Living Maps Review launched today

Living Maps review is a new online journal about maps, map making and thinking of mapping (I’m on the editorial board of the journal). As the launch email describes: “map making as a democratic medium for visual artists, writers, social  researchers and community activists. The journal has its roots in the highly successful series of … Continue reading Living Maps Review launched today

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Extreme Citizen Science in Esri ArcNews

The winter edition of Esri ArcNews (which according to Mike Gould of Esri, is printed in as many copies as Forbes) includes an article on the activities of the Extreme Citizen Science group in supporting indigenous groups in mapping. The article highlights the Geographical Information Systems (GIS) aspects of the work, and mentioning many members of … Continue reading Extreme Citizen Science in Esri ArcNews

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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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UCL Institute for Global Prosperity Talk: Extreme Citizen Science – Current Developments

The slides below are from a talk that I gave today at UCL Institute for Global Prosperity The abstract for the talk is: With a growing emphasis on civil society-led change in diverse disciplines, from International Development to Town Planning, there is an increasing demand to understand how institutions might work with the public effectively … Continue reading UCL Institute for Global Prosperity Talk: Extreme Citizen Science – Current Developments

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Citizen Science Data & Service Infrastructure

Following the ECSA meeting, the Data & tools working group workshop was dedicated to progressing the agenda on data & infrastructure. Jaume Piera (chair, Data and Tools working group of ECSA) covered the area of citizen science data – moving from ideas, to particular solutions, to global proposals – from separate platforms (iNaturalist, iSpot, GBIF, … Continue reading Citizen Science Data & Service Infrastructure

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Environmental information: between scarcity/abundance and emotions/rationality

The Eye on Earth Summit, which was held in Abu Dhabi last week, allowed me to immerse myself in the topics that I’ve been researching for a long time: geographic information, public access to environmental information, participation, citizen science, and the role of all these in policy making. My notes (day 1 morning, day 1 afternoon, … Continue reading Environmental information: between scarcity/abundance and emotions/rationality

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Environmental information: between scarcity/abundance and emotions/rationality

The Eye on Earth Summit, which was held in Abu Dhabi last week, allowed me to immerse myself in the topics that I’ve been researching for a long time: geographic information, public access to environmental information, participation, citizen science, and the role of all these in policy making. My notes (day 1 morning, day 1 afternoon, … Continue reading Environmental information: between scarcity/abundance and emotions/rationality

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Being philosophical about crowdsourced geographic information

Originally posted on Geo: Geography and Environment:
By Renée Sieber (McGill University, Canada) and Muki Haklay (University College London, UK) Our recent paper, The epistemology(s) of volunteered geographic information: a critique, started from a discussion we had about changes within the geographic information science (GIScience) research communities over the past two decades. We’ve both been working in the…

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Being philosophical about crowdsourced geographic information

Originally posted on Geo: Geography and Environment:
By Renée Sieber (McGill University, Canada) and Muki Haklay (University College London, UK) Our recent paper, The epistemology(s) of volunteered geographic information: a critique, started from a discussion we had about changes within the geographic information science (GIScience) research communities over the past two decades. We’ve both been working in the…

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Eye on Earth (Day 3 – Afternoon) Remote sensing, conservation monitoring and closing remarks

The afternoon of the last day of Eye on Earth included two plenary sessions, and a discussion (for the morning, see this post). The first plenary focused on Remote sensing and location enabling applications: Taner Kodanaz (digitalglobe) technology that looking out to the sky now allow us to look at the Earth from 400 miles. Digital … Continue reading Eye on Earth (Day 3 – Afternoon) Remote sensing, conservation monitoring and closing remarks

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Eye on Earth (Day 3 – Afternoon) Remote sensing, conservation monitoring and closing remarks

The afternoon of the last day of Eye on Earth included two plenary sessions, and a discussion (for the morning, see this post). The first plenary focused on Remote sensing and location enabling applications: Taner Kodanaz (digitalglobe) technology that looking out to the sky now allow us to look at the Earth from 400 miles. Digital … Continue reading Eye on Earth (Day 3 – Afternoon) Remote sensing, conservation monitoring and closing remarks

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Eye on Earth (Day 2 – Afternoon) – Cost of knowledge, citizen science & visualisation

The first afternoon session was dedicated to Understanding the Costs of Knowledge – Cost of Data Generation and Maintenance (my second day morning post is here) The session was moderated by Thomas Brooks (IUCN) – over the last couple of days we heard about innovation in mobilisation of environmental and socio-economic data. All these innovations have … Continue reading Eye on Earth (Day 2 – Afternoon) – Cost of knowledge, citizen science & visualisation

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Eye on Earth (Day 2 – Afternoon) – Cost of knowledge, citizen science & visualisation

The first afternoon session was dedicated to Understanding the Costs of Knowledge – Cost of Data Generation and Maintenance (my second day morning post is here) The session was moderated by Thomas Brooks (IUCN) – over the last couple of days we heard about innovation in mobilisation of environmental and socio-economic data. All these innovations have … Continue reading Eye on Earth (Day 2 – Afternoon) – Cost of knowledge, citizen science & visualisation

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Eye on Earth (Day 2 – Morning) – moving to data supply

Eye on Earth (Day 2 – Morning) – moving to data supply The second day of Eye on Earth moved from data demand to supply . You can find my posts from day one, with the morning and the afternoon sessions. I have only partial notes on the plenary Data Revolution-data supply side, although I’ve posted separately the slides from … Continue reading Eye on Earth (Day 2 – Morning) – moving to data supply

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Eye on Earth (Day 2 – Morning) – moving to data supply

Eye on Earth (Day 2 – Morning) – moving to data supply The second day of Eye on Earth moved from data demand to supply . You can find my posts from day one, with the morning and the afternoon sessions. I have only partial notes on the plenary Data Revolution-data supply side, although I’ve posted separately the slides from … Continue reading Eye on Earth (Day 2 – Morning) – moving to data supply

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Eye on Earth Summit 2015 talk – Extreme Citizen Science – bridging local & global

Thanks to the organisers of the Eye on Earth Summit, I had an opportunity to share the current state of technological developments within the Extreme Citizen Science (ExCiteS) group with the audience of the summit: people who are interested in the way environmental information sharing can promote sustainability. The talk, for which the slides are … Continue reading Eye on Earth Summit 2015 talk – Extreme Citizen Science – bridging local & global

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Eye on Earth Summit 2015 talk – Extreme Citizen Science – bridging local & global

Thanks to the organisers of the Eye on Earth Summit, I had an opportunity to share the current state of technological developments within the Extreme Citizen Science (ExCiteS) group with the audience of the summit: people who are interested in the way environmental information sharing can promote sustainability. The talk, for which the slides are … Continue reading Eye on Earth Summit 2015 talk – Extreme Citizen Science – bridging local & global

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Eye on Earth (Day 1 – afternoon) – policy making demand for data and knowledge for healthy living

The afternoon of the first day of Eye on Earth (see previous post for an opening ceremony and the morning sessions) had multiple tracks. I selected to attend Addressing policy making demand for data; dialogue between decision makers and providers The speakers were asked to address four points that address issues of data quality control and … Continue reading Eye on Earth (Day 1 – afternoon) – policy making demand for data and knowledge for healthy living

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Eye on Earth (Day 1 – afternoon)

The afternoon of the first day of Eye on Earth (see previous post for an opening ceremony and the morning sessions) had multiple tracks. I selected to attend Addressing policy making demand for data; dialogue between decision makers and providers The speakers were asked to address four points that address issues of data quality control and … Continue reading Eye on Earth (Day 1 – afternoon)

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Eye on Earth (day 1 – morning)

Four years after the first Eye on Earth Summit (see my reflections about the 2011 event here, and the Dublin meeting in 2013 here), the second summit is being held in Abu Dhabi. Eye on Earth is a meeting that is dedicated to the coordination of environmental information sharing at all scales so it can … Continue reading Eye on Earth (day 1 – morning)

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Eye on Earth (day 1 – morning) – opening and the need for data

Four years after the first Eye on Earth Summit (see my reflections about the 2011 event here, and the Dublin meeting in 2013 here), the second summit is being held in Abu Dhabi. Eye on Earth is a meeting that is dedicated to the coordination of environmental information sharing at all scales so it can … Continue reading Eye on Earth (day 1 – morning) – opening and the need for data

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New paper: The epistemology(s) of volunteered geographic information: a critique

Considering how long Reneé Sieber  (McGill University) and I know each other, and working in similar areas (participatory GIS, participatory geoweb, open data, socio-technical aspects of GIS, environmental information), I’m very pleased that a collaborative paper that we developed together is finally published. The paper ‘The epistemology(s) of volunteered geographic information: a critique‘ took some … Continue reading New paper: The epistemology(s) of volunteered geographic information: a critique

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New paper: The epistemology(s) of volunteered geographic information: a critique

Considering how long Reneé Sieber  (McGill University) and I know each other, and working in similar areas (participatory GIS, participatory geoweb, open data, socio-technical aspects of GIS, environmental information), I’m very pleased that a collaborative paper that we developed together is finally published. The paper ‘The epistemology(s) of volunteered geographic information: a critique‘ took some … Continue reading New paper: The epistemology(s) of volunteered geographic information: a critique

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Building Centre – from Mapping to Making

The London based Building Centre organised an evening event – from Mapping to Making –  which looked at the “radical evolution in the making and meaning of maps is influencing creative output. New approaches to data capture and integration – from drones to crowd-sourcing – suggest maps are changing their impact on our working life, … Continue reading Building Centre – from Mapping to Making

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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)

 

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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)

 

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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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