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Small Area Estimation of Public Confidence

Williams, Dawn; Howarth, James; Cheng, Tao; Blangiardo, Marta

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:creator>Williams, Dawn</dc:creator>
<dc:creator>Howarth, James</dc:creator>
<dc:creator>Cheng, Tao</dc:creator>
<dc:creator>Blangiardo, Marta</dc:creator>
<dc:date>2017-09-04</dc:date>
<dc:description>This paper explores the use of a spatio-temporal approach to small area estimation for improving understanding of attitudes to policing. The study focusing on confidence in the police in London using sample survey data. The Public Attitudes Survey (PAS) collects data on the experiences and perceptions of Londoners with respect to crime, policing and anti-social behavior.  While the most robust survey of its kind in the world, it is not designed for use at the neighborhood level but rather to produce annual, Borough level estimates on a rolling average basis. However, there is a demand for reliable, local level data for quarterly assessment and planning. In this study, we present a Bayesian spatio-temporal hierarchical modeling approach to small area estimation to address this. In this approach, information is “borrowed” from neighboring regions in space and time to increase effective sample size. This enables reliable estimates, forecasts and classification of trends in confidence at the neighborhood-level.   </dc:description>
<dc:identifier>https://zenodo.org/record/884184</dc:identifier>
<dc:identifier>10.5281/zenodo.884184</dc:identifier>
<dc:identifier>oai:zenodo.org:884184</dc:identifier>
<dc:relation>doi:10.5281/zenodo.884183</dc:relation>
<dc:relation>url:https://zenodo.org/communities/dfp17</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:rights>http://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
<dc:subject>forecasting</dc:subject>
<dc:subject>Bayesian</dc:subject>
<dc:subject>spatiotemporal</dc:subject>
<dc:subject>small area estimation</dc:subject>
<dc:subject>policing</dc:subject>
<dc:title>Small Area Estimation of Public Confidence</dc:title>
<dc:type>info:eu-repo/semantics/preprint</dc:type>
<dc:type>publication-preprint</dc:type>
</oai_dc:dc>

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