{"689960":{"#nid":"689960","#data":{"type":"event","title":"PhD Defense by Sichen Jin","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E \u003Cem\u003EBridging Spatial and Social Network Analysis through Visual Analytics\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E\u0026nbsp;Monday, May 11, 2026\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E\u0026nbsp;4:30\u20136:30 PM Eastern time (U.S.)\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELocation:\u0026nbsp;\u003C\/strong\u003ETechnology Square Research Building (\u003Ca href=\u0022https:\/\/nam12.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fmaps.app.goo.gl%2Fi8yusVJT3cryf5yC7\u0026amp;data=05%7C02%7Ctm186%40gtvault.onmicrosoft.com%7Cd28854b9c817485399e808dea08fd6ff%7C482198bbae7b4b258b7a6d7f32faa083%7C1%7C0%7C639124736818490949%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C\u0026amp;sdata=eIFtmA4rwcYB3Eut2yHKkrW5ucEP8uZjQ%2BIJmsU1ggo%3D\u0026amp;reserved=0\u0022 title=\u0022https:\/\/maps.app.goo.gl\/i8yusVJT3cryf5yC7\u0022\u003ETSRB\u003C\/a\u003E)\u0026nbsp;Room 334 (VIS Lab)\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EVirtual Meeting (hybrid):\u003C\/strong\u003E\u0026nbsp;\u003Ca href=\u0022https:\/\/nam12.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fgatech.zoom.us%2Fj%2F6902674214%3Fpwd%3DWXgrZ1RkOVlhaWhUci83R1h0UER1QT09\u0026amp;data=05%7C02%7Ctm186%40gtvault.onmicrosoft.com%7Cd28854b9c817485399e808dea08fd6ff%7C482198bbae7b4b258b7a6d7f32faa083%7C1%7C0%7C639124736818531092%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C\u0026amp;sdata=mhTF1qaUXjqKjhd1b0WcPCiaCFcLJzvrgWGDg9JjO60%3D\u0026amp;reserved=0\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/6902674214?pwd=WXgrZ1RkOVlhaWhUci83R1h0UER1QT09\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ESichen Jin\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EPh.D. Candidate in Computer Science\u003C\/p\u003E\u003Cp\u003ESchool of Interactive Computing, College of Computing\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Clio Andris (Advisor) - School of Interactive Computing, School of City \u0026amp; Regional Planning, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Alex Endert - School of Interactive Computing, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. John Stasko - School of Interactive Computing, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Yalong Yang\u0026nbsp;- School of Interactive Computing, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Shrirang Abhyankar - Principal - AI \u0026amp; Optimization, The Electric Reliability Council of Texas\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003Cbr\u003ESpatial social networks (SSNs) represent social relationships embedded in geographic space, where nodes and edges are associated with geographic locations. Spatial social network analysis (SSNA) requires methods from both social network analysis (SNA) and geographic information systems (GIS), with applications in domains such as criminal network investigation, geosocial science, epidemiology, transportation and mobility analysis, urban planning, and location-based services. However, existing tools and workflows remain fragmented: SNA often ignores the influence of underlying geographic context, while GIS tools lack support for modeling relational structures. As a result, analysts often rely on disconnected pipelines that hinder effective SSNA.\u003C\/p\u003E\u003Cp\u003EThis dissertation addresses these challenges by investigating how visual analytics can bridge SNA and GIS for integrated SSNA through design studies, long-term empirical evaluation, and pedagogical implementation. These research goals and contributions are organized into four main thrusts:\u003C\/p\u003E\u003Cp\u003E(1) \u003Cstrong\u003EA visual analytics system for integrated SSNA.\u003C\/strong\u003E We present SNoMaN, an interactive visual analytic system that supports simultaneous exploration of network and geospatial contexts through coordinated views, interactions, and dynamically computed spatial\u2013network metrics. The system enables users to examine how spatial distributions relate to network structures, such as identifying whether densely connected communities are geographically clustered or dispersed. It also provides interactive visualizations for exploring relationships between geographic metrics (e.g., Euclidean distance, spatial dispersion, and average connection distance) and network metrics (e.g., degree, betweenness and closeness centralities, network density, and shortest path length), as well as for identifying anomalies.\u003C\/p\u003E\u003Cp\u003E(2) \u003Cstrong\u003EUncovering interdisciplinary needs and challenges.\u003C\/strong\u003E We conducted a workshop-based study using SNoMaN as a design probe with researchers from SNA and GIS backgrounds. The findings reveal both the value of visual analytics as a boundary object for cross-disciplinary collaboration and key sources of science friction, including differences in terminology, analytical expectations, and familiarity with spatial versus network concepts. These insights informed iterative refinement of the system.\u003C\/p\u003E\u003Cp\u003E(3) \u003Cstrong\u003EEmpirical evaluations of integrated SSNA.\u003C\/strong\u003E Building on iterative refinements of the system, we evaluated SNoMaN through longitudinal case studies with 12 domain experts using real-world datasets across diverse application domains. Over multiple sessions spanning several months, the results demonstrate that SNoMaN effectively supports exploratory spatial data analysis, hypothesis generation, and insight discovery. At the same time, the study highlights limitations related to transparency and trust in computed metrics, suggesting the need for improved interpretability and interoperability with programmable tools.\u003C\/p\u003E\u003Cp\u003E(4) \u003Cstrong\u003ESSNA teaching modules using visual analytics.\u003C\/strong\u003E We developed a learner-centered SSNA teaching module built around SNoMaN, designed to lower barriers to interdisciplinary learning across SNA and GIS. The curriculum was implemented across eight universities in multiple disciplines and evaluated through instructor observations, student engagement, and learning outcomes, leading to insights for broader interdisciplinary education.\u003C\/p\u003E\u003Cp\u003EOverall, this work shows that integrating geographic and network perspectives through visual analytics enables new forms of reasoning about SSNs that are difficult to achieve with fragmented workflows. By combining design study, empirical evaluation, and pedagogical contributions, this dissertation contributed to the use of visual analytics for interdisciplinary SNA and GIS analysis and supports broader adoption of SSNA in both research and education.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cem\u003EBridging Spatial and Social Network Analysis through Visual Analytics\u003C\/em\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Bridging Spatial and Social Network Analysis through Visual Analytics"}],"uid":"27707","created_gmt":"2026-04-23 12:00:25","changed_gmt":"2026-04-23 12:02:21","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-05-11T16:30:00-04:00","event_time_end":"2026-05-11T18:30:00-04:00","event_time_end_last":"2026-05-11T18:30:00-04:00","gmt_time_start":"2026-05-11 20:30:00","gmt_time_end":"2026-05-11 22:30:00","gmt_time_end_last":"2026-05-11 22:30:00","rrule":null,"timezone":"America\/New_York"},"location":" Technology Square Research Building (TSRB) Room 334 (VIS Lab) ","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}