People and their livestock represent today more than 95% of the terrestrial vertebrates biomass and have a massive global ecological impact. High resolution global data on people and livestock distribution are hence essential in geography and environmental and agricultural sciences. Generating these data requires the collection of recent census counts, the use of algorithms to disaggregate these counts at higher spatial resolution (pixels), and application of the method over large regions, hence generating large amount of data. Recent developments in the field have been driven by strong needs for updated data and aimed at the rapid production of new outputs, without necessarily taking the time to examine, or re-examine, the method and concepts supporting them.
The main objective of this project was to evaluate the different approaches used to predict and represent people and livestock distribution in global databases, to improve the existing methods by addressing some of their limitations, and to provide recommendations for future developments. This implied to examine both the conceptual and statistical models that support methods, as well as dealing with complex scale and real-world representation more theoretical issues.
More specifically, the project firstly aimed to compare quantitatively and qualitatively the different methods and database available to map the distribution of people and livestock in terms of quantitative goodness of fit (how good are numbers that are predicted), spatial patterns (how similar are observed and predicted distributions in terms of heterogeneity and level of clustering) and processing performances. Secondly, the project aimed to examine how uncertainty in the predicted values can be estimated and best represented. The third aim of the project was to identify innovative ways to improve the spatial realism of outputs at different scales, either by changing the modeling process itself or by using alternative forms of representation.
SpELL staff involved in the project:
External partners of the project:
Spatial analysis and characteristics of pig farming in Thailand
W. Thanapongtharm, C. Linard, P. Chinson, S. Kasemsuwan, M. Visser, A. E. Gaughan, M. Epprech, T. P. Robinson, and M. Gilbert.
"BMC Veterinary Research", Vol. 12, Issue 1, 2016.
Using Random Forest to Improve the Downscaling of Global Livestock Census Data
G. Nicolas, T. P. Robinson, G. R. W. Wint, G. Conchedda, G. Cinardi, and M. Gilbert.
"PLOS ONE", Vol. 11, Issue 3, Pages e0150424, 2016.
H7N9 and H5N1 avian influenza suitability models for China: accounting for new poultry and live-poultry markets distribution data
J. Artois, S. Lai, L. Feng, H. Jiang, H. Zhou, X. Li, M. S. Dhingra, C. Linard, G. Nicolas, X. Xiao, T. P. Robinson, H. Yu, and M. Gilbert.
"Stochastic Environmental Research and Risk Assessment", Pages 1-10, 2016.
Income Disparities and the Global Distribution of Intensively Farmed Chicken and Pigs
M. Gilbert, G. Conchedda, T. P. Van Boeckel, G. Cinardi, C. Linard, G. Nicolas, W. Thanapongtharm, L. D'Aietti, W. Wint, S. H. Newman, and T. P. Robinson.
"PLOS ONE", Vol. 10, Issue 7, Pages e0133381, 2015.
Mapping the Global Distribution of Livestock
T. P. Robinson, G. R. W. Wint, G. Conchedda, T. P. Van Boeckel, V. Ercoli, E. Palamara, G. Cinardi, L. D'Aietti, S. I. Hay, and M. Gilbert.
"PLoS ONE", Vol. 9, Issue 5, Pages e96084, 2014.