Cross-section and Spatial Approaches for the Agricultural Contexts in the 27 Countries of the European Union - The Agricultural Economics of the 21st Century - page 31

Fig. 3.19 Local spatial autocorrelation (one neighbor in a queen contiguity matrix) for the
logarithm of the average utilized agricultural area (ha) among European Union countries
Fig. 3.20 Local spatial autocorrelation (one neighbor in a queen contiguity matrix) for the
logarithm of the average AWU Labour force—directly employed by the holding among
European Union countries
Fig. 3.21 Local spatial autocorrelation (one neighbor in a queen contiguity matrix) for the
logarithm of the average number of holdings among European Union countries
with high values and low values, respectively, for the variable considered. The
values high-low and low-high represent negative local spatial autocorrelation. The
figures show namely positive local spatial autocorrelation for high values where
France appears in all figures, as a country that is positively influenced by high
values for neighboring countries. In Fig. 3.21 (for the variable number of holdings)
Belgium is positively autocorrelated for low values with neighboring countries.
This analysis of global and local spatial autocorrelation may prove to be an
important information for the design of future policies.