Integrating Spatial Data to Mathematical Models
As part of student’s studies at the School of Geography, they have been tasked with planning, creating and publishing blog posts in their tutor groups, summarising lectures from internal and external guest speakers. The collaborative blog posts are then reviewed by School Academics and graded on style, subject and content.
Tutor Group 3, (comprising Amelia, Sarah, David, Connor, Bailey & Declan) wrote the following blog post, which was selected by School of Geography Academics as this week’s winning blog post!
“Integrating Spatial Data to Mathematical Models
This week’s lecture from Dr Dilkushi de Alwis Pitts explained how spatial data can be integrated into a range of models, as well as discussing the future of Geographical Information Systems (GIS) and how it is becoming ever more accessible.
The first of the models included agent-based modelling, which essentially simulates the interactions between two entities. Within Dr Dilkushi’s work, this has been used effectively to identify a trend between Mosquito Blood meals and wet seasons to help reduce the risk of malaria. Given the ever-changing resilience of the insect, however, she went on to state that this can be somewhat difficult to capture.
Other applications of integrating spatial data to mathematical models include hydrological and hydrodynamic modelling. The latter considers the factors affecting heat change within Lake Ontario to simulate a thermal bar, while hydrological models, on the other hand, was used to model the hydrological parameters of the Mekong river basin and the flow to Tonle Sap lake. Remotely sensed spatial resources such as NDVI or Land cover data were used in the model as input data.
While concluding, Dr Dilkushi explained the SPACES feature which provides Species Distribution Model algorithms with formatted data layers. So, with the increased accessibility of remotely sensed data, it is likely that GIS will become even more useful in the years to come.”