Elin Charles-Edwards Dominic Brown Martin Bell

1. Factors affecting the magnitude and timing of temporary moves in Australia: A Poisson Regression Analysis Elin Charles-Edwards Dominic Brown Martin Bell Presentation to the 2007 International Population Geographies Conference, Hong Kong, 10-13th July 2007

2. Background • Study background • Service population estimates (Cook 1996; Lee 1999) • Those persons who demand goods or services from providers… (s)uch persons may be permanent or temporary residents of an area (Cook 1996) Service populations Permanent residents (ERP) Temporary Residents Daytime population Temporary mobility: those moves more that one night in duration that do not entail a change in usual residence. Lower bound: 24 hrs Upper bound: 12 months

3. Background • Estimating temporary populations • Direct • Census –temporal resolution • Travel surveys (NVS)- spatial resolution • Expensive and time consuming • Indirect • Accommodation surveys–visitors in private dwellings • Symptomatic data (e.g. electricity, water usage) – accessibility, benchmarking • (e.g. Smith 1989, Happel et. al 2002) • 3. Simulation • Based on the underlying dimensions of temporary population mobility

4. Background Magnitude Frequency Seasonality Number of visitors? When do they arrive? How long do they stay? Impact Duration Temporal Spatial Connectivity Periodicity Distance Circuits Seasonality: the systematic intra-year variation in visitation caused by exogenous factors (e.g. climatic), institutional factors (e.g. timing of public holidays) or a combination of the two.

5. What do we know? • Few large scale studies of temporary population mobility • No accepted conceptual framework within which to situate this mobility • Currently no scientific theory of visitor seasonality • Tourism literature has identified a number of different causes of tourism seasonality • Natural (e.g. Climate) • Institutional (e.g. School Holidays) • Calendar effects (e.g. Easter) • How do we start thinking about temporary mobility and the ways in which it varies through space and time?

6. What will get us there? Diaspora Diaspora Economic function Population size Population size Economic function Business cycles Climate Climate Time Harvest Calendar School Holidays School Holidays Distance Weather Weather Festivals Origin Destination • Scale – spatial and temporal • Fully saturated model – sparsely populated

7. What will get us there? • Data • National Visitor Survey • Comprehensive source of data of temporary population mobility in Australia • Continuous sample ~80 000 persons per annum • Variables: destination, origin, timing, purpose and duration of visit/trip • Sampling variability • Precludes the direct estimation of temporary visitors to small regions • Precludes use of fully saturated model • Dependent variable - monthly inflows to 68 Australian Tourism Regions

8. What will get us there? 2005 ASGC Tourism Regions

9. What will get us there? Data: Explanatory Variables

10. What will get us there? - Models -Approach separates model into temporal and spatial components

11. What will get us there? • Methodology • Run stepwise Poisson Regression Models • Model 1 (68 time series models) • Dependent variable – monthly inflows to Tourism Region • Independent variables – max. temp, sunshine hrs, precipitation • Offset- monthly inflows to all Tourism Regions • Apportionment model • Model 2 (12 cross-sectional models) • Dependent variable- inflows to all tourism regions • Independent variables – max. temp, sunshine hrs, precipitation, Tourism Quotient, ARIA score • Offset- Estimated Resident Populations • In-migration rate model

12. Results – Model 1 • Model fits are poor overall • Independent variables in 26/68 models accounted > 50 per cent of null deviance (G2 > 50 per cent) • 12 models had a G2 statistic greater than 70 per cent

13. Selected Results – Model 1 Model 1

14. Results – Model 1 • Poor model fits overall suggest that key determinants are missing from the model • Temperature accounts for most of the deviance in these models – direction of effect varies • Snowy Mountains (-ve) • Phillip Island (+ve) • Precipitation accounts for a moderate proportion of deviance for a number of regions- direction of effect varies • Snowy Mountains (+ve) • Outback QLD (-ve)

15. Results – Model 2 • Model fits are good overall

16. Results – Model 2 • Tourism Quotient accounts for most of the model deviance for all months (+ve) followed by ARIA score (+ve) • Maximum monthly temperature is the only factor varying at a monthly scale accounting for even moderate amounts of deviance

17. Conclusions • Early stages of research – major findings • Models work for some types of regions more than others • Not a common set of factors that apply to all regions • Easier to model baseline flows • Time series model better captures the seasonal variation in flows • Where to next? • Need to refine conceptual framework • Include more explanatory variables – difficult!!! • Disaggregate by purpose of trip? • Reintegration of temporal and spatial dimensions?