RESULT T2 972011007 Full text
Volume 72 – No.2, June 2013
guidelines. Then calculate the spatial contiguity matrix, of which in this study uses queen
contiguity matrix: the calculation of neighbor matrix by dividing any part of the common
boundary region. Figure 4 shows the queen contiguity matrix in Boyolali Regency. Dot
represents a sub district, and the red line shows the region of the neighboring districts. If sub districts
are connected with a red line, then the value
on the sub-value is 1. When a sub district is not connected, then the value
= 0. Figure 4 shows there are 70 connected areas in Boyolali Regency.
Figure 4 Queen Contiguity Matrix
The next stage is to calculate GISA. GISA
calculation results are shown in Table 3. From these calculations it is found that the value of E I
is -0.05. If the value of I E I, then the autocorrelation is positive. This means that the data
pattern to form clusters. Random data pattern forms if I = E I, meaning that there is no spatial
autocorrelation. If I E I, then the autocorrelation has a negative value, meaning that the data pattern
spreads. GISA calculation results in the form of an index value of Moran on seven indicators of food
insecurity. Moran index value in 2008-2010 on seven indicators shows the high level of spatial
correlation indicator types can be seen in Table 2
. There are approximately five indicators that make up the cluster pattern. This means that
neighboring sub districts have correlation on one another. While indicators have random spatial
pattern, meaning the neighboring districts have less correlation on one another. The highest correlation
between regions close +1 is owned by the indicator percentage of population living below
poverty line, with Moran index of 0.464. This index has potential to have converging spatial
patterns clusters. It means that percentage of population
living below
poverty line
in neighboring sub districts in Boyolali Regency still
have mutual correlation on each other.
Table 3. Calculation of GISA
Year Indicator Moran ’s Index
I Spatial
patterns 2008
1 -0.017
Uniform 2
0.464 Cluster
3 -0.05
Random 4
0.178 Cluster
5 0.45
Cluster 6
0.13 Cluster
7 0.05
Cluster 2009
1 0.053
Cluster 2
0.501 Cluster
3 -0.05
Random 4
0.246 Cluster
5 0.45
Cluster 6
0.13 Cluster
7 0.06
Cluster 2010
1 -0.025
Uniform 2
0.448 Cluster
3 -0.05
Random 4
0.262 Cluster
5 0.45
Cluster
Volume 72 – No.2, June 2013
6 0.13
Cluster 7
0.03 Cluster
Figure 5
is a LISA map of underprivileged
population in 2008. From Figure 5, shows that there are patterns of spatial clusters clustered and
correlated in sub district Wonosegoro and Kemusu. These sub districts are marked with red
color, which is a hotspot area High-High. It means that these two sub districts have a high
percentage of underprivileged population, and surrounded by sub districts that have a high
percentage of underprivileged populations as well. Sub district that categorized as hotspot is a sub
district that has potentially in food insecurity. Thus, such sub districts can be the focus in
governments efforts to improve the welfare of the population. In addition, there is a district that has a
High-Low value, the district Juwangi marked in green. It shows that the percentage of the
underprivileged population in Juwangi sub district is high, while the percentage in the surrounding
regions are low. However, this condition does not affect the surrounding districts.
Figure 5 underprivileged population in 2008
In 2009-2010, there was a change in the spatial pattern of sub district Wonosegoro and
Sambi. Wonosegoro sub district as hotspot region has become a non-significant region Figure 6. It
means that this sub district does not have a spatial correlation with the surrounding districts. A region
can be a non-significant because there are factors limiting the distribution of the population from one
region to the other regions, such as lakes, damaged roads, and other physical factors [Prasetyo, 2011].
On the contrary, in Sambi sub district, there are significant spatial pattern changes from non-
significant region to hotspot region. This means the percentage of the underprivileged population of
Sambi sub district is quite high, at the surrounding sub
districts with
high percentage
of underprivileged population as well.
Figure 6 underprivileged population in 2009-2010
Figure 7 shows that in 2008, there are spatial
pattern of local cluster in Juwangi, Wonosegoro and Boyolali sub districts. Hotspot regions that
marked in red is Wonosegoro sub district. It is has potentially in food insecurity regions because it has
high percentage of households without access to electricity and surrounded by the region with high
percentage of households without access to electricity as well. Meanwhile, Juwangi and
Boyolali sub districts included in the coldspot Low-Low. It means that households residing in
these two sub districts and the surrounding area have access to electricity.
Figure 7
households without access to electricity
2008
In 2009-2010, Boyolali sub district turned into a non-significant region, i.e. region which does not
have a spatial correlation with the surrounding areas Figure 8.
Volume 72 – No.2, June 2013
Figure 8 households without access to electricity
2009-2010
Figure 10 shows a map of LISA with seven
indicators of food insecurity in 2008. Food insecurity area is area that included in the hotspot
region in all indicators of food insecurity. From the figure shows that no region is experienced food
insecurity. However, some districts has potentially affected by food insecurity because of the districts
included in the hotspot regions. There are hotspot regions on three indicators of food insecurity,
which is on the indicator of percentage underprivileged population, the percentage of
households without access to electricity. This means that these three indicators has correlation
the occurrence of food insecurity in Boyolali Regency
in 2008.
On the
indicator of
underprivileged population percentage, there are two sub districts that included in hotspot region
and have potential become food insecurity region i.e. namely Wonosegoro, and Kemusu sub districts.
Wonosegoro district is also included in the hotspot region on indicator of household percentage
without access to electricity.
Figure 10 LISA map 2008
In 2009, there has been a hotspot change region with three same indicators as in 2008
Figure 12.
Changes occurred in the hotspot region
on the
percentage indicator
of underprivileged population in two districts, namely
Wonosegoro and Sambi sub districts. Wonosegoro sub district that originally was a hotspot area
turned into non-significant regions. Meanwhile, the Sambi sub district that was as non-significant
region turned into hotspot region.
In 2010, there were no changes in the spatial patterns of both the indicator and the region.
Figure 12
shows a map of food insecurity regions in 2009 and 2010. Potentially food insecure
regions are marked with red color. Based on the map, there are five potentially food insecure
districts, i.e. Wonosegoro, Kemusu, Ampel, Cepogo and Sambi sub districts. Potentially food insecurity
in Wonosegoro sub district affected by the high percentage indicator of households without access
to electricity. This indicates that the percentage of underprivileged people who last year had a high
level turned into low level. Causes of potentially food insecurity in Kemusu sub district is still the
same as in 2008, namely the high percentage of underprivileged population. Similarly in Sambi sub
Volume 72 – No.2, June 2013
district, the high percentage of the underprivileged population causes this sub district experienced
potentially food insecurity.
Figure 12 LISA map 2009-2010