↓ Skip to main content

A spatial approach to combatting wildlife crime

Overview of attention for article published in Conservation Biology, March 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

news
1 news outlet
twitter
37 tweeters
facebook
5 Facebook pages

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
96 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A spatial approach to combatting wildlife crime
Published in
Conservation Biology, March 2018
DOI 10.1111/cobi.13027
Pubmed ID
Authors

S. C. Faulkner, M. C. A. Stevens, S. S. Romañach, P. A. Lindsey, S. C. Comber

Abstract

Poaching can have devastating impacts on animal and plant numbers, and in many countries has reached crisis levels, with illegal hunters employing increasingly sophisticated techniques. Here, we show how geographic profiling - a mathematical technique originally developed in criminology and recently applied to animal foraging and epidemiology - can be adapted for use in investigations of wildlife crime, using data from an eight-year study in Savé Valley Conservancy, Zimbabwe that in total includes more than 10,000 incidents of illegal hunting and the deaths of 6,454 wild animals. Using a subset of these data for which the illegal hunters' identities are known, we show that the model can successfully identify the illegal hunters' home villages using the spatial locations of hunting incidences (for example, snares) as input, and show how this can be improved by manipulating the probability surface inside the Conservancy to reflect the fact that - although the illegal hunters mostly live outside the Conservancy, the majority of hunting occurs inside (in criminology, 'commuter crime'). The results of this analysis - combined with rigorous simulations - show for the first time how geographic profiling can be combined with GIS data and applied to situations with more complex spatial patterns - for example, where landscape heterogeneity means that some parts of the study area are unsuitable (e.g. aquatic areas for terrestrial animals, or vice versa), or where landscape permeability differs (for example, forest bats tending not to fly over open areas). More broadly, these results show how geographic profiling can be used to target anti-poaching interventions more effectively and more efficiently, with important implications for the development of management strategies and conservation plans in a range of conservation scenarios. This article is protected by copyright. All rights reserved.

Twitter Demographics

The data shown below were collected from the profiles of 37 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 96 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 21%
Researcher 16 17%
Student > Master 13 14%
Student > Doctoral Student 10 10%
Student > Bachelor 9 9%
Other 19 20%
Unknown 9 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 29%
Environmental Science 28 29%
Social Sciences 11 11%
Biochemistry, Genetics and Molecular Biology 4 4%
Medicine and Dentistry 3 3%
Other 9 9%
Unknown 13 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 31. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 July 2020.
All research outputs
#826,735
of 18,090,383 outputs
Outputs from Conservation Biology
#541
of 3,395 outputs
Outputs of similar age
#21,369
of 285,141 outputs
Outputs of similar age from Conservation Biology
#11
of 28 outputs
Altmetric has tracked 18,090,383 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,395 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 285,141 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.