Caglar Koylu

Assistant Professor
PhD, University of South Carolina
303 Jessup Hall
Curriculum Vitae: 
Research Interests: 
GIScience, spatial data mining, information visualization, human-computer interaction, mobility and geo-social networks

In addition to my appointment in Geographical and Sustainability Sciences, I am a cluster faculty at Iowa Informatics Initiative (UI3) and a coordinating committee member for the Geoinformatics program.   

Personal Website

To Prospective Graduate Students: 

I am looking for MS/PhD or PhD students starting from Fall 2019. Desirable research interests are (but not limited to):

  • Spatial network analysis (e.g., network measures, community detection, spatial interaction modeling, diffusion models)
  • Spatial data mining (e.g., clustering, association rule mining, classification, machine learning, and deep learning for object classification)
  • Geovisual analytics and human computer interaction (e.g., interactive cartography, flow mapping, coordinated views, utility and usability evaluation)
  • Big data analytics for social media and networking applications (e.g., natural language processing, topic modeling, sentiment analysis)
  • High performance computing, Hadoop, and big data storage systems (MongoDB)
  • Application areas such as human migration and mobility, geographically-embedded social networks (e.g., interpersonal communication, family tree), patient mobility, human communication, transportation, and energy networks; movement and pass networks in sports (e.g., soccer, basketball); analysis of social media data; social sensing and networks for disaster response, recovery and resilience

In addition to the above research interests, students should have, or be interested in developing, ability in:

  • One of the programming languages such as Java, C++, or Python;
  • Statistical computing such as R or Matlab
  • GIS Software such as ArcGIS or QGIS. 
  • Database management systems such as Postgresql, PostGIS, and MongoDB
  • English Proficiency (TOEFL)

Upon admittance you will be a member of The Geo-Social Lab, which is home to research projects aimed at developing innovative computational and visual tools to better understand and analyze massive and complex geo-social networks. 


We offer Master's and PhD degrees both at the Department of Geographical and Sustainability Sciences, and the interdisciplinary program at Geoinformatics. I recommend students with Computer Science background to apply to the program in Geoinformatics. Graduate teaching and research fellowships, and assistantships are available for competitive students. Before applying, please contact me with your brief research interests and CV attached. Visit The GeoSocial Lab website and read my publications below to learn more about my research. Below is a video recording of an invited seminar I gave in the Department of Biostatistics. The seminar was about the application of spatial data mining methods for the analysis of big and longitudinal patient mobility data.

Watch the Seminar

I invite competitive students for a Skype interview. The interview starts with a 6 minute, 40 second, a Pecha Kucha style presentation focused on your background, skills, and research interests.

Grants & Funding: 

Investigator, Distinguishing high-crime neighborhoods from low-crime neighborhoods: A spatial examination integrating a diversity of social and ecological factors, Interdisciplinary Research Grant with James Wo, Socilogy at UI, Obermann Center for Advanced Studies,  $6000, 7/15/2018 – 8/15/2018. 

Selected Publications: 

Koylu, C. (2018) Modeling and visualizing semantic and spatio-temporal evolution of topics in interpersonal communication on Twitter, International Journal of Geographical Information Science. doi:10.1080/13658816.2018.1458987

Koylu, C. (2018) Uncovering geo-social semantics from the Twitter mention network: An integrated approach using spatial network smoothing and topic modeling. “Human Dynamics Research in Smart and Connected Communities”, Springer GeoJournal Library Book Series.

Koylu, C. (2017) Discovering multi-scale community structures from the interpersonal communication network on Twitter. “Agent-Based Models and Complexity Science in the Age of Geospatial Big Data – Selected Papers from GIScience 2016”, Springer. 

Koylu, C., & Guo, D. (2017). Design and evaluation of line symbolizations for origin–destination flow maps. Information Visualization, 16(4), 309-331. doi:10.1177/1473871616681375 (Featured on the cover).

Koylu, C. (2016). Extracting and Visualizing Geo-Social Semantics from the User Mention Network on Twitter. Proceedings of GIScience 2016 Workshop on Rethinking the ABCs: Agent-Based Models and Complexity Science in the age of Big Data, CyberGIS, and Sensor Networks, Montreal, Canada, September 27, 2016.

Guo, D., Kasakoff, A. B., Koylu, C., Huang, Y., & Grieve, J.  (2015) “Historical Population Informatics: Comparing Big Data of Family Trees and the U.S. 1880 Census for Migration Analysis” Population Informatics for Big Data (PopInfo'15) in conjunction with the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) , August 10, 2015, Sydney

Koylu, C., Guo, D., Kasakoff, A., & Adams, J. W. (2014). Mapping family connectedness across space and timeCartography and Geographic Information Science, 41(1), 14-26 (Featured on the cover)

Koylu, C., & Guo, D. (2013). Smoothing locational measures in spatial interaction networks. Computers, Environment and Urban Systems, 41(0), 12-25. doi: