I would analyze this using the algorithm I learned from my history teacher in high school - Social, Political, Economic, Moral, Emotional - drawing on openly available information from the World-Wide Web. In some ways, this is a big data exercise, albeit drawing from previous big data sources.
Social - I would start with http://aschmann.net/AmEng/#SmallMapUnitedStates, overlay that with this - http://upload.wikimedia.org/wikipedia/commons/a/a7/Census-2000-Data-Top-US-Ancestries-by-County.svg - and this - http://www.nytimes.com/interactive/2009/03/10/us/20090310-immigration-explorer.html . The latter two are overly simplistic, like all big data projects, lumping together disparate groups under one label. A major example is New Mexico - the Wikimedia article recognizes the Spanish distinction for many counties in New Mexico, but the NYT (typically racially insensitive) lumps all Hispanics together under the "Latin America" origin. However, they do seem to show what people believe is their cultural heritage. This article - http://en.wikipedia.org/wiki/History_of_immigration_to_the_United_States - provides a reasonable summary of immigration to the United States. I haven't yet found a good article about migration within the United States. That is also important in understanding the resultant cultures. Political - I would use a selection of red/blue/other maps starting at the local level up to state level. If I was being pedantic, I'd go on to analyze the actual votes of elected officials with respect to party platforms - there are groups who monitor voting records whose information might be useful. The point being to show the past and current political atmospheres of the various regions. I suspect this would find a lot of contradictions - places where the local politicians are one variety while the national-level are another. This reflects the changing political atmosphere - voters understand that politicians in DC gain power with longevity and are reluctant to change even when political views are not a match. Economic - Per-state GDP information plus industry type information plus growth/decline of both. General categories of industry are also useful - extractive versus manufacturing, for example. Government spending at all levels is important - New Mexico is shaped by the Federal spending in this state. That reminds me of another factor - the percentage of property rights owned by the government (land, water, mineral, air, et cetera). Moral - Crime statistics, trends, criminal/victim demographics, religiosity (numbers of churches, numbers of churchgoers, church/sect/cult), marriage/divorce rates, maybe throw some modern-day Kinsey reports into the mix to account for marital fidelity and extreme deviancy. Opinion poll results about moral issues would be a major source. Emotional - Music - http://martinprosperity.org/papers/Geography%20of%20Music%20Preferences%20formatted.pdf Literature is another emotional indicator. I would look at various electronic media emotional content analysis, but I fear that does not adequately reflect the total population. Hobbies are a possible emotional indicator - how much people in a region indulge in hobbies vs not having time or energy or interest for them, possibly the type of hobby. The trick is to collect all of this data and then analyze for various geographic areas, both historical and current, to determine where these change significantly enough to show a different culture. The analysis should start with the smallest feasible geographic units and slowly build up if it isn't stable at that level. If enough variables, maybe 10 out of 100, are different from one county to the next - they are different enough to be marked as such. The sub-nations or regions are collections of geographic regions that fit within a fuzzy filter of themselves. I'd expect that some of the data would not vary enough to be a major input to this process, while others (language) have much greater variance. That would lend itself to weighting by variability of the indicator. Another part of the analysis might look at the variability of indicators in a region - if that's possible - so that cultures that tolerate more variability would stand out. Ray Parks Consilient Heuristician/IDART Program Manager V: 505-844-4024 M: 505-238-9359 P: 505-951-6084 NIPR: [email protected] SIPR: [email protected] (send NIPR reminder) JWICS: [email protected] (send NIPR reminder) On Nov 9, 2013, at 9:37 PM, Steve Smith wrote: > Ray - > > I agree that (especially with Woodard's attempt to address the Tea Party > directly) his biases do show, but I would still contend that while "tainted" > by that, his general structure is far from invalidated. > > Can you provide an alternative geo-political structuring or modifications to > his that you believe are more accurate (or even align with your own biases > better)? > > I'm much less interested in the conclusions that Woodard draws from his > framework than the reframing of the conversation it implies. I'm very tired > of the traditional Blue/Grey, Rural/Urban, Coastal/Heartland, North/South, > East/West divisions.... they have their place but do not suffice to explain a > lot and seem to serve mainly to keep us high-centered. > > - Steve > > PS Response to StephenT in progress... >> The concept is tainted by the cultural biases of the author. >> >> >> ----- Original Message ----- >> From: Steve Smith [mailto:[email protected]] >> Sent: Friday, November 08, 2013 10:27 PM >> To: The Friday Morning Applied Complexity Coffee Group <[email protected]> >> Subject: [EXTERNAL] [FRIAM] 11 American Nations >> >> An alternative view to the (I can't help but hear it in Dr. Suess' >> cadence) Red-State Blue-State version of Murrica. I don't agree with >> it in detail but in sweeping generalizations (5.5x less general than >> red/blue?) it captures what I know our cultural "melting pot" to be >> crufted into: >> >> http://www.washingtonpost.com/blogs/govbeat/wp/2013/11/08/which-of-the-11-american-nations-do-you-live-in/ >> >> >> ============================================================ >> FRIAM Applied Complexity Group listserv >> Meets Fridays 9a-11:30 at cafe at St. John's College >> to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com >> >> ============================================================ >> FRIAM Applied Complexity Group listserv >> Meets Fridays 9a-11:30 at cafe at St. John's College >> to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com > > > ============================================================ > FRIAM Applied Complexity Group listserv > Meets Fridays 9a-11:30 at cafe at St. John's College > to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com
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