 |
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 20th
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 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 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 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 15th
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 15th August 2015)
