Thermal Comfort, Weather-Type, and Consumer Behavior: Influences on Visitor Attendance at Four U.S. Metropolitan Zoos

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1 Thermal Comfort, Weather-Type, and Consumer Behavior: Influences on Visitor Attendance at Four U.S. Metropolitan Zoos David R. Perkins Doctoral Candidate, ABD & NSF Graduate Fellow Department of Geography University of North Carolina at Greensboro Dr. Keith G. Debbage Professor Department of Geography University of North Carolina at Greensboro

2 We must understand today before we can successfully plan for tomorrow

3 Research Purpose To highlight how weather can impact visitor attendance, particularly at large metropolitan zoological parks

4 Were you aware? Zoos and aquariums contributed over $16 billion to the U.S. economy in 2012, supported 142,000 jobs, and attracted 175 million visitors (AZA 2013)

5 Tourism is a Weather-Sensitive Industry

6 Slide Courtesy of Dr. Daniel Scott, University of Waterloo

7 Slide Courtesy of Dr. Daniel Scott, University of Waterloo

8 Slide Courtesy of Dr. Daniel Scott, University of Waterloo

9 Slide Courtesy of Dr. Daniel Scott, University of Waterloo

11 Weather impacts on visitor behavior: A spatio-temporal study of select metropolitan zoos

12 Background Weather can impact visitor attendance in substantive ways, and this research attempts to determine how biometeorologically-derived thermal comfort categories coincide with decisions to attend zoos.

14 Zoo Summary Comparison Zoo State Data Period Size (Acres) CSA Title 2010 CSA Population Visitor Rank (USA) Annual Visitation Atlanta Georgia 40 Atlanta-Sandy Springs- Gainesville, GA-AL 5,618, million Indianapolis Indiana September 2001 to June 2011 (n~3564) 64 Phoenix Arizona 125 St. Louis Missouri 90 Indianapolis- Anderson-Columbus, IN Phoenix-Mesa- Glendale, AZ St. Louis-St. Charles- Farmington, MO-IL 2,080, million 4,192, million 2,878, million

15 Period of Record Data September 2001 June 2011 Weather Nearest ASOS weather station (hourly records) Variables Temperature, Humidity, Cloud Cover, Wind Speed, Elevation Attendance Daily visitor counts obtained from gate admissions

16 Climate typologies

17 Biometeorological Indices Indices which assess how differing combinations of weather variables actually feel to a human Physiologically Equivalent Temperature (PET) Standard Effective Temperature (SET) Predicted Mean Vote (PMV) Fanger, 1972; Hoppe, 1999; Gagge et al., 1986; Vanos et al., 2010; de Freitas, 1985; Staiger et al., 2011; Sprague & Munson, 1974; Davis et al., 2006; ASHRAE, 2001, 2004; de Dear, 1991, 1994, 1998, 2002; de Freitas et al., 2007; Hwang et al., 2007; Matzarakis, 1996; Lin et al., 2007, 2009; Matzarakis, 1996; Staiger et. al., 2011; Matzarakis & Mayer 1996

18 The Munich Energy balance Model for Individuals (MEMI) is a thermo-physiological heat balance model It is the basis for the calculation of the Physiologically Equivalent Temperature (PET) M+W+R+C+E D +E Re +E Sw +S=0 M= Metabolic rate W= Physical work R= Net radiation of the body C= Convective heat flow E D = Imperceptible perspiration E Re = Σ heat flows for heating and humidifying inspired air E Sw = Heat flow due to evaporation of sweat S= Storage heat flow for heating or cooling the body mass Additional thermo-physiological parameters required in calculation: Heat resistance of clothing (clo units) Activity level (watts) Corresponding heat flows and controlling meteorological parameters: Air temperature C, E Re Air humidity E D,E Re, E Sw Wind velocity C, E Sw Mean radiant temperature R

19 RayMan Biometeorological Model Matzarakis, Rutz & Mayer (2000)

21 P. Hӧppe (1999)

22 RayMan Inputs Physical Input Clothing Values (CLO) Season CLO Winter 1.1 Shoulder Seasons 0.8 Summer 0.3 Age 37 Gender Female Effort 145W Height 1.65m Weight 63kg

23 Biometeorological Classification Matzarakis and Mayer (1996)

24 Zoo Atlanta Indianapolis Zoo 8% 10% 10% 3% 16% 6% 30% 9% 14% 16% 11% 11% 8% 13% 11% 7% 8% 9% 0% Phoenix Zoo 1% St. Louis Zoo 3% 7% 8% 39% 11% 13% 24% 15% 13% 7% 10% 9% 12% 12% 8% 8%

25 Attendance Day Typologies Descriptor Prime Excellent Marginal Poor Definition above 2 s.d. 1 to 2 s.d. -1 to +1 s.d. Below -1 s.d.

26 MARGINAL

27 Prime Attendance Days ATL INDY PHX STL 33% 30% 30% 28% 27% 27% 26% 18% 15% 14% 20% 19% 19% 16% 13% 7% 10% 8% 6% 11% 4% 1% 0% 0% 0% 1% 1% 1% 0% 1% 0% 3% 4% 4% 4% 2% Very Cold Cold Cool Slightly Cool Neutral Slightly Warm Warm Hot Very Hot

28 MARGINAL

29 Excellent Attendance Days ATL INDY PHX STL 30% 28% 25% 26% 20% 23% 21% 20% 22% 21% 18% 20% 16% 14% 13% 8% 6% 7% 6% 10% 7% 7% 10% 4% 2% 0% 0% 0% 0% 0% 0% 4% 4% 3% 1% 3% Very Cold Cold Cool Slightly Cool Neutral Slightly Warm Warm Hot Very Hot

30 MARGINAL

31 Marginal Attendance Days ATL INDY PHX STL 42% 25% 17% 16% 17% 7% 9% 6% 9% 12% 10% 11% 4% 11% 11% 10% 10% 10% 9% 8% 8% 13% 13% 11% 10% 14% 12% 11% 12% 12% 10% 9% 3% 9% 0% 0% Very Cold Cold Cool Slightly Cool Neutral Slightly Warm Warm Hot Very Hot

32 MARGINAL

33 Poor Attendance Days ATL INDY PHX STL 85% 87% 84% 54% 1% 14% 13% 7% 8% 2% 5% 6% 3% 11% 2% 3% 2% 5% 0% 1% 0% 1% 0% 1% 0% 1% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% Very Cold Cold Cool Slightly Cool Neutral Slightly Warm Warm Hot Very Hot

34 Weather Nuances: the difference between success and failure?

35 Zoo Atlanta St. Louis Zoo EXCELLENT DAYS PRIME DAYS EXCELLENT DAYS PRIME DAYS 30% 35% 25% 30% 20% 15% 10% 25% 20% 15% 10% 5% 0% Very Cold Cold Cool Slightly Cool Neutral 3 thermal categories Slightly Warm Warm Hot Very Hot 5% 0% Very Cold Cold Cool Slightly Cool Neutral Slightly Warm Warm Hot Very Hot Indianapolis Zoo Phoenix Zoo EXCELLENT DAYS PRIME DAYS EXCELLENT DAYS PRIME DAYS 35% 35% 30% 30% 25% 25% 20% 20% 15% 15% 10% 10% 5% 0% Very Cold Cold Cool Slightly Cool Neutral Slightly Warm Warm Hot Very Hot 5% 0% Very Cold Cold Cool Slightly Cool 3 thermal categories Neutral Slightly Warm Warm Hot Very Hot

36 Spatial Synoptic Classification (SSC) A multi-disciplinary weather classification method Kalkstein, 1996; Crowe 1971; Kalkstein et al., 1987; Hondula et al., 2012

37 Synoptic Scale Classification (SSC) Name Dry Polar (DP) Dry Moderate (DM) Dry Tropical (DT) Moist Polar (MP) Moist Moderate (MM) Moist Tropical (MT) Transitional (TR) Description Dry air usually from polar regions; coldest temperatures during the year Mild and dry air; often found when a traditional air mass is moderated Dry air representing the hottest and driest conditions of the year Cloudy, humid, and cool weather types Variable in its seasonality; considerably warmer than moist polar conditions Warm and humid air; often oppressive conditions Air mass transition from one to another Adapted from: Kalkstein et al., 1987

40 SSC Climatology 60% 50% 40% 30% 20% 10% 0% Dry Moderate Dry Polar Dry Tropical Moist Moderate Moist Polar Moist Tropical ATLANTA INDIANAPOLIS PHOENIX SAINT LOUIS Transition

41 SSC Prime Visitor Days 60% 50% 40% 30% 20% 10% 0% Dry Moderate Dry Polar Dry Tropical Moist Moderate Moist Polar Moist Tropical Transition ATLANTA INDIANAPOLIS PHOENIX SAINT LOUIS

42 Prime versus Climatology 30% 25% 20% 15% 10% 5% 0% -5% Atlanta Indy Phx STL -10% -15% -20% Dry Moderate Dry Polar Dry Tropical Moist Moderate Moist Polar Moist Tropical Transition

43 Prime versus excellent 15% 10% 5% 0% -5% Atlanta Indy Phx STL -10% -15% Dry Moderate Dry Polar Dry Tropical Moist Moderate Moist Polar Moist Tropical Transition

44 Important Takeaways Many findings are intuitive... However the quantification of such intuition is anything but intuitive! Sensitivity to nuances in weatherattendance relationships The value-added product Geography matters!

45 Strategies for Resilience

49 How can this research be applied to tourism businesses? The Immediacy of Social Media While weather impacts attendance flows at tourism locations, so do promotions and advertisement. It is seen in the data that if the weather is not extreme, its impact can be strategically overcome. Solution: Automatic social-media price promotions based upon business-specific weather triggers

50 Acknowledgements University of North Carolina at Greensboro Department of Geography East Carolina University Center for Sustainability: Tourism, Natural Resources, and the Built Environment GK-12 NSF Project # Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of the National Science Foundation Zoo Atlanta St. Louis Zoo Indianapolis Zoo Phoenix Zoo

51 We must understand today before we can successfully plan for tomorrow Thank you for your time!

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