Exercise::

The purpose of this exercise is to gain a very basic understanding of uneven geographic distributions and of the use of the "location quotient" to measure such unevenness, however inadequately, across regions within a country or across countries.

In the context of our class exercises and portfolio, the idea is to identify some very initial empirical manifestations of spatial concentration of some economic activity of your choice.

1. Formulate one or two simple questions or propositions which are related to the relative importance of some facet related to your topical specialization or area of interest (e.g. aerospace employment of legal services) in some local, regional or national geographic context....)
2. Identify one prime facet (and its regional "container" or "focal" region or country) which directly relate(s) to what you might be after (aerospace or software development employment in Puget Sound Region; Travel agencies in King County; information service exports as part of total GNP or GDP of a country; certain financial transactions as part of total balance of payments of a country, etc.)
3. Think about which larger geographic context could or should be the basis for a geographic comparison. Do not automatically select the United States as a "benchmark" for comparison. What about the "world as a whole" or a continent if you are interested in international facets. If you are interested in the phenomenon of urban homelessness, it might be more appropriate for your specific topic to compare Seattle with all American cities, or all cities of a certain size or West Coast cities only. If you can't make a decision between two alternative benchmark regions, take them both and calculate two location quotients.
4. Think about which larger "systematic" context could or should be the basis for your comparison. If you are interested in aerospace employment, total employment would probably be appropriate, but total employment in manufacturing, high-tech manufacturing, or all employment including services?
5. Suggest, search for and find some data which you feel adequately portrays your facet in a comparative geographic context (i.e. is available for different levels of "systematic" and "geographic" aggregation.
6. The search for appropriate data (whether it is ultimately the Census Bureau, some other federal, state or local agency, some professional group or non-profit organization, Chamber of Commerce, the World Bank, OECD, the Asian Development Bank, etc. ), may be very instructive as to who may also be interested in your topic and to what extent the relative lack of data may have influenced the amount and nature of past research into the nature of an uneven distribution.
7. There are lots of data sources some of which we are able to access through the Lab computers (such as Internet based data).
8. Calculate two location quotients. For your second location quotient, either select a second activity within the same regional/interregional context, or, alternatively, take the same activity as for your first LQ, but vary either the focal region or the benchmark region. Be very explicit as to how you set-up and execute all of your calculations.
9. Research & write two or more paragraphs of interpretation of your results. As part of your write-up comment on the extent to which the selection of your particular focal or benchmark region(s) and the type of activity (e.g. level of aggregation) may have influenced the size of your two different location quotients.

		employment in health care services in the City of Seattle
                    ---------------------------------------------
		       	    total employment of Seattle
  L.Q. Formula:     =============================================
		 health care service employment in West Coast Cities
	            ---------------------------------------------
	             Total Employment of these West Coast cities

Suggested total length: 1 page.

Caution:

1. Anti-Plagiarism rules will be strictly enforced!

1. Barney's Statistical Wake-Up-Call Barney the purple dinosaur spoke to the Association of American Criminal Statisticians last week about the need to "get tough" with the numbers....

2. Data Sites:
   • World Bank Data
   • OECD Data
   • Asian Development Bank Statistics
   • European Community: EuroStat
   • U.S. Census Data
   • Demographic & Population Data
   • Washington State Statistics

3. Location Quotients:
   • Membership Location Quotient, National Council for Geographic Education (NCGE) is an organization established to enhance the status and quality of geography teaching and learning.
   • Excel based L.Q. Exercise (Foust, Wisconsin)
   • Professor Stutz' Location Quotient site
   • A Primer on Using the L.Q. for Identifying the Local Economic Base
   • Location Quotients for King County (WA)

   • Wales: forecast changes in the industrial structure [ Table with LQs]

   • LQ Exercise for the Steel & Chemical Industries in the UK

   • Tim Chapin's Location Quotient Analysis [Florida State University]
     • King County Examples [Chapin]
   • Grant Thrall's Location Quotients [Location Quotient: Descriptive Geography for the Community Reinvestment Act]
   • Which Industries are Important to Connecticut? [By Jungmin Charles Joo, Associate Research Analyst, State of Connecticut]
   • High-Tech (Employment) Location Quotients for Wales vs. U.K.
   • Use of Location Quotients for calculating regional coefficients
   • Geog350 Version of the Location Quotient Exercise

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4. Literature:

   "Identifying Service-Sector Exports from Major Texas Cities," Economic Review (Federal Reserve Bank of Dallas), July 1990.

   Flegg, A.T., C.D. Webber, and M.V. Elliott, "On the Appropriate Use of Location Quotients in Generating Regional Input-Output Tables," Regional Studies 29(6), October 1995, pp.547-61.

   Hoover, Regional Economics (Full text online)

   • Appendix with introductory explanation of Location Quotient
   • Table with L.Q.s for Urban Places by Size (1954)