Thanks for the links, what I was actually trying to argue were two things, one was that the larger the data set the more homogenized and useless the data becomes, and two that looking at data for a given city returns different results.

There are lots of ways to come up with insights into what is happening, one way is to take the prices experienced by an individual in a single location over time, then to take the population of each set of locations and compute p(I|L) where I is your experienced inflation rate, give L your location. Then you can ask questions like "What is the likelyhood that a given individual has experienced an inflation rate that differs significantly from the reported rate? (either positively or negatively)"

Charts like http://www.city-data.com/ which can show you housing pricing changes by city for example you can see the wide disparity in changes between coastal vs more central states. Some states would have experienced very little change, others quite a bit. Now overlay that on population. Then you start to get to the answer of "What do 'most' people' experience in terms of inflation." The newspaper archive is a great way to check the numbers.

Thanks again for the links, one of the criticisms for the CPI scale has always been this : "The CPI follows the prices of a sample of items in various categories of consumer spending—such as food, clothing, shelter, and medical services—that people buy for day-to-day living."

Which historically did not include changes in quantity. So a 'can of soup' which went from $1.00 -> $1.25 over 10 years seems like it has a 25% increase in cost, but it went from 16 -> 10 ounces. Which means its actual cost went from $1.00 -> $2.00 when computing the per ounce price. This is another great thing you can see/check with newspaper archives as grocery prices are exceptionally well documented.