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Journals / A|Z ITU Mimarlık Fakültesi Dergisi / 2018 / Cilt: 15 - Sayı: 1

Web scraping and mapping urban data to support urban design decisions

Elif ENSARİ
Bilge KOBAŞ
Pages
5–21
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We propose that this method and resulting visualizations present valuable potential in supporting urban design decision-making processes.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Cities generate data in increasing speed, volume and variety which is more eas- ily accessed and processed by the advance of technology every day.
📊
Key Findings & Data:Consequently, the potential for this data to feedback into the city to improve living conditions and efficiency of utilizing resources grows.
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Conclusion & Impact:We propose that this method and resulting visualizations present valuable potential in supporting urban design decision-making processes.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

Cities generate data in increasing speed, volume and variety which is more eas- ily accessed and processed by the advance of technology every day. Consequently, the potential for this data to feedback into the city to improve living conditions and efficiency of utilizing resources grows. Departing from this potential, this pa- per presents a study that proposes methods to collect and visualize urban data with the aim of supporting urban design decisions. We employed web scraping techniques to collect a variety of publicly available data within the Kadıköy mu- nicipal boundaries of Istanbul and utilized a visual programming software to map and visualize this information. Through this method and superposition of our re- sulting maps, we visually communicate urban conditions including demographic and economic trends based on online real estate listings as well as spatial distri- bution and accessibility of public and commercial resources. We propose that this method and resulting visualizations present valuable potential in supporting urban design decision-making processes.

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Cite This Article

ENSARİ, E., KOBAŞ, B. (2018). Web scraping and mapping urban data to support urban design decisions. A|Z ITU Mimarlık Fakültesi Dergisi, Cilt: 15 - Sayı: 1, 5–21. https://ajindex.com/articles/143113

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References (11)

  1. Balaban, O., & Tuncer, B. (2016). Vi- sualizing Urban Sports Movement. In Complexity & Simplicity - Proceedings of the 34th eCAADe Conference (Vol. 2, pp. 89–94).
  2. Oululu, Finland: eCAADe and University of Oulu. Batty, M. (2013). Urban Informatics and Big Data. London. Batty, M., Axhausen, K., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wa- chowicz, M., Ouzounis, G., Portugali, Y. (2012). Smart cities of the future. Eu- ropean Physical Journal: Special Topics, 214, 481–518.
  3. Bency, A., Rallapalli, S., Ganti, R., Srivatsa, M., & Manjunath, B. (2017). Beyond Spatial Auto-Regressive Mod- els: Predicting Housing Prices with Satellite Imagery. In 2017 IEEE Winter Conference on Applications of Comput- er Vision (pp. 320–329).
  4. Santa Rosa, CA, USA: IEEE. Boeing, G., & Waddell, P. (2016). New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings. Journal of Planning Ed- ucation and Research, 1–20.
  5. Chen, N. C. (2012). Urban Data Mining: Social Media Data Analysis as a Complementary Tool for Urban De- sign. MIT. Clarke, A., & Steele, R. (2011). How personal fitness data can be re-used by smart cities. In 7th International Con- ference on Intelligent Sensors, Sensor Networks and Information Processing (pp. 395–400).
  6. Adelaide, SA, Australia: IEEE. Cohen, J. P., & Coughlin, C. C. (2008). Spatial hedonic models of air- port noise, proximity, and housing prices. Journal of Regional Science, 48(5), 859–878.
  7. Cranshaw, J., Schwartz, R., Hong, J. I., & Sadeh, N. (2012). The Livehoods Project: Utilizing Social Media to Un- derstand the Dynamics of a City. In Proceedings of the Sixth International AAAI Conference on Weblogs and So- cial Media (pp. 58–65).
  8. Palo Alto, CA, USA: The AAAI Press.
  9. Geoghegan, J., Wainger, L. a., & Bockstael, N. E. (1997). Analysis Spa- tial landscape indices in a hedonic framework:an ecological economics analysis using GIS. Ecological Econom- ics, 23, 251–264.
  10. Glaeser, E. L., Duke-Kominers, S., Luca, M., & Nalk, N. (2015). Big Data and Big Cities: The Promises and Lim- itations of Improved Measures of Urban Life. HKS Faculty Research Working Pa- per Series. Cambridge, MA. Jacobs, J. (1961). The Death and Life of Great American Cities. New York, NY, USA: Random House. Jenkins, A., Croitoru, A., Crooks, A. T., & Stefanidis, A. (2016). Crowd- sourcing a collective sense of place. PLoS ONE, 11(4), 1–20.
  11. Jiang, S., Alves, A., Rodrigues, F., Ferreira, J., & Pereira, F. C. (2015). Mining point-of-i