icegov2012 proceedings

Ranking and Hotspot Detection Methods on Infant Health for
Districts in Java, Indonesia: E-Governance Micro Tools
Yekti Widyaningsih
Department of Mathematics
Faculty of Mathematics and Natural Sciences
University of Indonesia
yekti@ui.ac.id
ABSTRACT
As the Academia stakeholder, this paper shares the method and
theory which extend the understanding of Electronic Governance.
The purpose of this research is to obtain the ranking of infant
health in Java based on five indicators and three indicators. These
indicators or variables are number of infant deaths (infd), number
of people in poverty (pov), number of infants with low birth
weight (lbw), number of deliveries in absence of health personnel
(abhp), and average education shortfall of women (avedsf). All
variables are district level aggregates. Besides ranking, hotspots
based on those indicators are also detected by ULS hotspot
detection method, while rankings are computed based on ORDIT,
implemented in R software. Rankings of the districts based on 5
(all) indicators and 3 indicators (infd, pov, lbw) are obtained. Also,
ranking is obtained based on salient scaling of 5 indicators and
salient scaling of 3 indicators. According to those results, the most
severe districts are districts 87 and 90, while the least severe
districts are districts 73, 31, and 35. There are many districts in
the hotspot area as the results of the ULS hotspot detection.
Districts 87, 90, 47, 58, 83, 44, and 45 are the worst areas of
infant health. This result is important information for the
government, especially the Health Department to make decisions
for the improvement of health programs. The methods can be used
as a micro tool to extend the function and understanding of egovernance.
Categories and Subject Descriptors
D.2.4 [Software/Program Verification - Statistical method]; G.3
[Probability and Statistics – Correlation];J.2 [Physical Sciences
and Engineering - Mathematics and statistics]; J.4 [Social and
Behavioral Sciences – Sociology]; K.6 [Management of
Computing and Information Systems]
General Terms
Measurement, Performance, Theory, Legal Aspects, Verification
Keywords
Ranking, Infant Health Indicators, ORDIT, R Software,
Computational Complexity, Hotspot, Upper Level Set Scan
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ICEGOV '12, October 22 - 25 2012, Albany, NY, United States, NY, USA
Copyright 2012 ACM 978-1-4503-1200-4/12/10…$15.00.
226
Wayne L. Myers
Penn State Institutes of Energy and Environment
Penn State University, University Park,
PA 16802 USA
wlm@psu.edu
Statistics
1. INTRODUCTION
Governments around the world wrestle with a wide range of social
and political challenges and look for innovations in technology,
not only in the government institutions of, but also in the
interactions between the government and citizens to help them
meet these challenges. Serving citizens efficiently and effectively,
engaging interested parties in decision making, and creating
sustainable economies, are among the many ways innovations in
policy and practice can directly impact the public value created by
any Electronic Governance investment. To justify continued or
new investments in Electronic Governance, decision makers must
increasingly provide evidence that the innovations they are
supporting create public value. Decision-makers play a very
important role in the management of a country. The decisions
made will have an impact, both at present and in the future. As the
Academia stakeholder, this paper shares the methods and theories
which extend the understanding of Electronic Governance.
Hopefully, this paper can contribute concrete solutions for
specific challenges faced by governments, especially in making a
decision about infant health improvement. To achieve effective
decision making, it is important to know the areas or districts most
in need of improvement. For a problem like this, ranking methods
are necessary.
To meet these social challenges, it is important for the
government to plan and arrange budgets at the districts level that
are appropriate to its need for improvement in particular matters.
This paper focuses on districts ranking based on infant health
indicators. Many researches about infant health have been done.
Some of them are as follows. A research has shown that
persistence of poverty and continuing unequal distribution of
health care resources to pregnant women and young mothers
render relative risks of neonatal and post-neonatal mortality 1.5
times greater than that experienced by infants not born in poverty
[1]. In addition, ‘poverty is infant health risk’ is a theoretical
proposition according to Centers for Disease Control and
Prevention [2]. OECD reported that the correlation between
percentage of low birth weight infants and infant mortality rates is
stronger in emerging countries [3]. Other research showed that
adding a maternity clinic to a village decreases the odds of infant
mortality by almost 15 per cent, in comparison to the risk before
the clinic was added. An additional doctor reduces the odds by
about 1.7 per cent [4]. Furthermore, the final model of Desai and
Alva, shows that maternal education has a statistically significant
impact on infant mortality and height-for-age in only a handful of
countries [5]. According to those findings, an abstract concept,
infant health relates to the six measurable indicators: poverty,
infant mortality, low birth weight, health personnel, and education
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