Urban computing

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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 the technological framework for smart cities. [1] [2]

Contents

The term "urban computing" was first introduced by Eric Paulos at the 2004 UbiComp conference [3] and in his paper The Familiar Stranger [4] 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. [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. [6] 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. [7]

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.

— Yu Zheng, Urban Computing with Big Data [8]

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. [9] 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." [10] 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. [11]

Energy consumption

Energy consumption and pollution throughout the world is heavily impacted by urban transportation. [12] 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. [13] 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. [14] Information and predictions of pollution density gathered in this way could also be used to generate localized air quality alerts. [14] 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. [13]

Health

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 Centers 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. [15] This discovery spurred a collaboration between the CDC and Google to create a map of predicted flu outbreaks based on this data. [16]

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. [17] [18] 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.

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. [4] 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. [19] 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.

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. 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. [8]

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. [20] [21]

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. [22]

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. [23]

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. [24] 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. [25]

Environment

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.

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. [8]

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. [17]

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. [26]

Another source of data is social media data. In particular, geo-referenced picture tags have been successfully used to infer smellscape maps [27] [28] (linked to air quality) and soundscape maps [29] (linked to sound quality) at city level.

See also

Related Research Articles

Ubiquitous computing is a concept in software engineering, hardware engineering and computer science where computing is made to appear anytime and everywhere. In contrast to desktop computing, ubiquitous computing can occur using any device, in any location, and in any format. A user interacts with the computer, which can exist in many different forms, including laptop computers, tablets, smart phones and terminals in everyday objects such as a refrigerator or a pair of glasses. The underlying technologies to support ubiquitous computing include Internet, advanced middleware, operating system, mobile code, sensors, microprocessors, new I/O and user interfaces, computer networks, mobile protocols, location and positioning, and new materials.

<span class="mw-page-title-main">Home automation</span> Building automation for a home

Home automation or domotics is building automation for a home. A home automation system will monitor and/or control home attributes such as lighting, climate, entertainment systems, and appliances. It may also include home security such as access control and alarm systems.

Computer-supported cooperative work (CSCW) is the study of how people utilize technology collaboratively, often towards a shared goal. CSCW addresses how computer systems can support collaborative activity and coordination. More specifically, the field of CSCW seeks to analyze and draw connections between currently understood human psychological and social behaviors and available collaborative tools, or groupware. Often the goal of CSCW is to help promote and utilize technology in a collaborative way, and help create new tools to succeed in that goal. These parallels allow CSCW research to inform future design patterns or assist in the development of entirely new tools.

Context awareness refers, in information and communication technologies, to a capability to take into account the situation of entities, which may be users or devices, but are not limited to those. Location is only the most obvious element of this situation. Narrowly defined for mobile devices, context awareness does thus generalize location awareness. Whereas location may determine how certain processes around a contributing device operate, context may be applied more flexibly with mobile users, especially with users of smart phones. Context awareness originated as a term from ubiquitous computing or as so-called pervasive computing which sought to deal with linking changes in the environment with computer systems, which are otherwise static. The term has also been applied to business theory in relation to contextual application design and business process management issues.

Wireless sensor networks (WSNs) refer to networks of spatially dispersed and dedicated sensors that monitor and record the physical conditions of the environment and forward the collected data to a central location. WSNs can measure environmental conditions such as temperature, sound, pollution levels, humidity and wind.

Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities.

<span class="mw-page-title-main">Tangible user interface</span>

A tangible user interface (TUI) is a user interface in which a person interacts with digital information through the physical environment. The initial name was Graspable User Interface, which is no longer used. The purpose of TUI development is to empower collaboration, learning, and design by giving physical forms to digital information, thus taking advantage of the human ability to grasp and manipulate physical objects and materials.

<span class="mw-page-title-main">Edge computing</span> Distributed computing paradigm

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth. Edge computing is an architecture rather than a specific technology, and a topology- and location-sensitive form of distributed computing.

A pervasive game is one where the gaming experience is extended out in the real world, or where the fictive world in which the game takes place blends with the physical world. The "It's Alive" mobile games company described pervasive games as "games that surround you", while Montola, Stenros and Waern's book, Pervasive Games defines them as having "one or more salient features that expand the contractual magic circle of play spatially, temporally, or socially." The concept of a "magic circle" draws from the work of Johan Huizinga, who describes the boundaries of play.

Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions. Since the 1980s, this research field has captured the attention of several computer science communities due to its strength in providing personalized support for many different applications and its connection to many different fields of study such as medicine, human-computer interaction, or sociology.

Visual privacy is the relationship between collection and dissemination of visual information, the expectation of privacy, and the legal issues surrounding them. These days digital cameras are ubiquitous. They are one of the most common sensors found in electronic devices, ranging from smartphones to tablets, and laptops to surveillance cams. However, privacy and trust implications surrounding it limit its ability to seamlessly blend into computing environment. In particular, large-scale camera networks have created increasing interest in understanding the advantages and disadvantages of such deployments. It is estimated that over 4 million CCTV cameras deployed in the UK. Due to increasing security concerns, camera networks have continued to proliferate across other countries such as the United States. While the impact of such systems continues to be evaluated, in parallel, tools for controlling how these camera networks are used and modifications to the images and video sent to end-users have been explored.

Urban informatics refers to the study of people creating, applying and using information and communication technology and data in the context of cities and urban environments. It sits at the conjunction of urban science, geomatics, and informatics, with an ultimate goal of creating more smart and sustainable cities. Various definitions are available, some provided in the Definitions section.

Implicit authentication (IA) is a technique that allows the smart device to recognize its owner by being acquainted with his/her behaviors. It is a technique that uses machine learning algorithms to learn user behavior through various sensors on the smart devices and achieve user identification. Most of the current authentication techniques, e.g., password, pattern lock, finger print and iris recognition, are explicit authentication which require user input. Comparing with explicit authentication, IA is transparent to users during the usage, and it significantly increases the usability by reducing time users spending on login, in which users find it more annoying than lack of cellular coverage.

Crowdsensing, sometimes referred to as mobile crowdsensing, is a technique where a large group of individuals having mobile devices capable of sensing and computing collectively share data and extract information to measure, map, analyze, estimate or infer (predict) any processes of common interest. In short, this means crowdsourcing of sensor data from mobile devices.

Animal–Computer Interaction (ACI) is a field of research for the design and use of technology with, for and by animals covering different kinds of animals from wildlife, zoo and domesticated animals in different roles. It emerged from, and was heavily influenced by, the discipline of Human–computer interaction (HCI). As the field expanded, it has become increasingly multi-disciplinary, incorporating techniques and research from disciplines such as artificial intelligence (AI), requirements engineering (RE), and veterinary science.

<span class="mw-page-title-main">Moustafa Youssef</span> Egyptian computer scientist

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<span class="mw-page-title-main">Bicycle counter</span>

Bicycle counters are electronic devices that detect the number of bicycles passing by a location for a certain period of time. Some advanced counters can also detect the speed, direction, and type of bicycles. These systems are sometimes referred to as bicycle barometers, but the term is misleading because it indicates the measurement of pressure. Most counting stations only consist of sensors, the internal computing device, although some use a display to show the total number of cyclists of the day and the current year. There are counting stations all over the world in over hundreds of cities, for example in Manchester, Zagreb, or Portland. The first bicycle counting station was installed in Odense, Denmark, in 2002.

Jofish Kaye is an American and British scientist specializing in human-computer interaction and artificial intelligence. He runs interaction design and user research at anthem.ai, and is an editor of Personal & Ubiquitous Computing.

<span class="mw-page-title-main">Spatial embedding</span>

Spatial embedding is one of feature learning techniques used in spatial analysis where points, lines, polygons or other spatial data types. representing geographic locations are mapped to vectors of real numbers. Conceptually it involves a mathematical embedding from a space with many dimensions per geographic object to a continuous vector space with a much lower dimension.

<span class="mw-page-title-main">Matthias Grossglauser</span> Swiss communication engineer

Matthias Grossglauser is a Swiss communication engineer. He is a professor of computer science at EPFL and co-director of the Information and Network Dynamics Laboratory (INDY) at EPFL's School of Computer and Communication Sciences School of Basic Sciences.

References

  1. ↑ Bouroche, Mélanie; Dusparic, Ivana (2020). "Urban Computing: The Technological Framework for Smart Cities". Handbook of Smart Cities. Springer International Publishing: 1–25. doi:10.1007/978-3-030-15145-4_5-1. ISBN   978-3-030-15145-4. S2CID   219809513.
  2. ↑ Kamilaris, Andreas; Pitsillides, Andreas; Prenafeta-Bold, Francesc X.; Ali, Muhammad Intizar (May 2017). "A Web of Things based eco-system for urban computing - towards smarter cities". 2017 24th International Conference on Telecommunications (ICT). pp. 1–7. doi:10.1109/ICT.2017.7998277. ISBN   978-1-5386-0643-8. S2CID   19278271.
  3. ↑ Paulos, Eric; Anderson, Ken; Townsend, Anthony (September 7, 2004). UbiComp in the Urban Frontier (workshop). Sixth International Conference on Ubiquitous Computing. Nottingham, England.
  4. 1 2 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.
  5. ↑ 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.
  6. ↑ 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. S2CID   1230643.
  7. ↑ 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.
  8. 1 2 3 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. S2CID   207214926.
  9. ↑ 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.
  10. ↑ 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.
  11. ↑ "About Cleveland Historical". Cleveland Historical. Retrieved 22 April 2015.
  12. ↑ "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)
  13. 1 2 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.
  14. 1 2 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.
  15. ↑ 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. S2CID   125775.
  16. ↑ "Google Flu Trends" . Retrieved 21 April 2015.
  17. 1 2 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.
  18. ↑ 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. S2CID   16801207. Archived from the original (PDF) on 2019-02-24.
  19. ↑ 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. S2CID   8694354.
  20. ↑ Magni, Marie (2012-06-06). "Cycle cities awarded bicycle counters". Cycling Embassy of Denmark. Archived from the original on 2020-07-19. Retrieved 2020-04-25.
  21. ↑ "Fahrradbarometer". hamburg.adfc.de (in German). Archived from the original on 2020-03-21. Retrieved 2020-04-25.
  22. ↑ "Pricing the surge" . Free exchange. The Economist . 2014-03-29.
  23. ↑ 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.
  24. ↑ 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.
  25. ↑ 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. S2CID   88518530.
  26. ↑ 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.
  27. ↑ 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.
  28. ↑ Quercia, Daniele; Schifanella, Rossano; Aiello, Luca Maria (2016). "The Emotional and Chromatic Layers of Urban Smells". AAAI Icwsm. arXiv: 1605.06721 . Bibcode:2016arXiv160506721Q.
  29. ↑ 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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