To listen to the recording of our latest webinar on Geoanalytics click webinar
Please see details of our speakers and topic below.
Having huge volumes of data is great, but the real issue is to derive meaning from the data. Two top Irish geospatial academics, Prof Chris Brunsdon and Dr Martin Charlton, presented at the webinar
They will deal with the following matters:
- What is ‘geoanalytics’
- Deriving meaningful insights … what does this mean?
- Data challenges and opportunities
- Types of geoanalytics, ranging from traditional statistical approaches to AI/machine learning
- What types of geoanalytics are suitable for deriving what types of meaningful insights
- Some examples of the use of different types of Geoanalytics
- Some likely trends in Geoanalytics over the coming years.
Professor Chris Brunsdon is the Director of The National Centre for Geocomputation at Maynooth University. Professor Brunsdon commenced his NCG post in 2014. He joined Maynooth University from the University of Liverpool where he served as Professor of Human Geography. He is one of the developers of the widely used Geographically Weighted Regression (GWR) data analysis technique. His work has a strong focus on both fundamental research and its practical applications. He has published extensively with contributions to spatial data analysis and geocomputation, and has a particular interest in geographical data visualisation, geocomputation and the R programming language, and applications of these to crime, health, and housing data.
Martin Charlton is currently Associate Professor in the National Centre for Geocomputation at Maynooth University, where he has been since 2004. He previously worked at the University of Newcastle upon Tyne, as Research Associate and Senior Researcher in the Centre for Urban and Regional Development Studies, and then as Lecturer in the Department of Geography. He is a graduate of Newcastle University. He is one of the co-developers of Geographic Weighted Regression. He has a long standing interest in Geographical Information Science and Spatial Epidemiology and has been dealing with the analysis of spatial data since the late 1970s.