Call For Papers: Rethinking the ABCs

Readers of the blog might be interested in a workshop being organized by Daniel Brown, Eun-Kyeong Kim, Liliana Perez, and Raja Sengupta entitled:

Rethinking the ABCs: Agent-Based Models and Complexity Science in the age of Big Data, CyberGIS, and Sensor networks

September 27th, 2016 in Montreal, Canada

To quote from the call:

“A broad scope of concepts and methodologies from complexity science – including Agent-Based Models, Cellular Automata, network theory, chaos theory, and scaling relations – has contributed to a better understanding of spatial/temporal dynamics of complex geographic patterns and process.

Recent advances in computational technologies such as Big Data, Cloud Computing and CyberGIS platforms, and Sensor Networks (i.e. the Internet of Things) provides both new opportunities and raises new challenges for ABM and complexity theory research within GIScience. Challenges include parameterization of complex models with volumes of georeferenced data being generated, scale model applications to realistic simulations over broader geographic extents, explore the challenges in their deployment across large networks to take advantage of increased computational power, and validate their output using real-time data, as well as measure the impact of the simulation on knowledge, information and decision-making both locally and globally via the world wide web.

The scope of this workshop is to explore novel complexity science approaches to dynamic geographic phenomena and their applications, addressing challenges and enriching research methodologies in geography in a Big Data Era.”

More information about the workshop can be found at https://sites.psu.edu/bigcomplexitygisci/

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“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Recently, Alison Heppenstall, Nick Malleson  and myself have just had a paper accepted in Systems entitled: “Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?” In the paper we critically examine how well agent-based models have  simulated a variety of urban processes. We discus what considerations are needed when choosing the appropriate level of spatial analysis and time frame to model urban phenomena and what role Big Data can play in agent-based modeling. Below you can read the abstract of the paper and see a number of example applications discussed.

Abstract: Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.

Keywords: cities; agent-based modeling; big data; crime; retail; space; simulation

Figure 1. (A) System structure; (B) System hierarchy; and (C) Related subsystems/processes (adapted from Batty, 2013).

Reference cited:

Batty, M. (2013).  The New Science of Cities; MIT Press: Cambridge, MA, USA.

Full reference to the open access paper:

Heppenstall, A., Malleson, N. and Crooks A.T. (2016). “Space, the Final Frontier”: How Good are Agent-based Models at Simulating Individuals and Space in Cities?, Systems, 4(1), 9; doi: 10.3390/systems4010009 (pdf)

 

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“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Recently, Alison Heppenstall, Nick Malleson  and myself have just had a paper accepted in Systems entitled: “Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?” In the paper we critically examine how well agent-based models have  simulated a variety of urban processes. We discus what considerations are needed when choosing the appropriate level of spatial analysis and time frame to model urban phenomena and what role Big Data can play in agent-based modeling. Below you can read the abstract of the paper and see a number of example applications discussed.

Abstract: Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.

Keywords: cities; agent-based modeling; big data; crime; retail; space; simulation

Figure 1. (A) System structure; (B) System hierarchy; and (C) Related subsystems/processes (adapted from Batty, 2013).

Reference cited:

Batty, M. (2013).  The New Science of Cities; MIT Press: Cambridge, MA, USA.

Full reference to the open access paper:

Heppenstall, A., Malleson, N. and Crooks A.T. (2016). “Space, the Final Frontier”: How Good are Agent-based Models at Simulating Individuals and Space in Cities?, Systems, 4(1), 9; doi: 10.3390/systems4010009 (pdf)

 

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Mapping London’s Twitter Activity in 3d

Image 1. The tweet density from 8am to 4pm on 20th June 2015, Central London




Twitter Mapping is increasingly useful method to link virtual activities and geographical space. Geo-tagged data attached to tweets containing the users’ location where they tweeted and it can visualise the locations of users on the map. Although the number of the geo-taggedtweets is a relatively small portion of all tweets, we can figure out the density, spatial patterns and other invisible relationships between online and offline.


Recently, studies with geo-tagged tweets have been developed to analyse the public response tospecific urban events, natural disasters and regional characteristics (Li et al., 2013) [1].  Furthermore, it is extending to traditional urban research topics, for example, revealing spatial segregation and inequality in cities (Shelton et al., 2015) [2].

 

Twitter mapping in 3D can augment 2d visualisation by providing built environment contexts and improved information. There are many examples of Twitter mapping in 3d such as A) #interactive/Andes [3] , B) London’s Twitter Island [4], C) Mapping London in real time, using Tweets [5]. A) and B) build up 3d mountains of the geo-tagged tweet on the map.  In the case of C), when the geo-tagged tweets are sent in the city, the heights of nearest buildings increase in the 3d model. These examples are creative and show different ways to view the integrated environments.

From a Networking City’s view, if we make a Twitter visualisation more tangible in a 3d urban model, it would help us to have a better understanding how urban environments are interconnected with the invisible media flow.

 

To make the visualisation, the Twitter data has been collected by using Big Data Toolkit developed by Steven Gray at CASA, UCL. All 53,750 geo-tagged tweets are collected on 20thJune, 2015 across the UK. As we can see from Table 1, the number of tweets was at the lowest point at 5am and reached to the highest point at 10pm with 3495 tweets. Moreover, Video 1 shows the location of the data in the UK and London on that day in real time.

 


Table 1. The Number of Geo-Coded Tweets in the UK on 20th June, 2015

 

https://www.youtube.com/watch?v=dg-2VlVfFaM



Video 1. The location of Geo-Coded Tweets in the UK on 20th June, 2015



When we calculate the density of the data, London, particularly Central London, contains the largest number of the tweets. (Image 2)

 

 

 

Image 2. The density of Geo-Coded Tweets in the UK on 20th June, 2015

In order to focus on the high density data, 6 km x 3.5 km area of Central London is chosen for the 3d model. Buildings, bridges, roads and other natural environments of the part of London have been set in the model based on OS Building Heights data[6]. Some Google 3d warehouse buildings are added to represent important landmark buildings like St.Pauls, London Eye and Tower Bridge as you can see from Image 3, Image 4 and Image 5.

 

 

Image 3. The plan view of Central London model

Image 4. The perspective view of Central London model

Image 5. The perspective view of Central London model (view from BT Tower)

The geo-tagged data set is divided into one hour periodsand distributed on the map to identify the tweet density in the area. Through this process, we can see how the density is changing depending on the time period. For example, the tweets are mainly concentrated around Piccadilly Circus and Trafalgar Square between 10am and 11am, but  there are two high-density areas between 12pm and 1pm (See Image 6, Image 7, Image 8 and Image 9)

Image 6. The tweet density between 10am and 11am on 20th June 2015

Image 7. The tweet density between 12pm and 1pm on 20th June 2015

Image 8. The tweet density from 12am to 12pm

Image 9. The tweet density from 12pm to Midnight

 


 

As we’ve seen above, the 2d mapping is useful to understand the relative density in one period such as which area is high and which area is low between 12pm and 1pm. However, we cannot understand the degree of intensity in the highest peak areas. It is believed that 3d mapping is needed at this stage. We can clearly see the density of the tweet data in each periodand the intensity of the tweet density across the time periods from Image 10 to Image 14.

West End area shows high density throughout the whole day but City area shows the peak only during lunch time. This pattern likely relates to the activities of office workers in City and leisure/tourist in West End.

Image 10. The tweet density in 3d between 10am and 11am on 20th June 2015

Image 11. The tweet density in 3d between 12pm and 1pm on 20th June 2015

 

Image 12. The tweet density in 3d from 12am to 8pm

Image 13. The tweet density in 3d from 8am to 4pm

Image 14. The tweet density from 4pm to Midnight

 

 

 ________________________________________

[1] Linna Li , Michael F. Goodchild & Bo Xu (2013) Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr, Cartography and Geographic Information Science, 40:2, 61-77

 

[2] Taylor Shelton, Ate Poorthuis & Matthew Zook (2015) Social Media and the City: Rethinking Urban Socio-Spatial Inequality Using User-Generated Geographic Information, Landscape and Urban Planning (Forthcoming), http://papers.ssrn.com/abstract=2571757

 

[3] Nicolas Belmonte, #interactive/Andes,   http://twitter.github.io/interactive/andes/  (Strived on 15th August 2015)

 

[4] Andy Hudson-Smith, London’s Twitter Island – From ArcGIS to Max to Lumion, http://www.digitalurban.org/2012/01/londons-twitter-island-from-arcgis-to.html#comment-7314


(Strived on 15thAugust 2015)

 
[5] Stephan Hugel and Flora Roumpani, Mapping London in real time, using Tweets, https://www.youtube.com/watch?feature=player_embedded&v=3fk_qxGZWFQ (Strived on 15th August 2015)

[6] OS Building Heights-Digimap Home Page  http://digimap.edina.ac.uk/webhelp/os/data_information/os_products/os_building_heights.htm  (Strived on 15th August 2015)

 

Continue reading »

Mapping London’s Twitter Activity in 3d

Image 1. The tweet density from 8am to 4pm on 20th June 2015, Central London




Twitter Mapping is increasingly useful method to link virtual activities and geographical space. Geo-tagged data attached to tweets containing the users’ location where they tweeted and it can visualise the locations of users on the map. Although the number of the geo-taggedtweets is a relatively small portion of all tweets, we can figure out the density, spatial patterns and other invisible relationships between online and offline.


Recently, studies with geo-tagged tweets have been developed to analyse the public response tospecific urban events, natural disasters and regional characteristics (Li et al., 2013) [1].  Furthermore, it is extending to traditional urban research topics, for example, revealing spatial segregation and inequality in cities (Shelton et al., 2015) [2].

 

Twitter mapping in 3D can augment 2d visualisation by providing built environment contexts and improved information. There are many examples of Twitter mapping in 3d such as A) #interactive/Andes [3] , B) London’s Twitter Island [4], C) Mapping London in real time, using Tweets [5]. A) and B) build up 3d mountains of the geo-tagged tweet on the map.  In the case of C), when the geo-tagged tweets are sent in the city, the heights of nearest buildings increase in the 3d model. These examples are creative and show different ways to view the integrated environments.

From a Networking City’s view, if we make a Twitter visualisation more tangible in a 3d urban model, it would help us to have a better understanding how urban environments are interconnected with the invisible media flow.

 

To make the visualisation, the Twitter data has been collected by using Big Data Toolkit developed by Steven Gray at CASA, UCL. All 53,750 geo-tagged tweets are collected on 20thJune, 2015 across the UK. As we can see from Table 1, the number of tweets was at the lowest point at 5am and reached to the highest point at 10pm with 3495 tweets. Moreover, Video 1 shows the location of the data in the UK and London on that day in real time.

 


Table 1. The Number of Geo-Coded Tweets in the UK on 20th June, 2015

 

https://www.youtube.com/watch?v=dg-2VlVfFaM



Video 1. The location of Geo-Coded Tweets in the UK on 20th June, 2015



When we calculate the density of the data, London, particularly Central London, contains the largest number of the tweets. (Image 2)

 

 

 

Image 2. The density of Geo-Coded Tweets in the UK on 20th June, 2015

In order to focus on the high density data, 6 km x 3.5 km area of Central London is chosen for the 3d model. Buildings, bridges, roads and other natural environments of the part of London have been set in the model based on OS Building Heights data[6]. Some Google 3d warehouse buildings are added to represent important landmark buildings like St.Pauls, London Eye and Tower Bridge as you can see from Image 3, Image 4 and Image 5.

 

 

Image 3. The plan view of Central London model

Image 4. The perspective view of Central London model

Image 5. The perspective view of Central London model (view from BT Tower)

The geo-tagged data set is divided into one hour periodsand distributed on the map to identify the tweet density in the area. Through this process, we can see how the density is changing depending on the time period. For example, the tweets are mainly concentrated around Piccadilly Circus and Trafalgar Square between 10am and 11am, but  there are two high-density areas between 12pm and 1pm (See Image 6, Image 7, Image 8 and Image 9)

Image 6. The tweet density between 10am and 11am on 20th June 2015

Image 7. The tweet density between 12pm and 1pm on 20th June 2015

Image 8. The tweet density from 12am to 12pm

Image 9. The tweet density from 12pm to Midnight

 


 

As we’ve seen above, the 2d mapping is useful to understand the relative density in one period such as which area is high and which area is low between 12pm and 1pm. However, we cannot understand the degree of intensity in the highest peak areas. It is believed that 3d mapping is needed at this stage. We can clearly see the density of the tweet data in each periodand the intensity of the tweet density across the time periods from Image 10 to Image 14.

West End area shows high density throughout the whole day but City area shows the peak only during lunch time. This pattern likely relates to the activities of office workers in City and leisure/tourist in West End.

Image 10. The tweet density in 3d between 10am and 11am on 20th June 2015

Image 11. The tweet density in 3d between 12pm and 1pm on 20th June 2015

 

Image 12. The tweet density in 3d from 12am to 8pm

Image 13. The tweet density in 3d from 8am to 4pm

Image 14. The tweet density from 4pm to Midnight

 

 

 ________________________________________

[1] Linna Li , Michael F. Goodchild & Bo Xu (2013) Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr, Cartography and Geographic Information Science, 40:2, 61-77

 

[2] Taylor Shelton, Ate Poorthuis & Matthew Zook (2015) Social Media and the City: Rethinking Urban Socio-Spatial Inequality Using User-Generated Geographic Information, Landscape and Urban Planning (Forthcoming), http://papers.ssrn.com/abstract=2571757

 

[3] Nicolas Belmonte, #interactive/Andes,   http://twitter.github.io/interactive/andes/  (Strived on 15th August 2015)

 

[4] Andy Hudson-Smith, London’s Twitter Island – From ArcGIS to Max to Lumion, http://www.digitalurban.org/2012/01/londons-twitter-island-from-arcgis-to.html#comment-7314


(Strived on 15thAugust 2015)

 
[5] Stephan Hugel and Flora Roumpani, Mapping London in real time, using Tweets, https://www.youtube.com/watch?feature=player_embedded&v=3fk_qxGZWFQ (Strived on 15th August 2015)

[6] OS Building Heights-Digimap Home Page  http://digimap.edina.ac.uk/webhelp/os/data_information/os_products/os_building_heights.htm  (Strived on 15th August 2015)

 

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Data and the City workshop (day 2)

The second day of the Data and City Workshop (here are the notes from day 1) started with the session Data Models and the City. Pouria Amirian started with Service Oriented Design and Polyglot Binding for Efficient Sharing and Analysing of Data in Cities. The starting point is that management of the city need data, and therefore … Continue reading Data and the City workshop (day 2)

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Data and the City workshop (day 1)

The workshop, which is part of the Programmable City project (which is funded by the European Research Council), is held in Maynooth on today and tomorrow. The papers and discussions touched multiple current aspects of technology and the city: Big Data, Open Data, crowdsourcing, and critical studies of data and software. The notes below are … Continue reading Data and the City workshop (day 1)

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Beyond quantification: a role for citizen science and community science in a smart city

The Data and the City workshop will run on the 31st August and 1st September 2015, in Maynooth University, Ireland. It is part of the Programmable City project, led by Prof Rob Kitchin. My contribution to the workshop is titled Beyond quantification: a role for citizen science and community science in a smart city and is extending a short article from … Continue reading Beyond quantification: a role for citizen science and community science in a smart city

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Geosimulation and Big Data: A Marriage made in Heaven or Hell?

http://www.pinterest.com/pin/101753272804937744/
Call for papers: AAG 2015 – Geosimulation and Big Data: A Marriage made in Heaven or Hell?

In recent years, human emotions, intentions, moods and behaviors have been digitized to an extent previously unimagined in the social sciences. This has been in the main due to the rise of a vast array of new data, termed ‘Big Data’. These new forms of data have the potential to reshape the future directions of social science research, in particular the methods that scientists use to model and simulate spatially explicit social systems. Given the novelty of this potential “revolution” and the surprising lack of reliable behavioural insight to arise from Big Data research, it is an opportune time to assess the progress that has been made and consider the future directions of socio-spatial modelling in a world that is becoming increasingly well described by Big Data sources.

We invite methodological, theoretical and empirical papers that that engage with any aspect of geospatial modelling and the use of Big Data. We are particularly interested in the ways that insight into individual or group behavior can be elucidated from new data sources – including social media contributions, volunteered geographical information, mobile telephone transactions, individually-sensed data, crowd-sourced information, etc. – and used to improve models or simulations. Topics include, but are not limited to:
  • Using Big Data to inform individual–based models of geographical systems;
  • Translating Big Data into agent rules;
  • Elucidating behavioral information from diverse data;
  • Improving simulated agent behavior;
  • Validating agent-based models (ABM) with Big Data;
  • Ethics of data collected en masse and their use in simulation.
Please e-mail the abstract and key words with your expression of intent to Nick Malleson by 28th October, 2014. Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at http://www.aag.org/cs/annualmeeting/call_for_papers. An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions as well as to include keywords.

Organizers

  • Alison Heppenstall, School of Geography, University of Leeds
  • Nick Malleson, School of Geography, University of Leeds
  • Andrew Crooks, Department of Computational Social Science, George Mason University
  • Paul Torrens, Department of Geographical Sciences, University of Maryland
  • Ed Manley, Centre for Advanced Spatial Analysis, University College London

Timeline

  • 28th October, 2014: Abstract submission deadline. E-mail Nick Malleson by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent.
  • 31st October, 2014: Session finalization and author notification
  • 3rd November, 2014: Final abstract submission to AAG, via www.aag.org. All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Nick Malleson. Neither the organizers nor the AAG will edit the abstracts.
  • 5th November, 2014: AAG registration deadline. Sessions submitted to AAG for approval.

 

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Geosimulation and Big Data: A Marriage made in Heaven or Hell?

http://www.pinterest.com/pin/101753272804937744/
Call for papers: AAG 2015 – Geosimulation and Big Data: A Marriage made in Heaven or Hell?

In recent years, human emotions, intentions, moods and behaviors have been digitized to an extent previously unimagined in the social sciences. This has been in the main due to the rise of a vast array of new data, termed ‘Big Data’. These new forms of data have the potential to reshape the future directions of social science research, in particular the methods that scientists use to model and simulate spatially explicit social systems. Given the novelty of this potential “revolution” and the surprising lack of reliable behavioural insight to arise from Big Data research, it is an opportune time to assess the progress that has been made and consider the future directions of socio-spatial modelling in a world that is becoming increasingly well described by Big Data sources.

We invite methodological, theoretical and empirical papers that that engage with any aspect of geospatial modelling and the use of Big Data. We are particularly interested in the ways that insight into individual or group behavior can be elucidated from new data sources – including social media contributions, volunteered geographical information, mobile telephone transactions, individually-sensed data, crowd-sourced information, etc. – and used to improve models or simulations. Topics include, but are not limited to:
  • Using Big Data to inform individual–based models of geographical systems;
  • Translating Big Data into agent rules;
  • Elucidating behavioral information from diverse data;
  • Improving simulated agent behavior;
  • Validating agent-based models (ABM) with Big Data;
  • Ethics of data collected en masse and their use in simulation.
Please e-mail the abstract and key words with your expression of intent to Nick Malleson by 28th October, 2014. Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at http://www.aag.org/cs/annualmeeting/call_for_papers. An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions as well as to include keywords.

Organizers

  • Alison Heppenstall, School of Geography, University of Leeds
  • Nick Malleson, School of Geography, University of Leeds
  • Andrew Crooks, Department of Computational Social Science, George Mason University
  • Paul Torrens, Department of Geographical Sciences, University of Maryland
  • Ed Manley, Centre for Advanced Spatial Analysis, University College London

Timeline

  • 28th October, 2014: Abstract submission deadline. E-mail Nick Malleson by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent.
  • 31st October, 2014: Session finalization and author notification
  • 3rd November, 2014: Final abstract submission to AAG, via www.aag.org. All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Nick Malleson. Neither the organizers nor the AAG will edit the abstracts.
  • 5th November, 2014: AAG registration deadline. Sessions submitted to AAG for approval.

 

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From Rhythmanalyst to Rhythmconductor- Rhythmanalysis: Space, Time and Everyday Life

 

 

 

 

Image 1. The book cover of ‘Rhythmanalysis: Space, Time and Everyday Life’

 
In the book, Rhythmanalysis: Space, Time and Everyday Life, French sociologist Henri Lefebvre suggests ‘Rhythm’ as an alternative tool to understand and analyse everyday urban life beyond visual recognition. He argues that we can examine the true nature of cities from the human body, the basic unit of urban life, to substantial urban structures through rhythms.

 

Invisible rhythms are generating, repeating and transforming in cities. Lefebvre categorizes types of rhythm, which deeply intervene the life and make a foundation of law, institution and culture, based on its characteristics. Among them, the author particularly insists to pay attention to two aspects of rhythms that Arrhythmia which is creating discordance between or among two or more rhythms, and Eurhythmia which is staying in the state of harmony and balance. He asserts that it is important to convert Arrhythmia in the city that causes inequality and injustice to Eurhythmia which sustains healthy urban condition.

 

‘Rhythmanlysist’ is a fresh idea from the book published in 1992. Rhythmanlysist hears sounds of the city and reveals hidden systems behind visual images with sensing and analysing the change of spatial aspects in timing. As a rhythmanlysist, Lefebvre investigates Mediterranean cities. He presents some insights that the rhythms of Mediterranean cities are derived from specific geographical and climate environments, and the rhythms have created different political system and exceptional cultural diversity in contrast to Atlantic cities. Physically, it leads the development of plazas and the importance of stairways which link sloping lands.

 

Rhythmanlysist could still be a valuable concept to understand complex urban situations. However, we are living in the digital era. As Mitchell (Mitchell, 1999) denoted, the rhythms of our ordinary life are changing by digital communication. Every day tremendous data, which are invisible and inaudible, are generating, and its flows push us into the massive ocean of heterogeneous rhythms. Therefore, new Rhythmanlysist in the digital age needs other capacities. Capturing the digital data in real time and synthesizing it should be essential requirements to create or maintain Eurhythmia. While the cities of the 20th century needed Rhythmanlysist, now it is the time of ‘Rhythmconductor’ who collects digital rhythms, reorganises its tempos-meters-articulations and resonates new contexts. We can easily find good examples of Rhythmconductor like below.
 

Image 2. London Public Bike share map by Oliver O’Brien. http://bikes.oobrien.com/london/

Image 3. Analysis of Happiness on Twitter during 9th September 2008 to 31stAugust 2011.

Dodds PS,  Harris KD,  Kloumann IM,  Bliss CA,  Danforth CM  (2011) Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter. PLoS ONE 6(12)

 

This radical change of the rhythm gives an opportunity to redefine the scopes of each social group. Citizens collect and utilize the data by their mobile devices; furthermore, they solve complex urban problems by themselves. (Desouza and Bhagwatwar, 2012) The role of planners is challenging to make new rhythms by spreading effective information and stimulating civic participation using social media instead traditional managers’ role within mainstream planning structures. (Tayebi, 2013) Also, Scientists’ role is shifting. According to Wright (Wright, 2013), scientific researchers had focused to find reasons of urban problems until the last decade, however; their voices are getting stronger to solve problems and provide alternatives in the decision making process with geospatial data and geographical analysis.

 

You can find the detail of Lefebvre’s book from Google and Amazon.

 

Desouza, K C and Bhagwatwar, A, 2012, “Citizen Apps to Solve Complex Urban Problems” Journal of Urban Technology 19(3) 107–136.

Mitchell, W J, 1999 E-topia: “Urban life, Jim–but not as we know it” (MIT Press, Cambridge, MA).

Tayebi, A, 2013, “Planning activism: Using Social Media to claim marginalized citizens’ right to the city” Cities 32 88–93.

Wright, D, 2013, “Bridging the Gap Between Scientists and Policy Makers: Whither Geospatial? | Esri Insider” Esri Insider, http://blogs.esri.com/esri/esri-insider/2013/02/11/bridging-the-gap-between-scientists-and-policy-makers-whither-geospatial/.

 

 

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