Category: SNA

Modeling the Emergence of Riots: A Geosimulation Approach

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As you might of guessed the paper is about riots but that is not all. In the paper we have a highly detailed cognitive model implemented through the PECS (Physical conditions, Emotional state, Cognitive capabilities, and Social status) framework based around identity theory. The purpose of the model (and paper) is to explore how the unique socioeconomic variables underlying Kibera, a slum in Nairobi, coupled with local interactions of its residents, and the spread of a rumor, may trigger a riot such as those seen in 2007. 
In order to explore this question from the “bottom up” we have developed a novel agent-based model that integrates social network analysis (SNA) and geographic information systems (GIS) for this purpose. In the paper we argue that this integration facilitates the modeling of dynamic social networks created through the agents’ daily interactions. The GIS is used to develop a realistic environment for agents to move and interact that includes a road network and points of interest which impact their daily lives.
Below is the abstract and a summary of its highlights in order to give you a sense of what our research contribution is. In addition to this we also provide some images either from the paper itself or the from Overview, Design Concepts, and Details (ODD) protocol. Finally at the bottom of this post you can see one of the simulation runs, details of where the model can be downloaded along with the full citation.

Paper Abstract:

Immediately after the 2007 Kenyan election results were announced, the country erupted in protest. Riots were particularly severe in Kibera, an informal settlement located within the nations capital, Nairobi. Through the lens of geosimulation, an agent-based model is integrated with social network analysis and geographic information systems to explore how the environment and local interactions underlying Kibera, combined with an external trigger, such as a rumor, led to the emergence of riots. We ground our model on empirical data of Kibera’s geospatial landscape, heterogeneous population, and daily activities of its residents. In order to effectively construct a model of riots, however, we must have an understanding of human behavior, especially that related to an individual’s need for identity and the role rumors play on a person’s decision to riot. This provided the foundation to develop the agents’ cognitive model, which created a feedback system between the agents’ activities in physical space and interactions in social space. Results showed that youth are more susceptible to rioting. Systematically increasing education and employment opportunities, however, did not have simple linear effects on rioting, or even on quality of life with respect to income and activities. The situation is more complex. By linking agent-based modeling, social network analysis, and geographic information systems we were able to develop a cognitive framework for the agents, better represent human behavior by modeling the interactions that occur over both physical and social space, and capture the nonlinear, reinforcing nature of the emergence and dissolution of riots.

Keywords: agent-based modeling; geographic information systems; social network analysis; riots; social influence; rumor propagation.

Paper Highlights:

  • An agent-based model integrates geographic information systems and social network analysis to model the emergence of riots. 
  • The physical environment and agent attributes are developed using empirical data, including GIS and socioeconomic data. 
  • The agent’s cognitive framework allowed for modeling their activities in physical space and interactions in social space. 
  • Through the integration of the three techniques, we were able to capture the complex, nonlinear nature of riots. 
  • Results show that youth are most vulnerable, and, increasing education and employment has nonlinear affects on rioting.

The high-level UML diagram of the model
A high-level representation of the model’s agent behavior incorporated into the PECS framework

An example of the evolution of social networks of ten Residents across the first two days of a simulation run.

The movie below shows the agent-based model which explores ethnic clashes in the Kenyan slum. The environment is made up of households, businesses, and service facilities (such data comes from OpenStreetMap). Agents within the model use a transportation network to move across the environment. As agents go about their daily activities, they interact with other agents – building out an evolving social network. Agents seek to meet their identity standard. Failure to reach their identity standard increases the agents frustration which can lead to an aggressive response (i.e. moving from blue to red color) such as rioting.

As with many of our models, we provide the data, model code and detailed model description in the form of the ODD protocol for others to use, learn more or to extend. Click here for more information.

Full Reference:

Pires, B. and Crooks, A.T. (2017), Modeling the Emergence of Riots: A Geosimulation Approach, Computers, Environment and Urban Systems, 61: 66-80. (pdf)

As normal, any thoughts or comments are most appreciated.
 

Continue reading »
ABM, ABM Examples, GeoMason, GIS, MASON, Slums, SNA, Social Networks

Modeling the Emergence of Riots: A Geosimulation Approach

Posted on by
As you might of guessed the paper is about riots but that is not all. In the paper we have a highly detailed cognitive model implemented through the PECS (Physical conditions, Emotional state, Cognitive capabilities, and Social status) framework based around identity theory. The purpose of the model (and paper) is to explore how the unique socioeconomic variables underlying Kibera, a slum in Nairobi, coupled with local interactions of its residents, and the spread of a rumor, may trigger a riot such as those seen in 2007. 
In order to explore this question from the “bottom up” we have developed a novel agent-based model that integrates social network analysis (SNA) and geographic information systems (GIS) for this purpose. In the paper we argue that this integration facilitates the modeling of dynamic social networks created through the agents’ daily interactions. The GIS is used to develop a realistic environment for agents to move and interact that includes a road network and points of interest which impact their daily lives.
Below is the abstract and a summary of its highlights in order to give you a sense of what our research contribution is. In addition to this we also provide some images either from the paper itself or the from Overview, Design Concepts, and Details (ODD) protocol. Finally at the bottom of this post you can see one of the simulation runs, details of where the model can be downloaded along with the full citation.

Paper Abstract:

Immediately after the 2007 Kenyan election results were announced, the country erupted in protest. Riots were particularly severe in Kibera, an informal settlement located within the nations capital, Nairobi. Through the lens of geosimulation, an agent-based model is integrated with social network analysis and geographic information systems to explore how the environment and local interactions underlying Kibera, combined with an external trigger, such as a rumor, led to the emergence of riots. We ground our model on empirical data of Kibera’s geospatial landscape, heterogeneous population, and daily activities of its residents. In order to effectively construct a model of riots, however, we must have an understanding of human behavior, especially that related to an individual’s need for identity and the role rumors play on a person’s decision to riot. This provided the foundation to develop the agents’ cognitive model, which created a feedback system between the agents’ activities in physical space and interactions in social space. Results showed that youth are more susceptible to rioting. Systematically increasing education and employment opportunities, however, did not have simple linear effects on rioting, or even on quality of life with respect to income and activities. The situation is more complex. By linking agent-based modeling, social network analysis, and geographic information systems we were able to develop a cognitive framework for the agents, better represent human behavior by modeling the interactions that occur over both physical and social space, and capture the nonlinear, reinforcing nature of the emergence and dissolution of riots.

Keywords: agent-based modeling; geographic information systems; social network analysis; riots; social influence; rumor propagation.

Paper Highlights:

  • An agent-based model integrates geographic information systems and social network analysis to model the emergence of riots. 
  • The physical environment and agent attributes are developed using empirical data, including GIS and socioeconomic data. 
  • The agent’s cognitive framework allowed for modeling their activities in physical space and interactions in social space. 
  • Through the integration of the three techniques, we were able to capture the complex, nonlinear nature of riots. 
  • Results show that youth are most vulnerable, and, increasing education and employment has nonlinear affects on rioting.

The high-level UML diagram of the model
A high-level representation of the model’s agent behavior incorporated into the PECS framework

An example of the evolution of social networks of ten Residents across the first two days of a simulation run.

The movie below shows the agent-based model which explores ethnic clashes in the Kenyan slum. The environment is made up of households, businesses, and service facilities (such data comes from OpenStreetMap). Agents within the model use a transportation network to move across the environment. As agents go about their daily activities, they interact with other agents – building out an evolving social network. Agents seek to meet their identity standard. Failure to reach their identity standard increases the agents frustration which can lead to an aggressive response (i.e. moving from blue to red color) such as rioting.

As with many of our models, we provide the data, model code and detailed model description in the form of the ODD protocol for others to use, learn more or to extend. Click here for more information.

Full Reference:

Pires, B. and Crooks, A.T. (2017), Modeling the Emergence of Riots: A Geosimulation Approach, Computers, Environment and Urban Systems, 61: 66-80. (pdf)

As normal, any thoughts or comments are most appreciated.
 

Continue reading »
ABM, ABM Examples, GeoMason, GIS, MASON, Slums, SNA, Social Networks

New Paper: Generating and Analyzing Spatial Social Networks

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We recently had a paper entitled “Generating and Analyzing Spatial Social Networks” accepted in Computational and Mathematical Organization Theory. In the paper we proposed and explored spatial versions of three well known networks, that of the Erdös-Rényi, Watts-Strogatz, and Barabási-Albert. Further details about the paper can be seen in the abstract below:

“In this paper, we propose a class of models for generating spatial versions of three classic networks: Erdös-Rényi (ER), Watts-Strogatz (WS), and Barabási-Albert (BA). We assume that nodes have geographical coordinates, are uniformly distributed over an m × m Cartesian space, and long-distance connections are penalized. Our computational results show higher clustering coefficient, assortativity, and transitivity in all three spatial networks, and imperfect power law degree distribution in the BA network. Furthermore, we analyze a special case with geographically clustered coordinates, resembling real human communities, in which points are clustered over k centers. Comparison between the uniformly and geographically clustered versions of the proposed spatial networks show an increase in values of the clustering coefficient, assortativity, and transitivity, and a lognormal degree distribution for spatially clustered ER, taller degree distribution and higher average path length for spatially clustered WS, and higher clustering coefficient and transitivity for the spatially clustered BA networks.”

Keywords: Spatial social networks, Network properties, Random network, Small-world network, Scale-free network.

The Python code for the models can be found here.

Full Reference: 

Alizadeh, M., Cioffi-Revilla, C. and Crooks, A. (2016), Generating and Analyzing Spatial Social Networks. Computational and Mathematical Organization Theory, DOI: 10.1007/s10588-016-9232-2 (pdf)



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Geography, SNA, Social Networks

New Paper: Generating and Analyzing Spatial Social Networks

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We recently had a paper entitled “Generating and Analyzing Spatial Social Networks” accepted in Computational and Mathematical Organization Theory. In the paper we proposed and explored spatial versions of three well known networks, that of the Erdös-Rényi, Watts-Strogatz, and Barabási-Albert. Further details about the paper can be seen in the abstract below:

“In this paper, we propose a class of models for generating spatial versions of three classic networks: Erdös-Rényi (ER), Watts-Strogatz (WS), and Barabási-Albert (BA). We assume that nodes have geographical coordinates, are uniformly distributed over an m × m Cartesian space, and long-distance connections are penalized. Our computational results show higher clustering coefficient, assortativity, and transitivity in all three spatial networks, and imperfect power law degree distribution in the BA network. Furthermore, we analyze a special case with geographically clustered coordinates, resembling real human communities, in which points are clustered over k centers. Comparison between the uniformly and geographically clustered versions of the proposed spatial networks show an increase in values of the clustering coefficient, assortativity, and transitivity, and a lognormal degree distribution for spatially clustered ER, taller degree distribution and higher average path length for spatially clustered WS, and higher clustering coefficient and transitivity for the spatially clustered BA networks.”

Keywords: Spatial social networks, Network properties, Random network, Small-world network, Scale-free network.

The Python code for the models can be found here.

Full Reference: 

Alizadeh, M., Cioffi-Revilla, C. and Crooks, A. (2016), Generating and Analyzing Spatial Social Networks. Computational and Mathematical Organization Theory, DOI: 10.1007/s10588-016-9232-2 (pdf)



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Geography, SNA, Social Networks

Linking Cyber and Physical Spaces

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We have just published a new paper in  Computers, Environment and Urban Systems entitled “Linking Cyber and Physical Spaces Through Community Detection And Clustering in Social Media Feeds“. In the paper we explore how geosocial media is providing us with  a new social communication avenue and a novel source of geosocial information. 
In particular, we discuss the notion of physical presence within social media and its importance for exploring the relation between the cyber and the physical domains. We discuss how communities and groups can be detected in both the cyber and physical space, and how they can be processed to form a ‘hybrid’ geosocial view of communities using social network analysis, community detection (the Louvain method) and DenStream. To showcase these concepts and their benefits, we present the analysis of two case studies that make use of Twitter data associated with two different types of events: a planned activity during the Occupy Wall Street (OWS) Day of Action (November 17th, 2011), and the response to the Boston Marathon Bombing (April 15, 2013). We conclude with a summary and outlook. Below is the abstract of the paper:

Over the last decade we have witnessed a significant growth in the use of social media. Interactions within their context lead to the establishment of groups that function at the intersection of the physical and cyber spaces, and as such represent hybrid communities. Gaining a better understanding of how information flows in these hybrid communities is a substantial scientific challenge with significant implications on our ability to better harness crowd-contributed content. This paper addresses this challenge by studying how information propagates and evolves over time at the intersection of the physical and cyber spaces. By analyzing the spatial footprint, social network structure, and content in both physical and cyber spaces we advance our understanding of the information propagation mechanisms in social media. The utility of this approach is demonstrated in two real-world case studies, the first reflecting a planned event (the Occupy Wall Street – OWS – movement’s Day of Action in November 2011), and the second reflecting an unexpected disaster (the Boston Marathon bombing in April 2013). Our findings highlight the intricate nature of the propagation and evolution of information both within and across cyber and physical spaces, as well as the role of hybrid networks in the exchange of information between these spaces.

Research highlights include:

    • Our analysis includes two major events as captured in Twitter.
    • The themes in cyber and physical communities tend to converge over time.
    • Messages among physical space users are more consistent at the onset of the event.
    • Geolocated users are consuming information more than they produce.

      Below are some of the images from the paper. Specifically the first image is how one can think of the relationships between physical and cyber spaces.  The next image provides an overview Our geosocial analysis framework for examining cyber and physical communities.

      Our Geosocial analysis framework

      In the figure below we show an example of using DenStream for spatiotemporal clustering and how the process can capture the protest activities that were planned for the Occupy Wall Street movement’s Day of Action. Each dot corresponds to the originating location of a geolocated tweet; The color of each point indicates the time of the corresponding tweet, ranging from dark blue (early morning, 0) to dark red (late night, 1). While the circles represent a specific spatiotemporal cluster. For example the circle labeled A marked the start of the day where people congregated around Wall Street while circle labeled C shows a cluster at Foley Square.
      Physical space groups identified in the lower Manhattan area. Each dot corresponds to the originating location of a geolocated tweet; The color of each point indicates the time of the corresponding tweet, ranging from dark blue (early morning, 0) to dark red (late night, 1).
      While in the figure below we show one example of linking the cyber and physical communities. Specifically in (a), the top five communities (node degree > 100) in the cyber space retweet network (each community is designated by one color) are shown; (b) shows the physical space groups; and (c) shows the resulting  hybrid meta-network where the connections between physical groups (P nodes), and cyber space communities (C nodes) are shown.

      We hope you enjoy the paper.

      Full Reference:

      Croitoru, A., Wayant, N., Crooks, A.T., Radzikowski, J. and Stefanidis, A. (2014), Linking Cyber and Physical Spaces Through Community Detection And Clustering in Social Media Feeds, Computers, Environment and Urban Systems.  doi:10.1016/j.compenvurbsys.2014.11.002

      Continue reading »
      GeoSocial, GIS, SNA, social media

      Linking Cyber and Physical Spaces

      Posted on by
      We have just published a new paper in  Computers, Environment and Urban Systems entitled “Linking Cyber and Physical Spaces Through Community Detection And Clustering in Social Media Feeds“. In the paper we explore how geosocial media is providing us with  a new social communication avenue and a novel source of geosocial information. 
      In particular, we discuss the notion of physical presence within social media and its importance for exploring the relation between the cyber and the physical domains. We discuss how communities and groups can be detected in both the cyber and physical space, and how they can be processed to form a ‘hybrid’ geosocial view of communities using social network analysis, community detection (the Louvain method) and DenStream. To showcase these concepts and their benefits, we present the analysis of two case studies that make use of Twitter data associated with two different types of events: a planned activity during the Occupy Wall Street (OWS) Day of Action (November 17th, 2011), and the response to the Boston Marathon Bombing (April 15, 2013). We conclude with a summary and outlook. Below is the abstract of the paper:

      Over the last decade we have witnessed a significant growth in the use of social media. Interactions within their context lead to the establishment of groups that function at the intersection of the physical and cyber spaces, and as such represent hybrid communities. Gaining a better understanding of how information flows in these hybrid communities is a substantial scientific challenge with significant implications on our ability to better harness crowd-contributed content. This paper addresses this challenge by studying how information propagates and evolves over time at the intersection of the physical and cyber spaces. By analyzing the spatial footprint, social network structure, and content in both physical and cyber spaces we advance our understanding of the information propagation mechanisms in social media. The utility of this approach is demonstrated in two real-world case studies, the first reflecting a planned event (the Occupy Wall Street – OWS – movement’s Day of Action in November 2011), and the second reflecting an unexpected disaster (the Boston Marathon bombing in April 2013). Our findings highlight the intricate nature of the propagation and evolution of information both within and across cyber and physical spaces, as well as the role of hybrid networks in the exchange of information between these spaces.

      Research highlights include:

        • Our analysis includes two major events as captured in Twitter.
        • The themes in cyber and physical communities tend to converge over time.
        • Messages among physical space users are more consistent at the onset of the event.
        • Geolocated users are consuming information more than they produce.

          Below are some of the images from the paper. Specifically the first image is how one can think of the relationships between physical and cyber spaces.  The next image provides an overview Our geosocial analysis framework for examining cyber and physical communities.

          Our Geosocial analysis framework

          In the figure below we show an example of using DenStream for spatiotemporal clustering and how the process can capture the protest activities that were planned for the Occupy Wall Street movement’s Day of Action. Each dot corresponds to the originating location of a geolocated tweet; The color of each point indicates the time of the corresponding tweet, ranging from dark blue (early morning, 0) to dark red (late night, 1). While the circles represent a specific spatiotemporal cluster. For example the circle labeled A marked the start of the day where people congregated around Wall Street while circle labeled C shows a cluster at Foley Square.
          Physical space groups identified in the lower Manhattan area. Each dot corresponds to the originating location of a geolocated tweet; The color of each point indicates the time of the corresponding tweet, ranging from dark blue (early morning, 0) to dark red (late night, 1).
          While in the figure below we show one example of linking the cyber and physical communities. Specifically in (a), the top five communities (node degree > 100) in the cyber space retweet network (each community is designated by one color) are shown; (b) shows the physical space groups; and (c) shows the resulting  hybrid meta-network where the connections between physical groups (P nodes), and cyber space communities (C nodes) are shown.

          We hope you enjoy the paper.

          Full Reference:

          Croitoru, A., Wayant, N., Crooks, A.T., Radzikowski, J. and Stefanidis, A. (2014), Linking Cyber and Physical Spaces Through Community Detection And Clustering in Social Media Feeds, Computers, Environment and Urban Systems.  doi:10.1016/j.compenvurbsys.2014.11.002

          Continue reading »
          GeoSocial, GIS, SNA, social media

          IR: State-Driven and Citizen-Driven Networks

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          Our work exploring how social media can be used to study events around the world has resulted in a new publication in the  Social Science Computer Review entitled “International Relations: State-Driven and Citizen-Driven Networks.” In essence what we are attempting to do is compare traditional international relations (e.g. from the United Nations General Assembly voting patterns) to those arising from the bottom up interactions (i.e from people on the ground). The abstract of the paper is below along with some of the images that accompany the paper.

          The international community can be viewed as a set of networks, manifested through various transnational activities. The availability of longitudinal datasets such as international arms trades and United Nations General Assembly (UNGA) allows for the study of state-driven interactions over time. In parallel to this top-down approach, the recent emergence of social media is fostering a bottom-up and citizen driven avenue for international relations (IR). The comparison of these two network types offers a new lens to study the alignment between states and their people. This paper presents a network-driven approach to analyze communities as they are established through different forms of bottom-up (e.g. Twitter) and top-down (e.g. UNGA voting records and international arms trade records) IR. By constructing and comparing different network communities we were able to evaluate the similarities between state-driven and citizen-driven networks. In order to validate our approach we identified communities in UNGA voting records during and after the Cold War. Our approach showed that the similarity between UNGA communities during and after the Cold War was 0.55 and 0.81 respectively (in a 0-1 scale). To explore the state- versus citizen-driven interactions we focused on the recent events within Syria within Twitter over a sample period of one month. The analysis of these data show a clear misalignment (0.25) between citizen-formed international networks and the ones established by the Syrian government (e.g. through its UNGA voting patterns).

          Full reference:

          Crooks, A.T., Masad, D., Croitoru, A., Cotnoir, A., Stefanidis, A. and Radzikowski, J. (2013), International Relations: State-Driven and Citizen-Driven Networks, Social Science Computer Review. DOI:10.1177/0894439313506851

          If you don’t have access to Social Science Computer Review, send us an email and we can send you an early version of the paper. This is also only part of our work on using multiple networks to explore international relations. One can of course also explore the networks in more detail. For example in the figure below we plot the actual transfer of arms between states during the 2001 and 2011 period. One can clearly see how different states are connected with Syria however, Russia has connections to many states.

          Arms transfers
          Or if we explore Twitter hastags and add an edge between any pair of hashtags when they are used in the same tweet we can explore an emergent ontology of topic labels users associate with each other. For example, the #Allepo hashtag is associated with other hashtags which appear to local events, including “#civilian”, “#airstrike”, “#hunger”, “#pictures”, many of which are only connected to the #Aleppo hashtag as shown below.

          Continue reading »
          GeoSocial, GIS, SNA, social media, Social network analysis, twitter

          IR: State-Driven and Citizen-Driven Networks

          Posted on by
          Our work exploring how social media can be used to study events around the world has resulted in a new publication in the  Social Science Computer Review entitled “International Relations: State-Driven and Citizen-Driven Networks.” In essence what we are attempting to do is compare traditional international relations (e.g. from the United Nations General Assembly voting patterns) to those arising from the bottom up interactions (i.e from people on the ground). The abstract of the paper is below along with some of the images that accompany the paper.

          The international community can be viewed as a set of networks, manifested through various transnational activities. The availability of longitudinal datasets such as international arms trades and United Nations General Assembly (UNGA) allows for the study of state-driven interactions over time. In parallel to this top-down approach, the recent emergence of social media is fostering a bottom-up and citizen driven avenue for international relations (IR). The comparison of these two network types offers a new lens to study the alignment between states and their people. This paper presents a network-driven approach to analyze communities as they are established through different forms of bottom-up (e.g. Twitter) and top-down (e.g. UNGA voting records and international arms trade records) IR. By constructing and comparing different network communities we were able to evaluate the similarities between state-driven and citizen-driven networks. In order to validate our approach we identified communities in UNGA voting records during and after the Cold War. Our approach showed that the similarity between UNGA communities during and after the Cold War was 0.55 and 0.81 respectively (in a 0-1 scale). To explore the state- versus citizen-driven interactions we focused on the recent events within Syria within Twitter over a sample period of one month. The analysis of these data show a clear misalignment (0.25) between citizen-formed international networks and the ones established by the Syrian government (e.g. through its UNGA voting patterns).

          Full reference:

          Crooks, A.T., Masad, D., Croitoru, A., Cotnoir, A., Stefanidis, A. and Radzikowski, J. (2013), International Relations: State-Driven and Citizen-Driven Networks, Social Science Computer Review. DOI:10.1177/0894439313506851

          If you don’t have access to Social Science Computer Review, send us an email and we can send you an early version of the paper. This is also only part of our work on using multiple networks to explore international relations. One can of course also explore the networks in more detail. For example in the figure below we plot the actual transfer of arms between states during the 2001 and 2011 period. One can clearly see how different states are connected with Syria however, Russia has connections to many states.

          Arms transfers
          Or if we explore Twitter hastags and add an edge between any pair of hashtags when they are used in the same tweet we can explore an emergent ontology of topic labels users associate with each other. For example, the #Allepo hashtag is associated with other hashtags which appear to local events, including “#civilian”, “#airstrike”, “#hunger”, “#pictures”, many of which are only connected to the #Aleppo hashtag as shown below.

          Continue reading »
          GeoSocial, GIS, SNA, social media, Social network analysis, twitter

          ElectionGauge

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          A project we have been working on at GMU called ElectionGauge has now gone live (although still under development). The idea about the project is tie geo-spatial analysis, linguistic analysis, and social network analysis to analyze Twitter responses to the upcoming US elections in real time with the aim of predicting election results.
          One question we are exploring is  do the tweets of users match the speech of candidates? For example, as Maksim Tsvetovat, one of the co-founders says “repeal Obamacare” might identify you as Tea Partier, while “legalize marijuana” puts you in Ron Paul’s camp. While still in beta, below is snapshot from the site:

          Find out more see: @maksim2042, @JackieKazil & @ElectionGauge or at Tech Cocktail

          Continue reading »
          SNA, twitter

          ElectionGauge

          Posted on by
          A project we have been working on at GMU called ElectionGauge has now gone live (although still under development). The idea about the project is tie geo-spatial analysis, linguistic analysis, and social network analysis to analyze Twitter responses to the upcoming US elections in real time with the aim of predicting election results.
          One question we are exploring is  do the tweets of users match the speech of candidates? For example, as Maksim Tsvetovat, one of the co-founders says “repeal Obamacare” might identify you as Tea Partier, while “legalize marijuana” puts you in Ron Paul’s camp. While still in beta, below is snapshot from the site:

          Find out more see: @maksim2042, @JackieKazil & @ElectionGauge or at Tech Cocktail

          Continue reading »
          SNA, twitter

          Harvesting ambient geospatial information from social media feeds

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          A paper I  recently co-authored with Anthony Stefanidis and Jacek Radzikowski from George Mason University entitled “Harvesting ambient geospatial information from social media feeds” is now available in  GeoJournal.   The abstract …

          Continue reading »
          AGI, ambient geospatial information, CSS, Papers, SNA, social media, Social network analysis, VGI, Volunteered Geographic Information

          Harvesting ambient geospatial information from social media feeds

          Posted on by

          A paper I  recently co-authored with Anthony Stefanidis and Jacek Radzikowski from George Mason University entitled “Harvesting ambient geospatial information from social media feeds” is now available in  GeoJournal.   The abstract …

          Continue reading »
          AGI, ambient geospatial information, CSS, Papers, SNA, social media, Social network analysis, VGI, Volunteered Geographic Information