1. Integrating public domain data to construct community profiles Ken Reed, Betsy Blunsdon, Nicola McNeil (Deakin University, Victoria, Australia) Steven McEachern (University of Ballarat, Victoria, Australia)

2. Multilevel data Two key developments: –Increased interested in contextual effects on individual outcomes –New statistical techniques for estimating effects at multiple levels (multilevel analysis), e.g. individual, household, neighborhood, community

3. Major projects Examples: –Human Development in Chicago Neighborhoods –The Los Angeles Family and Neighborhood Survey (LA FANS) Both attempt to disentangle effects of individual, family and community factors

4. Our goal Develop a general resource, useful for a range of projects Use publicly available data sources Construct datasets with capacities for measuring change over time Use geographical unit as unit of analysis

5. Postcode level Disadvantage: an administrative boundary that may not reflect social ecology of place Advantage: many different types data can be collected –Other data can be aggregated to postcode level

6. Starting point Selected 2 points in time: 1996 & 2001 Australian census in both years –Demographic data by postcode Australian federal election in both years –Voting behaviour by postcode

7. Types of data Incidence and perceptions of crime Licensed premises Churches and religious institutions Other cultural institutions Schools Recreational facilities Social services Businesses, government offices and agencies

12. Extensions Victorian pilot study, easily extended to Australia Provides context for mail out and phone survey –(postcode used in cluster or stratified sample) Can be complemented by ethnography or social observation

13. Current applications Why do people become volunteer firefighters? Social connectedness and health