ellipses. Hence, these two methods were compared to see which scan method is most suitable for detecting hotspots in Java Island, that has an arbitrary shape.
The evaluation used in this study was based on the stages presented in Figure 4. First, scan statistics methods were run multiple times, starting from a
small maximum-size 5 and systematically increased to the 50 default value. Second, the results were visualized in a map matrix for side-by-side comparison
of different maximum-sizes presented in Appendix 4 and Appendix 7. Third, the reliability of a region in a map was calculated and interpreted. Fourth, core
clusters would be discriminated from heterogeneous clusters through interpretation of the reliability. Fifth, the interpretation of core clusters has been
confirmed by comparing the results to other independent techniques and consultation with domain experts. In this study for poverty and food scarcity, the
results of Satscan and ULS were compared with the Food Security Map and Poverty Map accomplished by CBS and FSA.
4.2.1. Poverty
The evaluation of poverty hotspots based on a map matrix for side-by-side comparison of different maximum-sizes was presented in Appendix 4 and
summary table was presented in Appendix 5, which showed that the sensitivity to the maximum-size parameter. It has been known that sensitivity was related to 1
clusters tend to contain heterogeneous contents, particularly when using large maximum-size values and 2 components relate to stability of clusters in terms of
location and size as the maximum-size value is varied. For an example clusters that tend to contain heterogeneous contents, particularly when using large
maximum-size values, Satcan detected Gresik and Lamongan while ULS detected Demak that had a poverty level below 22 as a significant hotspot at a
50 maximum spatial cluster size. Gresik and Lamongan were aside Bojonegoro and Tuban meanwhile Demak was aside Batang that had high poverty levels.
These areas were not detected in lower maximum spatial cluster size of 30, 20, 10, and 5. Hence, these kinds of regions were described as
heterogeneous clusters. Such clusters were composed of not only the high-risk
locations that are of interest in hotspot detection, but also many low-risk locations that are not of interest.
After taking notice of these heterogeneous clusters, the discrimination of stablecore clusters from heterogeneous andor unstable ones were done. A core
cluster is considered as an often smaller, homogeneous subsets within heterogeneous clusters that exhibit values high enough to reject the null
hypothesis on their own strength. Or in other words a core cluster is a cluster that contains homogeneous, high reliability scores and high percentage of cases. Both
ULS and Satscan had 20 municipalities having a value of 1 meaning that the location was within a significant cluster in all scans. Between these 20
municipalities 16 of them were the same. All of the 6 first priority poverty areas and 10 second priority areas FSA had a reliability of 1, hence it was certain that
Trenggalek, Ponorogo, Pacitan and Wonogiri; Lumajang and Jember; Bojonegoro, Ngawi and Kab Blora; Batang Magelang, Temanggung,
Banjarnegara; and Garut were reliable core clusters. The average reliability of ULS 0.724 was slightly higher that Satscan
0.708. Both ULS and Satscan had 20 municipalities having a value of 1 meaning that the location is within a significant cluster in all scans. Satscan had 3
municipalities having a value 0.17 the municipality was only detected once at a 10 maximum spatial cluster size. While ULS detected 9 municipalities having a
value 0.17 the municipality was only detected once at a 50 maximum spatial cluster size. These various values of reliability indicates that one should be
precautious in choosing the most suitable maximum spatial cluster size. This study has also conducted an evaluation of Satscan and ULS’s poverty
hotspots based on the priority criteria of FSA shown in Figure 8. By using a maximum spatial cluster size of 50, 40, 30, 20, 10, and 5 ULS and
Satscan were able to detect all of the first priority areas. When detecting the second priority areas ULS performed better than Satscan. ULS detected an
average of 90.71 second priority areas, while Satscan detected an average of 77.14 second priority areas. The average false detection, which is the average
percentage of having detected a non critical area as a hotspot, of ULS 3.52 is lower compared to Satscan 10.38. In detecting first and second priority areas,
ULS also indicated mo maximum spatial cluste
indicated more precise a output and comparison t
Figure 8 ULS a size o
Based on the resul see whether these two
spatial cluster size of municipalities and also
ULS and Satcan. For th Satscan were detected b
areas that was detected b Cilacap, Pemalang, and
Cilacap, Pemalang, Pu Wonosobo, and Pekalo
compared to the surro
10 20
30 40
50 60
70 80
90 100
50 40
First Priority-ULS Second Priority-Sat
OK-ULS
more stable performance compared to Satscan ter size is changed. Therefore, based on this rese
e and stable performance. A more detail povert table can be seen in Appendix 6.
S and Satscan Performance with a maximum spati e of 50, 40, 30, 20, 10, and 5
ults above, a comparison of hotspots was also co o methods detected similar hotspots. For the
of 50, there were 23 82 similar secon o 7 54 similar third priority municipalities d
the second and third priority all of the hotspots d by ULS. As an example, there were four secon
d by ULS and not detected by Satscan, which we nd Purbalingga. It can be seen that Figure 9,
Purbalingga, Tegal, Banyumas, Banjarnegara, alongan were to be scanned by the circle w
rrounding areas, Banjarnegara, Batang, Purwo
40 30
20 10
5 First Priority-Satscan
Second Priority Satscan
Third Priority-ULS Third Priority-
OK-Satscan
n when the esearch ULS
erty hotspots
atial cluster
conducted to e maximum
ond priority detected by
s detected by cond priority
were Brebes, , if Brebes,
, Kebumen, window A,
worejo, and
5 rity-ULS
-Satscan
Wonosobo had higher occurrence of poor see Table 3. Hence, only Banjarnegara, Batang, Purworejo, and Wonosobo became candidate hotspots.
Table 3 The Percentage of Poor in Scan Window A and B
Scan Window A Scan Window B
Municipality of Poor
Municipality of Poor
Batang 43.07 Cilacap
28.61
Banjarnegara 32.57 Banjar
26.91 Wonosobo
29.15 Brebes 25.89
