Scholarships

The Geospatial Research Institute Toi Hangarau (GRI) is pleased to offer one PhD scholarship as a supplement to the University of Canterbury PhD scholarship. This scholarship is available only to a new PhD applicant who will complete research towards an approved geospatial project. The scholarship value is NZ$9,000 per annum plus up to NZ$2,000 for travel and other costs per annum, in addition to the University of Canterbury scholarship.

 

The scholarship is open to national and international students. To be eligible for the GRI scholarship, you need to:

  • fulfil the University of Canterbury criteria for a Doctoral scholarship and submit your application (click here)
  • select one PhD project from the list below
  • get in touch with the named supervisor of the project of your interest to discuss the project content and expectations.
  • apply for the GRI  scholarship using the link below.

 

Deadline: 30 April 2020 (extended from 28 February).

 

 Click here for further information and to apply

 

 

 

GRI scholarships eligible projects 2020:

 

 

Project 1

Title: mGeohealth: emerging applications of mobile technology

 

UC Principle Supervisor (Project Leader)

Ass. Prof. Malcolm Campbell: malcolm.campbell@canterbury.ac.nz

 

UC Department/School

School of Earth and Environment

 

Project outline 

Health and medical geography approaches have normally focused on residence-based conceptualisations as an approximation for multiple types of exposure. This is a ‘static’ place or point in time, usually a home address or a fixed administrative boundary, perhaps measured once every several years. However, we now are able to more accurately measure, the ‘true’ exposure, which can be captured ‘dynamically’; people moving between many places, places with differing environments and characteristics. This changes the quantum of data involved, the processing required and complexity of the challenges when using established statistical and geographical methods. From the literature, it is well recognised that place can be an important influence on health, either positive or negative; where we live matters for our health.

New methods and technologies, such as real-time personal mobile location data, afford an opportunity for new(er) approaches, with richer, large, fine-grained spatial data sources. However, these new data sources and accompanying methods bring with them a variety of new challenges, biases and opportunities. We propose an mGeoHealth, that is, the use of location-based applications for smart devices (e.g. smartphones and smart watches) in health. This means that mGeoHealth does not necessarily focus on adopting completely new technologies, but it aims to utilise readily available of smart devices, that are subsequently enhanced by suitable software or applications.

We wish to explore an mGeoHealth that sits at the intersection of the fields of mHealth (mobile health) and Health and Medical Geography (GeoHealth). As such it is a combination of two distinct, yet relatively unconnected domains. A focus on mGeoHealth is timely as it is a burgeoning area of specialised endeavour which is often missed broad(er) fields of mHealth or Health and Medical Geography. This new project can draw on a rich heritage of geographical endeavour, whilst adapting to new(er) methods of data collection and associated methods. To demonstrate the approach, we will use a series of real-life examples of data collections and associated analytics for discussion. We are particularly focused on mobility and movement as a source of exposure to environments (e.g. physical, social and so on) and also how mobility could exacerbate or ameliorate existing inequalities in health. This could be as diverse as exposure to air pollution or social connections. We will also explore the possibility of technology itself, to further alter health inequality.

 

 

Project 2

Title: Analysis of spatio-temporal trends in New Zealand Traffic in 1998-2018 and implications for benchmarking and implementation of autonomous self-driving vehicles.

 

UC Principle Supervisor (Project Leader)

Ass. Prof. Elena Moltchanova (School of Mathematics and Statistics): elena.moltchanova@canterbury.ac.nz

 

Other supervisor(s)

Ass. Prof. Christoph Bartneck (HIT Lab NZ):  christoph.bartneck@canterbury.ac.nz

 

UC Department/School

Collaboration between the School of Mathematics and Statistics and the HIT Lab NZ

 

Project outline

Holiday news coverage is unfortunately never complete without reports of traffic jams and the road death toll. Autonomous vehicles are expected to solve both problems. Always sober, always alert, always obedient to the rules. In New Zealand, they have the potential to reduce the number of traffic accidents caused by drunk or distracted drivers, as well as those involving tourists who are unfamiliar with driving on the left side of the road. However, it would be unrealistic to assume that the autonomous vehicles could achieve a perfect driving record and avoid fatalities altogether. So how safe do they need to be before we can accept them onto our roads? To move this discussion from philosophy to policy we need to establish empirical benchmarks. The goal of this project is to use past road accident data to develop a simulation framework for the cost-benefit analysis of autonomous self-driving vehicles.

In the first phase of this project we will analyse the data in the New Zealand Crash Analysis System (CAS) to determine the factors most influencing the probability and severity of accident occurrence. CAS includes detailed, spatially-explicit traffic accident information which we intend to complement with other relevant information, such as weather conditions. In the second phase we intend to create a simulation framework that will empower us to formulate recommendations regarding successful deployment of autonomous vehicles. Will they be most useful in large densely populated metropolitan areas during the morning rush hour or should they better be deployed in remote areas instead? Would it make tourist trips safer? By establishing how safe human drivers are we will be able to establish safety performance standards for autonomous vehicles. Once autonomous vehicles meet this benchmark it could be argued that human driving should become illegal. Given the size and complexity of the data, the candidate is expected to utilise state-of-the art modelling, such as Bayesian spatio-temporal regression modelling, Bayesian networks, and random forests.

 

 

Project 3

Title: Distributed, dynamic path planning and control of Unmanned Aerial Vehicle Swarms for 3D mapping and geospatial data collection.

 

UC Principle Supervisor (Project Leader)

Dr Graeme Woodward (Research Leader, Wireless Research Centre): graeme.woodward@canterbury.ac.nz

 

Other supervisor(s)

Prof. Andreas Willig (Computer Science and Software Engineering): andreas.willig@canterbury.ac.nz

 

UC Department/School

CSSE / WRC.

 

Project outline

The ubiquity of Geospatial Information Systems (GIS) is fuelling a growing need to collect spatial data quickly, efficiently and regularly. This project will develop novel methods to aid the creation of rich and timely data and in so doing will empower people to make information-based decisions.

Many geospatial data collection tasks require many images taken from various perspectives. Unmanned Aerial Vehicles (drones) can be used for this task, but with a single drone it can take several hours before data collection is completed. Furthermore, with a pre-determined flight path, and data post-processing (e.g. synthetic aperture methods based upon a “lawn-mower” flight trajectory), it is often only known after the campaign whether the collected data is of sufficient  quality for the geospatial application at hand, and the data may suffer ‘gaps’, e.g. from feature occlusion.  Instead, we’d like to investigate the use of a drone swarm to speed up capture.  By enabling the drones to co-ordinate in real-time, we believe that completion times can be significantly shortened.  This requires new distributed and scalable drone co-ordination and flight-planning algorithms.  Research will include new real-time collaborative and distributed algorithms for allocating data collection tasks to drones, maintaining the correct spatial relationship between each drone, and for time-efficient path planning based on data observed and current drone positions.  Network connectivity is required to ensure that drones make consistent decisions and to guarantee that the entire swarm can be controlled by a single operator instead of having one operator per drone.  Such communication requirements need to be included into the dynamic path planning algorithm.

The innovations in drone co-ordination and dynamic path planning can be applied to important geospatial sensing and data-collection tasks, including computer vision, RADAR and LIDAR surveying and tracking of targets through complex terrains.

 

 

Project 4

Title: Integrating methods of machine learning into computational models of flood risk assessment

 

UC Principle Supervisor (Project Leader)

Prof. Matthew Wilson: matthew.wilson@canterbury.ac.nz

 

UC Department/School

School of Earth and Environment

 

Project outline 

The impacts of flooding and society’s vulnerability to it are increasing under climate change, with more than 79 million people affected, 5,500 fatalities and US$33.5 billion of damages each year, according to the EM-DAT database. In order to manage and reduce these impacts, flood risk assessments are required, which enable spatial planning of communities – e.g. to define flood zones or other mitigation measures. These assessments are often completed using computational numerical modelling of surface water flow hydraulics, in a process which integrates large volumes of data for boundary conditions (i.e. water inflows, topography, bathymetry and land cover), model accuracy assessment (e.g. maps derived from SAR images from satellite remote sensing) and data for impact quantification (e.g. building locations and types, or data of other vulnerable infrastructure). However, while these methods can provide excellent assessments, even reduced complexity numerical models are computational expensive, meaning that a challenging trade-off is required between model spatial resolution and extent, model structure, and number of simulations. In addition, full assessment of uncertainty is rarely completed, due to the high computational demands which results from a requirement to complete many 100s or 1000s of simulations. On top of this, the data used in flood risk assessments are rarely without significant gaps or errors, which add to this uncertainty and reduce the reliability of estimates. The integration of machine learning methods into flood risk assessments can, potentially, provide solutions which address some of these issues and lead to a drastic improvement in flood risk assessment. For example, it may enable improved data processing of model boundary conditions, improve the quantification of uncertainties associated with flood risk assessments, or facilitate the rapid near-real time prediction of flooded areas during an event. This PhD will assess the suitability of machine learning for flood risk assessment, and explore methods for integrating these methods into numerical models.

 

 

Project 5

Title:Empowering youth through participatory geospatial technologies: Community food security in Otautahi Christchurch

 

UC Principle Supervisor (Project Leader)

Associate Professor Sara Tolber (College of Education, Health, and Human Development): sara.tolbert@canterbury.ac.nz

 

Other supervisor(s)

Associate Professor Diane Mollenkopf (College of Business and Law): diane.mollenkopf@canterbury.ac.nz

 

UC Department/School

School of Teacher Education

 

Project outline 

While a global phenomenon, food insecurity plays out in local communities, and remains a significant problem in Aotearoa (Ministry of Health, 2019). Some reports indicate that food insecurity within Aotearoa has been exacerbated over the past three decades, particularly among Māori/Pasifika and low-income communities as well as among young adolescents (O’Brien, 2014, Utter et al., 2017). The UC Community Food Security/Hidden Hunger (CFS-HH) Research Cluster, comprised of community partners as well as university researchers, engages in transdisciplinary research that seeks to better understand how communities can be empowered to address food security concerns in the face of climate change.

The focus of the PhD research will be exploring how local youth can be empowered through geospatial tools to understand and act upon food security challenges that most directly impact them and their families. In the proposed study, the PhD candidate will work with CFS -HH researchers and community partners to lead high school students from lower decile schools in using GIS tools to map food vulnerabilities and strengths in local community food systems. The proposed methodology for the study is Youth Participatory Action Research (YPAR), a widely used critical pedagogical approach to engaging young people in understanding and transforming their local physical, educational, social, and/or political environments. Local high school students will use interactive geospatial tools, such as Maptionnaire and/or Streetwyze, to map the availability of culturally appropriate, nutritious foods, as well as to document if/how their peers as well as community residents have experienced and are currently experiencing food vulnerabilities. Researchers and students will work together to identify how the data they collect can best be applied and disseminated toward the mitigation of food vulnerabilities in low-income communities in Otautahi Christchurch.