Spatial weighting approach in numerical method for disaggregation of MDGs indicators

Disaggregation use to separate and classify the data based on certain characteristics or on administrative level. Disaggregated data is very important because some indicators not measured on all characteristics. Detailed disaggregation for development indicators is important to ensure that everyone...

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Bibliographic Details
Published inIOP conference series. Materials Science and Engineering Vol. 332; no. 1; pp. 12049 - 12055
Main Authors Permai, S D, Mukhaiyar, U, Satyaning PP, N L P, Soleh, M, Aini, Q
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.03.2018
Subjects
Euclidean geometry
Indicators
Numerical methods
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Summary:Disaggregation use to separate and classify the data based on certain characteristics or on administrative level. Disaggregated data is very important because some indicators not measured on all characteristics. Detailed disaggregation for development indicators is important to ensure that everyone benefits from development and support better development-related policymaking. This paper aims to explore different methods to disaggregate national employment-to-population ratio indicator to province- and city-level. Numerical approach applied to overcome the problem of disaggregation unavailability by constructing several spatial weight matrices based on the neighbourhood, Euclidean distance and correlation. These methods can potentially be used and further developed to disaggregate development indicators into lower spatial level even by several demographic characteristics.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/332/1/012049