Evolution of London’s Rush Hour Traffic Mix

My latest London data visualisation crunches an interesting dataset from the Department of Transport. The data is available across England, although I’ve chosen London in particular because of its more interesting (i.e. not just car dominated) traffic mix. I’ve also focused on just the data for 8am to 9am, to examine the height of the … Continue reading Evolution of London’s Rush Hour Traffic Mix

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Lives on the Line v2: Estimated Life Expectancy by Small Areas

I’ve produced an updated version of a graphic that my colleague Dr James Cheshire created a few years ago, showing how the estimated life expectancy at birth various throughout the capital, using a geographical tube map to illustrate the sometimes short-distance changes. You can see an interactive version on my tube data visualisation platform. Click … Continue reading Lives on the Line v2: Estimated Life Expectancy by Small Areas

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Smart Mobility Meeting in Mexico City

Below is a presentation that combined my talks last Thursday and Friday at the Smart Mobility forums in central Mexico City, organised by ITDP Mexico and funded by the Foreign and Commonwealth Office’s Prosperity Fund (respresented by the British Embassy in Mexico). The Thursday presentation focused on the third-party app ecosystem that exists around bikesharing … Continue reading Smart Mobility Meeting in Mexico City

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Big Data Here: The Code

So Big Data Here, a little pop-up exhibition of hyperlocal data, has just closed, having run continuously from Tuesday evening to this morning, as part of Big Data Week. We had many people peering through the windows of the characterful North Lodge building beside UCL’s main entrance on Gower Street, particularly during the evening rush … Continue reading Big Data Here: The Code

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Big Data Here

The Consumer Data Research Centre (CDRC) at UCL is organising a short pop-up exhibition on hyperlocal data: Big Data Here. The exhibition is taking place in North Lodge, the small building right beside UCL’s main entrance. The exhibition materials are supplied by the Centre for Advanced Spatial Analysis (CASA). Inside, a big projection shows local … Continue reading Big Data Here

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Busiest Tube Station Times

Here are the busiest tube station quarter-hour periods, based on the Transport for London 2015 RODS data (modelled, based on typical autumn weekday), used in TubeHeartbeat, adding together entries, exits and interchange stats and excluding Kensington Olympia. The main pattern shows that stations further out from London’s main work areas (The West End, the City … Continue reading Busiest Tube Station Times

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Open Source Public Transport Accessibility Modelling

The RGS-IBG annual conference has been on this week, and I presented as part of a series of geocomputation sessions arranged in advance of the 21st anniversary Geocomputation conference in Leeds next year. The topic was current CASA research from the RESOLUTION project, looking at developing fast and consistent methods of measuring public transport accessibility between different … Continue reading Open Source Public Transport Accessibility Modelling

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Tate Modern Switch House: a New Perspective on London

High rise developments are often exclusive private spaces, as attested by the current glut of luxury flats, hotels and offices rising across Inner London. Even recent developments advertising their public space credentials have come up short, with for example the Shard’s fantastic views costing £25 entry fee, or the Walkie-Talkie’s ‘skygarden’ amounting to an expensive restaurant and some pot plants. … Continue reading Tate Modern Switch House: a New Perspective on London

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London’s Bikeshare Needs A Redistribution of Stations

Here’s an interesting graph, which combines data on total journeys per day on London’s bicycle sharing system (currently called “Santander Cycles”) from the London Data Store, with counts of available bicycles per day to hire, from my own research database. The system launched in summer 2010 and I started tracking the numbers almost from the … Continue reading London’s Bikeshare Needs A Redistribution of Stations

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New Citizen Science for air quality campaign

Mapping for Change, the social enterprise that I co-founded, has been assisting community groups to run air quality studies for the past 5 years. During this period we have worked in 30 communities across London, carrying out studies with different tools – from collecting leaves, to examining lichens, to using diffusion tubes. We have also followed … Continue reading New Citizen Science for air quality campaign

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Mapping Protest in 3D with Twitter Data




As one part of my docotoral thesis, I have made the video that shows the relationship between ‘London End Austerity Now’ Protest on 20thJune 2015 and the Twitter acitivity on that day.

The video gives you some details about the protest, the data and 3D visualisation.
If the following YouTube video is not displayed on your device, please use this link. 





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Mapping Protest in 3D with Twitter Data




As one part of my docotoral thesis, I have made the video that shows the relationship between ‘London End Austerity Now’ Protest on 20thJune 2015 and the Twitter acitivity on that day.

The video gives you some details about the protest, the data and 3D visualisation.
If the following YouTube video is not displayed on your device, please use this link. 





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)

 

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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Living Somewhere Nice, Cheap and Close In – Pick Two!

When people decide to move to London, one very simple model of desired location might be to work out how important staying somewhere nice, cheap, and well located for the centre of the city is – and the relative importance of these three factors. Unfortunately, like most places, you can’t get all three of these … Continue reading Living Somewhere Nice, Cheap and Close In – Pick Two!

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OpenStreetMap: London Building Coverage

OpenStreetMap is still surprisingly incomplete when it comes to showing buildings for the London area, this is a real contrast to other places (e.g. Birmingham, New York City, Paris) when it comes to completeness of buildings, this is despite some good datasets (e.g Ordnance Survey OpenMap Local) including building outlines. It’s one reason why I … Continue reading OpenStreetMap: London Building Coverage

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Tube Line Closure Map

The Tube Line Closure Map accesses Transport for London’s REST API for line disruption information (both live and planned) and uses the information there to animate a geographical vector map of the network, showing closed sections as lines flashing dots, with solid lines for unaffected parts. The idea is similar to TfL’s official disruption map, … Continue reading Tube Line Closure Map

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Street Trees of Southwark

Above is an excerpt of a large, coloured-dot based graphic showing the locations of street trees in Rotherhithe, part of the London Borough of Southwark in London, as released by them to the OpenStreetMap database back in 2010. You can download the full version (12MB PDF). Street trees are trees on public land managed by … Continue reading Street Trees of Southwark

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Taking the Scenic Route – Quantitatively?

A friend forwarded me this article which discusses this paper by researchers at the Yahoo Labs offices in Barcelona and the University of Turin. The basic idea is that they crowdsourced prettiness of places in central London, via either/or pairs photographs, to build up a field of attractiveness, then adjusted a router based on this … Continue reading Taking the Scenic Route – Quantitatively?

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Seeing Red: 15 Ways the Boris Bikes of London Could be Better

I’ll be gradually tweaking this article to add/amend sources, clarifications and develop some of my arguments. A big announcement for the “Boris Bikes” today, aka Barclays Cycle Hire. London’s bikeshare system, the second largest in the western world after Paris’s Velib and nearly five years old, will be rebranded as Santander Cycles, and the bikes … Continue reading Seeing Red: 15 Ways the Boris Bikes of London Could be Better

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Understanding Household Energy Use in England & Wales

Household energy use is a key indicator for understanding urban sustainability and fuel poverty, and is a timely topic now that winter has arrived. The LuminoCity3D site maps domestic energy use in England and Wales at 1km2 scale using data from DECC. This map has also just been published as a featured graphic in Regional Studies Regional Science.…

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Is Developing the Greenbelt the Answer to London’s Housing Crisis?

Following rapid growth and a chronic lack of new development, housing affordability has reached crisis levels in London. Median house prices are at £300k (8 times median household income) while average prices have passed half a million. London is now amongst the most expensive cities in the world, a situation with severe consequences for economic competitiveness and…

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