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3.5 ULS Hotspot Detection for Bad Nutrition Case in Some Districts in Java Island
Nutrition is a process of functioning food normally consumed by process of digestion, absorption, transportation, storage, metabolism and secretion of
substances that are used to sustain life, growth and normal functioning of organs, as well as generate energy. In other words, nutrition is the kind of food that human
eat and the way it affects their health Longman 2004. Good nutrition leads to good health, and may include a diet that contains vitamins, proteins, etc.
Bad nutrition leads to bad health with many diseases such as malnutrition. Bad nutrition is a severe form of occurrence of chronic malnutrition.
The objective of this part is to obtain the bad nutrition hotspot subdistricts in Java Island. The data is taken from BPS 2008, i.e. the aggregate number of bad
nutrition in sub districts. The data is assumed to follows the Poisson process, so the hotspot detection using Poisson distribution concept to detect the hotspot area.
A Poisson process is a non-deterministic process where events occur continuously and independently of each other. The Poisson distribution is used to
model the number of events occurring within a given time interval or a particular area or in a size of population. A Poisson distribution is a discrete probability
distribution that represents the probability of events having a Poisson process occurring in a certain period of time Ross 2002. Some examples of a Poisson
process are the radioactive decay of radio nuclides, a people suffering a disease bad nutrition among a large number of people population in an area. Based on
the description about Poisson process from Ross, the bad nutrition case can be assumed follows the Poisson process and have Poisson distribution.
3.6 The Results of Bad-nutrition Hotspot Detection
This part is the result of bad nutrition ULS hotspot detection in 9 districts of Java Island. Figure 16 shows hotspots areas dark color of Kuningan, Karawang,
Majalengka, Temanggung, Boyolali, and Cilacap, while Figure 17 shows hotspots area of Blitar, Ngawi, and Jember. Significance levels of all hotspots in these 9
districts are less than 0.05 or p-values are less than 0.05. There are two hotspots areas in Kuningan, in the west and east parts. Actually, p-values of the hotspot
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Kuningan Karawang
Majalengka Temanggung
Boyolali Cilacap
Figure 16 ULS Hotspot of bad nutrition in Kuningan, Karawang, Majalengka, Temanggung, Boyolali, and Cilacap
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Blitar Ngawi
Jember Figure 17 ULS Hotspot of bad nutrition in Blitar, Ngawi, and Jember
in east part is smaller than in west area, so this hotspot is more significant as the hotspot. Karawang district has hotspot area from the North to the middle of area,
while hotspots in other districts are vary in positions. Kuningan, Karawang, Majalengka, and Temanggung districts have 7 subdistricts as the hotspot area,
Boyolali has 5 subdistrict, Cilacap has 8 subdistricts, Blitar, Jember, and Ngawi have 4, 10, and 6 subdistricts, respectively as the bad nutrition hotspot areas. At
the map, the darker red color areas are the hotspot areas. Appendix 7 to 14 shows the detail results.
3.7 Conclusion According to the result of comparison two hotspot detection methods, it is
believed, ULS hotspot detection is better than scan statistic. For application, ULS hotspot detection method gave the result of bad nutrition hotspot areas in 9
districts in Java Island. This hotspot detection result will be used as a dichotomy explanatory in modeling in Chapter 4.
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Chapter 4
NESTED GENERALIZED LINEAR MIXED MODEL FOR CORRELATED DATA
4.1 Introduction
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