PRR 844 : Secondary data analysis
Secondary data analysis : use of data gathered by someone else for a different purpose – reanalysis of
existing data. See methods links page for links to secondary sources of data about recreation & tourism
Sources:
Government agencies: e.g. Population, housing & economic censuses, tax collections, traffic counts,
employment, environmental quality measures, park use, ,…
Internal records of your organization – sales, customers, employees, budgets, web logs,..
Private sector - industry associations often have data on size and characteristics of industry
Previous surveys – as printed reports or raw data, survey research firms sell data
Library & Electronic sources – the WWW, on-line & CD-ROM literature searches, …
Previously published research – reports have data in summary form, original data often available from
the authors.
Issues in using secondary data.
1) data availability – know what is available & where to find it
2) relevance – data must be relevant to your problem & situation
3) accuracy – need to understand accuracy & meaning of the data
4) sufficiency – often must supplement secondary data with primary data or judgement to
completely address the problem
Since you did not collect the secondary data it is imperative that you fully understand the meaning and
accuracy of the data before you can intelligently use it. This usually requires you to know how it was
collected and by whom. Find complete documentation of the data or ask about details from source of data.
For example, most standard government data providers have extensive documentation on methods, data
reliability, etc. at their websites. Beware of data that isn’t clearly documented. BTS Guide to Good
Statistical Practice identifies some useful guidelines.
Kinds of Secondary data
1. Regularly gathered time series data : useful for tracking trends and forecasting. Most common sources
here are mostly governmental and often economic - international arrivals, sales, jobs, payroll in various
industries, census of population and housing, budgets, revenues, employees, some regularly conducted
surveys and industry reporting.
2. Reporting for geographic units: useful for spatial analysis, many of the above time series are also
reported for countries, states, counties and sometimes smaller geographic units. Again, mostly from
governmental sources.
3. Park visit/facility use data : many park systems have regular reports of visitor counts, although accuracy
and consistency is sometimes questionable. NPS public use data a good example, also most state parks,
and some other park systems. Private sector has good sales data but usually proprietary. Only a few
museums
Examples
a)
b)
c)
d)
e)
Trends – Compare surveys in different years or plot time series data - many tables in Spotts
Travel & Tourism Statistical Abstract, Michigan county tourism profiles, economic time
series at BLS site, REIS data at Gov Info Clearing House.
Spatial variations - gather data across spatial units, map the result, compare geog. Areas.
Recreation participation – apply rates from national, state and local surveys to local
population data from Census, rates at NSGA, ARC web pages (Roper-Starch study), NSRE
1994-95 survey
Internal records: use zipcodes or telephone area codes to map market area, track trends in
regularly gathered variables (use, sales, costs, employee turnover, customer
compliants/satisfaction, environmental variables, web logs, …)
Combining sources in models : e.g. gravity model would utilize population data, an inventory
of supply of facilities, and distances.
TOURISM: Example of Use of Secondary Data in Estimating Tourism Activity, Spending and
Impacts in Michigan
I have series of models for estimating tourism activity, spending and economic impacts at state and county
level. These rely almost completely on secondary data sources – lodging room use taxes, motel,
campground and seasonal home inventories, occupancy rates by region, average spending by segment and
statewide travel counts. See my economic impact web site. Also see Leones paper
(http://ag.arizona.edu/pubs/marketing/az1113/) on measuring tourism activity in your community.
Secondary data used in my tourism models:
Room tax collections (state and local CVB’s)
Resident Population by county (Census)
Seasonal homes by county (Census)
Lodging inventory by county (rooms, campsites)
Hotel, restaurant, amusement, retail sales, employment, income by county (IMPLAN, REIS, CBP,
BEA, BLS, ES-202)
National tourism industry ratios (BEA Satellite acounts)
BLS price indices by commodity
State & local tourism estimates by others (TIA, TTRRC, D.K. Shifflet, ATS 95, CVB’s)
Local area multipliers and ratios (employment to sales, income to sales) for tourism sectors
(IMPLAN)
County to county distance matrix
Michigan Airport enplaning and deplaning passengers by airport
Parameters from various tourism surveys (ATS 1995, D.K. Shifflet, TTRRC household, variety of ovcal
surveys in Michigan)
Average length of stay, party size by subgroups of visitors
Hotel room and campsite ocupancy rates
Average room and campground rates (prices)
Average days seasonal homes are occupied, average party size
Average spending for different visitor segments
State Total day trips and stays with friends and relatives (ATS95)
For trips of 50 miles or more, percent that are 100 miles or more.
See my economic impacts of tourism website. Models and data sources are discussed briefly in MITEIM
model documentation and at bottom of county tourism spending table
(http://www.prr.msu.edu/miteim/michtsm00.htm).
RECREATION : Estimating/forecasting participants, days of participation (Lakes States Recreation
Estimates by County). Forecast by using population projections in the model.
Population by age (Census) - POPi
Activity participation rates for Michigan (NSGA) – by age group (PARTi)
Frequency of participation (NSGA ) – by age group (FREQi)
 POP * PART
Number of days of participation =  POP * PART * FREQ
Number of participants =
i
i
i
i
i
See Stynes, D.J. 1998. Recreation activity and tourism spending in the Lakes States. IN Lake States
Regional Forest Resources Assessment: Techical Papers. J. M. Vasievich and H.H. Webster (eds). USDA
Forest Service, NC Forest Expmt Station, Gen’l Tech. Report NC-189., pp. 139-164.
Exercise to practice downloading data for the above recreation model:
http://www.msu.edu/course/prr/389/PRR389Exercises.htm#ex4