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I would like to perform an individual-level multivariate analysis at small levels of geographic aggregation (Australian census collection districts, typical population ~= 250). Clearly, the census isn't available at these small levels of aggregation for privacy reasons, however the 1% census sample is available at a much greater level of spatial aggregation so I am investigating other alternatives. Almost Almost all the variables of interest are categorical. I have two datasets at my disposal:

  • The 1% census sample is available at a much greater level of spatial aggregation (an area with a population of ~190,000 and vast spatial segregation of demographics).

  • Frequency tables for the variables I am interested in at the small area level (500 small areas, mean pop = 385, sd = 319, median = 355).

How can I use these two datasets to simulate a population distribution at the small area level that is as close as possible to the actual population of the small area?

I appreciate that these methodsthere may well be routine, so methods for doing this; if so a pointer to a textbook or relevant journal articles would be vastly appreciated.

I would like to perform an individual-level multivariate analysis at small levels of geographic aggregation (Australian census collection districts, typical population ~= 250). Clearly, the census isn't available at these small levels of aggregation for privacy reasons, however the 1% census sample is available at a much greater level of spatial aggregation. Almost all the variables of interest are categorical.

How can I use these two datasets to simulate a population at the small area level that is as close as possible to the actual population of the small area?

I appreciate that these methods may well be routine, so if so a pointer to a textbook or relevant journal articles would be appreciated.

I would like to perform an individual-level multivariate analysis at small levels of geographic aggregation (Australian census collection districts). Clearly, the census isn't available at these small levels of aggregation for privacy reasons so I am investigating other alternatives. Almost all the variables of interest are categorical. I have two datasets at my disposal:

  • The 1% census sample is available at a much greater level of spatial aggregation (an area with a population of ~190,000 and vast spatial segregation of demographics).

  • Frequency tables for the variables I am interested in at the small area level (500 small areas, mean pop = 385, sd = 319, median = 355).

How can I use these two datasets to simulate a population distribution at the small area level that is as close as possible to the actual population of the small area?

I appreciate that there may well be routine methods for doing this; if so a pointer to a textbook or relevant journal articles would be vastly appreciated.

1
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How can I simulate census microdata for small areas using a 1% microdata sample at a large scale and aggregate statistics at the small area scale?

I would like to perform an individual-level multivariate analysis at small levels of geographic aggregation (Australian census collection districts, typical population ~= 250). Clearly, the census isn't available at these small levels of aggregation for privacy reasons, however the 1% census sample is available at a much greater level of spatial aggregation. Almost all the variables of interest are categorical.

How can I use these two datasets to simulate a population at the small area level that is as close as possible to the actual population of the small area?

I appreciate that these methods may well be routine, so if so a pointer to a textbook or relevant journal articles would be appreciated.