Multilevel modelling of refusal and non-contact in household surveys: evidence from six UK Government surveys

We analyse household unit non-response in six major UK Government surveys by using a multilevel multinomial modelling approach. The models are guided by current conceptual frameworks and theories of survey participation. One key feature of the analysis is the investigation of the extent to which effects of household characteristics are survey specific. The analysis is based on the 2001 UK Census Link Study, which is a unique source of data containing an unusually rich set of auxiliary variables. The study contains the response outcome of six surveys, linked to census data and interviewer observations for both respondents and non-respondents

Multilevel modelling of refusal and noncontact nonresponse in household surveys: evidence from six UK government surveys

This paper analyses household unit nonresponse and interviewer effects in six major UK government surveys using a multilevel multinomial modelling approach. The models are guided by current conceptual frameworks and theories of survey participation. One key feature of the analysis is the investigation of survey dependent and independent effects of household and interviewer characteristics, providing an empirical exploration of the leverage-salience theory. The analysis is based on the 2001 UK Census Link Study, a unique data source containing an unusually rich set of auxiliary variables, linking the response outcome of six surveys to census data, interviewer observation data and interviewer information, available for respondents and nonrespondents

Multidimensional Urban Segregation - Toward A Neural Network Measure

We introduce a multidimensional, neural-network approach to reveal and measure urban segregation phenomena, based on the Self-Organizing Map algorithm (SOM). The multidimensionality of SOM allows one to apprehend a large number of variables simultaneously, defined on census or other types of statistical blocks, and to perform clustering along them. Levels of segregation are then measured through correlations between distances on the neural network and distances on the actual geographical map. Further, the stochasticity of SOM enables one to quantify levels of heterogeneity across census blocks. We illustrate this new method on data available for the city of Paris.Comment: NCAA S.I. WSOM+ 201

Carstairs Scores for Scottish Postcode Sectors, Datazones and Output Areas from the 2011 Census

Carstairs deprivation scores, originally created in 1981, provide a measure of material deprivation. Four census variables (male unemployment, no car ownership, overcrowding and low social class) were used in the creation of the score. As near as possible the same four variables have been used to update Carstairs scores decennially, despite changes to the definition of some of the variables over time. Researchers at the MRC/CSO Social and Public Health Sciences Unit, University of Glasgow have now updated Carstairs scores for 2011 for Scottish postcode sectors and for the first time datazones and output areas

Anchorage Community Indicators: Public Use Data Files

The four SPSS datasets described and included here include: ACSPUBFILE.SAV: The data collected in the course of the 2005 Anchorage Community Survey in SPSS format. / ACSCT.SAV: a merged dataset consisting of composite measures extracted from the 2005 Anchorage Community Survey, 2000 U.S. Census, and Anchorage Police Department Dispatches 2003–2005. / BLOCKGROUPMEASURES.SAV: A description of the 214 census block groups within the city of Anchorage, including composite measures derived from the 2000 Census of Population and Housing (documented in ACI Technical Report Initial Measures derived from Census) and dispatch measures derived from APD dispatch data files (documented in Anchorage Police Department Dispatch Data). / CTRACTMEASURES.SAV: A census tract level of aggregation of the 214 census blocks into the 55 census tracts that compose the city of Anchorage. Also includes composite measures derived from the 2000 Census of Population and Housing (documented in ACI Technical Report Initial Measures derived from Census) and dispatch measures derived from APD dispatch data files (documented in Anchorage Police Department Dispatch Data).The Anchorage Community Survey is a biannual study conducted by the Justice Center at the University of Alaska Anchorage as a principal component of the Community Indicators Project at UAA. As the premier source of data on Anchorage Community Indicators, the ACS also provides insight into the communities of Anchorage, Girdwood and Eagle River. This document explains the various SPSS datasets, collection methods, and variables of the 2005 Anchorage Community Survey (https://scholarworks.alaska.edu/handle/11122/3729).[Introduction] / 2005 Anchorage Community Survey / 2000 U.S. Census Extracts / Appendices — Variables / 1: ACSPubFile.sav / 2: ACSCT.sav / 3: CTractMeasures.sav / 4: BlockGroupMeasures.sa

Harmonising Databases for the Cross National Study of Internal Migration: Lessons from Australia and Britain

This project involves the development of a Web interface to origin-destination statistics from the 1991 Census (in a form that will be compatible with planned 2001 outputs). It provides the user with a set of screen-based tools for setting the parameters governing each data extraction (data set, areas, variables) in the form of a query. Traffic light icons are used to signal what the user has set so far and what remains to be done. There are options to extract different types of flow data and to generate output in different formats. The system can now be used to access the interaction flow data contained in the 1991 Special Migration Statistics Sets 1 and 2 and Special Workplace Statistics Set C. WICID has been demonstrated at the Origin-Destination Statistics Roadshows organised by GRO Scotland and held during May/June 2000 and the Census Offices have expressed interest in using the software in the Census Access Project

Mapping poverty in rural China: how much does the environment matter?

A recently developed small area estimation technique is used to geographically derive detailed estimates of consumption-based poverty and inequality in rural Shaanxi, China. These estimates may be helpful for targeting since there is wide variability in poverty rates within Shaanxi but low levels of inequality within most counties and townships. We also investigate whether including environmental variables in the equation used to predict consumption and poverty improves upon typical approaches that only use household survey and census data. Ignoring environmental variables appears likely to produce targeting errors

Spatio-Temporal Wildland Arson Crime Functions

Wildland arson creates damages to structures and timber and affects the health and safety of people living in rural and wildland urban interface areas. We develop a model that incorporates temporal autocorrelations and spatial correlations in wildland arson ignitions in Florida. A Poisson autoregressive model of order p, or PAR(p) model, is estimated for six high arson Census tracts in the state for the period 1994-2001. Spatio-temporal lags of wildland arson ignitions are introduced as dummy variables indicating the presence of an ignition in previous days in surrounding Census tracts and counties. Temporal lags of ignition activity within the Census tract are shown to be statistically significant and larger than previously reported for non-spatial variants of the PAR(p) model. Spatio-temporal lagged relationships with current arson that were statistically significant show that arson activity up to a county away explains arson patterns, and spatio-temporal lags longer than two days were not significant. Other variables showing significance include weather and wildfire activity in the previous six years, but prescribed fire and several variables that provide evidence that such activity is consistent with an economic model of crime were less commonly significant.Resource /Energy Economics and Policy,