The data is taken from the Places Rated Almanac, by Richard Boyer and David Savageau, copyrighted and published by Rand McNally. The nine rating criteria used by Places Rated Almanac are:

Climate & Terrain
Housing
Health Care & Environment
Crime
Transportation
Education
The Arts
Recreation
Economics
For all but two of the above criteria, the higher the score, the better. For Housing and Crime, the lower the score the better. The scores are computed using the following component statistics for each criterion (see the Places Rated Almanac for details):

Climate & Terrain: very hot and very cold months, seasonal temperature variation, heating- and cooling-degree days, freezing days, zero-degree days, ninety-degree days.
Housing: utility bills, property taxes, mortgage payments.
Health Care & Environment: per capita physicians, teaching hospitals, medical schools, cardiac rehabilitation centers, comprehensive cancer treatment centers, hospices, insurance/hospitalization costs index, flouridation of drinking water, air pollution.
Crime: violent crime rate, property crime rate.
Transportation: daily commute, public transportation, Interstate highways, air service, passenger rail service.
Education: pupil/teacher ratio in the public K-12 system, effort index in K-12, accademic options in higher education.
The Arts: museums, fine arts and public radio stations, public television stations, universities offering a degree or degrees in the arts, symphony orchestras, theatres, opera companies, dance companies, public libraries.
Recreation: good restaurants, public golf courses, certified lanes for tenpin bowling, movie theatres, zoos, aquariums, family theme parks, sanctioned automobile race tracks, pari-mutuel betting attractions, major- and minor- league professional sports teams, NCAA Division I football and basketball teams, miles of ocean or Great Lakes coastline, inland water, national forests, national parks, or national wildlife refuges, Consolidated Metropolitan Statistical Area access.
Economics: average household income adjusted for taxes and living costs, income growth, job growth.
In addition latitude and longitude, population and state and case number are also given. Use principal components analysis to identify the major components of variation in the ratings amongst cities.

In particular

How many principal components are needed?
Interpret your principal components.
If you could only use a few variables from the original dataset, which would they be?
Identify unusual cities.
To identify the unusual cities, the identify() function is useful. here is an example of its use:
> plot(places[,1],places[,2])
> identify(places[,1],places[,2],row.names(places))
Now click on the plot with the right mouse button to identify points
and use the middle button to finish.