A machine learning application to Google Maps Reviews as a participatory planning tool

References

• Afifah, K., Yulita, I. N., & Sarathan, I. (2021). Sentiment analysis on telemedicine App reviews using XGBoost classifier. In 2021 international conference on artificial intelligence and Big data analytics (pp. 22–27). doi:10.1109/ICAIBDA53487.2021.9689762.
   Google Scholar
• Afzalan, N., & Muller, B. (2018). Online participatory technologies: Opportunities and challenges for enriching participatory planning. Journal of the American Planning Association, 84(2), 162–177. doi:10.1080/01944363.2018.1434010
   Web of Science ®Google Scholar
• Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence, and smart cities. Cities, 89, 80–91. doi:10.1016/j.cities.2019.01.032
   Web of Science ®Google Scholar
• Antunes, P., Santos, R., & Videira, N. (2006). Participatory decision making for sustainable development—the use of mediated modelling techniques. Land Use Policy, 23(1), 44–52. doi:10.1016/j.landusepol.2004.08.014
   Web of Science ®Google Scholar
• Arnstein, S. R. (1969). A ladder of citizen participation. Journal of the American Institute of Planners, 35(4), 216–224. doi:10.1080/01944366908977225
   Web of Science ®Google Scholar
• Bala, B. K., Arshad, F. M., & Noh, K. M. (2017). Systems thinking: system dynamics (pp. 15–35). doi:10.1007/978-981-10-2045-2_2
   Google Scholar
• Batty, M. (2018). Artificial intelligence and smart cities. Environment and Planning B: Urban Analytics and City Science, 45(1), 3–6. Sage. doi:10.1177/2399808317751169
   Google Scholar
• Berry, B. J. L. (1964). Cities as systems within systems of cities. Papers in Regional Science, 13(1), 147–163. doi:10.1111/j.1435-5597.1964.tb01283.x
   Google Scholar
• Bracken, I. (1981). Urban planning methods: Research and policy analysis. London: Methuen & Co. Ltd.
   Google Scholar
• Bryson, J. M., Quick, K. S., Slotterback, C. S., & Crosby, B. C. (2013). Designing public participation processes. Public Administration Review, 73(1), 23–34. doi:10.1111/j.1540-6210.2012.02678.x
   Web of Science ®Google Scholar
• Çakır, M. U., & Güldamlasıoğlu, S. (2016). Text mining analysis in Turkish language using Big data tools. In 2016 IEEE 40th annual computer software and applications conference (COMPSAC) (pp. 614–618). doi:10.1109/COMPSAC.2016.203
   Google Scholar
• Caragliu, A., del Bo, C., & Nijkamp, P. (2009). Smart cities in Europe. In Proceedings of the 3rd central European conference in regional science—CERS 2009, (pp. 49–59). doi:10.1080/10630732.2011.601117
   Google Scholar
• Chandurkar, T., & Tijare, D. P. (2021). Sentiment analysis: A review and comparative analysis on colleges. International Journal of Scientific Research in Computer Science, Engineering, and Information Technology, 526–531. doi:10.32628/CSEIT217266
   Google Scholar
• Chang, Y. C., Ku, C. H., & Chen, C. H. (2019). Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. International Journal of Information Management, 48, 263–279. doi:10.1016/j.ijinfomgt.2017.11.001
   Web of Science ®Google Scholar
• Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., … Zhou, T. (2015). Xgboost: eXtreme gradient boosting. R Package Version 0.4-2, 1(4), 1–4.
   Google Scholar
• Chen, Y., Liu, X., Gao, W., Wang, R. Y., Li, Y., & Tu, W. (2018). Emerging social media data on measuring urban park use. Urban Forestry & Urban Greening, 31, 130–141. doi:10.1016/j.ufug.2018.02.005
   Web of Science ®Google Scholar
• Clark, G. (2020). The future of cities: Middle East, North Africa, and Türkiye. https://associationhubs.org/wp-content/uploads/2020/06/Greg-Clark-2020-MENAT-cities-C19-report-1.pdf
   Google Scholar
• Coxall, W., Robins, L., & Leach, R. (2003). Contemporary British politics. London: Palgrave Macmillan.
   Google Scholar
• Cugurullo, F. (2021). Frankenstein urbanism. Routledge. doi:10.4324/9781315652627.
   Google Scholar
• Currie, D. J., Smith, C., & Jagals, P. (2018). The application of system dynamics modeling to environmental health decision-making and policy - a scoping review. BMC Public Health, 18(1), 402. doi:10.1186/s12889-018-5318-8
   PubMedGoogle Scholar
• Derdzakyan, N., Pourmand, A., Shesser, R., Ganguli, S., & Trevino, J. (2022). The potential use of Google reviews to assess patient satisfaction in the emergency department. American Journal of Emergency Medicine, 52, 110–113. doi:10.1016/j.ajem.2021.12.013
   PubMed Web of Science ®Google Scholar
• Doğuç, Ö, Aytaç, ÖB, & Silahtaroğlu, G. (2020). Lemmatizer: Akıllı Türkçe Kök Bulma Yöntemi. Turkish Studies-Information Technologies and Applied Sciences, 15(3), 289–299. doi:10.47844/TurkishStudies.44220
   Google Scholar
• Dong, Q., Cai, J., Chen, S., He, P., & Chen, X. (2022). Spatiotemporal analysis of urban green spatial vitality and the corresponding influencing factors: A case study of Chengdu, China. Land, 11(10), 1820. doi:10.3390/land11101820
   Web of Science ®Google Scholar
• Du, J., Ye, X., Jankowski, P., Sanchez, T. W., & Mai, G. (2023). Artificial intelligence enabled participatory planning: A review. International Journal of Urban Sciences, 1–28. doi:10.1080/12265934.2023.2262427
   Web of Science ®Google Scholar
• Fainstein, S., & Campbell, S. (2012). Readings in planning theory. Malden, MA: Wiley-Blackwell.
   Google Scholar
• Fang, L., Adelakun, F., & Goetemann, T. (2022). Brewing entrepreneurship: Coffee and startups. Journal of Planning Education and Research. doi:10.1177/0739456X211067272
   Google Scholar
• Fernandez, J., Song, Y., Padua, M., & Liu, P. (2022). A framework for urban parks: Using social media data to assess bryant park, New York. Landscape Journal, 41(1), 15–29. University of Wisconsin Press. doi:10.3368/lj.41.1.15
   Web of Science ®Google Scholar
• Gebbels, M., McIntosh, A., & Harkison, T. (2021). Fine-dining in prisons: Online TripAdvisor reviews of the Clink training restaurants. International Journal of Hospitality Management, 95. doi:10.1016/j.ijhm.2021.102937
   Web of Science ®Google Scholar
• Gholipour, S., MahdiNejad, J.-D., & Saleh Sedghpour, B. (2022). Security and urban satisfaction: Developing a model based on safe urban park design components extracted from users’ preferences. Security Journal, 35(3), 863–892. doi:10.1057/s41284-021-00303-4
   Web of Science ®Google Scholar
• Gibson, R., Lusoli, W., & Ward, S. (2005). Online participation in the UK: Testing a ‘contextualised’ model of internet effects. British Journal of Politics and International Relations, 7(4), 561–583.
   Google Scholar
• Golicnik, B., & Ward Thompson, C. (2010). Emerging relationships between design and use of urban park spaces. Landscape and Urban Planning, 94(1), 38–53. doi:10.1016/j.landurbplan.2009.07.016
   Web of Science ®Google Scholar
• Granier, B., & Kudo, H. (2016). How are citizens involved in smart cities? Analysing citizen participation in Japanese “smart communities”. Information Polity, 21(1), 61–76. doi:10.3233/IP-150367
   Google Scholar
• Hao, J., & Ho, T. K. (2019). Machine learning made easy: A review of Scikit-learn package in python programming language. Journal of Educational and Behavioral Statistics, 44(3), 348–361. doi:10.3102/1076998619832248
   Web of Science ®Google Scholar
• Heikinheimo, V., Minin, E. d., Tenkanen, H., Hausmann, A., Erkkonen, J., & Toivonen, T. (2017). User-generated geographic information for visitor monitoring in a national park: A comparison of social media data and visitor survey. ISPRS International Journal of Geo-Information, 6(3), 85. doi:10.3390/ijgi6030085
   Web of Science ®Google Scholar
• Homaid, M. S., & Moulitsas, I. (2022). Measuring airport service quality using machine learning algorithms. In 2022 the 6th International conference on advances in artificial intelligence (pp. 8–14). doi:10.1145/3571560.3571562.
   Google Scholar
• Hu, Y., Deng, C., & Zhou, Z. (2019). A semantic and sentiment analysis on online neighborhood reviews for understanding the perceptions of people toward their living environments. Annals of the American Association of Geographers, 109(4), 1052–1073. doi:10.1080/24694452.2018.1535886
   Web of Science ®Google Scholar
• Huai, S., & van de Voorde, T. (2022). Which environmental features contribute to positive and negative perceptions of urban parks? A cross-cultural comparison using online reviews and Natural Language Processing methods. Landscape and Urban Planning, 218, 104307. doi:10.1016/j.landurbplan.2021.104307
   Web of Science ®Google Scholar
• Huang, Y., Li, Z., & Huang, Y. (2022). User perception of public parks: A pilot study integrating spatial social media data with park management in the city of Chicago. Land, 11(2), 211. doi:10.3390/land11020211
   Web of Science ®Google Scholar
• International Organization for Migration (IOM). (2019). Analysis: Baseline assessment in Istanbul field observation report October–December 2018. Retrieved from https://displacement.iom.int/sites/default/files/public/reports/DTM_Baseline_Observation_Istanbul_ENG_digital.pdf
   Google Scholar
• Istanbul Metropolitan Municipality (IMM). (2020a). Esenyurt land use. https://sehirplanlama.ibb.istanbul/esenyurt-ilcesi/
   Google Scholar
• Istanbul Metropolitan Municipality (IMM). (2020b). İstanbul Göç Araştırması (Istanbul Migration Research). https://www.ibb.istanbul/Uploads/2021/3/goc-arastirmalari-24.03.2021.pdf
   Google Scholar
• Ju, J., Liu, L., & Feng, Y. (2018). Citizen-centered big data analysis-driven governance intelligence framework for smart cities. Telecommunications Policy, 42(10), 881–896. doi:10.1016/j.telpol.2018.01.003
   Web of Science ®Google Scholar
• Jung, C., al Qassimi, N., Arar, M., & Awad, J. (2022). The improvement of user satisfaction for two urban parks in dubai, UAE: Bay avenue park and Al Ittihad Park. Sustainability (Switzerland), 14(6), 3460. doi:10.3390/su14063460
   Web of Science ®Google Scholar
• Kaur, G., Mehta, P., Kaur, A., & Khurana, M. (2022). A review of opinion mining techniques. ECS Transactions, 107(1), 10125–10132. doi:10.1149/10701.10125ecst
   Google Scholar
• Kemp, S. (2022). Digital 2022: Türkiye. Datareportal. https://datareportal.com/reports/digital-2022-Türkiye
   Google Scholar
• Khan, A. M., & Loan, F. A. (2022). Exploring the reviews of Google Maps to assess the user opinions about public libraries. Library Management, 43(8–9), 601–615. doi:10.1108/LM-05-2022-0053
   Web of Science ®Google Scholar
• Kitchin, R. (2019). Toward a genuinely humanizing smart urbanism. In P. Cardullo, C. di Feliciantonio, & R. Kitchin (Eds.), The right to the smart city (pp. 193–204). Emerald Publishing Limited. doi:10.1108/978-1-78769-139-120191014
   Google Scholar
• Koh, Y. F., Loc, H. H., & Park, E. (2022). Towards a “city in nature”: evaluating the cultural ecosystem services approach using online public participation GIS to support urban green space management. Sustainability (Switzerland), 14(3), 1499. doi:10.3390/su14031499
   Web of Science ®Google Scholar
• Kumar, D., Mittal, G., Choubey, A., & Singh, P. (2020). Restaurant review classification and analysis. Journal of Engineering Sciences, 11(8), 169–179.
   Google Scholar
• Lee, K., & Yu, C. (2018). Assessment of airport service quality: A complementary approach to measure perceived service quality based on Google reviews. Journal of Air Transport Management, 71, 28–44. doi:10.1016/j.jairtraman.2018.05.004
   Web of Science ®Google Scholar
• Li, L., Mao, Y., Wang, Y., & Ma, Z. (2022). How has airport service quality changed in the context of COVID-19: A data-driven crowdsourcing approach based on sentiment analysis. Journal of Air Transport Management, 105, 102298. doi:10.1016/j.jairtraman.2022.102298
   PubMed Web of Science ®Google Scholar
• Liu, H., Gegov, A., & Cocea, M. (2017). Complexity control in rule based models for classification in machine learning context (pp. 125–143). doi:10.1007/978-3-319-46562-3_9
   Google Scholar
• Liu, R., & Xiao, J. (2020). Factors affecting users’ satisfaction with urban parks through online comments data: Evidence from Shenzhen, China. International Journal of Environmental Research and Public Health, 18(1), 253. doi:10.3390/ijerph18010253
   PubMed Web of Science ®Google Scholar
• Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In I. Guyon, U. v. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 30, pp. 4765–4774). New York: Curran Associates, Inc.
   Google Scholar
• Martí, P., Serrano-Estrada, L., & Nolasco-Cirugeda, A. (2019). Social media data: Challenges, opportunities, and limitations in urban studies. Computers, Environment and Urban Systems, 74, 161–174. doi:10.1016/j.compenvurbsys.2018.11.001
   Web of Science ®Google Scholar
• Milusheva, S., Marty, R., Bedoya, G., Williams, S., Resor, E., & Legovini, A. (2021). Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning. PLoS One, 16(2), e0244317. doi:10.1371/journal.pone.0244317
   PubMed Web of Science ®Google Scholar
• Ministry of Environment, Urbanization, and Climate Change. (2014). Spatial plans regulation. Retrieved from https://www.mevzuat.gov.tr/File/GeneratePdf?mevzuatNo=19788andmevzuatTur=KurumVeKurulusYonetmeligiandmevzuatTertip=5
   Google Scholar
• Mishra, D. N., & Panda, R. K. (2021). How delightful is Indian wellness tourism? An ethnographic study. Advances in Hospitality and Tourism Research, 9(1), 132–156. doi:10.30519/ahtr.784232
   Google Scholar
• Munawir, M., Fujianti, L. N., Koerniawan, M. D., & Dewancker, B. (2021). Text mining by social network data towards developing attractiveness of urban park. Case study: Thematic parks in Bandung City, Indonesia. IOP Conference Series: Earth and Environmental Science, 738(1). doi:10.1088/1755-1315/738/1/012057
   Google Scholar
• Nizomutdinov, B., & Kazak, V. (2021). Development of a method for assessing the accessibility of the urban environment for low-mobility groups of the population based on google maps reviews. In 14th International conference on theory and practice of electronic governance (pp. 532–534). doi:10.1145/3494193.3494276.
   Google Scholar
• Niță, M. R., Arsene, M., Barbu, G., Cus, A. G., Ene, M., Serban, R. M., … Stoia, L. N. (2021). Using social media data to evaluate urban parks use during the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 18(20), 10860. doi:10.3390/ijerph182010860
   PubMed Web of Science ®Google Scholar
• OECD. (2020). Smart cities and inclusive growth (pp. 1–59). https://www.oecd.org/cfe/cities/OECD_Policy_Paper_Smart_Cities_and_Inclusive_Growth.pdf
   Google Scholar
• Plunz, R. A., Zhou, Y., Carrasco Vintimilla, M. I., Mckeown, K., Yu, T., Uguccioni, L., & Sutto, M. P. (2019). Twitter sentiment in New York city parks as measure of well-being. Landscape and Urban Planning, 189, 235–246. doi:10.1016/j.landurbplan.2019.04.024
   Web of Science ®Google Scholar
• Porwol, L., Ojo, A., & Breslin, J. G. (2016). An ontology for next generation e-participation initiatives. Government Information Quarterly, 33(3), 583–594. doi:10.1016/j.giq.2016.01.007
   Web of Science ®Google Scholar
• Purcell, A. T., & Lamb, R. J. (1998). Preference and naturalness: An ecological approach. Landscape and Urban Planning, 42(1), 57–66. doi:10.1016/S0169-2046(98)00073-5
   Web of Science ®Google Scholar
• Rabiei-Dastjerdi, H., McArdle, G., & Aghajani, M. A. (2022). User-generated data in cultural mapping: Analyzing Google point of interest reviews in Dublin. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-4-2022, 107–112. doi:10.5194/isprs-annals-V-4-2022-107-2022
   Google Scholar
• Ranjan, S., & Mishra, S. (2020). Comparative sentiment analysis of app reviews. 11th International conference on computing, communication and networking technologies (ICCCNT) (pp. 1–7).
   Google Scholar
• Resch, B., Summa, A., Zeile, P., & Strube, M. (2016). Citizen-centric urban planning through extracting emotion information from Twitter in an interdisciplinary space-time-linguistics algorithm. Urban Planning, 1(2), 114–127. doi:10.17645/up.v1i2.617
   Web of Science ®Google Scholar
• Review Tracker. (2022). Online reviews statistics and trends: A 2022 Report by ReviewTrackers. https://www.reviewtrackers.com/reports/online-reviews-survey/
   Google Scholar
• Sahu, M. K., & Selot, S. (2021). Support vector machine and random forest machine learning algorithms for sentiment analysis on tourism reviews: A performance analysis. I-Manager’s Journal on Computer Science, 9(3), 1. doi:10.26634/jcom.9.3.18479
   Google Scholar
• Santos, I. N., & Azevedo, J. (2019). Running after Time (pp. 31–45). doi:10.4018/978-1-5225-8163-5.ch002
   Google Scholar
• Saumya, S., Singh, J. P., Baabdullah, A. M., Rana, N. P., & Dwivedi, Y. K. (2019). Ranking online consumer reviews. Electronic Commerce Research and Applications, 29, 78–79.
   Web of Science ®Google Scholar
• Shah, M., Pabel, A., & Martin-Sardesai, A. (2020). Assessing Google reviews to monitor student experience. International Journal of Educational Management, 34(3), 610–625. doi:10.1108/IJEM-06-2019-0200
   Web of Science ®Google Scholar
• Silva, B. N., Khan, M., & Han, K. (2018). Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society, 38, 697–713. doi:10.1016/j.scs.2018.01.053
   Web of Science ®Google Scholar
• Skotis, A., & Livas, C. (2022). A data-driven analysis of experience in urban historic districts. Annals of Tourism Research Empirical Insights, 3(2), 100052. doi:10.1016/j.annale.2022.100052
   Google Scholar
• Smith, G. (2009). Democratic innovations: Designing institutions for citizen participation. Cambridge: Cambridge University Press.
   Google Scholar
• Stave, K. (2010). Participatory system dynamics modeling for sustainable environmental management: Observations from four cases. Sustainability, 2(9), 2762–2784. doi:10.3390/su2092762
   Google Scholar
• Thakuriah, P., Tilahun, N., & Zellner, M. (2017). Big data and urban informatics: Innovations and challenges to urban planning and knowledge discovery. In P. Thakuriah, N. Tilahun, & M. Zellner (Eds.), Seeing cities through big data (1st ed., pp. 11–48). Springer International Publishing. doi:10.1007/978-3-319-40902-3
   Google Scholar
• Turkish Statistical Institute (TUIK). (2022). Address based population registration system results, 2022. Retrieved from https://data.tuik.gov.tr/Bulten/Index?p=Adrese-Dayali-Nufus-Kayit-Sistemi-Sonuclari-2022-49685
   Google Scholar
• van Weerdenburg, D., Scheider, S., Adams, B., Spierings, B., & van der Zee, E. (2019). Where to go and what to do: Extracting leisure activity potentials from Web data on urban space. Computers, Environment and Urban Systems, 73, 143–156. doi:10.1016/j.compenvurbsys.2018.09.005
   Web of Science ®Google Scholar
• Vasudevan, A. (2017). The autonomous city: A history of urban squatting. London: Verso Books.
   Google Scholar
• Yalçınkaya, B., & Just, D. R. (2022). Comparison of customer reviews for local and chain restaurants: Multilevel approach to google reviews data. Cornell Hospitality Quarterly. doi:10.1177/19389655221102388
   Web of Science ®Google Scholar
• Yücebaş, S., & Tintin, R. (2021). Govdeturk: A novel Turkish natural language processing tool for stemming, morphological labelling and verb negation. The International Arab Journal of Information Technology, 18(2), 148–157. doi:10.34028/iajit/18/2/3
   Web of Science ®Google Scholar
• Zabelskyte, G., Kabisch, N., & Stasiskiene, Z. (2022). Patterns of urban green space use applying social media data: A systematic literature review. Land, 11(2), 238. doi:10.3390/land11020238
   Web of Science ®Google Scholar
• Zengel, R., & Dogrusoy, I. T. (2014). The importance of sociocultural habits in park design, leisure behaviour and user satisfaction. A comparative study on Two Parks in İzmir, Türkiye. Journal of Settlements and Spatial Planning, 5(2), 107–117.
   Google Scholar