Map simulation of dogs’ behaviour using population density of probabilistic model

Abstract

This paper proposes a simulator to demonstrate dogs’ behaviours considering individual and group habits, which is designed to be purposefully expandable for disease control. The proposed system is developed using Unity and Mapbox SDK. The normal distribution, kernel density method and probabilistic model are applied to simulate the movement behaviour, world interaction and behaviour rates, respectively. The simulation is validated on an area of Saibai, located in the northwestern of Torres Strait islands, Australia. This reports a median tie-strength of 0.0106 which is slightly different from the value calculated from the GPS information of 0.0113. It thus contains the relative error of 6.19%. Then, the simulation is applied to three cities in Thailand. They are all reported with higher tie-strengths, when compared to Saibai. This is because of the significantly higher average numbers of dogs and group distances, with the larger connections between dogs and their communities.