In addition to my appointment in Geographical and Sustainability Sciences, I am a cluster faculty at Iowa Informatics Initiative (UI3) . I am also a faculty member of two interdisciplinary graduate programs: (1) Informatics (also a coordinating committee member for the Geoinformatics program) and (2) Applied Mathematical and Computational Sciences.
To Prospective Graduate Students:
I am always looking for MS/PhD or PhD students with the following desirable research interests:
- Spatial interaction analysis and visualization (e.g., network measures, spatial community detection, spatial interaction modeling and diffusion models, flow mapping and clustering)
- Spatial data mining (e.g., clustering, association rule mining, machine learning and deep learning)
- Geovisual analytics and human computer interaction (e.g., interactive cartography, flow mapping, coordinated views, and utility and usability evaluation)
- Big data analytics for social media and networking applications (e.g., geospatial semantics, natural language processing, topic modeling and 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 geospatial data and 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.
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.
- Foundations of GIS (GEOG: 1050) - FALL 2018
- Introduction to Geographic Databases (GEOG: 4580) - FALL 2018
- Introduction to Geographic Visualization (GEOG: 3540) - SPRING 2018
- Spatial Analysis and Modeling (GEOG: 6500) - FALL 2016
GEOG 3540 Example Student Portfolio: https://geog3540.github.io/ryan-p-larson/
Example Final Projects:
Amariah Fischer (Ph.D. in Geography)
Hoeyun Kwon (Ph.D. in Geography)
Bin Zhang (B.S. in Geography / M.S. in Geoinformatics)
Investigator, The People’s Weather Map and Social Media: Iowans Talking about Weather Hazards, Digital Bridges Summer Collaborative Research Grant with Barbara Eckstein, Professor of English at the University of Iowa and Casey Oberlin, Assistant Professor of Socilogy at the Grinnel College, Obermann Center for Advanced Studies, $10,000, 5/1/2018 – 12/1/2018.
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, $12,000, 7/15/2018 – 8/15/2018.
Koylu, C., Zhao, C. & Shao, W. (2019). Deep neural networks and kernel density estimation for detecting human activity patterns from geo-tagged images: A case study of birdwatching on Flickr, ISPRS International Journal of Geo-Information, 8(1), 45. DOI: https://doi.org/10.3390/ijgi8010045
Sit, M., Koylu, C. & Demir I (2019). Identifying disaster related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: A case study of Hurricane Irma, International Journal of Digital Earth. DOI: https://doi.org/10.1080/17538947.2018.1563219
Koylu, C., Larson, R., Dietrich, B. & Lee, K.P (2019). CarSenToGram: Geovisual text analytics for exploring spatio-temporal variation in public discourse on Twitter, Cartography and Geographic Information Science, 46:1, 57-71. DOI: https://doi.org/10.1080/15230406.2018.1510343 (Featured on the cover).
Koylu, C., Delil, S., Guo, D. & Celik, R.N. (2018) Analysis of Big Patient Mobility Data for Identifying Medical Regions, and Spatio-temporal Characteristics and Care Needs of Patients on the Move, International Journal of Health Geographics, vol.17, p.32. DOI: https://doi.org/10.1186/s12942-018-0152-x
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: https://doi.org/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, Cham. DOI: https://doi.org/10.1007/978-3-319-73247-3_9
Koylu, C. (2018) Discovering multi-scale community structures from the interpersonal communication network on Twitter. In L. Perez, E.-K. Kim, & R. Sengupta (Eds.), Agent-Based Models and Complexity Science in the Age of Geospatial Big Data: Selected Papers from a workshop on Agent-Based Models and Complexity Science (GIScience 2016) (pp. 87-102). Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-319-65993-0_7
Koylu, C., & Guo, D. (2017). Design and evaluation of line symbolizations for origin–destination flow maps. Information Visualization, 16(4), 309-331. DOI: https://doi.org/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 time. Cartography and Geographic Information Science, 41(1), 14-26. DOI: https://doi.org/10.1080/15230406.2013.865303 (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: http://dx.doi.org/10.1016/j.compenvurbsys.2013.03.001