Serie II numero 81 - Dipartimento di Matematica e Informatica

A DISTANCE DECAY MODEL FOR LOCAL SPATIAL STATISTICS 293
(results not showed). Having established the parameters for the distance decay
model, we can now concentrate on analysing the results obtained for local
spatial index considering the various neighbourhood models. It should be
highlighted that in formula (1), the quantity ij j
j ji
z w for the “Classic Binary”
,
and “MaxMin” methods becomes a simple mean, while for the “DSMA” and
“Distance Decay” methods it becomes a weighted mean with weightings
sensitive to the effective distance between the interconnected units. The main
results are shown in tables 1,2 and 3.
Distance decay function
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Distance (Km)
n
=1; =1
=0.5; =2
=5; =2
threshold dist.
Fig. 1 – Distance decay function for various values assumed by the parameters
At the level of overall spatial autocorrelation, we note that the four methods do
not show statistically significant autocorrelation of the data (tab. 1). In practice,
the variable railway density does not show at an overall level to be statistically
correlated with the territory. The situation is differentiated, however, by
analysing the results at a local level. As said before, the spatial data may often
fail to show territorial dependence at a global level but show it at a local level.
In the case in point, tables 2 and 3 show the Local Moran values only in the
provinces in which the data was found to be statistically significant. For the
various methods compared, the analysis identifies situations of marked local
autocorrelation. Specifically, the spatial outliers detected were found to be the
same for the “MaxMin” and “DSMA” methods, while they differed for the
“Classic Binary” and “Distance Decay” methods. In particular, this latter