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Contextualizing local-scale point sample data using global-scale spatial datasets

Lessons learnt from the analysis of large-scale land acquisitions

24 February 2016

This paper examines how the geospatial accuracy of samples and sample size influence conclusions from geospatial analyses. It does so using the example of a study investigating the global phenomenon of large-scale land acquisitions and the socio-ecological characteristics of the areas they target.

Authors: Sandra Eckert, Markus Giger, Peter Messerli

Highlights 

  • Contribution of spatially explicit evidence for policy requires accurate, detailed spatial data. 
  • Avoid using national-scale statistics to characterize phenomena having local-scale impact.
  • The more geographic contexts vary within a country the more detailed the spatial scale is required.
  •  Focus on improving the geospatial accuracy of data rather than obtaining a larger sample.
  • Uncertainty is introduced by inadequate definition of the shape/size of analysed geographic contexts.

Read the full paper here

Abstract

This paper examines how the geospatial accuracy of samples and sample size influence conclusions from geospatial analyses. 

It does so using the example of a study investigating the global phenomenon of large-scale land acquisitions and the socio-ecological characteristics of the areas they target. 

First, we analysed land deal datasets of varying geospatial accuracy and varying sizes and compared the results in terms of land cover, population density, and two indicators for agricultural potential: yield gap and availability of uncultivated land that is suitable for rainfed agriculture. We found that an increase in geospatial accuracy led to a substantial and greater change in conclusions about the land cover types targeted than an increase in sample size, suggesting that using a sample of higher geospatial accuracy does more to improve results than using a larger sample.
The same finding emerged for population density, yield gap, and the availability of uncultivated land suitable for rainfed agriculture. Furthermore, the statistical median proved to be more consistent than the mean when comparing the descriptive statistics for datasets of different geospatial accuracy. 

Second, we analysed effects of geospatial accuracy on estimations regarding the potential for advancing agricultural development in target contexts. Our results show that the target contexts of the majority of land deals in our sample whose geolocation is known with a high level of accuracy contain smaller amounts of suitable, but uncultivated land than regional- and national-scale averages suggest. Consequently, the more target contexts vary within a country, the more detailed the spatial scale of analysis has to be in order to draw meaningful conclusions about the phenomena under investigation. 

We therefore advise against using national-scale statistics to approximate or characterize phenomena that have a local-scale impact, particularly if key indicators vary widely within a country.