Factors Influencing Visitor's Choices of Urban Destinations in North ...

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Figure 4: Overseas Visitors to U.S. (in millions)Mnemonics 1998 1999 2000 2001 2002V_OVS_PHO 0.43 0.29 0.34 0.26 0.25V_OVS_LOS 3.56 3.57 3.53 2.82 2.26V_OVS_SAC 0.17 0.22 0.21 0.15 0.13 Legend:V_OVS_SAF 2.58 2.79 2.83 1.97 1.64 Cities State CityV_OVS_SAN 0.78 0.81 0.70 0.59 0.44 Phoenix, AZ AZ PHOV_OVS_DEN 0.26 0.27 0.29 0.24 0.25 San Diego, CA CA SANV_OVS_WAS 1.40 1.30 1.48 1.20 1.03 San Francisco, CA CA SAFV_OVS_FOT 0.52 0.47 0.47 0.42 0.29 Los Angeles, CA CA LOSV_OVS_MIA 3.27 2.86 2.94 2.55 2.20 Sacramento, CA CA SACV_OVS_ORL 2.87 2.86 3.01 2.47 1.87 Denver, CO CO DENV_OVS_TAM 0.74 0.49 0.52 0.50 0.36 Washington, DC DC WASV_OVS_ATL 0.57 0.54 0.70 0.70 0.54 Orlando, FL FL ORLV_OVS_CHI 1.21 1.27 1.35 1.07 1.01 Tampa, FL FL TAMV_OVS_IND 0.10 0.07 0.08 0.00 0.08 Miami, FL FL MIAV_OVS_NEO 0.36 0.29 0.36 0.39 0.23 Fort Lauderale, FL FL FOTV_OVS_BOS 1.04 1.20 1.33 1.07 0.82 Atlanta, GA GA ATLV_OVS_BAL 0.14 0.17 0.18 0.15 0.13 Chicago, IL IL CHIV_OVS_DET 0.26 0.29 0.34 0.28 0.25 Indianapolis, IN IN INDV_OVS_MIN 0.19 0.20 0.31 0.26 0.13 New Orleans, LA LA NEOV_OVS_STL 0.10 0.15 0.10 0.09 0.06 Boston, MA MA BOSV_OVS_CHR 0.05 0.07 0.13 0.11 0.08 Baltimore, MD MD BALV_OVS_LAS 1.80 2.25 2.26 1.51 1.22 Detroit, MI MI DETV_OVS_NEY 5.00 5.51 5.71 4.80 4.24 Minneapolis, MN MN MINV_OVS_CIN 0.12 0.12 0.10 0.11 0.10 St. Louis, MO MO STLV_OVS_CLE 0.10 0.10 0.10 0.11 0.06 Charlotte, NC NC CHRV_OVS_COU 0.07 0.05 0.08 0.09 0.10 Las Vegas, NV NV LASV_OVS_OKL 0.00 0.00 0.00 0.00 0.00 New York City, NY NY NEYV_OVS_POT 0.19 0.20 0.16 0.11 0.12 Columbus, OH OH COUV_OVS_PHI 0.36 0.34 0.39 0.42 0.42 Cincinnati, OH OH CINV_OVS_PIT 0.12 0.10 0.10 0.11 0.10 Cleveland, OH OH CLEV_OVS_MEM 0.10 0.10 0.00 0.00 0.06 Oklahoma City, OK OK OKLV_OVS_NAH 0.10 0.07 0.16 0.00 0.08 Portland, OR OR POTV_OVS_AUS 0.12 0.12 0.13 0.13 0.08 Philadelphia, PA PA PHIV_OVS_DAL 0.40 0.42 0.49 0.35 0.33 Pittsburgh, PA PA PITV_OVS_HOU 0.50 0.42 0.44 0.42 0.36 Nashville, TN TN NAHV_OVS_SAZ 0.12 0.12 0.00 0.11 0.08 Memphis, TN TN MEMV_OVS_SAY 0.12 0.12 0.16 0.15 0.12 San Antonio, TX TX SAZV_OVS_SEA 0.47 0.47 0.42 0.35 0.31 Houston, TX TX HOUV_OVS_MIL 0.00 0.00 0.00 0.00 0.00 Dallas, TX TX DALV_OVS_KAN 0.00 0.00 0.00 0.00 0.00 Austin, TX TX AUSSalt Lake City, UT UT SAYSeattle, WA WA SEAMilwaukee, WI WI MILSource: OTTI website61
Figure 5: Total U.S. Cities Visits from Canada: Including En Route, ExcludingSame Day Auto and Non-Leisure (in millions)Legend:Mnemonics 2000 2001 2002 Cities State CityV_CAN_PHO 0.24 0.23 0.19 Phoenix, AZ AZ PHOV_CAN_LOS 0.14 0.14 0.12 San Diego, CA CA SANV_CAN_SAC 0.08 0.07 0.06 San Francisco, CA CA SAFV_CAN_SAF 0.17 0.16 0.15 Los Angeles, CA CA LOSV_CAN_SAN 0.20 0.19 0.17 Sacramento, CA CA SACV_CAN_DEN 0.07 0.06 0.06 Denver, CO CO DENV_CAN_WAS 0.08 0.07 0.06 Washington, DC DC WASV_CAN_FOT 0.09 0.09 0.08 Orlando, FL FL ORLV_CAN_MIA 0.14 0.13 0.11 Tampa, FL FL TAMV_CAN_ORL 1.23 1.11 0.94 Miami, FL FL MIAV_CAN_TAM 0.27 0.27 0.24 Fort Lauderale, FL FL FOTV_CAN_ATL 0.59 0.63 0.59 Atlanta, GA GA ATLV_CAN_CHI 0.22 0.25 0.22 Chicago, IL IL CHIV_CAN_IND 0.18 0.21 0.20 Indianapolis, IN IN INDV_CAN_NEO 0.09 0.08 0.06 New Orleans, LA LA NEOV_CAN_BOS 0.27 0.28 0.22 Boston, MA MA BOSV_CAN_BAL 0.34 0.40 0.37 Baltimore, MD MD BALV_CAN_DET 1.10 1.04 1.01 Detroit, MI MI DETV_CAN_MIN 0.41 0.39 0.38 Minneapolis, MN MN MINV_CAN_KAN 0.06 0.05 0.04 St. Louis, MO MO STLV_CAN_STL 0.03 0.03 0.02 Charlotte, NC NC CHRV_CAN_CHR 0.59 0.60 0.57 Las Vegas, NV NV LASV_CAN_LAS 0.38 0.30 0.26 New York City, NY NY NEYV_CAN_NEY 0.29 0.25 0.23 Columbus, OH OH COUV_CAN_CIN 0.16 0.17 0.15 Cincinnati, OH OH CINV_CAN_CLE 0.16 0.15 0.13 Cleveland, OH OH CLEV_CAN_COU 0.15 0.17 0.17 Oklahoma City, OK OK OKLV_CAN_OKL 0.03 0.03 0.03 Portland, OR OR POTV_CAN_POT 0.26 0.23 0.22 Philadelphia, PA PA PHIV_CAN_PHI 0.41 0.41 0.42 Pittsburgh, PA PA PITV_CAN_PIT 0.40 0.41 0.37 Nashville, TN TN NAHV_CAN_MEM 0.14 0.16 0.14 Memphis, TN TN MEMV_CAN_NAH 0.21 0.20 0.20 San Antonio, TX TX SAZV_CAN_AUS 0.02 0.02 0.02 Houston, TX TX HOUV_CAN_DAL 0.03 0.03 0.03 Dallas, TX TX DALV_CAN_HOU 0.03 0.03 0.03 Austin, TX TX AUSV_CAN_SAZ 0.05 0.05 0.04 Salt Lake City, UT UT SAYV_CAN_SAY 0.12 0.12 0.11 Seattle, WA WA SEAV_CAN_SEA 1.23 1.18 1.17 Milwaukee, WI WI MILV_CAN_MIL 0.12 0.10 0.11Source: Global Insight, Inc. and Statistics Canada62
Page 2 and 3:
Table of ContentsI. EXECUTIVE SUMMA
Page 4 and 5:
I. Executive SummaryHighlightsThe r
Page 6 and 7:
Next StepsThis project has surfaced
Page 8 and 9:
IV.coefficients from these models p
Page 10 and 11:
series, simple autoregressive and
Page 12 and 13:
Arts & Culture Museums Historymuseu
Page 14 and 15: The tourist visitations data was al
Page 16 and 17: Hotel Room Count and Hotel Property
Page 18 and 19: Table 9: Michelin’s RatingsRating
Page 20 and 21: value below one suggests that the s
Page 22 and 23: Table 13: Individual Attractions Th
Page 24 and 25: Table 16: Significant Results 14Equ
Page 26 and 27: The adjusted R 2 for this model is
Page 28 and 29: • On average across all cities, b
Page 30 and 31: Model 5C 17 : In Model 5C, the numb
Page 32 and 33: The model overpredicts the number o
Page 34 and 35: Attraction Elasticities for Ottawa
Page 36 and 37: Table 22: Marketing Budget Elastici
Page 38 and 39: VII.Technical AppendixA. Attraction
Page 40 and 41: • Metropolitan Opera House in New
Page 42 and 43: Model 3: Three-Star Quality-Rated A
Page 44 and 45: Model 5B: Three-Star Quality-Rated
Page 46 and 47: 3. The cognitive perspective involv
Page 48 and 49: also found that almost half of the
Page 50 and 51: Also, the model seems to completely
Page 52 and 53: strengths and its weaknesses, devel
Page 54 and 55: Items from QuestionnaireTable 23: M
Page 56 and 57: Tourism Demand/Econometric Modellin
Page 58 and 59: Table 25: Types of Explanatory Vari
Page 60 and 61: Results: Lim and McAleer found that
Page 62 and 63: Richards, Greg. (2002). “Tourism
Page 66 and 67: Figure 6: Cities Person-Trips Volum
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