aggregating census tract level data

I am trying to arrive at the population data at the city level.

I understand that the variable that comes closest to a city is a "place" or msa.

my question is how can I connect the census tract level data with the msa and place within bigquery?

Thanks and regards to all the contributors!!

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  • City level data is available. Place refers to incorporated places or Census Designated Places. I am not familiar with Bigquery but if it uses standard Census summary level codes then it would be summary level 160. Refer to the ACS summary file documentation for that. (see the appendices file): www.census.gov/.../summary-file-documentation.html

  • by the way MSA is a metropolitan statistical area which is not equivalent to a city. They are made up of counties (or a single county). OMB defines these as "Metropolitan Statistical Area—A Core Based Statistical Area associated with at least one urbanized area that has a population of at least 50,000. The Metropolitan Statistical Area comprises the central county or counties containing the core, plus adjacent outlying counties having a high degree of social and economic integration with the central county or counties as measured through commuting."

  • Yes.  In my case I think aggregation either at the level of a city or at the level of an msa (or a cbsa) will work.  I was having difficulty arriving at city (or msa, or cbsa..) level aggregations with some identifier.  And since I am trying to build a solution that will live in bigquery, I will try to understand and find the equivalent of the standard Census summary level codes.  Thank you so much!

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  • Yes.  In my case I think aggregation either at the level of a city or at the level of an msa (or a cbsa) will work.  I was having difficulty arriving at city (or msa, or cbsa..) level aggregations with some identifier.  And since I am trying to build a solution that will live in bigquery, I will try to understand and find the equivalent of the standard Census summary level codes.  Thank you so much!

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