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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/4527E75
Repositorysid.inpe.br/mtc-m21d/2021/07.02.12.50
Last Update2021:07.02.12.50.04 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2021/07.02.12.50.04
Metadata Last Update2022:04.03.22.27.26 (UTC) administrator
DOI10.3390/ijgi10060364
ISSN2220-9964
Citation KeyRamos:2021:GeWeRe
TitleImproving victimization risk estimation: A geographically weighted regression approach
Year2021
MonthJune
Access Date2024, May 19
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size4050 KiB
2. Context
AuthorRamos, Rafael Blakeley Guimarães
GroupDIIAV-CGCT-INPE-MCTI-GOV-BR
AffiliationInstituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Addressrafaelgramos@gmail.com
JournalISPRS International Journal of Geo-Information
Volume10
Number6
Pagese364
Secondary MarkB3_GEOCIÊNCIAS B5_CIÊNCIAS_AMBIENTAIS
History (UTC)2021-07-02 12:50:04 :: simone -> administrator ::
2021-07-02 12:50:05 :: administrator -> simone :: 2021
2021-07-02 12:50:12 :: simone -> administrator :: 2021
2022-04-03 22:27:26 :: administrator -> simone :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsCrime
Denominator dilemma
Geographically weighted regression
Mapping
Risk
Standardization
AbstractStandardized crime rates (e.g., homicides per 100,000 people) are commonly used in crime analysis as indicators of victimization risk but are prone to several issues that can lead to bias and error. In this study, a more robust approach (GWRisk) is proposed for tackling the problem of estimating victimization risk. After formally defining victimization risk and modeling its sources of uncertainty, a new method is presented: GWRisk uses geographically weighted regression to model the relation between crime counts and population size, and the geographically varying coefficient generated can be interpreted as the victimization risk. A simulation study shows how GWRisk outperforms naïve standardization and Empirical Bayesian Estimators in estimating risk. In addition, to illustrate its use, GWRisk is applied to the case of residential burglaries in Belo Horizonte, Brazil. This new approach allows more robust estimates of victimization risk than other traditional methods. Spurious spikes of victimization risk, commonly found in areas with small populations when other methods are used, are filtered out by GWRisk. Finally, GWRisk allows separating a reference population into segments (e.g., houses, apartments), estimating the risk for each segment even if crime counts were not provided per segment.
AreaCST
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data URLhttp://urlib.net/ibi/8JMKD3MGP3W34T/4527E75
zipped data URLhttp://urlib.net/zip/8JMKD3MGP3W34T/4527E75
Languageen
Target Fileramos_improving.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/46KUATE
DisseminationPORTALCAPES
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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