In Table 5 above it can be seen that there were 9 areas that were considered as the most critical areas that needed more attention from the government. These
areas were located in the northern Central and East Java. There were only three cities that were not either poverty, food scarcity, nor unemployment hotspots,
which were Kota Batu, Kota Salatiga, and Kota Serang. Areas which were considered more secure to poverty were located in West Java. A joint hotspot map
of poverty, food insecurity, and unemployment can be seen in Appendix 13. After locating these areas, the core cluster of each case was determined.
These core clusters gave indications of which cluster should be given prioritization. The core clusters that had a reliability score above 0.6 or it is at
least detected four times when using the maximum spatial cluster size of 50, 40, 30, 20, 10, and 5. The stability of clusters can also be seen in
Appendix 11. Afterwards an ordinal logistic model was built in order to identify the main factors causing these joint hotspots. By having knowledge on the factors
causing these hotspots, hopefully precise actions can be done to alleviate critical areas in Java.
4.4. Determining Factors Causing Poverty, Food Insecurity, and
Unemployment
In a model to determine the main factors causing poverty, food insecurity and unemployment joint hotspots, there were 12 variables Table 2 from various
sectors analyzed. These sectors included Citizenship and Labour, Education, Economy, Politics and Security, Location, and also Housing and Environment.
Before selecting these 12 variables 23 indicators were used Appendix 15, but there were 11 indicators that were highly correlated with other variables.
Therefore to prevent multicolinearity in the model these variables were excluded. The correlation table between of these variables can be seen in Appendix 16.
At first an ordinal logistic model for the 6 categories stated in Table 1 was built. It can be seen from Table 6 that there were 7 significant variables at a 15
level of significance, which were School facilities, credit facilities, the percentage of trade village, industrial village, and services village, ratio of farm industry,
and proportion of villages without electricity. Hence these factors should be given
prioritization in alleviating poverty and eradicating food scarcity and unemployment.
Table 6. Ordinal Logistic Regression Table
Predictor Coef
SE Coef Wald
P Odds
Ratio Const1
-4.56 1.21
-3.78 0.00
Const2 -2.08
1.11 -1.88
0.06 Const 3
-1.30 1.10
-1.18 0.24
Const4 -0.83
1.10 -0.75
0.45 Const5
0.21 1.11
0.19 0.85
Ratio of School per Village -0.44
0.14 -3.18
0.00 0.64
of Credit Facilities 0.94
0.37 2.54
0.01 2.56
of Industry Village -4.90
2.93 -1.67
0.10 0.01
of Trade Village 6.66
1.91 3.49
0.00 782.22
of Service Village -4.21
2.11 -1.99
0.05 0.01
of Villages Without Electricity
0.05 0.02
2.56 0.01
1.05 Ratio of Farm Industry
0.21 0.10
2.05 0.04
1.23 Based on the result above, the government should take notice that the
increase of school facilities, stimulating economical potential of a village in industry and services, decreased the possibility of an area to become critical areas.
The government should give more attention to credit facilities, economical potential of a village in trade, villages without electricity, and small scale farm
industry. It turned out that the increase of these factors increased the possibility of a municipality to become a critical area. From this study it was pointed out that
credit facilities, farm Industry and trade in a village did not show indication that it could improve the welfare of people living in critical areas. Hence, these factors
should be revitalized. Areas that had a high ratio of families living without electricity were also critical points in solving the problem of poverty,
unemployment, and food scarcity. Therefore the government should have given more attention to people who lived in these areas.
Evaluation towards the model was also done by conducting the likelihood- ratio test which uses G statistic. Based on the results given, G = 63.438 with a p-
value=0.000 indicated that H will be rejected or there is at least one explanatory
variable had a significant influence on the joint hotspots. Further evaluation was
carried out by using measures of association, Correct Classification Rate CCR, and Goodness of fit test that would be explained in more detail below.
For measure of association, concordant and discordant pairs indicated how well your model predicted data. The more concordant pairs, the better the models
predictive ability. In the model above there were 77.2 concordant pairs, which was a good indicator. Goodness of fit test intended to test whether the observed
data were inconsistent with the fitted model. If they were not indicated by the significance values that are larger-then it can be concluded that the data and the
model predictions were similar and that the model was good. Another evaluation used was the Correct Classification Rate CCR. The CCR of the model above for
all response categories were 52 while the CCR for responses categories 1, 5, and 6 was 78.87. The CCR for response categories 2, 3, and 6 were 0. This indicated
that the categories with low response had very low precision. Hence it was suggested to try other link functions or reorder the response value. Other link
functions have been used such as the complementary log-log suggested for skewed distribution. The results of the CCR was still low. While reordering the
response value in to three categories established higher CCR but it will be difficult to interpret. Further results on the model can be seen in Appendix 16.
V. CONCLUSION AND RECOMMENDATION
5.1. Conclusion
By using Geoinfarmatics techniques the research concluded that: a.
By comparing ULS and Satscan on poverty and food insecurity cases in Java, this research pointed out that ULS had a more precise and stable
performance compared to Satscan. ULS was suggested as an alternative to thematic maps often used by government institutions. Maps based on
SatscanULS were more precise compared to thematic maps because spatial scanning methods could not only detect whether an area was a
critical area or not, but also conducted hypothesis testing whether the area was significantly different or not compared to surrounding areas and used
the geographical information data to enhance the accuracy of results. b.
Based on the joint hotspots of poverty, unemployment, and food scarcity there were nine areas Banyumas, Batang, Cilacap, Demak, Kab. Madiun,
Kota Pekalongan, Kulonprogo, Pemalang, Purworejo considered as the most critical area that needed more attention from the government. Most
of these areas were located in Central Java. There were only three cities that were not either a poverty, food scarcity, nor an unemployment
hotspots, which were Kota Batu, Kota Salatiga, Kota Serang. c.
Main factors causing the joint hotspots were identified by using Ordinal Logistic Regression Model. Factors related to the hotspot were school
facilities, village trade, village industry, village services, slum areas, and proportion of families without electricity, and proportion of credit
facilities.
5.2. Recommendation
Further research on other methods used for hotspot detection should be done. In this research, ULS has better performance than Satscan, it should be
simulatedapplied not only in Java but also in Indonesia where there is also a large body of oceans separating the islands. Development towards tools that can be used
to enhance the practicality in SatscanULS outputs is also needed. This study hopefully would become a pioneer in further studies at a national level and
