Electricity consumption as a predictor of household income: an spatial statistics approach

dc.contributorEscolas::EAESP
dc.contributorFGV
dc.creatorFrancisco, Eduardo de Rezende
dc.creatorAranha, Francisco
dc.creatorZambaldi, Felipe
dc.creatorGoldszmidt, Rafael Guilherme Burstein
dc.date.accessioned2018-10-25T18:23:31Z
dc.date.accessioned2022-11-03T20:36:47Z
dc.date.available2018-10-25T18:23:31Z
dc.date.available2022-11-03T20:36:47Z
dc.date.created2018-10-25T18:23:31Z
dc.date.issued2006
dc.identifier8517000277; 9788517000270
dc.identifierhttp://hdl.handle.net/10438/25248
dc.identifier2-s2.0-84870682567
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5041654
dc.description.abstractThis paper investigates the relationship between electricity consumption, economic classification and household income, by means of comparing Brazilian Census Micro-Data with the customer database of AES Eletropaulo, a large Brazilian electric distribution company, using traditional statistics and spatial auto-regressive models. Income and economic classification are recognized as efficient proxies for purchasing power. Income indicators based on Electricity Consumption can be almost automatically generated by electric companies using GIS techniques, and this is a potential new business model for electric companies.
dc.languageeng
dc.relationGEOINFO 2006 - 8th Brazilian Symposium on GeoInformatics
dc.rightsopenAccess
dc.sourceScopus
dc.subjectAuto regressive models
dc.subjectAutomatically generated
dc.subjectCustomer database
dc.subjectElectric distribution company
dc.subjectElectricity-consumption
dc.subjectHousehold income
dc.subjectNew business models
dc.subjectPurchasing power
dc.subjectSpatial statistics
dc.subjectIndustry
dc.titleElectricity consumption as a predictor of household income: an spatial statistics approach
dc.typeConference Proceedings


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