Potato cyst nematodes (PCN) cost Scottish agriculture over £25 million/year and threatens food security in the developed and developing world. Improved understanding of PCN epidemiology is a priority for the Scottish potato industry, with spatial and temporal modelling identified by the recent PHC PCN working group as essential components. The aim of this project was to gain a better understanding of the spatial epidemiology of PCN in Scotland, by applying mapping, statistical and artificial intelligence (machine learning) techniques to existing landscape-scale datasets. Several factors were shown to influence the presence of PCN in fields, either positively or negatively, which may help to better understand how management practices could be used to help reduce the presence of the pest. These principal drivers of PCN incidence were used to create a machine learning model that can predict PCN incidence to an accuracy of 82%.