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)    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.

b)   Spatial variations - gather data across spatial units, map the result, compare geog. Areas.

c)    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    

d)   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, …)

e)  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) - POP_i

Activity participation rates for Michigan (NSGA) – by age group (PART_i)

Frequency of participation  (NSGA ) – by age group (FREQ_i)

Number of participants = S POP_i * PART_i

Number of days of participation = S POP_i * PART_i * FREQ_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