Caglar Koylu

Koylu portrait 2023
Associate Professor

caglar-koylu@uiowa.edu

Education: 
PhD, University of South Carolina
Office: 
303 Jessup Hall
Phone: 
319-335-0161
Office Hours: 
Mondays: 12:30 PM - 2:00 PM
Wednesdays: 2:30 PM - 4:00 PM
Curriculum Vitae: 
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Research Interests: 
GIScience, geovisualization, human-computer interaction, human mobility and migration, population-scale kinship networks

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. 

Lab Website

Check out FlowMapper https://flowmapper.org, a web-based tool we developed for visualization and analysis of origin-destination flow data!

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
  • Web-based GIS and Geovisualization: JavaScript, D3, React, etc.
  • 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. 

Applications

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.

Courses: 

Geoviz flyer  Geoviz projects  

GEOG 3540 Example Student Portfolio: 

Example Final Projects:

2023

Past Projects

Graduate Advisees: 

Maryam Torkashvand (Ph.D. in Geography)

Jinyi Cai (Ph.D. in Geography)

Loretta Nwajiaku (M.A. in Geography)

Alumni:

Hoeyun Kwon, Ph.D. in Geography, 2023

Geng Tian, M.A. in Geography, 2021

Grants & Funding: 

Principal Investigator, HNDS-R: Population-scale Kinship Networks and Migration, National Science Foundation (NSF), with CO-PI Jonas Helgertz (Co-Principal), $477,734.00, 09/01/2022-08/31/2025.

Co-Principal Investigator, Development of Small Area Interactive Risk Maps for Cancer Control Efforts, National Institutes of Health (NIH), with PI Jacob Oleson, Co-PIs Grant Brown, Mary Charlton, Sarah Nash, $1,188,804, 06/15/2022-05/31/2025.

Co-Principal Investigator, Project Haystack, GoDaddy Inc., with PI Caroline Tolbert, Political Science, University of Iowa, $234,491, 8/1/18 – 8/1/20.

Selected Publications: 

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).

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