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Geosense an Open Publishing Platform for Visualization, Social Sharing, and Analysis of Geospatial Data

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Geosense an Open Publishing Platform for Visualization, Social Sharing, and Analysis of Geospatial Data GeoSense An open publishing platform for visualization, social sharing, and analysis of geospatial data. ARCHNES Anthony DeVincenzi TT I T B.F.A. Visual Communication, Seattle Art Institute 2007 Submitted to the Program in Media Arts and Sciences, Shlf A- hi dlI c, oo~ o rcecur an- annng11, in partial fulfillment of the requirements for the degree of Master of Science in Media Arts and Sciences at the Massachusetts Institute of Technology June 2012 @ 2012 Massachusetts Institute of Technology. All rights reserved Aut or Anthony DeVincenzi Program in Media Arts and Sciences May 11, 2012 Certified by Dr. Hiroshi Ishii Jerome B. Wiesner Professor of Media Arts and Sciences Associate Director, MIT Media Lab Program in Media Arts and Sciences Accepted by Dr. Mitchel Resnick Chairperson, Departmental Committee on Graduate Students Program in Media Arts and Sciences GeoSense An open publishing platform for visualization, social sharing, and analysis of geospatial data. Anthony DeVincenzi ;~ Thesis Supervisor Dr. Hiroshi Ishii Jerome B. Wiesner Professor of Media Arts and Sciences Associate Director, MIT Media Lab Program in Media and Sciences Thesis Reader Cesar A. Hidalgo Assistant Professor, MIT Media Lab {' 34> Thesis Reader Joi Ito Director, MIT Media Lab Acknowledgments THANK YOU, Hiroshi, my advisor, for allowing me to diverge greatly from our group's pri- mary area of research to investigate an area I believe to be strikingly mean- ingful; for no holds barred in critique, and providing endless insight. The Tangible Media Group, my second family, who adopted me as a designer and allowed me to play pretend engineer. Samuel Luescher, for co-authoring GeoSense alongside me. My thesis readers Joichi Ito and Cesar Hidalgo for providing feedback, inspi- ration, and guidance over the course of this work. The people of Safecast, who support an idea larger than what any one man could accomplish. You are truly inspiring. Divid Lakatos, and Matthew Blackshaw, for our many adventurous projects to date, and for those to come in the near future. Mom and Dad, for allowing me to explore my passions despite how inappli- cable they may have seemed at times. My family, and Jessica for loving me. I learn from your patience. My friends in Seattle, and around the world. TABLE OF CONTENTS Introduction 13 Related Work 18 Contemporaries 20 Safecast 23 A call for help 23 Keeping quarters 24 Application Design 27 Balancing simplicity and complexity 27 Data mobility 28 Summary of system 28 Second order observation 30 Data features 30 Development timeline 31 Design Theory 33 Geovisualization 33 Aesthetics 36 Spatial-temporal narratives 39 Process 42 Concept 43 Safecast worldmap (V1) 45 Generalizing the platform (V2) 48 User interfacefor data management 49 GeoSense (V3) 50 9 Spatial comments and chat 52 Continued:Beyond the screen 53 Technical Design 55 Server structure 56 Amazon EC2 56 Ubuntu 56 Satellite & satellite API 56 Architecture 56 GeoSense Database 57 Data import 58 Aggregation and reduction through MapReduce 58 Spatial indexing and grid queries 59 TeamdataDatabase 61 Application Structure 61 Views 61 Models 62 Collections 62 ExternalLibraries 62 Challenges 65 Data purity 65 Performance 66 Scale 67 Custom instances 67 Use Cases 69 Safecast 69 Sourcemap 71 The Lace Race 71 10 Results 72 Future Work 74 Tile servers 74 Expanded visualization types 75 Models & mechanistic explanations 75 Boolean conditions and spatially bound alerts 75 Conclusion 78 References 81 Appendix 87 Tablet AR installation 87 11 12 Introduction Throughout this document we refer to two projects: GeoSense, a visualization platform, and Safecast [1], a sensing and data collection organization. Their differences will be described at length as well as their commonality and shared resources. ONWARD - Geovisualization is a common form of information visualization, or scientific data visualization that when combined with visual pattern recognition allows for increased human understanding in effort to enhance the decision making process around a given view of data. [2] Geospatial data has become abundant, and so have the many sensors that we use to collect it. With over 1.2 billion web and GPS enabled devices in our pockets [3], the amount of geotagged meta data ranging from tweets to photos has skyrocketed to enormous proportions. As more data becomes coupled with geospatial coordinates the intrinsic relationship between the meaning of the data and the place-in-space from where it came can be visual- ized, observed, and analyzed to inform decision making processes. However, this poses a problem as growing amounts of data can become more and more difficult to parse and understand. 13 Today, the tools available for geospatial mapping remain highly spe- cialized with significant technical overhead often outweighing the capabili- ties of the user. We use maps to codify the physical existence of immaterial media and without accessible tools for visualization, the meaning of data is lost in the columns and rows of spreadsheets. Further, the inability to quickly and simply create and share geovisualizations in a lightweight manner has slowed the evolution of sharing and collaboration in GIS [4]. How could a community, a university, or an entire industry benefit from having the complexity of geospatial data visualization reduced to that of email, or a single tweet? To be more specific, what if we could seamlessly share and engage with social features such as comments and live interaction around geospatial data? We believe that empowering users with the tools necessary to construct visual and social narratives around contextual data will enhance their collective ability to respond to current events while simultane- ously planning for the future. To achieve this we must first build a platform that can interpret the many disparate forms of data and enable them to co-exist in a single unified visualization. Without this tool, our data and voices are left in singular silos - never able to engage and interact with the voices of many. The visualization may take a number of forms, two or three-dimensional, varied in aesthetics per the author's discretion yet constrained within a sandbox as to guide the user - in short, not too much control, but not too little. A simple interface for sharing and socializing the new geovisualization invites multiuser collabora- tion, where each user may contribute and discuss the current datasets; sup- porting the claim that the shared knowledge of many users is often more valuable than that of one [5]. Finally, data pertaining to user interaction in- volving comments, tweets, and physical location may be aggregated to create a second order dataset which in turn may be incorporated into the visualiza- tion for communal behavior analysis. GeoSense aims to provide such a tool, where the user can perform tasks of both the visual artist and data analyst all while contributing to the shared cognition and collective intelligence of a broader community. Geo- 14 Sense is an easy-to-use web based platform for the organization and upload of multiple datasets, a framework platform for 2D and 3D visualization, as well as a suite of social and analysis tools. GeoSense explores generating visual correlation models based on data layering and the aggregate of community analysis in lieu of unified theories, or known mechanistic explanations. After the 311 disasters in Japan involving the Thhoku earthquake, tsunami, and Fukushima Daiichi reactor meltdown, the community was left with little information around the outcome of the crisis. The public struggled to obtain answers to even the most basic questions: "Is it safe for me to stay in my home?" and "Is my food safe to eat?" Thousands rose to aid, and amongst the responders was Safecast, an independently organized crowd-sourced mapping network. Despite the great amount of information and data that was collected, there was no clear path towards displaying, juxtaposing, and dis- cussing the multivariate sources of critical information. GeoSense was founded to support the efforts of Safecast and the many communities of Ja- pan. 15 16 17 Related Work We have, for hundreds of years, refined our use of visual language in the art of data visualization. As early as the 18th century men such as Joseph Priestley, an English theologist and academic had begun exploring the graphical repre- sentation of statistical methods through what is believed to be one of the ear- liest implementations of a timeline; designed to illustrate the contemporane- ity of ancient philosophers and statesman [44]. During a similar time William Playfair, a contemporary of Priestly, debuted what are believed to be the first known instances of bar and pie charts in his two books The Commercial and Political Atlas, and Statistical Breviary respectively. These early exploration laid a foundation upon which nearly three hundred years of related work has been conducted. In more contemporary times, an enumerable amount of work has been done in the field of data visualization, much of which stems from the foundational work of Edward Tufte and his many visual definitions described in "Visual Explanations" [6]. Tufte's seminal work in visual explanation and analysis has provided the foundation for an even wider field of informational graphic design: a notable trend covering a massive spectrum of content rang- ing from visualizations for geospatial data [7] to social and emotional observa- tions through data analysis [8]. 18 Exports and Imports to and from DENMARK Se NORWAY from r/oo TO178Q The .Bottom ise is dsqd nt 1arrs the Ryht- hand her bzto L.QOOO eark One of the first time series graphs: William Playfair'strade-balance time-series chart,pub- lished in PoliticalAtlas, 1786 In the area of visualization for geospatial applications much work has been done by the GIS community to provide tools which allow for the exploration and visualization of location based datasets. Of these, Google Earth [9] and NASA World Wind [10] have been widely adopted as platforms for plotting sets of data ranging from tracking glacier footprints [11] to the displacement and distribution of refugees located in remote areas of the world [12].
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