Guest Lecture at the University of Melbourne Urban Informatics '16: Big and Open Data Analytics and Visualization
Dr. Saberi was invited to deliver a guest lecture at an intensive postgraduate unit on Urban Informatics at the University of Melbourne. As part of the lecture, he presented three of the recent projects developed by the CityX team: Changing Melbourne 2.0, Places by Metro, and Melbourne Pedestrian Activities and Safety.
"Urban Informatics is the study of cities using digital data, information, knowledge and models to understand trends, complexities and inform the formulation and evaluation of sustainable urban futures. This subject aims to arm the student with the necessary fundamental concepts and practical understanding of the rise of the Smart City and how urban informatics can assist in evidenced-based and collaborative decision-making. This subject utilizes the Australian Urban Research Infrastructure Network (AURIN) portal as an e-learning resource for exploring what is possible in emerging in the new discipline of urban informatics. Students will also be exposed to a range of other complementary digital environments including open data repositories, urban modeling and visualization tools and open source geospatial information technologies." - ABPL90366 Urban Informatics Unit Guide, University of Melbourne.
PhD student Richard Amoh-Gyimah presents his research at the Australian Road Safety Conference (ARSC2016)
Richard Amoh-Gyimah presented his research on Modelling Crash Unobserved Heterogeneity Using Semi-Parametric Geographically Weighted Poisson Regression at the Australian Road Safety Conference (ARSC2016) held in Canberra, 6-8 September, 2016.
Abstract. Crash data are typically collected with reference to location dimension. Such data suffer from unobserved heterogeneity. The objective of this research is twofold: (1) to develop zonal crash prediction models using the Semi-Parametric Geographically Weighted Poisson Regression (S- GWPR) to address the issue of unobserved heterogeneity and (2) compare the performance of the S- GWPR with a non-spatial negative binomial (NB) model. The result indicates that by accounting for unobserved heterogeneity, the S-GWPR models performed better than the NB models. It was also found that unlike the NB models that show fixed parameters, all the variables except three have spatially varying coefficients in the S-GWPR.
Dr. Meead Saberi, lecturer in transportation engineering, data guru, and urban scientist