Spatio-temporal interpolation methods for data integration: Application on studying environmental effects on asthma, diabetes and cataracts

Primary Supervisor: Dr Liwan Liyanage (opens in a new window)

Spatio-temporal data is becoming increasingly prevalent in our society. This has largely been spurred on from the capability of building arrays and sensors into everyday items, along with highly specialised measuring equipment becoming cheaper. The result of this prevalence can be seen in the wealth of data of this kind that is now available for analysis. This Spatio-temporal data is particularly useful for contextualising events in other data sets by providing background information for a point in space and time. Problems arise however, when the contextualising data and the data set of interest do not align in space and time in the exact way needed. This problem is becoming more common due to the precise data recorded from GPS systems not overlapping with points of interest and not being easily generalised to a region. Thus, Interpolating Data for the points of interest in space and time is important and a number of methods have been proposed with varying levels of success. These methods are all lacking in usability and the models are limited by strict assumptions and constraints. This project proposes developing new improved methods for the interpolation of points accurately in the patio-temporal scope, based on a set of known points.

Data on discrete spatio-temporal events can be enriched through the inclusion of ancillary data occurring at the exact point in space and time. Unfortunately, useful background data is often not measured at the exact point that is needed. In order to generate more relevant data, point based interpolation can be performed, using the observed points as inputs to predict for the needed points. These improved methods will be used to study the environmental effects such as weather and pollution on diseases such as asthma, diabetes and cataract.

This project would suit candidates with a background in data science, computing or statistics. Candidates are also required to have experience using R.