My research interests lie at the intersection of computational and visual analytical methods, big data analytics and usability in geovisualization. I am particularly interested in the development of new theory, methodologies and applications to analyze and understand large geo-social networks, such as population-scale kinship networks (family trees), human mobility and migration, and commodity and information flows.
To Prospective Graduate Students:
I am always looking for MS/PhD or PhD students with the following desirable research interests:
- Human mobility and migration
- Population-scale family trees and other geographically-embedded social networks
- 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)
- Other application areas such as social media analytics, patient mobility, human communication, movement and pass networks in sports (e.g., soccer, basketball), and social sensing for disaster response, recovery and resilience
In addition to the above research interests, students should have, or be interested in developing, ability in:
- Geospatial programming in Java and/or Python
- Statistical computing in R
- Interactive computing using Jupyter and Observable Notebooks
- GIS Software such as ArcGIS or QGIS
- Database management systems such as PostgreSQL/PostGIS
- High performance computing, Spark, Hadoop and big data storage and management systems (MongoDB)
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 at the Department of Geographical and Sustainability Sciences. Our graduate degrees are classified as STEM with the CIP code 45.0702 (Cartography and Geographic Information Systems). 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. Please read my publications below and visit The GeoSocial Lab website to learn more about our research in the lab. 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
- Introduction to Geospatial Programming (GEOG: 3050) - SPRING
- Introduction to Geographic Visualization (GEOG: 3540) - SPRING
- Introduction to Geographic Databases (GEOG: 4580) - FALL
- Spatial Analysis and Modeling (GEOG: 6500) - FALL
GEOG 3540 Example Student Portfolio:
Example Final Projects:
Hoeyun Kwon (Ph.D. in Geography)
Geng Tian (M.A. in Geography)
Co-Principal Investigator, Project Haystack, GoDaddy Inc., with PI Caroline Tolbert, Political Science, University of Iowa, $234,491, 8/1/18 – 8/1/20.
Investigator, Distinguishing high-crime neighborhoods from low-crime neighborhoods: A socio-spatial examination integrating a diversity of social and ecological factors, Public Policy Center, University of Iowa, with James Wo, Assistant Professor of Sociology and Mark Berg, Associate Professor of Sociology, $3,000, 7/22/2019 – 8/19/2019.
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. & Kasakoff, A. (2022). Measuring and mapping long-term changes in migration flows using population-scale family trees. Cartography and Geographic Information Science. DOI: https://doi.org/10.1080/15230406.2021.2011419 (Featured on the cover).
Koylu, C., Tian, G.* & Windsor, M. (2022) FlowMapper.org: A web‐based framework for designing origin‐destination flow maps. Journal of Maps. DOI: https://doi.org/10.1080/17445647.2021.1996479
Kwon, H.*, Hom, K.*, Rifkin, M.*, Tian B.* & Koylu, C. (2021). Exploring the spatiotemporal heterogeneity in the relationship between human mobility and COVID-19 prevalence using dynamic time warping. Proceedings of GIScience 2021 Workshop on Advancing Movement Data Science (AMD’ 2021), September 27, 2021, World Wide Web. DOI: https://arxiv.org/abs/2109.13765
Koylu, C., Guo, D., Huang, Y., Kasakoff, A. B., & Grieve, J. (2021) Connecting family trees to construct a population-scale and longitudinal geo-social network for the U.S., International Journal of Geographical Information Science. 35:12, 2380-2423, DOI: https://doi.org/10.1080/13658816.2020.1821885
Koylu, C., & Kasakoff, A. (2020). Mapping Temporal Trends of Parent-Child Migration from Population-Scale Family Trees, AutoCarto 2020 - The 23rd International Research Symposium on Cartography and GIScience, November 18, 2020, World Wide Web. DOI: https://arxiv.org/abs/2012.11007
Zhu, X., Guo, D., Koylu, C. & Chen, C. (2019) Density-Based Multi-scale Flow Mapping and Generalization, Computers, Environment and Urban Systems, 77, 101359. DOI: https://doi.org/10.1016/j.compenvurbsys.2019.101359
Xu, H., Demir, I. Koylu, C. & Muste, M. (2019) A web-based geovisual analytics platform for identifying potential contributors to culvert sedimentation, Science of the Total Environment. DOI: https://doi.org/10.1016/j.scitotenv.2019.07.157
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. (2019) Modeling and visualizing semantic and spatio-temporal evolution of topics in interpersonal communication on Twitter, International Journal of Geographical Information Science, 33:4, 805-832, DOI: https://doi.org/10.1080/13658816.2018.1458987
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) 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