Spatial and Complex Network Data Analysis using Julia

Giulio Valentino Dalla Riva

Abstract:

This tutorial explores Complex Network and Spatial Data Analysis in Julia, targeting data miners, spatial scientists, network analysts, urban planners, and researchers. Initially, it covers network analysis foundations with Graphs.jl and LightGraphs.jl, advancing to community detection and visualization using CommunityDetection.jl and GraphPlot.jl. Subsequently, it delves into spatial data analysis, elucidating data manipulation and visualization with GeoIO.jl, Makie.jl, and GeoMakie.jl, and exploring spatial statistics with GeoStats.jl. The final segment unveils the interplay between spatial and network data through case studies, emphasizing spatial proximity evaluation and SpatialGraphs.jl for handling geospatially embedded networks. Through hands-on exercises, attendees will gain practical insights into leveraging Julia for complex network and spatial data analysis.

Giulio Valentino Dalla Riva is a Senior Lecturer in Data Science in the School of Mathematics and Statistics, University of Canterbury, and he is the founder of Baffelan – Data Climbing, a boutique data consultancy company. His research explores and tries to make sense of what happens in complex, dynamical networks. He is interested in ecological networks and the evolutionary processes that modify them in time. Giulio develops mathematical and statistical tools to study the relationship between ecological biodiversity and evolutionary diversity. He is also interested in social networks, especially online, and trying to understand what makes them work in the way they work.

A tiny warmup has been prepared for anybody who would like to attend this tutorial (it makes the experience smoother by already installing some stuff). It can be found here: https://gvdr.github.io/megachile/23/ausdm/0_setup.html

Data Privacy: Access and Consent Management using Personal Online Datastores

Anushka Vidanage, Jessica Moore, Graham Williams

Abstract:

Cloud computing services typically collect personal data for analysis and modelling for many different purposes. The past few decades has seen an increasing uptake of useful online services, including social media, online shopping, and more. Over time, more complex and sensitive data has been collected and stored in centralised databases, with inherent lack of interoperability and little control provided to individuals over their own data. Centralised data has also become vulnerable to security and privacy attacks. There is a clear need for a paradigm shift in how data is collected, secured, controlled, and stored.

Personal online datastores (PODs) hosted on Solid (social linked data) servers presents such a paradigm shift. Personal data is stored in a distributed manner with individuals having access controls (self-sovereignty) over their own data. The open Solid (https://solidproject.org/) specification enables hosting and sharing of PODs on servers, under an individual’s own control. The individual, through an ecosystem of apps, collects, controls, stores, and manages their own data within their POD, having complete control over with whom, when and how they share their data, and even where their POD is hosted.

In this tutorial you will learn: (1) why data privacy is so important today and in the future; (2) about the technology behind Solid PODs; (3) how to utilise Solid PODS to provide fine grain access control and consent mechanisms for data sharing, and (4) to develop an app for storing and sharing personal data between PODs utilising a Solid PODs data architecture.

Anushka Vidanage is a research fellow at the Software Innovation Institute (SII) in the ANU School of Computing, with over 6 years of experience research fields of data privacy. His primary research interests include data privacy and security through PODs, privacy-preserving record linkage, and distributed machine learning.

Jess Moore is an applied data scientist in the ANU School of Computing and Chief Operating Officer of the Software Innovation Institute (SII), with over 15 years post PhD experience. Her research interests are in consent management, analytics and engineering with PODs.

Graham Williams is Chief Scientist of the Software Innovation Institute, ANU School of Computing. He returned to the ANU after a career as Microsoft’s Director of Data Science, Lead Data Scientist for the Australian Government’s Data Analytics Centre of Excellence, and Principal Research Scientist with CSIRO Australia. His current research interest is in AI and ML in a privacy based world.