Spatial weighting approach in numerical method for disaggregation of MDGs indicators

Indonesian Operations Research Association - International Conference on Operations Research 2017

Permai, S.D.^1 ; Mukhaiyar, U.^2 ; Satyaning Pp, N.L.P.^3 ; Soleh, M.^4 ; Aini, Q.^5

Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta

11480, Indonesia^1

Statistics Research Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung

40132, Indonesia^2

Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya

60111, Indonesia^3

Department of Statistics, Bogor Agricultural University, Bogor

16680, Indonesia^4

Regional Balance and Statistical Analysis Sub-Division, Central Statistics Agency, West Nusa Tenggara

84455, Indonesia^5

关键词: Demographic characteristics;    Disaggregation;    Euclidean distance;    Numerical approaches;    Population ratio;    Spatial levels;    Weight matrices;    Weighting approaches;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/332/1/012049/pdf
DOI  :  10.1088/1757-899X/332/1/012049

来源: IOP

【 摘 要 】

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.