<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<site>mtc-m16c.sid.inpe.br 804</site>
		<holdercode>{isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}</holdercode>
		<identifier>8JMKD3MGPDW34P/3KP34LS</identifier>
		<repository>sid.inpe.br/mtc-m16c/2015/12.10.17.37</repository>
		<lastupdate>2015:12.10.17.37.22 sid.inpe.br/mtc-m18@80/2008/03.17.15.17 simone</lastupdate>
		<metadatarepository>sid.inpe.br/mtc-m16c/2015/12.10.17.37.22</metadatarepository>
		<metadatalastupdate>2023:01.30.13.10.06 sid.inpe.br/mtc-m18@80/2008/03.17.15.17 administrator {D 2015}</metadatalastupdate>
		<issn>2179-4820</issn>
		<citationkey>FeitosaRoseJaco:2015:SpMiAp</citationkey>
		<title>Small area housing deficit estimation: a spatial microsimulation approach</title>
		<format>CD-ROM, On-line.</format>
		<year>2015</year>
		<secondarytype>PRE CN</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>1096 KiB</size>
		<author>Feitosa, Flávia da Fonseca,</author>
		<author>Rosemback, Roberta Guerra,</author>
		<author>Jacovine, Thiago Correa,</author>
		<affiliation>niversidade Federal do ABC (CECS/UFABC)</affiliation>
		<affiliation>Universidade Federal de Minas Gerais (CEDEPLAR/UFMG)</affiliation>
		<affiliation>niversidade Federal do ABC (CECS/UFABC)</affiliation>
		<editor>Fileto, Renato,</editor>
		<editor>Korting, Thales Sehn,</editor>
		<e-mailaddress>lubia@dpi.inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Geoinformática, 16 (GEOINFO)</conferencename>
		<conferencelocation>Campos do Jordão</conferencelocation>
		<date>27 nov. a 02 dez. 2015</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>131-136</pages>
		<booktitle>Anais</booktitle>
		<tertiarytype>Short papers</tertiarytype>
		<transferableflag>1</transferableflag>
		<abstract>This paper presents our first attempts to develop a new methodology for measuring housing deficit at small areas. It combines the advantages of two types of census data: (a) individual-level sample data, which are very useful for depicting many dimensions of the housing deficit, but do not present detailed geographic information; and (b) universal data with detailed spatial resolution (census tracts), but aggregated. For that, we explore an approach based on spatial microsimulation. We simulate spatial microdata by using aggregate data as constraints to expand and allocate individual-level data to census tracts. This procedure allowed us to estimate a particular dimension of the housing déficit (housing cost) at higher spatial resolution.</abstract>
		<area>SRE</area>
		<language>en</language>
		<targetfile>proceedings2015_p12.pdf</targetfile>
		<usergroup>lubia@dpi.inpe.br</usergroup>
		<visibility>shown</visibility>
		<documentstage>not transferred</documentstage>
		<mirrorrepository>dpi.inpe.br/banon-pc2@80/2006/07.04.20.21</mirrorrepository>
		<nexthigherunit>8JMKD3MGPDW34P/42T288P</nexthigherunit>
		<nexthigherunit>8JMKD3MGPDW34P/48F29JE</nexthigherunit>
		<citingitemlist>sid.inpe.br/mtc-m16c/2020/07.21.21.26 2</citingitemlist>
		<hostcollection>sid.inpe.br/mtc-m18@80/2008/03.17.15.17</hostcollection>
		<username>simone</username>
		<lasthostcollection>sid.inpe.br/mtc-m18@80/2008/03.17.15.17</lasthostcollection>
		<url>http://mtc-m16c.sid.inpe.br/rep-/sid.inpe.br/mtc-m16c/2015/12.10.17.37</url>
	</metadata>
</metadatalist>