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Spatial weighting approach in numerical method for disaggregation of MDGs indicators
Permai S.D.a, Mukhaiyar U.b, Satyaning Pp N.L.P.c, Soleh M.d, Aini Q.e
a Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta, 11480, Indonesia
b Statistics Research Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung, 40132, Indonesia
c Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia
d Department of Statistics, Bogor Agricultural University, Bogor, 16680, Indonesia
e Regional Balance and Statistical Analysis Sub-Division, Central Statistics Agency, West Nusa Tenggara, 84455, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Published under licence by IOP Publishing Ltd.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.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Demographic characteristics,Disaggregation,Euclidean distance,Numerical approaches,Population ratio,Spatial levels,Weight matrices,Weighting approaches[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1757-899X/332/1/012049[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]