Difference between revisions of "VisualizeR Overview"
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[[visualizeR_Overview| <font color="#FFFFFF">Overview</font>]] | [[visualizeR_Overview| <font color="#FFFFFF">Overview</font>]] | ||
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| + | [[visualizeR_Application| <font color="#FFFFFF">Application</font>]] | ||
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[[File:Crowdfundingov.png|center|500px]] | [[File:Crowdfundingov.png|center|500px]] | ||
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| + | =Abstract= | ||
| + | Crowdfunding is the practice of using small amounts of capital from a relatively large number of individuals to fund a project or venture typically through the Internet. Crowdfunding makes use of the easy accessibility of vast networks of friends, family and colleagues through social media websites like Facebook, Twitter and LinkedIn to get the word out about a new business or campaign and attract investors. Mobile Apps are a popular growing medium along with the above mentioned social media websites for helping campaigns and projects to publicize and seek funding for their work. Campaigns can range anywhere from technology, business, nonprofit, political, charity, commercial, or financing for a startup. With the rise of such online platforms allowing people to easily create campaigns, crowdfunding has emerged as an area that is ripe for research. | ||
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| + | As an area of analysis, crowdfunding has largely featured literature that focused more on predicting the success/failure of campaigns. However, as a field of visualization, the data has relatively been left untapped; most visualizations that exist simply show the accuracy of these prediction algorithms. | ||
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| + | Through this project and application of R and its tools, we set a platform to explore the datasets gathered by the crowdfunding apps for understanding and visualizing patterns between the viewers and investors. The application sets the tone for performing exploratory data analysis (via choropleths and heatmaps and calendar maps) by way of communicating the age group that contributes most or the states that contribute highly on crowd funding projects. The application helps us find specific segments of users who show interest on specific category of project (Health/Environmental/ Technological/ Sports/Politics, etc.) that the app launches/publishes. It helps unleash the user behavior through sunburst charts for various regions/states and help us find the regions that indulge in cautious investing or impulsive funding. Usage of clustering algorithms (k means and parallel coordinates visualization) demonstrated in CFVAR help us segment the users in ways or methods that matter to individual users or corporations for their ongoing as well as upcoming projects. Both researchers of crowdfunding as well as people interested in starting their own campaigns can benefit from such tools as they can utilize these visualizations to make better sense of the data. Because of this emerging domain, the visualizations explored would just be the beginning of what can be an ever-increasing domain of research and analysis for this growing field. | ||
Latest revision as of 11:50, 6 August 2017
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Abstract
Crowdfunding is the practice of using small amounts of capital from a relatively large number of individuals to fund a project or venture typically through the Internet. Crowdfunding makes use of the easy accessibility of vast networks of friends, family and colleagues through social media websites like Facebook, Twitter and LinkedIn to get the word out about a new business or campaign and attract investors. Mobile Apps are a popular growing medium along with the above mentioned social media websites for helping campaigns and projects to publicize and seek funding for their work. Campaigns can range anywhere from technology, business, nonprofit, political, charity, commercial, or financing for a startup. With the rise of such online platforms allowing people to easily create campaigns, crowdfunding has emerged as an area that is ripe for research.
As an area of analysis, crowdfunding has largely featured literature that focused more on predicting the success/failure of campaigns. However, as a field of visualization, the data has relatively been left untapped; most visualizations that exist simply show the accuracy of these prediction algorithms.
Through this project and application of R and its tools, we set a platform to explore the datasets gathered by the crowdfunding apps for understanding and visualizing patterns between the viewers and investors. The application sets the tone for performing exploratory data analysis (via choropleths and heatmaps and calendar maps) by way of communicating the age group that contributes most or the states that contribute highly on crowd funding projects. The application helps us find specific segments of users who show interest on specific category of project (Health/Environmental/ Technological/ Sports/Politics, etc.) that the app launches/publishes. It helps unleash the user behavior through sunburst charts for various regions/states and help us find the regions that indulge in cautious investing or impulsive funding. Usage of clustering algorithms (k means and parallel coordinates visualization) demonstrated in CFVAR help us segment the users in ways or methods that matter to individual users or corporations for their ongoing as well as upcoming projects. Both researchers of crowdfunding as well as people interested in starting their own campaigns can benefit from such tools as they can utilize these visualizations to make better sense of the data. Because of this emerging domain, the visualizations explored would just be the beginning of what can be an ever-increasing domain of research and analysis for this growing field.
