Matching espacial para georreferenciar datos de encuestas de hogar

Mónica Navarrete; Patricio Aroca; Jorge Bernal

doi:10.4067/s0718-52862017000100053

Matching espacial para georreferenciar datos de encuestas de hogar

Translated title of the contribution: Spatial matching for georeferencing data of household surveys

Mónica Navarrete, Patricio Aroca, Jorge Bernal

Business School

Research output: Contribution to journal › Article › peer-review

Abstract

This paper develops a methodology using business intelligence (data warehouse) and OLAP tools (Online Analytical Processing) to match individuals from a household survey data to a census one. In order to geo-reference the household data, the method takes advantage of the geographical information of the census. Using the 2003 Household Survey and the 2002 Chilean Census of Population and Housing, the average household income is projected to intra-municipality levels and the results are compared to Elbers et al. (2003) methodology. The results show that the proposed methodology allows for taking into account and display the impact of intra-municipality heterogeneity and the spatial interaction among the spatial units.

Translated title of the contribution	Spatial matching for georeferencing data of household surveys
Original language	Spanish
Pages (from-to)	53-80
Number of pages	28
Journal	Estudios de Economia
Volume	44
Issue number	1
DOIs	https://doi.org/10.4067/s0718-52862017000100053
State	Published - 2017

Access to Document

10.4067/s0718-52862017000100053

Cite this

@article{85babf630f2c4b9194172f51329378d3,

title = "Matching espacial para georreferenciar datos de encuestas de hogar",

abstract = "This paper develops a methodology using business intelligence (data warehouse) and OLAP tools (Online Analytical Processing) to match individuals from a household survey data to a census one. In order to geo-reference the household data, the method takes advantage of the geographical information of the census. Using the 2003 Household Survey and the 2002 Chilean Census of Population and Housing, the average household income is projected to intra-municipality levels and the results are compared to Elbers et al. (2003) methodology. The results show that the proposed methodology allows for taking into account and display the impact of intra-municipality heterogeneity and the spatial interaction among the spatial units.",

keywords = "Business intelligence, Household income, Household surveys, Small areas estimation, Spatial matching",

author = "M{\'o}nica Navarrete and Patricio Aroca and Jorge Bernal",

note = "Funding Information: Los autores agradecen los aportes realizados por los {\'a}rbitros an{\'o}nimos del art{\'i}culo, quienes contribuyeron a mejorar sustancialmente el trabajo. M{\'o}nica Navarrete y Jorge Bernal agradecen el financiamiento del Proyecto 8741-15 otorgado por el Fondo de Investigaci{\'o}n Cient{\'i}fica y Tecnol{\'o}gica de la Universidad de Tarapac{\'a}. Patricio Aroca agradece el apoyo de los proyectosCONICYT/FONDAP/15130009 and FONDECYT 1140082. Publisher Copyright: {\textcopyright} 2017, Universidad de Chile. All rights reserved.",

year = "2017",

doi = "10.4067/s0718-52862017000100053",

language = "Spanish",

volume = "44",

pages = "53--80",

journal = "Estudios de Economia",

issn = "0304-2758",

publisher = "Universidad de Chile",

number = "1",

}

TY - JOUR

T1 - Matching espacial para georreferenciar datos de encuestas de hogar

AU - Navarrete, Mónica

AU - Aroca, Patricio

AU - Bernal, Jorge

N1 - Funding Information: Los autores agradecen los aportes realizados por los árbitros anónimos del artículo, quienes contribuyeron a mejorar sustancialmente el trabajo. Mónica Navarrete y Jorge Bernal agradecen el financiamiento del Proyecto 8741-15 otorgado por el Fondo de Investigación Científica y Tecnológica de la Universidad de Tarapacá. Patricio Aroca agradece el apoyo de los proyectosCONICYT/FONDAP/15130009 and FONDECYT 1140082. Publisher Copyright: © 2017, Universidad de Chile. All rights reserved.

PY - 2017

Y1 - 2017

N2 - This paper develops a methodology using business intelligence (data warehouse) and OLAP tools (Online Analytical Processing) to match individuals from a household survey data to a census one. In order to geo-reference the household data, the method takes advantage of the geographical information of the census. Using the 2003 Household Survey and the 2002 Chilean Census of Population and Housing, the average household income is projected to intra-municipality levels and the results are compared to Elbers et al. (2003) methodology. The results show that the proposed methodology allows for taking into account and display the impact of intra-municipality heterogeneity and the spatial interaction among the spatial units.

AB - This paper develops a methodology using business intelligence (data warehouse) and OLAP tools (Online Analytical Processing) to match individuals from a household survey data to a census one. In order to geo-reference the household data, the method takes advantage of the geographical information of the census. Using the 2003 Household Survey and the 2002 Chilean Census of Population and Housing, the average household income is projected to intra-municipality levels and the results are compared to Elbers et al. (2003) methodology. The results show that the proposed methodology allows for taking into account and display the impact of intra-municipality heterogeneity and the spatial interaction among the spatial units.

KW - Business intelligence

KW - Household income

KW - Household surveys

KW - Small areas estimation

KW - Spatial matching

UR - http://www.scopus.com/inward/record.url?scp=85019073249&partnerID=8YFLogxK

U2 - 10.4067/s0718-52862017000100053

DO - 10.4067/s0718-52862017000100053

M3 - Article

AN - SCOPUS:85019073249

SN - 0304-2758

VL - 44

SP - 53

EP - 80

JO - Estudios de Economia

JF - Estudios de Economia

IS - 1

ER -

Matching espacial para georreferenciar datos de encuestas de hogar

Abstract

Access to Document

Fingerprint

Cite this