Use this URL to cite or link to this record in EThOS:
Title: Integrating GIS approaches with geographic profiling as a novel conservation tool
Author: Faulkner, Sally
ISNI:       0000 0004 7653 9985
Awarding Body: Queen Mary University of London
Current Institution: Queen Mary, University of London
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Access from Institution:
Geographic profiling (GP) was originally developed to solve the problem of information overload when dealing with cases of serial crime. In criminology, the model uses spatial data relating to the locations of connected crimes to prioritise the search for the criminal's anchor point (usually a home or workplace), and is extremely successful in this field. Previous work has shown how the same approach can be adapted to biological data, but to date the model has assumed a spatially homogenous landscape, and has made no attempt to integrate more complex spatial information (eg, altitude, land use). It is this issue that I address here. In addition, I show for the first time how the model can be applied to conservation data and - taking the model back to its origins in criminology - to wildlife crime. In Chapter 2, I use the Dirichlet Process Mixture (DPM) model of geographic profiling to locate sleep trees for tarsiers in dense jungle in Indonesia, using as input the locations at which calls were recorded, demonstrating how the model can be applied to locating the nests, dens or roosts of other elusive animals and potentially improving estimates of population size, with important implications for management of both species and habitats. In Chapter 3, I show how spatial information in the form of citizen science could be used to improve a study of invasive mink in the Hebrides. In Chapter 4, I turn to the issue of 'commuter crime' in a study of poaching in Savé Valley Conservancy (SVC) in Zimbabwe, in which although poaching occurs inside SVC the majority of poachers live outside, showing how the model can be adjusted to reflect a simple binary classification of the landscape (inside or outside SVC). Finally, in Chapter 5, I combine more complex land use information (estimates of farm density) with the GP model to improve predictions of human-wildlife conflict.
Supervisor: Not available Sponsor: National Environment Research Council ; Queen Mary University of London
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Geographic profiling ; human-wildlife conflict ; conservation ; population size estimation