What further differentiates urban computing from traditional remote sensing networks is the variety of devices, inputs, and human interaction involved. In traditional sensor networks, devices are often purposefully built and specifically deployed for monitoring certain phenomenon such as temperature, noise, and light. As an interdisciplinary field, urban computing also has practitioners and applications in fields including civil engineering, anthropology, public history, health care, urban planning, and energy, among others.

在计算机科学领域，城市计算从无线网络和传感器网络、信息科学和人机交互计算领域获得灵感。城市计算使用了许多由普适计算引入的范式，即设备集合用于收集城市环境的数据，以帮助改善受城市影响的人们的生活质量。城市计算与传统遥感网络的进一步区别在于涉及的设备、输入和人机交互的多样性。在传统的传感器网络中，设备通常是有目的地构建和专门用于监测某些现象，如温度、噪音和光线。作为一个跨学科的领域，城市计算在土木工程、人类学、公共历史、卫生保健、城市规划和能源等领域也有实践和应用。


Applications and examples

Urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human, to tackle the major issues that cities face. Urban computing connects unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods, to create win-win-win solutions that improve urban environment, human life quality, and city operation systems.

— 模板:Comma separated entries


Cultural archiving

Cities are more than a collection of places and people - places are continually reinvented and re-imagined by the people occupying them. As such, the prevalence of computing in urban spaces leads people to supplement their physical reality with what is virtually available.[7] Toward this end, researchers engaged in ethnography, collective memory, and public history have leveraged urban computing strategies to introduce platforms that enable people to share their interpretation of the urban environment. Examples of such projects include CLIO—an urban computing system that came out of the Collective City Memory of Oulu study—which "allows people to share personal memories, context annotate them and relate them with city landmarks, thus creating the collective city memory."[8] and the Cleveland Historical project which aims to create a shared history of the city by allowing people to contribute stories through their own digital devices.[9]

Cities are more than a collection of places and people - places are continually reinvented and re-imagined by the people occupying them. As such, the prevalence of computing in urban spaces leads people to supplement their physical reality with what is virtually available. Toward this end, researchers engaged in ethnography, collective memory, and public history have leveraged urban computing strategies to introduce platforms that enable people to share their interpretation of the urban environment. Examples of such projects include CLIO—an urban computing system that came out of the Collective City Memory of Oulu study—which "allows people to share personal memories, context annotate them and relate them with city landmarks, thus creating the collective city memory." and the Cleveland Historical project which aims to create a shared history of the city by allowing people to contribute stories through their own digital devices.

城市不仅仅是一个地方的集合，人们不断地对这些地方进行改造和重新想象。因此，计算机在城市空间的普及导致人们用虚拟可用的东西来补充他们的物理现实。为此，从事人种学、集体记忆和公共历史研究的研究人员利用城市计算策略引入平台，使人们能够分享他们对城市环境的解释。这类项目的例子包括 clio ——一个来自奥卢集体城市记忆研究的城市计算系统——该系统“让人们分享个人记忆，对其进行背景注释，并将其与城市地标联系起来，从而创造集体城市记忆。”以及克利夫兰历史项目，该项目旨在通过允许人们通过自己的数字设备发表故事，创造一个共享的城市历史。


Energy consumption

Energy consumption and pollution throughout the world is heavily impacted by urban transportation.[10] In an effort to better utilize and update current infrastructures, researchers have used urban computing to better understand gas emissions by conducting field studies using GPS data from a sample of vehicles, refueling data from gas stations, and self-reporting online participants.[11] From this, knowledge of the density and speed of traffic traversing a city's road network can be used to suggest cost-efficient driving routes, and identify road segments where gas has been significantly wasted.[12] Information and predictions of pollution density gathered in this way could also be used to generate localized air quality alerts.[12] Additionally, these data could produce estimates of gas stations’ wait times to suggest more efficient stops, as well as give a geographic view of the efficiency of gas station placement.[11]

Energy consumption and pollution throughout the world is heavily impacted by urban transportation. In an effort to better utilize and update current infrastructures, researchers have used urban computing to better understand gas emissions by conducting field studies using GPS data from a sample of vehicles, refueling data from gas stations, and self-reporting online participants. From this, knowledge of the density and speed of traffic traversing a city's road network can be used to suggest cost-efficient driving routes, and identify road segments where gas has been significantly wasted. Information and predictions of pollution density gathered in this way could also be used to generate localized air quality alerts. This discovery spurred a collaboration between the CDC and Google to create a map of predicted flu outbreaks based on this data.

全世界的能源消耗和污染都受到城市交通的严重影响。为了更好地利用和更新现有的基础设施，研究人员使用城市计算机进行实地研究，利用车辆样本的全球定位系统数据、加油站的加油数据以及在线参与者的自我报告，从而更好地了解气体排放。由此，可以利用关于穿越城市道路网的交通密度和速度的知识，提出具有成本效益的驾驶路线，并确定哪些道路段的汽油被大量浪费。以这种方式收集的污染密度信息和预测也可用于产生局部空气质量警报。这一发现促使 CDC 和谷歌合作，根据这些数据创建了一个预测流感爆发的地图。


Health

Urban computing can also be used to track and predict pollution in certain areas. Research involving the use of artificial neural networks (ANN) and conditional random fields (CRF) has shown that air pollution for a large area can be predicted based on the data from a small number of air pollution monitoring stations. These findings can be used to track air pollution and to prevent the adverse health effects in cities already struggling with high pollution. On days when air pollution is especially high, for example, there could be a system in place to alert residents to particularly dangerous areas.

城市计算机也可以用来跟踪和预测某些地区的污染。人工神经网络(ANN)和条件随机场(CRF)的研究表明，利用少量空气污染监测站的数据，可以预测大面积的空气污染。这些发现可以用来跟踪空气污染，并防止对已经在与高污染作斗争的城市的不利健康影响。例如，在空气污染特别严重的日子里，可以建立一个系统，提醒居民注意特别危险的地区。

Smart phones, tablets, smart watches, and other mobile computing devices can provide information beyond simple communication and entertainment. In regards to public and personal health, organizations like the Center for Disease Control and Prevention (CDC) and World Health Organization (WHO) have taken to Twitter and other social media platforms, to provide rapid dissemination of disease outbreaks, medical discoveries, and other news. Beyond simply tracking the spread of disease, urban computing can even help predict it. A study by Jeremy Ginsberg et al. discovered that flu-related search queries serve as a reliable indicator of a future outbreak, thus allowing for the tracking of flu outbreaks based on the geographic location of such flu-related searches.[13] This discovery spurred a collaboration between the CDC and Google to create a map of predicted flu outbreaks based on this data.[14]


Urban computing can also be used to track and predict pollution in certain areas. Research involving the use of artificial neural networks (ANN) and conditional random fields (CRF) has shown that air pollution for a large area can be predicted based on the data from a small number of air pollution monitoring stations.[15][16] These findings can be used to track air pollution and to prevent the adverse health effects in cities already struggling with high pollution. On days when air pollution is especially high, for example, there could be a system in place to alert residents to particularly dangerous areas.

Mobile computing platforms can be used to facilitate social interaction. In the context of urban computing, the ability to place proximity beacons in the environment, the density of population, and infrastructure available enables digitally facilitated interaction. Paulos and Goodman's paper The Familiar Stranger introduces several categories of interaction ranging from family to strangers and interactions ranging from personal to in passing. Examples of geographically aware applications include Yik Yak, an application that facilitates anonymous social interaction based on proximity of other users, Ingress which uses an augmented reality game to encourage users to interact with the area around them as well as each other, and Foursquare, which provides recommendations about services to users based on a specified location.

移动计算平台可以用来促进社交互动。在城市计算的背景下，在环境中放置近距离信标的能力、人口密度和可用的基础设施使得数字化便利的互动成为可能。波洛斯和古德曼的论文《熟悉的陌生人》介绍了几类互动，从家庭到陌生人，从个人互动到过客互动。地理感知应用程序的例子包括 Yik Yak，一个基于接近其他用户促进匿名社交互动的应用程序，Ingress 使用扩增实境游戏来鼓励用户与他们周围的区域以及彼此之间进行互动，还有 Foursquare，它根据特定的位置向用户提供服务建议。


Social Interaction

Mobile computing platforms can be used to facilitate social interaction. In the context of urban computing, the ability to place proximity beacons in the environment, the density of population, and infrastructure available enables digitally facilitated interaction. Paulos and Goodman's paper The Familiar Stranger introduces several categories of interaction ranging from family to strangers and interactions ranging from personal to in passing.[2] Social interactions can be facilitated by purpose-built devices, proximity aware applications, and “participatory” applications. These applications can use a variety techniques for users to identify where they are ranging from “checking in” to proximity detection, to self-identification.[17] Examples of geographically aware applications include Yik Yak, an application that facilitates anonymous social interaction based on proximity of other users, Ingress which uses an augmented reality game to encourage users to interact with the area around them as well as each other, and Foursquare, which provides recommendations about services to users based on a specified location.

One of the major application areas of urban computing is to improve private and public transportation in a city. The primary sources of data are floating car data (data about where cars are at a given moment). This includes individual GPS’s, taxi GPS’s, WiFI signals, loop sensors, and (for some applications) user input.

城市计算的主要应用领域之一是改善城市的私人和公共交通。数据的主要来源是浮动汽车数据(关于汽车在给定时刻位置的数据)。这包括个人 GPS、出租车 GPS、 WiFI 信号、循环传感器，以及(对于某些应用)用户输入。


Urban computing can help select better driving routes, which is important for applications like Waze, Google Maps, and trip planning. Wang et al. built a system to get real-time travel time estimates. They solve the problems: one, not all road segments will have data from GPS in the last 30 minutes or ever; two, some paths will be covered by several car records, and it’s necessary to combine those records to create the most accurate estimate of travel time; and three, a city can have tens of thousands of road segments and an infinite amount of paths to be queried, so providing an instantaneous real time estimate must be scalable. They used various techniques and tested it out on 32670 taxis over two months in Beijing, and accurately estimated travel time to within 25 seconds of error per kilometer.

城市计算可以帮助选择更好的行车路线，这对于像 Waze、谷歌地图和旅行计划这样的应用程序很重要。王等人。建立了一个系统来得到实时旅行时间的估计。它们解决了以下问题: 第一，并不是所有的道路段都能在最后30分钟或更长的时间内获得 GPS 的数据; 第二，一些道路将被多条车辆记录覆盖，有必要将这些记录结合起来，以创建最准确的旅行时间估计; 第三，一个城市可能有数以万计的道路段和无限数量的路径需要查询，因此提供一个即时的实时估计必须是可伸缩的。他们使用了各种技术，并在北京的32670辆出租车上进行了为期两个月的测试，准确地估计出行时间在每公里误差25秒内。

Transportation

One of the major application areas of urban computing is to improve private and public transportation in a city. The primary sources of data are floating car data (data about where cars are at a given moment). This includes individual GPS’s, taxi GPS’s, WiFI signals, loop sensors, and (for some applications) user input.

Uber is an on-demand taxi-like service where users can request rides with their smartphone. By using the data of the active riders and drivers, Uber can price discriminate based on the current rider/driver ratio. This lets them earn more money than they would without “surge pricing,” and helps get more drivers out on the street in unpopular working hours.

Uber 是一种类似出租车的按需服务，用户可以用智能手机申请搭车。通过使用活跃乘客和司机的数据，优步可以根据当前的乘客/司机比例进行价格歧视。这使得他们比没有“动态定价”的情况下挣得更多的钱，并且有助于让更多的司机在不受欢迎的工作时间上街。

Urban computing can help select better driving routes, which is important for applications like Waze, Google Maps, and trip planning. Wang et al. built a system to get real-time travel time estimates. They solve the problems: one, not all road segments will have data from GPS in the last 30 minutes or ever; two, some paths will be covered by several car records, and it’s necessary to combine those records to create the most accurate estimate of travel time; and three, a city can have tens of thousands of road segments and an infinite amount of paths to be queried, so providing an instantaneous real time estimate must be scalable. They used various techniques and tested it out on 32670 taxis over two months in Beijing, and accurately estimated travel time to within 25 seconds of error per kilometer.[6]


Urban computing can also improve public transportation cheaply. A University of Washington group developed OneBusAway, which uses public bus GPS data to provide real-time bus information to riders. Placing displays at bus stops to give information is expensive, but developing several interfaces (apps, website, phone response, SMS) to OneBusAway was comparatively cheap. Among surveyed OneBusAway users, 92% were more satisfied, 91% waited less, and 30% took more trips.

城市计算也可以提高公共交通的成本。华盛顿大学的一个团队开发了 OneBusAway，它使用公共汽车的 GPS 数据向乘客提供实时的公共汽车信息。在公交车站放置显示器来提供信息是昂贵的，但是开发几个界面(应用程序、网站、电话响应、短信)到 OneBusAway 相对便宜。在接受调查的 OneBusAway 用户中，92% 的用户更满意，91% 的用户等待时间更短，30% 的用户出行次数更多。

Bicycle counters are an example of computing technology to count the number of cyclists at a certain spot in order to help urban planning with reliable data.[18][19]


Making decisions on transportation policy can also be aided with urban computing. London’s Cycle Hire system is a heavily used bicycle sharing system run by their transit authority. Originally, it required users to have a membership. They changed it to not require a membership after a while, and analyzed data of when and where bikes were rented and returned, to see what areas were active and what trends changed. They found that removing membership was a good decision that increased weekday commutes somewhat and heavily increased weekend usage. Based on the patterns and characteristics of a bicycle sharing system, the implications for data-driven decision supports have been studied for transforming urban transportation to be more sustainable.

制定交通政策的决策也可以借助城市计算。伦敦的自行车租赁系统是一个广泛使用的自行车共享系统，由他们的交通管理部门运营。最初，它要求用户拥有会员资格。一段时间后，他们将其改为不需要会员资格，并分析了自行车出租和归还的时间和地点的数据，以了解哪些地区是活跃的，哪些趋势发生了变化。他们发现，取消会员资格是一个很好的决定，工作日通勤次数有所增加，周末的使用量也大幅增加。基于自行车共享系统的模式和特点，研究了数据驱动决策支持对城市交通系统可持续改造的意义。

Uber is an on-demand taxi-like service where users can request rides with their smartphone. By using the data of the active riders and drivers, Uber can price discriminate based on the current rider/driver ratio. This lets them earn more money than they would without “surge pricing,” and helps get more drivers out on the street in unpopular working hours.[20]


Urban computing can also improve public transportation cheaply. A University of Washington group developed OneBusAway, which uses public bus GPS data to provide real-time bus information to riders. Placing displays at bus stops to give information is expensive, but developing several interfaces (apps, website, phone response, SMS) to OneBusAway was comparatively cheap. Among surveyed OneBusAway users, 92% were more satisfied, 91% waited less, and 30% took more trips.[21]

Urban computing has a lot of potential to improve urban quality of life by improving the environment people live in, such as by raising air quality and reducing noise pollution. Many chemicals that are undesirable or poisonous are polluting the air, such as PM 2.5, PM 10, and carbon monoxide. Many cities measure air quality by setting up a few measurement stations across the city, but these stations are too expensive to cover the entire city. Because air quality is complex, it’s difficult to infer the quality of air in between two measurement stations.

通过改善人们生活的环境，例如提高空气质量和减少噪音污染，城市计算机在改善城市生活质量方面有很大的潜力。许多不良或有毒的化学物质正在污染空气，如 pm2.5、 pm10和一氧化碳。许多城市通过在全市建立一些测量站来测量空气质量，但是这些测量站太昂贵了，无法覆盖整个城市。由于空气质量复杂，很难推断两个测量站之间的空气质量。


Making decisions on transportation policy can also be aided with urban computing. London’s Cycle Hire system is a heavily used bicycle sharing system run by their transit authority. Originally, it required users to have a membership. They changed it to not require a membership after a while, and analyzed data of when and where bikes were rented and returned, to see what areas were active and what trends changed. They found that removing membership was a good decision that increased weekday commutes somewhat and heavily increased weekend usage.[22] Based on the patterns and characteristics of a bicycle sharing system, the implications for data-driven decision supports have been studied for transforming urban transportation to be more sustainable.[23]

Various ways of adding more sensors to the cityscape have been researched, including Copenhagen wheels (sensors mounted on bike wheels and powered by the rider) and car-based sensors. While these work for carbon monoxide and carbon dioxide, aerosol measurement stations aren’t portable enough to move around.

为城市景观添加更多传感器的各种方式已经被研究，包括哥本哈根车轮(安装在自行车车轮上的传感器，由骑手提供动力)和汽车传感器。虽然这些工作的一氧化碳和二氧化碳，气溶胶测量站是不便携的足以移动。


Environment

Another source of data is social media data. In particular, geo-referenced picture tags have been successfully used to infer smellscape maps

另一个数据来源是社交媒体数据。特别是，地理参考图片标签已经成功地用于推断[ http://goodcitylife.org/smellymaps/project.php 气味][ http://goodcitylife.org/smellymaps/index.php 地图]

Urban computing has a lot of potential to improve urban quality of life by improving the environment people live in, such as by raising air quality and reducing noise pollution. Many chemicals that are undesirable or poisonous are polluting the air, such as PM 2.5, PM 10, and carbon monoxide. Many cities measure air quality by setting up a few measurement stations across the city, but these stations are too expensive to cover the entire city. Because air quality is complex, it’s difficult to infer the quality of air in between two measurement stations. 

(linked to air quality) and soundscape maps  (linked to sound quality) at city level.

(与空气质素有关)及( http://goodcitylife.org/chattymaps/project.php 声景)( http://goodcitylife.org/chattymaps/index.php 地图)(与声音质素有关)。


Various ways of adding more sensors to the cityscape have been researched, including Copenhagen wheels (sensors mounted on bike wheels and powered by the rider) and car-based sensors. While these work for carbon monoxide and carbon dioxide, aerosol measurement stations aren’t portable enough to move around.[6]


There are also attempts to infer the unknown air quality all across the city from just the samples taken at stations, such as by estimating car emissions from floating car data. Zheng et al. built a model using machine learning and data mining called U-Air. It uses historical and real-time air data, meteorology, traffic flow, human mobility, road networks, and points of interest, which are fed to artificial neural networks and conditional random fields to be processed. Their model is a significant improvement over previous models of citywide air quality.[15]


Chet et al. developed a system to monitor air quality indoors, which were deployed internally by Microsoft in China. The system is based in the building’s HVAC (heating, ventilation, air conditioning) units. Since HVACs filter the air of PM 2.5, but don’t check if its necessary, the new system can save energy by preventing HVACs from running when unnecessary.[24]


Another source of data is social media data. In particular, geo-referenced picture tags have been successfully used to infer smellscape maps [25]

[26] (linked to air quality) and soundscape maps [27] (linked to sound quality) at city level.


See also

Category:Human–computer interaction

类别: 人机交互

Category:Urban design

类别: 城市设计

Category:Urban planning

类别: 城市规划

Category:Information society

类别: 信息社会

Category:Urban society

类别: 城市社会


This page was moved from wikipedia:en:Urban computing. Its edit history can be viewed at 差分隐私/edithistory

  1. ↑ 模板:Cite speech
  2. ↑ 2.0 2.1 Paulos, Eric; Goodman, Elizabeth (2004). The familiar stranger: anxiety, comfort, and play in public places. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York, New York, USA: ACM Press. pp. 223–230. doi:10.1145/985692.985721. ISBN 1-58113-702-8.
  3. ↑ Foth, Marcus (2009). Handbook of Research on Urban Informatics: The Practice and Promise of the Real-Time City. Hershey, PA: Information Science Reference. ISBN 978-1-60566-152-0. OCLC 227572898. http://eprints.qut.edu.au/13308/. 
  4. ↑ Akyildiz, I.F.; Su, W.; Sankarasubramaniam, Y.; Cayirci, E. (2002). "Wireless sensor networks: a survey". Computer Networks. 38 (4): 393–422 [395]. CiteSeerX 10.1.1.320.5948. doi:10.1016/S1389-1286(01)00302-4.
  5. ↑ Kukka, Hannu; Ylipulli, Johanna; Luusua, Anna; Dey, Anind K. (2014). Urban computing in theory and practice. Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational (NordiCHI '14). New York, New York, USA: ACM Press. pp. 658–667. doi:10.1145/2639189.2639250. ISBN 978-1-4503-2542-4.
  6. ↑ 6.0 6.1 6.2 Zheng, Yu; Capra, Licia; Wolfson, Ouri; Yang, Hai (2014-09-18). "Urban Computing". ACM Transactions on Intelligent Systems and Technology. Association for Computing Machinery (ACM). 5 (3): 1–55. doi:10.1145/2629592. ISSN 2157-6904.
  7. ↑ Kukka, Hannu; Luusua, Anna; Ylipulli, Johanna; Suopajärvi, Tiina; Kostakos, Vassilis; Ojala, Timo (2014). "From cyberpunk to calm urban computing: Exploring the role of technology in the future cityscape". Technological Forecasting and Social Change. 84: 29–42. doi:10.1016/j.techfore.2013.07.015.
  8. ↑ Christopoulou, Eleni; Ringas, Dimitrios; Stefanidakis, Michail (2012). Experiences from the Urban Computing Impact on Urban Culture. 16th Panhellenic Conference on Informatics (PCI). IEEE. pp.56,61. doi:10.1109/pci.2012.53. ISBN 978-1-4673-2720-6.
  9. ↑ "About Cleveland Historical". Cleveland Historical. Retrieved 22 April 2015.
  10. ↑ "Greenhouse Gas Emissions: Transportation Sector Emissions - Climate Change - US EPA". epa.gov. 2012-03-16. Archived from the original on 2014-07-04.{{cite web}}: CS1 maint: unfit URL (link)
  11. ↑ 11.0 11.1 Zhang, Fuzheng; Wilkie, David; Zheng, Yu; Xie, Xing (2013). Sensing the pulse of urban refueling behavior. UbiComp '13: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing. New York, New York, USA: ACM Press. pp. 13–22. doi:10.1145/2493432.2493448. ISBN 978-1-4503-1770-2.
  12. ↑ 12.0 12.1 Shang, Jingbo; Zheng, Yu; Tong, Wenzhu; Chang, Eric; Yu, Yong (2014). Inferring gas consumption and pollution emission of vehicles throughout a city. KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, New York, USA: ACM Press. pp. 1027–1036. doi:10.1145/2623330.2623653. ISBN 978-1-4503-2956-9.
  13. ↑ Ginsberg, J; et al. (2009). "Detecting influenza epidemics using search engine query data". Nature. 457 (7232): 1012–1014. Bibcode:2009Natur.457.1012G. doi:10.1038/nature07634. PMID 19020500.
  14. ↑ "Google Flu Trends". Retrieved 21 April 2015.
  15. ↑ 15.0 15.1 Zheng, Yu; Liu, Furui; Hsieh, Hsun-Ping (2013). U-Air: when urban air quality inference meets big data. KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, New York, USA: ACM Press. pp. 1436–1444. doi:10.1145/2487575.2488188. ISBN 978-1-4503-2174-7.
  16. ↑ Zheng, Yu; Chen, Xuxu; Jin, Qiwei; Chen, Yubiao; Qu, Xiangyun; Liu, Xin; Chang, Eric; Ma, Wei-Ying; Rui, Yong; Sun, Weiwei (2014). "A Cloud-Based Knowledge Discovery System for Monitoring Fine-Grained Air Quality" (PDF). MSR-Tr-2014-40. Archived from the original (PDF) on 2019-02-24.
  17. ↑ Jabeur, Nafaâ; Zeadally, Sherali; Sayed, Biju (2013-03-01). "Mobile social networking applications". Communications of the ACM. Association for Computing Machinery (ACM). 56 (3): 71. doi:10.1145/2428556.2428573. ISSN 0001-0782.
  18. ↑ Magni, Marie (2012-06-06). "Cycle cities awarded bicycle counters". Cycling Embassy of Denmark (in English). Retrieved 2020-04-25.
  19. ↑ "Fahrradbarometer". hamburg.adfc.de (in Deutsch). Retrieved 2020-04-25.
  20. ↑ "Pricing the surge". Free exchange. The Economist. 2014-03-29.
  21. ↑ Ferris, Brian; Watkins, Kari; Borning, Alan (2010). OneBusAway: results from providing real-time arrival information for public transit. CHI '10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York, New York, USA: ACM Press. pp. 1807–1816. doi:10.1145/1753326.1753597. ISBN 978-1-60558-929-9.
  22. ↑ Lathia, Neal; Ahmed, Saniul; Capra, Licia (2012). "Measuring the impact of opening the London shared bicycle scheme to casual users". Transportation Research Part C: Emerging Technologies. Elsevier BV. 22: 88–102. doi:10.1016/j.trc.2011.12.004. ISSN 0968-090X.
  23. ↑ Xie, Xiao-Feng; Wang, Zunjing (2018). "Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports: Case study in the Washington DC area". Journal of Transport Geography. 71: 84–102. arXiv:1901.02061. Bibcode:2019arXiv190102061X. doi:10.1016/j.jtrangeo.2018.07.010.
  24. ↑ Chen, Xuxu; Zheng, Yu; Chen, Yubiao; Jin, Qiwei; Sun, Weiwei; Chang, Eric; Ma, Wei-Ying (2014). Indoor air quality monitoring system for smart buildings. UbiComp '14: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, New York, USA: ACM Press. pp. 471–475. doi:10.1145/2632048.2632103. ISBN 978-1-4503-2968-2.
  25. ↑ Quercia, Daniele; Schifanella, Rossano; Aiello, Luca Maria; Kate, McLean (2015). "Smelly maps: the digital life of urban smellscapes". AAAI Icwsm. 3 (3). arXiv:1505.06851. Bibcode:2015arXiv150506851Q.
  26. ↑ Quercia, Daniele; Schifanella, Rossano; Aiello, Luca Maria (2016). "The Emotional and Chromatic Layers of Urban Smells". AAAI Icwsm. arXiv:1605.06721. Bibcode:2016arXiv160506721Q.
  27. ↑ Aiello, Luca Maria; Schifanella, Rossano; Quercia, Daniele; Aletta, Francesco (2016). "Chatty maps: constructing sound maps of urban areas from social media data". Royal Society Open Science. 3 (3): 150690. arXiv:1603.07813. Bibcode:2016RSOS....350690A. doi:10.1098/rsos.150690. PMC 4821272. PMID 27069661.
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