Bir büyük veri görselleştirme uygulaması olarak konut tercih infografikleri

Year 2021, Volume: 23 Issue: 1, 36 - 52, 02.06.2021

Elif Erkurt , Esen Yıldırım

https://doi.org/10.33707/akuiibfd.733379

Cited By: 1

Abstract

Günümüz dünyasında büyük veri olgusunun gittikçe önem kazanması ile birlikte istatistikçilere, veri analistlerine ve bu analizleri mümkün kılacak teknolojik alt yapının geliştirilmesi için de yazılımcılara duyulan ihtiyaç artmaktadır. İlgili alan uzmanları için temel hedef, ele alınan büyük veriden rafine edilebilecek enformasyonu ortaya çıkarmak ve mümkünse bulguları kapsamlı, işlevsel ve sade görsellere yansıtabilmektir. Bu çalışmada, emlak sektörüne ilişkin bir uygulama üzerinde, büyük veri analizi ile üretilebilecek görsellerin, ilgili hedef kitleye sunabileceği önemli, hatta kimi zaman alternatifsiz kolaylığa dikkat çekilmeye çalışılmıştır. Bu amaçla; R programı yardımıyla, konut tercihini kolaylaştırıcı bir araç olarak, kullanımı kolay ve dinamik yapıda infografikler üretilmiştir. Böylelikle, konut satın almayı planlayan bireylerin konutun yaşı, yüzölçümü, oda sayısı, fiyatı gibi önemli buldukları kriterleri dikkate alan, veri değişimine duyarlı, iller/ilçeler arasında karşılaştırma yapılmasına imkân veren infografiklerden yararlanarak tercih yapabilmelerine olanak sağlanmıştır.

Keywords

Büyük Veri, Görselleştirme, İnfografik, Konut, Emlak

Real estate preference infographics as big data visualization application

Abstract

Statisticians, data scientists and software programmers/engineers who develop technological infrastructure to enable performing analytics are in high demand with the increasing importance and usage of big data in the last decades. The main goal of subject matter experts is to extract actionable insight from big data, and if possible, to deliver these insights with effective and simple visualizations. This study aims to draw attention to important, even sometimes unrivaled convenience offered to the relevant target audience by visualizations, which were created with big data analysis on an application related to real estate sector. For this purpose, by means of R program, and as a tool to ease the housing selections; user-friendly and dynamic-structured infographics were created. Thus, individuals, who are planning to buy houses, are given the opportunity to make selections by using infographics which enable them to take essential criteria into consideration - such as the age of the property, area measures, number of rooms, price; are sensitive to data variations and allow to make comparison between cities/districts.

Keywords

Big Data, Visualization, Infographics, Housing, Real Estate.

References

• Aktan, E. (2018), Büyük veri: uygulama alanları, analitiği ve güvenlik boyutu, Bilgi Yönetimi Dergisi, 1(1), 1-22. https://dergipark.org.tr/tr/download/article-file/482194
• Anuşlu, M., & Anuşlu, T. (2018). Application of big data visualization with agricultural value-added of all countries, Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 10(2), 94-98.
• Çelik, S., & Akdamar, E. (2018). Büyük veri ve veri görselleştirme, Akademik Bakış Dergisi, S.65, 253-264. https://www.researchgate.net/publication/325604426_BUYUK_VERI_VE_VERI_GORSELLESTIRME
• Davenport, T. (2014). Big data@work. İstanbul: Türk Hava Yolları Yayınları.
• Davenport, T. (2012). Three big benefits of big data analytics. http://book.itep.ru/depository/big_data/AST- 0147176_Three_Big_Benefits_of_Big_Data_Analytics.pdf
• Demirtaş, B., & Argan, M. (2015). Büyük veri ve pazarlamadaki dönüşüm: kuramsal bir yaklaşım. Pazarlama ve Pazarlama Araştırmaları Dergisi, S.15, 1-21. https://dergipark.org.tr/en/download/articlefile/1672065
• Dialani, P. (2020, September 24). Top 10 Big Data Trends Of 2020. https://www.analyticsinsight.net/top-10-big-data-trends-2020/
• Du, D., Li, A., & Zhang, L. (2014). Survey on the applications of big data in chinese real estate enterprise. Procedia Computer Science, 30, 24–33. https://www.sciencedirect.com/science/article/pii/S1877050914005547
• Eken, S. (2020). Büyük verinin interaktif görselleştirilmesi: tableau üzerine öğrenci deneyimleri, Avrupa Bilim ve Teknoloji Dergisi, S.18, 262,271. https://dergipark.org.tr/tr/pub/ejosat/issue/52599/659823
• Erbay, H, ve Kör, H., (2016, 3-6 Ekim), Büyük veri ve büyük verinin analizi, Uluslararası Bilim ve Teknoloji Konferansı, Ankara.
• Erkurt, E. (2020). Büyük veri görselleştirme ve türkiye’de konut sektörüne ilişkin infografikler,[Yayımlanmamış doktora tezi]. Marmara Üniversitesi.
• Feinleib, D. (2013). Big Data Demystified: How Big Data is Changing The Way We Live, Love and Learn. Big Data Group.
• Fox, P., & Hendler, J. (2011, February). Changing the equation on scientific data visualization. Science, V.331, Issue:6018, 705-709.
• Gast, A. (2020, January 9). Why We Need to Talk About Big Data. https://www.weforum.org/agenda/2020/01/privacy-in-a-world-of-ai-and-big-data/.
• Gürsakal, N. (2014). Büyük veri (Genişletilmiş 2. Baskı). Dora Yayınevi.
• Hewage, T. N., Halgamuge, M. N., Syed, A., & Ekici, G. (2018, February). Review: big data techniques of google, amazon, facebook and twitter. Journal of Communications, 13(2), 94-100.
• Iliinsky, N. & Steele, J. (2011). Designing Data Visualizations. O’Reilly.
• Khan, N., Alsaquer, M., Shah, H., Badsha, G., Abbasi, A.A., ve Salehian, S. (2018). The 10 vs, issues and challenges of big data. ICBDE '18: Proceedings of the 2018 International Conference on Big Data and Education, 52-56.
• Lavalle, A., Teruel, M., Maté, A., & Trujillo, J. (2020). Improving sustainability of smart cities through visualization techniques for big data from IoT devices. Sustainability, 12(14),1-17.
• Loberto, M., Luciani, A., & Pangallo, M. (2018, April). The potential of big housing data: an application to the italian real-estate market. Banca D'Italia, No.1171. https://www.bancaditalia.it/pubblicazioni/temidiscussione/2018/2018-1171/en_tema_1171.pdf?language_id=1
• Lohr, S. (2012, August 11). How big data became so big. http://www.nytimes.com/2012/08/12/business/how-big-data-became-so-big-unboxed.html?_r=0
• Lohr, S. (2013, February 1). The origins of big data: an etymological detective story.https://bits.blogs.nytimes.com/2013/02/01/the-origins-of-big-data-an-etymological-detectivestory/?_r=0
• Maçãs, C., Cruz, P., Amaro, H., Polisciuci E., Carvalho, T., Santos, F., ve Machado, P. (2015). Time-series application on big data visualization of consumption in supermarkets. 6th International Conference on Information Visualization Theory and Applications. https://www.researchgate.net/publication/280039332_Time- Series_Application_on_Big_Data_Visualization_of_Consumption_in_Supermarkets
• Padmavalli, M. (2016, November-December). Big data: emerging challenges of big data and techniques for handling. IOSR Journal of Computer Engineering (IOSR-JCE), V.18, I.6, 13-18.
• Press, G. (2020, January 6). 6 predictions about data in 2020 and the coming decade. https://www.forbes.com/sites/gilpress/2020/01/06/6-predictions-about-data-in-2020-and-thecoming- decade/?sh=122605c74fc3
• Reinsel, D., Gantz, J., & Rydning, J. (2018, November). The digitization of the world from edge to core. IDC White Paper. https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagatedataage-whitepaper.pdf
• Sakyi, K. T. (2016). Big data: understanding big data. https://arxiv.org/ftp/arxiv/papers/1601/1601.04602.pdf
• Soto, A. J., Ryan, C., Silva, F. P., Das, T., Wolkowicz, J., Milios, E. E., ve Brooks, S. (2018). Data quality challenges in twitter content analysis for informing policy making in health care. Proceedings of the 51st Hawaii International Conference on System Sciences, 760-769. https://pdfs.semanticscholar.org/009b/ea25c5e4712fa6e9b266380511f807891bde.pdf
• Su, K., Liu, C., & Wang, Y. (2018). A principle of designing infographic for visualization representation of tourism social big data. Journal of Ambient Intelligence and Humanized Computing. Journal of Ambient Intelligence and Humanized Computing, https://link.springer.com/article/10.1007/s12652-018-1104-9
• Sun, D., Du, Y., Xu, W., Zuo, M., Zhang, C., & Zhou, J. (2015). Combining online news articles and web search to predict the fluctuation of real estate market in big data context. Pacific Asia Journal of the Association for Information Systems,.6(4), 19-37.
• Techopedia, (2018). Data Visulization. https://www.techopedia.com/definition/30180/data-visualization
• Tole, A. A. (2013). Big data challenges. Database Systems Journal, V. IV, No. 3, 31-40.
• Türk Keneşi & Dünya Telekomünikasyon ve Bilgi Toplumu Günü Etkinliği. (2017, 17 Mayıs).
• https://www.timeturk.com/turk-kenesi-dunya-telekomunikasyon-ve-bilgi-toplumu-gunuetkinligi/ haber-637200
• Uğur, N. G., & Akbıyık, A. (2020). Impacts of COVID-19 on global tourism industry: a cross-regional comparison. Tourism Management Perspectives, 36, 1-13.
• Vuleta, B. (2021, January 28). How much data is created every day? [27 Staggering Stats]. https://seedscientific.com/how-much-data-is-created-every-day/
• Watson, H. J. (2014). Tutorial: big data analytics: concepts, technologies, and applications. Communications of the Association for Information Systems, V.34, A.65, 1244-1268.
• Yılmazel, S., Afşar, A., & Yılmazel, Ö. (2017). Türkiye’de satışı bulunan konutların il ve bölgeler bazında dağılımının büyük veri teknolojisi ile incelenmesi. Sakarya İktisat Dergisi, 6(3), 1-21.
• Zhang, P., Zhou, J., & Zhang, T. (2017). Quantifying and visualizing jobs-housing balance with big data: a case study of shanghai. Cities, V:66, 10-22.