Study on Spatial Structure Characteristics of the Tourism and Leisure Industry
Abstract
:1. Introduction
2. Research Methodology and Data Sources
2.1. Research Methodology
2.1.1. Average Nearest Neighbor Index
2.1.2. Kernel Density Estimation
2.1.3. Standard Deviational Ellipse
2.1.4. Spatial Autocorrelation Index
2.2. Data Sources
3. Analysis of Results
3.1. Spatial Distribution Patterns
3.2. Spatial Cluster Characteristics
3.3. Spatial Hot Spot Detection
4. Discussion
4.1. Contribution
4.2. Limited Factors
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Illeris, S.; Sjoholt, P. The nordic countries: High quality service in a low density environment. Prog. Plan. 1995, 43, 205–221. [Google Scholar] [CrossRef]
- Cui, Z.; Shen, L.; Liu, Z. Spatial agglomeration characteristics of service industry in Xinjiekou CBD of Nanjing City and change: Based on micro enterprise data. Prog. Geogr. 2020, 39, 1832–1844. (In Chinese) [Google Scholar] [CrossRef]
- Meng, L.L.; Li, J.G.; Li, M.Y.; Song, Y.Y. Study on the agglomeration characteristics and influence factors of modern service industry in Henan Province. World Reg. Stud. 2020, 29, 1202–1212. (In Chinese) [Google Scholar]
- Boiteux, O.C.; Guillain, R. Changes in the intrametropolitan location of producer services in France (1978–1997): Do information technologies promote a more dispersed spatial pattern? Urban Geogr. 2004, 25, 550–578. [Google Scholar] [CrossRef]
- Coll, M.E.; Arauzo, C.J.; Moreno, M.A. Agglomeration of creative industries: An intra-metropolitan analysis for Barcelona. Pap. Reg. Sci. 2019, 98, 409–431. [Google Scholar] [CrossRef]
- Wyrwich, M. New knowledge-intensive business services on the bloc: The role of local manufacturing for start-up activity in knowledge-intensive business services. Reg. Stud. 2019, 53, 320–329. [Google Scholar] [CrossRef]
- Widaningrum, D.L.; Surjandari, I.; Sudiana, D. Discovering spatial patterns of fast-food restaurants in Jakarta, Indonesia. J. Ind. Prod. Eng. 2020, 37, 403–421. [Google Scholar] [CrossRef]
- Desmarchelier, B.; Djellal, F.; Gallouj, F. Knowledge intensive business services and long term growth. Struct. Chang. Econ. Dyn. 2013, 25, 188–205. [Google Scholar] [CrossRef] [Green Version]
- Li, Z.M.; Fan, Z.X. Analysis of agglomeration and temporal spatial evolution of high and new technology industries in Poyang lake eco-economic zone. Rev. Fac. Ing. 2017, 32. [Google Scholar]
- Huang, D.W. The space fitting between spatial links of services with spatial distribution of urbanization in Guangdong province. Econ. Geogr. 2017, 37, 114–123. (In Chinese) [Google Scholar]
- Zeng, Y.; Han, F.; Liu, J.F. Does the agglomeration of producer services promote the quality of urban economic growth? J. Quant. Tech. Econ. 2019, 36, 83–100. (In Chinese) [Google Scholar]
- Wu, L.F.; Hao, L.H.; Wang, X.G.; Zhang, L. Development potential pattern of service industry in the eastern Chinese cities based on high-speed railway connection: From the perspective of social network analysis. Econ. Geogr. 2020, 40, 145–154. (In Chinese) [Google Scholar]
- Corrocher, N.; Cusmano, L. The knowledge-intensive business services engine of regional innovation systems: Empirical evidence from European regions. Reg. Stud. 2014, 48, 1212–1226. [Google Scholar] [CrossRef] [Green Version]
- Kamp, B.; Sisti, E. Assessing the relationship between ICT services and the manufacturing industry from a meso-economice perspective: Insights from the Basque country. Eur. Rev. Serv. Econ. 2018, 2, 123–151. [Google Scholar]
- Taylor, P.J.; Derudder, B.; Faulconbridge, J.; Hoyler, M.; Ni, P. Advanced Producer Service Firms as Strategic Networks, Global Cities as Strategic Places. Econ. Geogr. 2013, 90, 267–291. (In Chinese) [Google Scholar] [CrossRef] [Green Version]
- Zhuang, D.L.; Liang, J.; Xu, J.L. China′s provincial capital network based on producer service industries. Geogr. Geo-Inf. Sci. 2020, 36, 113–120+128. (In Chinese) [Google Scholar]
- Lee, Y.-J.A.; Jang, S.; Kim, J. Tourism clusters and peer-to-peer accommodation. Ann. Tour. Res. 2020, 83, 102960. [Google Scholar] [CrossRef]
- Gao, Y.H.; Yang, Q.Q.; Liang, L.; Zhao, Y.H. Spatial pattern and influencing factors of retailing industries in Xi’an based on POI data. Sci. Geogr. Sin. 2020, 40, 710–719. (In Chinese) [Google Scholar]
- Luan, H.; Law, J.; Quick, M. Identifying food deserts and swamps based on relative healthy food access: A spatio-temporal Bayesian approach. Int. J. Health Geogr. 2015, 14, 1–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chen, M.; Creger, T.; Howard, V.; E Judd, S.; Harrington, K.F.; Fontaine, K.R. Association of community food environment and obesity among US adults: A geographical information system analysis. J. Epidemiol. Community Health 2019, 73, 148–155. [Google Scholar] [CrossRef]
- Han, Z.; Song, W. Identification and Geographic Distribution of Accommodation and Catering Centers. ISPRS Int. J. Geo-Inf. 2020, 9, 546. [Google Scholar] [CrossRef]
- Tang, C.G.; Sun, M.Y.; Wan, Z.W. Spatial distribution characteristics of high-level scenic spots and its influencing factors in Beijing-Tianjin-Hebei urban agglomeration. Econ. Geogr. 2019, 39, 204–213. (In Chinese) [Google Scholar]
- Nahiduzzaman, K.M.; Aldosary, A.S.; Mohammed, I. Framework Analysis of E-Commerce Induced Shift in the Spatial Structure of a City. J. Urban Plan. Dev. 2019, 145, 04019006. [Google Scholar] [CrossRef]
- Cebeillac, A.; Daude, E.; Vaguet, A. Spatial discontinuities, health and mobility: What do the Google’s POIs and tweets tell us about Bangkok’s (Thailand) structures and spatial dynamics. Rev. Int. Geomat. 2018, 28, 389–407. [Google Scholar] [CrossRef]
- Yang, J.; Yang, R.; Chen, M.H.; Su, C.H.; Zhi, Y.; Xi, J. Effects of rural revitalization on rural tourism. J. Hosp. Tour. Manag. 2021, 47, 35–45. [Google Scholar] [CrossRef]
- Jin, S.H.; Yang, J.; Wang, E.X.; Liu, J. The influence of high-speed rail on ice–snow tourism in northeastern China-Science Direct. Tour. Manag. 2020, 78, 104070. [Google Scholar] [CrossRef]
- Chen, H.X. Evolution of spatial distribution and agglomeration features of producer services in Beijing. Econ. Geogr. 2018, 38, 108–116. (In Chinese) [Google Scholar]
- Liu, J.; Zhao, M. Study on Evolution and Interaction of Service Industry Agglomeration and Efficiency of Hebei Province China. Complexity 2020, 2020, 1750430. [Google Scholar] [CrossRef]
- Zhao, M.Y.; Liu, J.G. Research on the Characteristics and Influenle of Beijing Tourism Flow Network. Urban Dev. Stud. 2020, 27, 13–18. (In Chinese) [Google Scholar]
- Hobbs, M.; Griffiths, C.; Green, M.; Jordan, H.; Saunders, J.; Christensen, A.; McKenna, J. Fast-food outlet availability and obesity: Considering variation by age and methodological diversity in 22,889 Yorkshire Health Study participants. Spat. Spatio-Temporal Epidemiol. 2018, 28, 43–53. [Google Scholar] [CrossRef]
- Haris, E.; Gan, K.H.; Tan, T.-P. Spatial information extraction from travel narratives: Analysing the notion of co-occurrence indicating closeness of tourist places. J. Inf. Sci. 2020, 46, 581–599. [Google Scholar] [CrossRef]
- Lim, K.H.; Chan, J.; Leckie, C.; Karunasekera, S. Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency. Knowl. Inf. Syst. 2018, 54, 375–406. [Google Scholar] [CrossRef]
- Dadashpoor, H.; Yousefi, Z. Centralization or decentralization? A review on the effects of information and communication technology on urban spatial structure. Cities 2018, 78, 194–205. [Google Scholar] [CrossRef]
- Sparks, K.; Thakur, G.; Pasarkar, A.; Urban, M. A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation. Int. J. Geogr. Inf. Sci. 2020, 34, 759–776. [Google Scholar] [CrossRef] [Green Version]
- Hamid, R.A.; Croock, M.S. A developed GPS trajectories data management system for predicting tourists’ POI. Telkomnika Telecommunication Comput. Electron. Control 2020, 18, 124–132. [Google Scholar] [CrossRef]
- Xue, S.; Li, G.; Yang, L.; Liu, L.; Nie, Q.; Mehmood, M.S. Spatial Pattern and Influencing Factor Analysis of Attended Collection and Delivery Points in Changsha City, China. Chin. Geogr. Sci. 2019, 29, 1078–1094. [Google Scholar] [CrossRef] [Green Version]
- Hopken, W.; Muller, M.; Fuchs, M.; Lexhagen, M. Flickr data for analysing tourists’ spatial behaviour and movement patterns: A comparison of clustering techniques. J. Hosp. Tour. Technol. 2020, 11, 69–82. [Google Scholar] [CrossRef]
- Negruşa, A.L.; Toader, V.; Sofică, A.; Tutunea, M.F.; Rus, R.V. Exploring Gamification Techniques and Applications for Sustainable Tourism. Sustainability 2015, 7, 11160–11189. [Google Scholar] [CrossRef] [Green Version]
- Moscardo, G.; Murphy, L. There Is No Such Thing as Sustainable Tourism: Re-Conceptualizing Tourism as a Tool for Sustainability. Sustainability 2014, 6, 2538–2561. [Google Scholar] [CrossRef] [Green Version]
- Dangi, T.B.; Jamal, T. An integrated approach to “Sustainable community-based tourism”. Sustainability 2016, 8, 475. [Google Scholar] [CrossRef] [Green Version]
- Peeters, P.; Landré, M. The Emerging Global Tourism Geography—An Environmental Sustainability Perspective. Sustainability 2011, 4, 42–71. [Google Scholar] [CrossRef] [Green Version]
POI | Classification | Number/Pcs | Percentage/% |
---|---|---|---|
Catering | Chinese food, western food, fast food, beverage shop | 105,059 | 29.00 |
Accommodation | Star-rated hotels, homestays, youth hostels | 28,983 | 8.00 |
Scenic spots | A-level scenic spots, parks, squares | 4792 | 1.32 |
Shopping and entertainment | Retail industry, integrated shopping center | 223,415 | 61.67 |
Tourism and leisure | 362,249 | 100 |
Tourism and Leisure | Catering | Accommodation | Scenic Spots | Shopping and Entertainment | |
---|---|---|---|---|---|
R value | 0.18 | 0.15 | 0.21 | 0.52 | 0.19 |
p value | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Tourism and Leisure | Catering | Accommodation | Scenic Spots | Shopping and Entertainment | |
---|---|---|---|---|---|
Azimuth (°) | 39.10 | 36.07 | 36.77 | 36.07 | 40.99 |
Major axis (km) | 32.53 | 28.92 | 40.17 | 48.44 | 32.55 |
Minor axis (km) | 22.59 | 21.47 | 24.28 | 32.16 | 22.48 |
Flatness | 1.44 | 1.35 | 1.65 | 1.50 | 1.44 |
Area (km2) | 2308.27 | 1950.57 | 3063.79 | 4893.28 | 2298.87 |
Tourism and Leisure | Catering | Accommodation | Scenic Spots | Shopping and Entertainment | |
---|---|---|---|---|---|
Global Moran’s I | 0.19 | 0.24 | 0.38 | 0.08 | 0.14 |
Z value | 16.55 | 20.28 | 32.74 | 6.94 | 11.91 |
p value | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Tourism and Leisure | Catering | Accommodation | Scenic Spots | Shopping and Entertainment | ||
---|---|---|---|---|---|---|
Grade I hot spot | Street/pcs | 138 | 141 | 131 | 12 | 134 |
Area/km2 | 1722.22 | 1788.35 | 1533.32 | 887.68 | 1583.46 | |
POI/pcs | 249,477.00 | 76,167.00 | 20,670.00 | 291.00 | 146,009.00 | |
Z mean value | 5.73 | 6.08 | 7.00 | 2.95 | 1.19 | |
Grade II hot spot | Street/pcs | 10 | 8 | 6 | 11 | 14 |
area/km2 | 394.47 | 196.03 | 220.88 | 662.91 | 549.78 | |
POI/pcs | 12,393.00 | 2534.00 | 1106.00 | 262.00 | 12,950.00 | |
Z mean value | 2.29 | 2.24 | 2.23 | 2.28 | 2.32 | |
Grade III hot spot | Street/pcs | 5 | 5 | 3 | 13 | 5 |
Area/km2 | 126.04 | 296.38 | 189.46 | 880.06 | 107.48 | |
POI/pcs | 5883.00 | 2225.00 | 382.00 | 331.00 | 3285.00 | |
Z mean value | 1.79 | 1.78 | 1.78 | 0.10 | 1.80 | |
Random distribution area | Street/pcs | 91 | 85 | 95 | 232 | 107 |
Area/km2 | 7883.31 | 7246.53 | 10,520.84 | 59.93 | 8915.54 | |
POI/pcs | 67,045.00 | 16,605.00 | 10,166.00 | 3553.00 | 45,749.00 | |
Z mean value | −0.54 | −0.56 | −0.52 | 0.87 | −0.58 | |
Grade I cold spot | Street/pcs | 16 | 19 | 42 | 18 | 6 |
Area/km2 | 1151.30 | 1207.50 | 2186.80 | 876.29 | 542.05 | |
POI/pcs | 4823.00 | 1546.00 | 658 | 114.00 | 367.00 | |
Z mean value | −2.76 | −2.81 | −2.91 | −2.90 | −2.80 | |
Grade II cold spot | Street/pcs | 30 | 36 | 27 | 21 | 22 |
Area/km2 | 2371.79 | 2817.18 | 1258.18 | 891.15 | 1911.81 | |
POI/pcs | 14,256.00 | 4127.00 | 627.00 | 155.00 | 5620.00 | |
Z mean value | −2.18 | −2.24 | −2.56 | −2.26 | −2.24 | |
Grade III cold spot | Street/pcs | 24 | 20 | 10 | 7 | 26 |
Area/km2 | 2743.85 | 1207.50 | 483.49 | 370.13 | 2782.84 | |
POI/pcs | 8372.00 | 1546.00 | 252.00 | 38.00 | 9291.00 | |
Z mean value | −1.78 | −2.81 | −1.84 | −1.79 | −1.77 |
Industry | Typical Streets (p < 0.01) | Z Value | |
---|---|---|---|
Tourism and Leisure | hot spot | Beixingqiao, Hepingli, Sanlitun, Hepingjie, Tuanjiehu | >7 |
cold spot | Fozizhuang, Wangping, Miaofeng, Tamzhesi, Heibeizhen | <−2 | |
Catering | hot spot | Beixingqiao, Tamzhesi, Sanlitun, Hepingli, Hepingjie | >8 |
cold spot | Fozizhuang, Dahuashan, Wangxinzhuang, Daxingzhuang, Yukou | <−2 | |
Accommodation | hot spot | Beixingqiao, Tuanjiehu, Hepingjie, Sanlitun, Hujialou | >8 |
cold spot | Qinglonghu, Hebei, Mulin, Tamzhesi, Beixiaoying | <−3 | |
Scenic Spots | hot spot | Pingguoyuan, Liulimiao, Bajiao, Shicheng, Xiangshan | >2 |
cold spot | Beixiaoying, Nancai, Yangzhen, Zhangzhen, Binhe | <−2 | |
Shopping and Entertainment | hot spot | Beixingqiao, Hepingli, Andingmen, Hepingjie, Sanlitun | >6 |
cold spot | Fozizhuang, Wangping, Miaofengshan, Tamzhesi, Datai | <−2 |
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Zhao, M.; Liu, J. Study on Spatial Structure Characteristics of the Tourism and Leisure Industry. Sustainability 2021, 13, 13117. https://doi.org/10.3390/su132313117
Zhao M, Liu J. Study on Spatial Structure Characteristics of the Tourism and Leisure Industry. Sustainability. 2021; 13(23):13117. https://doi.org/10.3390/su132313117
Chicago/Turabian StyleZhao, Mingyu, and Jianguo Liu. 2021. "Study on Spatial Structure Characteristics of the Tourism and Leisure Industry" Sustainability 13, no. 23: 13117. https://doi.org/10.3390/su132313117