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-tagged tweets 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 to specific 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 20^th June, 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

 



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 periods and 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 20^th June 2015

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

Image 11. The tweet density in 3d between 12pm and 1pm on 20^th 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 15^th 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 15^th August 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 15^th 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 15^th August 2015)

 

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 20^th 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 9^th September 2008 to 31^st August 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/.

 

 

Luminous Cities: offering an alternative way of geotag

Image1. The webpage of Luminous Cities_Manhattan

Studying human behaviours and communication in time and space has been regarded as the important factor of modern urban planning. In this digital era, collecting online data and analysing the data provide an opportunity to understand the intention and the process of the behaviours and the communication which had not been revealed.

Geotag, which is attached on Social Network Service (SNS), is concerned as one of connecting link between the internet and urban. Mainly, there are two types of geotag. One is user-generated geotag that SNS users identify the places on their contents. The other is automatically generated with spatial coordination by the services. It represents the political, social and economic characteristics of the places as well as the physical location of the user or the data produced.

There are many good examples of mapping the geotag data of SNS. Eric Fischer’s well known mapping images reveal not only the density of the geotag data but also social aspects in cities such as the invisible dimensions of tourism in New York (Image 2). Twitter Languages in London by James Cheshire and Ed Manley shows the popularity of languages depends on different locations in London ((Image 3).

Image2. The mapping geotag data of locals and tourists by Eric Fischer 

Image3. Twitter Languages in London, James Cheshire and Ed Manley

Luminous Cities is the project to demonstrate the interactive map of Flickr geotag data supported by CASA at UCL and CSAP at the University of Leeds. It has developed by Gavin Baily and Sarah Bagshaw. The project does not remain the displaying density and distribution of the geotag, but offers in-detail contents of the geotag such as user, tag, time of the day and timeline over 50 cities in the world. With the multiple contents, Luminous Cities could be a platform to check out the geotag data of Flickr based on personal interest, and to view their cities from a different side. When it comes to Networking City, who is interested in protest and demonstration in the city, it would be a helpful tool to examine the relationship between protests or occupy tags of Flickr in London and actual events of them. Also, some interesting results may be emerging when we compare two data sets: Flickr and Twitter.

Image4. Berlin user geotag map from the webpage of Luminous Cities

Image5. London occupy geotag map from the webpage of Luminous Cities

Image6. Tokyo geotag map, Zoom out, from the webpage of Luminous Cities

Image7. Tokyo geotag map, Zoom in, from the webpage of Luminous Cities

You can find more things from following links.

Flickr was shown as the highest growing application in 2013 by Mashable

http://mashable.com/2013/10/21/fastest-growing-apps/?utm_cid=mash-com-fb-main-link

Mapping the world with Flickr and Twitter by Guardian

http://www.theguardian.com/technology/pda/2011/jul/13/flickr-twitter-maps

Infographic Of The Day: Using Twitter And Flickr Geotags To Map The World

http://www.fastcodesign.com/1664462/infographic-of-the-day-using-twitter-and-flickr-geotags-to-map-the-world

The World Protests by GDELT

 


Image 1. All GDELT protest data for 2013. The image was captured from GDELT's work. (See below)

Global Database of Events, Language, and Tone (GDELT) is a remarkable organisation to provide, freely, the data of all human behaviours, particularly protest, over the world since 1979. They are trying to make "real-time social sciences earth observatory" by updating the data every day. It is running by three researchers, Kalev Leetaru, Philip Schrodt and Patrick Brandt. 

 

If you visit their website and their blog, you would be surprised by their enormous data set as well as effective and nice visualisation. For example, recent GDELT’s work is showing protest movement in 2013. (Image 1) This interactive map illustrates how many protests have been raising in the world a year including Egypt, Brazil and Turkey, and we can recognise that the flame of protests are covering the world even though the data would not report all hidden protests.

 

Image 2. Syria's civil war. The image was captured from GDELT's work. (See below) 

 

Another map describes the terrific condition of Syria’s civil war in detail. (Image 2) Visualising the location, the number of violence per day and the period of the civil war together warns us how the situation is significant much more than just some sentences and images of broadcasting news. It was issued on The Guardian.  

 

However, the most important thing is their continuous effort to collect the data, sort it out and provide the valuable data for further research. Opening the data might not be an easy decision and it would be a extremely time-consuming work.

 

After visiting the website of GDELT, Networking City understood the importance of open data, its impacts and the power of visualisation, and promised to work hard and being more opened.