MUNAWIRText Mining by Social Network Data towards Developing Attractiveness of Urban Park. Case Study: Thematic Parks in Bandung City, Indonesia.

Munawir1, Lia Nurbanillah Fujianti 2, M Donny Koerniawan 3 ,Bart Dewancker 2 1 Universitas Pendidikan Indonesia2 Kitakyushu University Japan3 Institut Teknologi Bandung, Indonesia

Munawir@upi.edu

Urban Retrofitting: Building, Cities and Communities in The Disruptive Era

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INTRODUCTION & LITERATURE REVIEW

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• One of City in Indonesia,Bandung city has its ownapproach to building thecity’s image. This citycreates its image throughthe development of publiccity parks.

• Based on Indonesian LawNumber 26 of 2007concerning spatial planningarticle 29, it is stated thatthe proportion of greenopen space is minimal inurban areas, which is 30%of the total area.

• The total area of Bandung City(PP. Nomor : 6/1987) = 16.729,65 Ha

• Green open space in 2015 = 2.032,21 Ha• Percentage of total area with total green open

space Bandung city is 12,15 %

• In 2013 the city governmentof Bandung revitalizedseveral parks into thematicparks.

• Thematic Park is the parkcreated with a certaintheme/concept as aparticular characteristic, bybrings out certain characters,so that when people see itthey can be able to capturethe impression of a morespecific function of the parks.Source:http://dpkp3.bandung.go.id/ruang-terbuka-hijau

Figure 1. Percentage of Green open space in Bandung city, 2015

INTRODUCTION & LITERATURE REVIEW

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• As public spaces, such as parks, can also befunctioned as a place to express opinion, visitor’sopinion plays an important role.

• Recently, the online reviews data particularly hasbeen considered as an important factor to influenceconsumers’ decision and are valued as assetsbased on valued information.

• Our approach is to analyze the attractiveness of thethematic parks in Bandung City and to evaluatevisitor’s perceptions by identifying sentences fromopinions or reviews regarding the fulfillment ofthematic park functions based on its user or visitor,facilities, community activities and atmosphere ofthe park.

Figure 2. The Location of Selected of Thematic Parks in Bandung

METHODSPlace Your Affiliation

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OnlineReviewData

TOPICEXTRACTIONLDAModel

(LatentDirichlet Allocation)*Word-TopicMatrix

TopicNaming

User/community Activities Facilities Atmosphere

SummarizingThetotalweightofeachtopicrepresenttherelativeImportanceof

eachtopic

Result

Figure 4. Procedure to identify attractiveness of parks

Figure 3. Google Maps user reviews

FINDINGS AND DISCUSSION

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Attractive Component from Topic Extraction• As for the appropriate topic with number K=4,

we can distinctly each topic by summarizingthe corresponding top 20 words. The fourtopics include “user/community”, “activities”,“facilities”, and “atmosphere”.

Topic1.User/Community0.1629batman 0.0018 entertainment 0.0018 good 0.0098 play 0.0141characters 0.0014 families 0.0011 hangout 0.0016 playground 0.0058children's 0.0377 family 0.0042 inviting 0.0014 statues 0.0068comfortable 0.0064 free 0.0084 park 0.0205 suitable 0.0094crowded 0.0038 fun 0.0033 place 0.0166 superhero 0.0070Topic2.Activities0.1414park 0.0205 enjoy 0.0005 relaxing 0.0020 especially 0.0012place 0.0166 activities 0.0003 refreshing 0.0002 recreation 0.0009inviting 0.0014 visiting 0.0007 children's 0.0377 meeting 0.0001play 0.0141 sport 0.0002 crowded 0.0038 comfortable 0.0064free 0.0084 cool 0.0072 good 0.0098 suitable 0.0094Topic3.Facilities0.2064superheroes 0.0020 Play 0.0250 Superman 0.0009 place 0.0173Playground 0.0012 comfortable 0.0064 toilet 0.0013 free 0.0084facilities 0.0030 Parking 0.0026 trees 0.0012 cool 0.0072statue 0.0068 free 0.0085 children's 0.0377 family 0.0042children's 0.0380 Wi-Fi 0.0048 suitable 0.0094 park 0.0205Topic4.Atmosphere0.084friendly 0.0008 play 0.0141 bad 0.0017 cozy 0.0004suitable 0.0094 playground 0.0058 unique 0.0006 happiness 0.0004atmosphere 0.0009 comfortable 0.0064 exiting 0.0010 enjoy 0.0005cool 0.0072 Recommended 0.0003 shady 0.0011 attractions 0.0008good 0.0098 park 0.0205 thematic 0.0013 entertainment 0.0018

Table1.TopicSummarywithK(thenumberoftopics=4)SuperheroParkReviews(Example)

NameofPark TopicsCategory

User/Community

Activities Facilities Atmosphere

SuperheroPark 0.1629 0.1414 0.2064 0.0847

CentrumMusicPark 0.0562 0.0899 0.0938 0.0941

PhotoPark 0.0998 0.0711 0.1108 0.0940

GesitPark 0.0622 0.0767 0.0897 0.1274

FitnessPark 0.0790 0.0818 0.0726 0.0640

JombloPark 0.0419 0.0607 0.0260 0.0528

FilmPark 0.0869 0.0496 0.0492 0.0295

LansiaPark 0.0665 0.0218 0.0408 0.0605

PetPark 0.0740 0.0307 0.0433 0.0303

InclusionPark 0.0901 0.0641 0.0780 0.0745

Average 0.0820 0.0688 0.0811 0.0712

Percentage 27.05% 22.70% 26.75% 23.49%

Table2.SummarizationofTopicCategoriesThematicParksReviews

Percentage corresponding weight is not significant different each factor from 10 thematic parks

FINDINGS AND DISCUSSION

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27.05% 26.75%

23.49% 22.70%

20.00%

21.00%

22.00%

23.00%

24.00%

25.00%

26.00%

27.00%

28.00%

Percen

tageAverageCorespo

ndingWeights

Attractivenessfactor

Figure 5.OverallPercentageAttractivenessFactorof10Thematic Parks

ANOVA:SingleFactor

SUMMARY

Groups Count Sum Average Variance

User/Community 10 0.8195593 0.081956 0.001103

Activities 10 0.6878566 0.068786 0.001119

Facilities 10 0.8105613 0.081056 0.002667

Atmosphere 10 0.7117668 0.071177 0.000924

ANOVA

SourceofVariation SS df MS F P-value Fcrit

BetweenGroups 0.001 3 0.000454 0.312126 0.816474 2.866266

WithinGroups 0.052 36 0.001453

Total 0.054 39

Table2. Analysisofvarianceattractivenessofthematicparks

CONCLUSIONS

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• Based on the result of topic extraction that the attractiveness of the thematic park can toconclude that there are 4 categories in the attraction of thematic parks namely user or groupcommunities, facilities, activities, and atmosphere. The result shows that we accept the nullhypothesis. The means of the four populations are equal.

• The dominant factor attractiveness of thematic parks is the user/community factor, related toone of the purposes of the thematic park in Bandung is to make different designs of parksbased on visitor characteristics.

• This research confirms the capacity of online reviews to the understanding of theattractiveness of thematic parks. Online reviews show great information to assess landscapeas there are volumes of data available which implicitly shows public opinions through text.Based on the topic extraction found, to developing or improving the park.

REFERENCES

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1. BAPPEDA Kota Bandung; Interplan, P.T.; Belaputra. Laporan Akhir Kajian Konsep Pengembangan danPengelolaan Taman Kota Menjadi Taman Tematik di Kota Bandung; BAPPEDA Kota Bandung: Bandung,Indonesia, 2014.

2. Housing and Settlement Area Office Land and Parks in Bandung City. Available online:http://dpkp3.bandung.go.i (last accessed on 10 august 2020).

3. Robby, Y.T.; Martheas, I.; Jian-Ping, S. The Effectiveness of Green Infrastructure Concept Design at City Parks,Bandung City, Indonesia. In Proceedings of the 12th International Conference on Hydroscience & Engineering,Tainan, Taiwan, 6–10 November 2016, pp.1-3

4. Kemperman, A.D.A.M. Temporal Aspects of Theme Park Choice Behavior: Modeling variety seeking, seasonalityand diversification to support theme park planning. Ph.D. Thesis, Eindhoven University of Technology,Netherlands. 2000.

5. Jamal, S.A; Aminudin, Norliza; Rahman, N.A. Visitors’ Experiences of Cluster Development at Thematic Parks inMalaysia. Asian Social Science; Volume 13, No.8, 2017. ISSN: 1911-2017

6. Hajmirsadeghi, R.S; Shamsuddin, S; Foroughi, A. The Impact of Physical Design Factor on the Effective Use ofPublic Squares. International Journal of Fundamental Psychology & Social Sciences, Volume 2, No.3, pp. 49-56,Sep, 2012. ISSN: 2231-9484.

7. Srivastava, Jaidep. Data Mining for Social Network Analysis. IEEE ISI 2008 Talk (III).8. Maskeri, G.; Sarkar, S.; Heafield, K. Mining business topics in source code using latent dirichlet allocation. In

Proceedings of the 1st India Software Engineering Conference—ISEC’08, Hyderabad, India, 19–22 February2008; p. 113.

Thank You

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