Big data analytics for enrichment of rural area content tourism in Okhotsk sub-prefecture of Japan

Transcription

1 Big data analytics for enrichment of rural area content tourism in Okhotsk sub-prefecture of Japan Ali Bakdur, Fumito Masui, Michal Ptaszynski Kitami Institute of Technology, Dept. of Computer Science, Japan

2 HELLO! I m Ali Bakdur PhD. Student Kitami Institute of Technology

3 Data sources

4 Topics Positioning of Japan, Hokkaido and Okhotsk Study plan Discussion

5 Positioning of Japan, Hokkaido, and Okhotsk

6 百万 Population forecast Population All Population 65+ Population 65- United Nations DESA / Population Division

7 The world's biggest economies Trillion USD United States China Japan Germany United Kingdom France India Italy Brazil Canada World Bank data,

8 Assessment of tourism value chain International Trade Centre (ITC)

9 United States Spain France United Kingdom Italy Germany Australia Turkey Austria Japan Korea Greece Canada Switzerland Mexico Netherlands Belgium Portugal Sweden Poland New Zealand Denmark Czech Republic Hungary Israel Norway Luxembourg Ireland Finland Slovenia Slovak Republic Chile Estonia Iceland Latvia 十億 Inbound tourism indicators (USD ) OECD statistics from Statistics Directorate Japan red

10 United States Germany United Kingdom France Canada Italy Australia Belgium Korea Netherlands Japan Norway Sweden Spain Switzerland Austria Denmark Mexico Poland Ireland Finland Czech Republic Turkey Portugal Israel New Zealand Luxembourg Greece Slovak Republic Chile Hungary Estonia Slovenia Iceland Latvia Billions Outbound tourism indicators (USD ) OECD statistics from Statistics Directorate Japan red

11 United States Germany United Kingdom Japan France Mexico Italy Australia Spain Canada Korea Turkey Austria Sweden Switzerland Netherlands Chile Norway New Zealand Greece Belgium Israel Portugal Denmark Finland Poland Hungary Czech Republic Ireland Slovak Republic Slovenia Luxembourg Latvia Iceland Estonia 十億 Domestic tourism indicators (USD ) OECD statistics from Statistics Directorate Japan red

12 Country Rankings (2014) : Absolute Contribution Travel & Tourism's Total Contribution to GDP (USD Billions) 1 United States 1, China Germany Japan United Kingdom WTTC Travel & Tourism Economic Impact

13 343,100,000,000 $ 7.5% of Japan s GDP in 2014

14 Domestic vs Foreign (2014) 8% 92% Foreign visitor spending WTTC Travel & Tourism Economic Impact Domestic spending

15 Japan GDP (% of total ) WTTC Travel & Tourism Economic Impact

16 Trend of People's Priorities in the Near Future Leisure/recreation Home environment improvement Improved eating habits Perchase of consumer durables Public Opinion Survey on National Lifestyle

17 Top 10 Leisure Activities Potential Demand Mountaineering Theater going Auto camping Picnicing, hiking and walking Events and exhibitions Barbecuing Onsen (hot spring) and spa facilities Passenger cruises Domestic leisure travel Overseas travel Participation Latent demand White Paper of Leisure 2015 (Japan Productivity Center)

18 百万 Trends in Domestic Travel (2015) Overnight travel (person) Same-Day trip (person) Survey of Trend in Travel and Tourism Consumption (JTA)

19 Guest Nights in Japan (2015) 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2, ,89 10,000 Guest-Night 7,668 6,006 5,386 4,224 4,263 3,217 2,506 2,089 1,295 Accommodation Survey (JTA)

20 Hokkaido is located on similar latitudes to major American and European cities.

21 Japan vs Hokkaido Place Hokkaido Japan Area (Km²) 83, ,970 Population ,431, ,110,000 Population Growth Rate Population Density Gross Domestic Production 2013 (Trillion YEN) MLIT (Minister of Land, Infrastructure, Transport and Tourism)

22 The Hokkaido Comprehensive Development Plan

23 OKHOTSK SUB-PREFECTURE of HOKKAIDO, JAPAN

24 Population Census 140, , ,000 80,000 60,000 40,000 20, , , , ,979 43,395 42,045 40,998 39,077 28,476 26,632 24,750 23, Abashiri-shi Kitami-shi Monbetsu-shi Statistics Japan, Ministry of Internal Affairs and Communications

25 6,000 5,000 4,000 3,000 2,000 1,000 0 Establishment s Existence (2014) 5,713 4,947 Abashiri-shi Kitami-shi Monbetsu-shi 1,936 1,701 1,296 1, Total Existing New Abolished Statistics Japan, Ministry of Internal Affairs and Communications

26 Accommodation, Food Service Business 1, Abashiri-shi Kitami-shi Monbetsu-shi Statistics Japan, Ministry of Internal Affairs and Communications

27 KIT Foreign Students Kitami Institute of Technology

28 China Malesia Indonesia Bangladesh Korea Cambodia Russia Tunisia Nicaragua Greece Iran Syria Finland Morocco Egypt Myanmar India Nigeria Poland Thai Mongolia Saudi Arabia Taiwan Vietnam USA Panama Algeria Peru Turkey KIT Foreign Student Distribution (2017) Kitami Institute of Technology

29 Study Plan Better and Sustainable Tomorrow!

30 Big Data

31 Data science process

32 Model process steps 1. Determine problem 2. Collect and observe the data sets 3. Bayesian Approaches 4. Model & Algorithms 5. Monte Carlo Simulation 6. Accumulation of results 7. Action and Decisions

33 Problem statement Why Japanese domestic travel and tourism industry has been declining gradually since 1998? Proof of Concept

34 Data Years Trillion Yen import pandas tourism_df = pandas.read_csv('/resources/japands.csv') jpn_spending_year = tourism_df[tourism_df.cc == 'JPN'].groupby('Year').mean()

35 Expen./Year Regression line graph

36 Model & Sampling model = tourism_model(jpn_spending_year) mcmc = pymc.mcmc(model) mcmc.sample(50000, 10000, 1) lambda_1_samples = mcmc.trace('lambda_1')[:] lambda_2_samples = mcmc.trace('lambda_2')[:] tau_samples = mcmc.trace('tau')[:]

37 Posterior distributions of λ1, λ2 and τ.

38 Conclusions It s been seen that Japanese domestic travel and tourism is a significant sector. Needs to be recovered. Combining more random variables and development of the model is required. Contribution of Hokkaido Development Plan is essential Tokyo Summer Olympics

39 THANKS!