CASA focuses on using formal methods and models to study cities. They work on projects involving modelling, visualizing, mapping, and sensing urban phenomena. Recent examples include analyzing Twitter data to understand urban activity, crowd-sourcing map annotations, and modelling pedestrian movement and disease spreading.
Big data analysis and scheduling optimization system oriented assembly proces...nexgentechnology
GET IEEE BIG DATA, JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
City lines designing hybrid hub and-spoke transit system with urban big datanexgentechnology
GET IEEE BIG DATA, JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Using DTMon to Monitor Free Flow Traffichadiarbabi
We present DTMon, a dynamic traffic monitiroing system using vehicular networks, and analyze its performance in free flow (i.e., non-congested) traffic. DTMon uses roadside infrastructure to gather and report current traffic conditions to traffic management centers and equipped vehicles. We analyze how traffic characteristics such as speed, flow rate, percentage of communicating vehicles, and distance from the DTMon measurement point to the roadside infrastructure affects the amount and quality of data that can be gathered and delivered. We evaluate five different methods of delivering data from vehicles to the roadside infrastructure, including pure vehicle-to-vehicle communication, store-and-carry, and hybrid methods. Methods that employ some amount of store-and-carry can increase the delivery rate, but also increase the message delay. We show that with just a few pieces of roadside infrastructure, DTMon can gather high-quality travel time and speed data even with a low percentage of communicating vehicles.
Kharita: Robust Road Map Inference Through Network Alignment of Trajectoriesvipyoung
In this work we address the challenge of inferring the road network
of a city from crowd-sourced GPS traces. While the problem has been
addressed before, our solution has the following unique characteristics:
(i) we formulate the road network inference problem as a network
alignment optimization
problem where both the nodes and edges of the network have to be inferred,
(ii) we propose both an offline (\kha) and an online (\khastar) algorithm
which are intuitive and capture the key aspects of the optimization
formulation but are scalable and accurate. The \khastar in particular is, to
the best of our knowledge, the first known online algorithm for map inference,
(iii) we test our approach on two real data sets
and both our code and data sets have been made available for research
reproducibility.
Big data analysis and scheduling optimization system oriented assembly proces...nexgentechnology
GET IEEE BIG DATA, JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
City lines designing hybrid hub and-spoke transit system with urban big datanexgentechnology
GET IEEE BIG DATA, JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Using DTMon to Monitor Free Flow Traffichadiarbabi
We present DTMon, a dynamic traffic monitiroing system using vehicular networks, and analyze its performance in free flow (i.e., non-congested) traffic. DTMon uses roadside infrastructure to gather and report current traffic conditions to traffic management centers and equipped vehicles. We analyze how traffic characteristics such as speed, flow rate, percentage of communicating vehicles, and distance from the DTMon measurement point to the roadside infrastructure affects the amount and quality of data that can be gathered and delivered. We evaluate five different methods of delivering data from vehicles to the roadside infrastructure, including pure vehicle-to-vehicle communication, store-and-carry, and hybrid methods. Methods that employ some amount of store-and-carry can increase the delivery rate, but also increase the message delay. We show that with just a few pieces of roadside infrastructure, DTMon can gather high-quality travel time and speed data even with a low percentage of communicating vehicles.
Kharita: Robust Road Map Inference Through Network Alignment of Trajectoriesvipyoung
In this work we address the challenge of inferring the road network
of a city from crowd-sourced GPS traces. While the problem has been
addressed before, our solution has the following unique characteristics:
(i) we formulate the road network inference problem as a network
alignment optimization
problem where both the nodes and edges of the network have to be inferred,
(ii) we propose both an offline (\kha) and an online (\khastar) algorithm
which are intuitive and capture the key aspects of the optimization
formulation but are scalable and accurate. The \khastar in particular is, to
the best of our knowledge, the first known online algorithm for map inference,
(iii) we test our approach on two real data sets
and both our code and data sets have been made available for research
reproducibility.
The Governing Council of Galapagos, the Galapagos National Park
Directorate and the Charles Darwin Foundation are pleased to present
the 2011-2012 Galapagos Report - a compendium of scientific and social
analyses and observations designed to stimulate cogent, thoughtful
discussion and public policy that will help to protect Galapagos
ecosystems and its biodiversity and promote human well-being (“Buen
Vivir”) in the archipelago.
The articles presented in this edition of the Galapagos Report reflect
a range of disciplines and opinions within the general areas of human
systems, tourism, marine management, and biodiversity and ecosystem
restoration. In addition, two articles present the framework for
establishing a knowledge management initiative and a citizen science
program for Galapagos. We are pleased to include articles by authors
based in Galapagos as well as colleagues from around the globe, all of
whom have shared valuable ideas and information on critical and timely
issues.
It is the intent of the Galapagos Report to inform and stimulate discussion,
as well as catalyze critical research, and effective public action and
management policy. We are grateful to the wide range of collaborators
who have shared their vision for Galapagos and whose work is so critical
to the health and future of the archipelago. Our three institutions remain
committed to working in coordination with all Galapagos stakeholders
to ensure the long-term sustainability of this natural treasure, symbol of
Ecuador’s natural patrimony.
Propositos que me mando mi esposa Sue...A ver si logro de aplicarlos....dieta equilibrada? que es esto??? caminar y no tmar un taxi??? a trabajar... espero que los inspire de hacer lo mismo...SUERTE...
Dr Vassilis Zachariadis explained the research activities that have been developed at CASA, UCL.
Flood impact assessment in mega cities under urban sprawl and climate change kick-off workshop
cf. city flows - A comparative visualization of bike sharing systemsTill Nagel
cf. city flows is a comparative visualization environment of urban bike mobility designed to help citizens casually analyze three bike-sharing systems in the context of a public exhibition space.
By Till Nagel and Christopher Pietsch.
Urban Complexity Lab, FH Potsdam
<a>http://uclab.fh-potsdam.de/</a>
This talk introduces the project and some of its goals and visualizations, and shows our design process in analyzing the data and designing the visualizations.
cf. city flows was exhibited at the Streams and Traces in November 2015 in Berlin. Find more information at http://streamsandtraces.com/
More information coming soon.
The Future of Maps for Mobility / Geography2050Janine Yoong
While high-resolution satellite imagery is the foundation of digital mapping, the demands of urban mobility require highly accurate, frequently updated data from a different vantage point – the ground. Recent advances in street-level imagery collection and data extraction are lifting up new trends in location-based services, smart cities, and autonomous vehicles. As map technologists push the boundaries of machine intelligence for extracting data from images, human collaboration will drive the creation of maps for mobility for all.
This presentation served as the AI Keynote during the second half of the NISO 2023 Humanities Roundtable, and was provided by Katherine McDonough of Lancaster University and the Alan Turing Institute. The event focused on both Open Access of Humanities Monographs, and AI in Generative Content & Authorship, and was held virtually on June 20, 2023.
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART CITIES Alg.docxjasoninnes20
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART
CITIES:
Algorithmic improvements on Big Data Analysis in the context of RADICAL city
applications
Evangelos Psomakelis12,Fotis Aisopos1, Antonios Litke1, Konstantinos Tserpes21, Magdalini
Kardara1 and Pablo Martínez Campo3
1Distributed Knowledge and Media Systems Group, National Technical University of Athens, Zografou Campus, Athens,
Greece
2Informatics and Telematics Dept, Harokopio University of Athens, Greece
3Communications Engineering department, University of Cantabria, Santander, Spain
{fotais, litke, nkardara, tserpes, vpsomak}@mail.ntua.gr,[email protected]
Keywords: Internet of Things, Social Networking, Big Data Aggregation and Analysis, Smart City applications,
Sentiment Analysis, Machine Learning
Abstract: In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and
analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices,
collected by smart city applications and socially-aware data aggregation services. A large set of city
applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout
participating cities is being applied, resulting into produced sets of millions of user-generated events and
online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such
as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating
algorithmic and configurations to minimize delays in dataset processing and results retrieval.
1 INTRODUCTION
Modern cities are increasingly turning towards
ICT technology for confronting pressures associated
with demographic changes, urbanization, climate
change (Romero Lankao, 2008) and globalization.
Therefore, most cities have undertaken significant
investments during the last decade in ICT
infrastructure including computers, broadband
connectivity and recently sensing infrastructures.
These infrastructures have empowered a number of
innovative services in areas such as participatory
sensing, urban logistics and ambient assisted living.
Such services have been extensively deployed in
several cities, thereby demonstrating the potential
benefits of ICT infrastructures for businesses and the
citizens themselves. During the last few years we
have also witnessed an explosion of sensor
deployments and social networking services, along
with the emergence of social networking (Conti et al.,
2011) and internet‐of‐things technologies (Perera et
al., 2013; Sundmaeker et al., 2010) Social and sensor
networks can be combined in order to offer a variety
of added‐value services for smart cities, as has
already been demonstrated by various early internet‐
of‐things applications (such as WikiCity(Calabrese et
al., 2007), CitySense(Murty et al., 2007),
GoogleLatitude(Page and Kobsa, 2010)), as ...
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART CITIES Alg.docxtangyechloe
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART
CITIES:
Algorithmic improvements on Big Data Analysis in the context of RADICAL city
applications
Evangelos Psomakelis12,Fotis Aisopos1, Antonios Litke1, Konstantinos Tserpes21, Magdalini
Kardara1 and Pablo Martínez Campo3
1Distributed Knowledge and Media Systems Group, National Technical University of Athens, Zografou Campus, Athens,
Greece
2Informatics and Telematics Dept, Harokopio University of Athens, Greece
3Communications Engineering department, University of Cantabria, Santander, Spain
{fotais, litke, nkardara, tserpes, vpsomak}@mail.ntua.gr,[email protected]
Keywords: Internet of Things, Social Networking, Big Data Aggregation and Analysis, Smart City applications,
Sentiment Analysis, Machine Learning
Abstract: In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and
analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices,
collected by smart city applications and socially-aware data aggregation services. A large set of city
applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout
participating cities is being applied, resulting into produced sets of millions of user-generated events and
online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such
as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating
algorithmic and configurations to minimize delays in dataset processing and results retrieval.
1 INTRODUCTION
Modern cities are increasingly turning towards
ICT technology for confronting pressures associated
with demographic changes, urbanization, climate
change (Romero Lankao, 2008) and globalization.
Therefore, most cities have undertaken significant
investments during the last decade in ICT
infrastructure including computers, broadband
connectivity and recently sensing infrastructures.
These infrastructures have empowered a number of
innovative services in areas such as participatory
sensing, urban logistics and ambient assisted living.
Such services have been extensively deployed in
several cities, thereby demonstrating the potential
benefits of ICT infrastructures for businesses and the
citizens themselves. During the last few years we
have also witnessed an explosion of sensor
deployments and social networking services, along
with the emergence of social networking (Conti et al.,
2011) and internet‐of‐things technologies (Perera et
al., 2013; Sundmaeker et al., 2010) Social and sensor
networks can be combined in order to offer a variety
of added‐value services for smart cities, as has
already been demonstrated by various early internet‐
of‐things applications (such as WikiCity(Calabrese et
al., 2007), CitySense(Murty et al., 2007),
GoogleLatitude(Page and Kobsa, 2010)), as.
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART CITIES Alg.docxhartrobert670
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART
CITIES:
Algorithmic improvements on Big Data Analysis in the context of RADICAL city
applications
Evangelos Psomakelis12,Fotis Aisopos1, Antonios Litke1, Konstantinos Tserpes21, Magdalini
Kardara1 and Pablo Martínez Campo3
1Distributed Knowledge and Media Systems Group, National Technical University of Athens, Zografou Campus, Athens,
Greece
2Informatics and Telematics Dept, Harokopio University of Athens, Greece
3Communications Engineering department, University of Cantabria, Santander, Spain
{fotais, litke, nkardara, tserpes, vpsomak}@mail.ntua.gr,[email protected]
Keywords: Internet of Things, Social Networking, Big Data Aggregation and Analysis, Smart City applications,
Sentiment Analysis, Machine Learning
Abstract: In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and
analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices,
collected by smart city applications and socially-aware data aggregation services. A large set of city
applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout
participating cities is being applied, resulting into produced sets of millions of user-generated events and
online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such
as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating
algorithmic and configurations to minimize delays in dataset processing and results retrieval.
1 INTRODUCTION
Modern cities are increasingly turning towards
ICT technology for confronting pressures associated
with demographic changes, urbanization, climate
change (Romero Lankao, 2008) and globalization.
Therefore, most cities have undertaken significant
investments during the last decade in ICT
infrastructure including computers, broadband
connectivity and recently sensing infrastructures.
These infrastructures have empowered a number of
innovative services in areas such as participatory
sensing, urban logistics and ambient assisted living.
Such services have been extensively deployed in
several cities, thereby demonstrating the potential
benefits of ICT infrastructures for businesses and the
citizens themselves. During the last few years we
have also witnessed an explosion of sensor
deployments and social networking services, along
with the emergence of social networking (Conti et al.,
2011) and internet‐of‐things technologies (Perera et
al., 2013; Sundmaeker et al., 2010) Social and sensor
networks can be combined in order to offer a variety
of added‐value services for smart cities, as has
already been demonstrated by various early internet‐
of‐things applications (such as WikiCity(Calabrese et
al., 2007), CitySense(Murty et al., 2007),
GoogleLatitude(Page and Kobsa, 2010)), as ...
The presentation was illustrated at the CEEM CoP Webinar: “Achieving Low Carbon Mobility: Urban Transportation Modelling, Public Awareness and Behavioural Change" on tge 10th of October 2013
CEEM CoP stands for Community Energy and Emissions Modelling (CEEM) Community of Practice (CoP).
CEEM CoP is an informal group supporting CEEM practitioners and local governments in furthering greenhouse gas modelling, target-setting and action in communities across BC – www.toolkit.bc.ca/ceem
Mobility is an important part of daily life. Progressive community planning and transportation design can greatly reduce the need for automobile travel, instead providing a diverse range of active transportation alternatives.
This presentation on the CATCH project looks at how transportation-related data can be used to understand a city’s travel footprint and help to inform city planning and programs to promote individual behaviour change.
It reviews the findings and lessons learned from the ‘CATCH Project’ (Carbon Aware Travel Choice): a 2 million euro-funded project, involving 11 partners across 6 European Union countries, aimed to develop a knowledge platform to help urban communities move to less carbon-intensive transportation systems. This presentation touches on the important role of developing a system to compare and contrast best practices, identify the many motivators for change to low carbon mobility, and use tools for engaging the public and decision makers to support innovation and change.
Presentation given during the first transportation workshop at Melbourne Uni. Focus on crowd monitoring and management. With examples from various projects (SAIL, Mekka, etc.)
The Governing Council of Galapagos, the Galapagos National Park
Directorate and the Charles Darwin Foundation are pleased to present
the 2011-2012 Galapagos Report - a compendium of scientific and social
analyses and observations designed to stimulate cogent, thoughtful
discussion and public policy that will help to protect Galapagos
ecosystems and its biodiversity and promote human well-being (“Buen
Vivir”) in the archipelago.
The articles presented in this edition of the Galapagos Report reflect
a range of disciplines and opinions within the general areas of human
systems, tourism, marine management, and biodiversity and ecosystem
restoration. In addition, two articles present the framework for
establishing a knowledge management initiative and a citizen science
program for Galapagos. We are pleased to include articles by authors
based in Galapagos as well as colleagues from around the globe, all of
whom have shared valuable ideas and information on critical and timely
issues.
It is the intent of the Galapagos Report to inform and stimulate discussion,
as well as catalyze critical research, and effective public action and
management policy. We are grateful to the wide range of collaborators
who have shared their vision for Galapagos and whose work is so critical
to the health and future of the archipelago. Our three institutions remain
committed to working in coordination with all Galapagos stakeholders
to ensure the long-term sustainability of this natural treasure, symbol of
Ecuador’s natural patrimony.
Propositos que me mando mi esposa Sue...A ver si logro de aplicarlos....dieta equilibrada? que es esto??? caminar y no tmar un taxi??? a trabajar... espero que los inspire de hacer lo mismo...SUERTE...
Dr Vassilis Zachariadis explained the research activities that have been developed at CASA, UCL.
Flood impact assessment in mega cities under urban sprawl and climate change kick-off workshop
cf. city flows - A comparative visualization of bike sharing systemsTill Nagel
cf. city flows is a comparative visualization environment of urban bike mobility designed to help citizens casually analyze three bike-sharing systems in the context of a public exhibition space.
By Till Nagel and Christopher Pietsch.
Urban Complexity Lab, FH Potsdam
<a>http://uclab.fh-potsdam.de/</a>
This talk introduces the project and some of its goals and visualizations, and shows our design process in analyzing the data and designing the visualizations.
cf. city flows was exhibited at the Streams and Traces in November 2015 in Berlin. Find more information at http://streamsandtraces.com/
More information coming soon.
The Future of Maps for Mobility / Geography2050Janine Yoong
While high-resolution satellite imagery is the foundation of digital mapping, the demands of urban mobility require highly accurate, frequently updated data from a different vantage point – the ground. Recent advances in street-level imagery collection and data extraction are lifting up new trends in location-based services, smart cities, and autonomous vehicles. As map technologists push the boundaries of machine intelligence for extracting data from images, human collaboration will drive the creation of maps for mobility for all.
This presentation served as the AI Keynote during the second half of the NISO 2023 Humanities Roundtable, and was provided by Katherine McDonough of Lancaster University and the Alan Turing Institute. The event focused on both Open Access of Humanities Monographs, and AI in Generative Content & Authorship, and was held virtually on June 20, 2023.
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART CITIES Alg.docxjasoninnes20
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART
CITIES:
Algorithmic improvements on Big Data Analysis in the context of RADICAL city
applications
Evangelos Psomakelis12,Fotis Aisopos1, Antonios Litke1, Konstantinos Tserpes21, Magdalini
Kardara1 and Pablo Martínez Campo3
1Distributed Knowledge and Media Systems Group, National Technical University of Athens, Zografou Campus, Athens,
Greece
2Informatics and Telematics Dept, Harokopio University of Athens, Greece
3Communications Engineering department, University of Cantabria, Santander, Spain
{fotais, litke, nkardara, tserpes, vpsomak}@mail.ntua.gr,[email protected]
Keywords: Internet of Things, Social Networking, Big Data Aggregation and Analysis, Smart City applications,
Sentiment Analysis, Machine Learning
Abstract: In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and
analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices,
collected by smart city applications and socially-aware data aggregation services. A large set of city
applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout
participating cities is being applied, resulting into produced sets of millions of user-generated events and
online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such
as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating
algorithmic and configurations to minimize delays in dataset processing and results retrieval.
1 INTRODUCTION
Modern cities are increasingly turning towards
ICT technology for confronting pressures associated
with demographic changes, urbanization, climate
change (Romero Lankao, 2008) and globalization.
Therefore, most cities have undertaken significant
investments during the last decade in ICT
infrastructure including computers, broadband
connectivity and recently sensing infrastructures.
These infrastructures have empowered a number of
innovative services in areas such as participatory
sensing, urban logistics and ambient assisted living.
Such services have been extensively deployed in
several cities, thereby demonstrating the potential
benefits of ICT infrastructures for businesses and the
citizens themselves. During the last few years we
have also witnessed an explosion of sensor
deployments and social networking services, along
with the emergence of social networking (Conti et al.,
2011) and internet‐of‐things technologies (Perera et
al., 2013; Sundmaeker et al., 2010) Social and sensor
networks can be combined in order to offer a variety
of added‐value services for smart cities, as has
already been demonstrated by various early internet‐
of‐things applications (such as WikiCity(Calabrese et
al., 2007), CitySense(Murty et al., 2007),
GoogleLatitude(Page and Kobsa, 2010)), as ...
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART CITIES Alg.docxtangyechloe
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART
CITIES:
Algorithmic improvements on Big Data Analysis in the context of RADICAL city
applications
Evangelos Psomakelis12,Fotis Aisopos1, Antonios Litke1, Konstantinos Tserpes21, Magdalini
Kardara1 and Pablo Martínez Campo3
1Distributed Knowledge and Media Systems Group, National Technical University of Athens, Zografou Campus, Athens,
Greece
2Informatics and Telematics Dept, Harokopio University of Athens, Greece
3Communications Engineering department, University of Cantabria, Santander, Spain
{fotais, litke, nkardara, tserpes, vpsomak}@mail.ntua.gr,[email protected]
Keywords: Internet of Things, Social Networking, Big Data Aggregation and Analysis, Smart City applications,
Sentiment Analysis, Machine Learning
Abstract: In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and
analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices,
collected by smart city applications and socially-aware data aggregation services. A large set of city
applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout
participating cities is being applied, resulting into produced sets of millions of user-generated events and
online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such
as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating
algorithmic and configurations to minimize delays in dataset processing and results retrieval.
1 INTRODUCTION
Modern cities are increasingly turning towards
ICT technology for confronting pressures associated
with demographic changes, urbanization, climate
change (Romero Lankao, 2008) and globalization.
Therefore, most cities have undertaken significant
investments during the last decade in ICT
infrastructure including computers, broadband
connectivity and recently sensing infrastructures.
These infrastructures have empowered a number of
innovative services in areas such as participatory
sensing, urban logistics and ambient assisted living.
Such services have been extensively deployed in
several cities, thereby demonstrating the potential
benefits of ICT infrastructures for businesses and the
citizens themselves. During the last few years we
have also witnessed an explosion of sensor
deployments and social networking services, along
with the emergence of social networking (Conti et al.,
2011) and internet‐of‐things technologies (Perera et
al., 2013; Sundmaeker et al., 2010) Social and sensor
networks can be combined in order to offer a variety
of added‐value services for smart cities, as has
already been demonstrated by various early internet‐
of‐things applications (such as WikiCity(Calabrese et
al., 2007), CitySense(Murty et al., 2007),
GoogleLatitude(Page and Kobsa, 2010)), as.
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART CITIES Alg.docxhartrobert670
BIG IOT AND SOCIAL NETWORKING DATA FOR SMART
CITIES:
Algorithmic improvements on Big Data Analysis in the context of RADICAL city
applications
Evangelos Psomakelis12,Fotis Aisopos1, Antonios Litke1, Konstantinos Tserpes21, Magdalini
Kardara1 and Pablo Martínez Campo3
1Distributed Knowledge and Media Systems Group, National Technical University of Athens, Zografou Campus, Athens,
Greece
2Informatics and Telematics Dept, Harokopio University of Athens, Greece
3Communications Engineering department, University of Cantabria, Santander, Spain
{fotais, litke, nkardara, tserpes, vpsomak}@mail.ntua.gr,[email protected]
Keywords: Internet of Things, Social Networking, Big Data Aggregation and Analysis, Smart City applications,
Sentiment Analysis, Machine Learning
Abstract: In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and
analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices,
collected by smart city applications and socially-aware data aggregation services. A large set of city
applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout
participating cities is being applied, resulting into produced sets of millions of user-generated events and
online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such
as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating
algorithmic and configurations to minimize delays in dataset processing and results retrieval.
1 INTRODUCTION
Modern cities are increasingly turning towards
ICT technology for confronting pressures associated
with demographic changes, urbanization, climate
change (Romero Lankao, 2008) and globalization.
Therefore, most cities have undertaken significant
investments during the last decade in ICT
infrastructure including computers, broadband
connectivity and recently sensing infrastructures.
These infrastructures have empowered a number of
innovative services in areas such as participatory
sensing, urban logistics and ambient assisted living.
Such services have been extensively deployed in
several cities, thereby demonstrating the potential
benefits of ICT infrastructures for businesses and the
citizens themselves. During the last few years we
have also witnessed an explosion of sensor
deployments and social networking services, along
with the emergence of social networking (Conti et al.,
2011) and internet‐of‐things technologies (Perera et
al., 2013; Sundmaeker et al., 2010) Social and sensor
networks can be combined in order to offer a variety
of added‐value services for smart cities, as has
already been demonstrated by various early internet‐
of‐things applications (such as WikiCity(Calabrese et
al., 2007), CitySense(Murty et al., 2007),
GoogleLatitude(Page and Kobsa, 2010)), as ...
The presentation was illustrated at the CEEM CoP Webinar: “Achieving Low Carbon Mobility: Urban Transportation Modelling, Public Awareness and Behavioural Change" on tge 10th of October 2013
CEEM CoP stands for Community Energy and Emissions Modelling (CEEM) Community of Practice (CoP).
CEEM CoP is an informal group supporting CEEM practitioners and local governments in furthering greenhouse gas modelling, target-setting and action in communities across BC – www.toolkit.bc.ca/ceem
Mobility is an important part of daily life. Progressive community planning and transportation design can greatly reduce the need for automobile travel, instead providing a diverse range of active transportation alternatives.
This presentation on the CATCH project looks at how transportation-related data can be used to understand a city’s travel footprint and help to inform city planning and programs to promote individual behaviour change.
It reviews the findings and lessons learned from the ‘CATCH Project’ (Carbon Aware Travel Choice): a 2 million euro-funded project, involving 11 partners across 6 European Union countries, aimed to develop a knowledge platform to help urban communities move to less carbon-intensive transportation systems. This presentation touches on the important role of developing a system to compare and contrast best practices, identify the many motivators for change to low carbon mobility, and use tools for engaging the public and decision makers to support innovation and change.
Presentation given during the first transportation workshop at Melbourne Uni. Focus on crowd monitoring and management. With examples from various projects (SAIL, Mekka, etc.)
A MetaLoop Executive Summary & Briefing Paper for Decision Makers.
The Station Comes To You with the fastest, most seamless Express A to B, to help us de-carbonise and de-congest our cities most easily and quickly, while sparking a Productivity & Revenue boom.
2013 Talk on Informatics tools for public transport re cities and healthPatrick Sunter
A presentation at the 2013 meeting of the UniMelb-based "Transport, Health & Chronic Diseases Research Network", on 13 Nov, 2013 (See http://cwhgs.unimelb.edu.au/knowledge/knowledge
). Talk title:- 'Some Remarks on Issues around Data and Tools for Understanding Public Transport Networks from My PhD Work'.
Similar to Michael | Bikeability Workshop December 2010 (20)
1. Visualising & Modelling Local Movement What We Are Doing in CASA Michael Batty http://www.casa.ucl.ac.uk/ Thursday, December 2 nd , 2010, Copenhagen
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3. What We Do in CASA We are a fairly eclectic group focussed on using formal methods, models, computation in relation to cities – we tend more to the cities side being architects and planners and engineers than to math/computation per se But we have a lot of programmers and applied maths people as RAs and Co-investigators in our projects We work to a strong sub-theme of the complexity sciences We started 15 years ago are interdisciplinary, cross disciplinary but we are embedded as one of 7 units that comprise the Faculty of the Built Environment – i.e. the Bartlett School
4. Modelling Cities: Our models draw from several traditions – primarily social physics which involve things like nonlinear dynamics, scaling, reaction-diffusion and so on, and we operationalise our models using ABM, microsimulation, aggregate econometrics and so on Our focus tends to be at the district level up – i.e. neighbourhoods in cities up to metro regions like Greater London, but with recent extensions to global dynamics There are also pressures in our work to extend down-scale because of our interests in sensing and new data sources
5. Four Broad Areas: Modelling, Visualising, Mapping, and Sensing We have projects in all these areas and I will catalogue these in a minute before I describe a few of them Our core technologies used to be GIS and still are but we extend to all sorts of applications such as CAD, multimedia, Web 2.0 applications and so on I have not mentioned data but we are immersed in large spatial data sets – and have strong interests in open data, public data and so on; obviously in map data, and increasingly in sensing data
6. Our Projects: From Blue Skies to Fairly Applied Modelling: ARCADIA (EPSRC) Climate change in Greater London (Tyndall) SCALE (EPSRC) – the impact of rapid changes in energy costs on movement dynamics in cities (with CS and Transport) Enfolding : Modelling and Visualising Global Dynamics – in trade, migration, development aid, etc. Mechanicity (ERC) – modelling cities and their morphology using allometry and network science, with a focus on energy Mapping : GENeSIS (ESRC) – generative spatial modelling and mapping (with Leeds) using web 2 style mapping – building new kinds
7. of GIS infrastructure for social scientists. This work is extending into sensing and into fine scale pedestrian modelling NeISS – an add-on to GENeSIS from JISC involving mapping Visualising: To an extent, all our projects involve visualisation as this is a major way of reaching out to stakeholders as well as embracing the complexity of our simulations and delivering our science to others Sensing : TOTEM (EPSRC) – tagging objects with codes – building memory into the built environment COSMIC (ERA-Complexity-Net) – a pilot network on spatial dynamics in cities at the fine scale involving sensing
13. The Kremer Collection http://www.thekremercollection.com/ Tooth courtesy of Johan Lundin Biomedical Informatics Research Group Department of Oncology University of Helinski http://www.webmicroscope.net/ http://chs75.harvard.edu/manuscripts/
22. Developing Agent-Based Models for Simulating Movement – Pedestrian Modelling We have looked at three projects: two in CASA on the Notting Hill Carnival and the Covent Garden Entertainment Hub and one from Anders Johansson now in CASA from his time in Dresden and ETHZ with Dirk Helbing Anders is now working on GPS tracing of movements in central London and we are planning some work on building an epidemics model of spreading diseases like the common cold in enclosed transport environments like the Tube system. Here are some of these examples
23. Above: Crowd Scenes and Emergency Vehicles at Hajj and in Notting Hill: Below: Our ABM of the Notting Hill Carnival
25. Ander’s Johannson’s Epidemic Model of Central London based on GPS tracks from Courier Data There is a very nice visualisation of this – animation – at http://www.ajohansson.com/london_epidemics.avi
26. Scraping Data: The London Bikes Experiment Locally called Boris’s Bikes 4200 bikes, 340 stations, access via online registration or by paying on a credit card at the local bike station – so all online data Measuring Online Demand and Supply for Transport (the London Bikes Project)
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30. As yet no records of demand from people logging on, so no management capabilities, but could happen probably from an App based software but maybe from the server