Graham's research focuses on network science and visualization. He is particularly interested in geospatial and temporal networks.
Dr Graham McNeill
Graham is a researcher at the OII. He is interested in network science, visualization and machine learning. Specific interests include estimating network properties from data and visualizing geospatial and temporal networks.
Graham has an undergraduate degree in mathematics from the University of Newcastle upon Tyne and postgraduate degrees from the University of Edinburgh. His doctoral research focused on probabilistic approaches to shape analysis. Following this, he worked on content-based video retrieval at the University of Bath. Graham has also worked as a catastrophe risk analyst in the insurance industry and as a teacher.
networks, visualization, machine learning, data science
Positions held at the OII
- Researcher, November 2015 –
Participants: Dr Scott A. Hale, Dr Jonathan Bright, Dr Graham McNeill, Chico Camargo
This project seeks to utilise newly available data to help urban policy makers improve transport infrastructure to cope with growing and increasingly mobile populations.
Participants: Dr Scott A. Hale, Dr Jonathan Bright, Dr Graham McNeill
Mining human mobility and migration patterns from social media and industry data sources as well as visualizing geo-temporal network data interactively with HTML5.
- (2019) Viz-Blocks: Building Visualizations and Documents in the Browser. EuroVis.
- (2017) Where’d it go? How geographic and force-directed layouts affect network task performance. EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization (EuroRV3).
- (2017) "Generating Tile Maps", Computer Graphics Forum. Eurographics Conference on Visualization (EuroVis), Barcelona, Spain, 12 – 16 June 2017. Wiley. 36 (3) 435-445.
- (2009) "Storyboard sketches for content based video retrieval", International Conference on Computer Vision (ICCV). 245-252.
- (2008) "Free-hand sketch grouping for video retrieval", International Conference on Pattern Recognition (ICPR).
- (2007) "Linear and nonlinear generative probabilistic class models for shape contours", International Conference on Machine Learning (ICML).
- (2006) "Part-based probabilistic point matching using equivalence constraints", Neural Information Processing Systems (NIPS).
- (2006) "A probabilistic approach to robust shape matching", International Conference on Image Processing (ICIP).
- (2006) "Hierarchical procrustes matching for shape retrieval", Computer Vision and Pattern Recognition (CVPR). 885-894.
- (2005) "2D shape classification and retrieval", International Joint Conference on Artificial Intelligence (IJCAI).
- (2017) "Estimating local commuting patterns from geolocated Twitter data", EPJ Data Science 6. 6 (24).
21 July 2016
Author: Graham McNeill
Not all networks have geographic information (e.g., friendship networks, hyperlink networks, etc.). However, for networks that do have geographic data (such as networks of ...
My work has been financially supported by InnovateUK and by Lloyd’s Register Foundation via the Alan Turing Institute.