Research Article
BibTex RIS Cite
Year 2023, Volume: 1 Issue: 1, 64 - 83, 15.06.2023
https://doi.org/10.26650/JTADP.01.004

Abstract

References

  • Abbasi, A., Rashidi, T. H., Maghrebi, M., & Waller, S. T. (2015). Utilising Location Based Social Media in Travel Survey Methods: Bringing Twitter Data into the Play. Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Net-works.https://doi.org/10.1145/2830657.2830660 google scholar
  • Adelfio, M., Serrano-Estrada, L., Marti-Ciriquiân, P., Kain, J.-H., & Stenberg, J. (2020). Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data. Applied Spatial Analysis and Policy, 13(4), 985-1017. https://doi.org/10.1007/s12061-020-09338-3 google scholar
  • Balaban, O., & Tuncer, B. (2016). Visualizing Urban Sports Movement. Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe, 2, 89-94. https://doi.org/10.52842/conf.ecaade.2016.2.089 google scholar
  • Batty (2001). Foreword. In R. Laurini (ed.), Information systems for urban planning: a hypermedia cooperavite approach. Taylor and Francis google scholar
  • Batty M. (2016). Big Data and The City. Built Environment 42 (3), 321-337.https://doi.org/10.2148/benv.42.3.321 google scholar
  • Batty M., Axhausen K., Fosca G., Pozdnoukov A., Bazzani A., Wachowicz M., Ouzonunis G., Portugali Y. (2012). Smart Cities of the Future. Eur. Phys. J. Special Topics 214, pp. 481-518. https://doi.org/10.1140/epjst/e2012-01703-3 google scholar
  • Batty, M. (2013a). Urban Informatics and Big Data. London: CASA, University College London. Retrieved date: 03/05/2019. Retrieved from:http://www.spatialcomplexity.info/files/2015/07/Urban-Informatics-and-Big-Data google scholar
  • Batty, M. (2013b). The New Science of Cities. Cambridge, MA: MIT Press. google scholar
  • Berry, D. (2011). The computational turn: thinking about the digital humanities. Culture Machine, 12. https://sro.sussex.ac.uk/id/eprint/49813/ google scholar
  • Boyd, D., Crawford, K. (2012). Critical Questions about big data: Provocations for a cultural,technological, and scholarly phenomenon. Information Communication and Society, 15 (5), 662-679. https://doi.org/10.1080/1369118X.2012.678878 google scholar
  • Çağdaş G., Bacınoğlu Z. S., Çavuşoğlu Ö., (2015). Mimarlıkta Hesaplamalı Yaklaşımlar. Mimaride Sayısal Fırsatlar, 34-42. google scholar
  • Chan, A. (2015). Big data interfaces and the problem of inclusion. Media, Culture & Society, 37(7), 1078-1083. https://doi.org/10.1177/0163443715594106 google scholar
  • Cranshaw, J., Schwartz, R., Hong, J., & Sadeh, N. (2021). The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City. Proceedings of the International AAAI Conference on Web and Social Media, 6(1 SE-Full Papers), 58-65. https://doi.org/10.1609/icwsm.v6i1.14278 google scholar
  • De Choudhury, M., Feldman, M., Amer-Yahia, S., Golbandi, N., Lempel, R., & Yu, C. (2010). Automatic Construction of Travel Itineraries Using Social Breadcrumbs. Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, 35-44. https://doi.org/10.1145/1810617.1810626 google scholar
  • Deng,X., & Newsam, S. D. (2017). Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use. CoRR, abs/1711.0.1711.03641 google scholar
  • Drudzel, M.J. & Fylnn R.R. (2002). Decision support systems. In A. Kent (ed.), Encyclopedia oflibrary and information science (2 nd ed., pp. 3-12). Marcel Dekker Inc. https://sites.pitt.edu/ druzdzel/psfiles/dss.pdf google scholar
  • Ensari, E., & Kobaş, B. (2018). Web scraping and mapping urban data to support urban design decisions. A/Z ITU Journal of the Faculty of Architecture, 15(1), 5-21. https://doi.org/10.5505/itujfa.2018.40360 google scholar
  • Fredriksson, C. (2018). Big data creating new knowledge as support in decision-making: practical examples of big data use and consequences of using big data as decision support. Journal of Decision System, 27(3), 1-18. http://dx.doi.org/10.1080/12460125.2018.1459068 google scholar
  • Garcıa-Palomares, J. C., Gutierrez, J., & Mınguez, C. (2015). Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography, 63, 408-417. https://doi.org/https://doi.org/10.1016/j.apgeog.2015.08.002 google scholar
  • Ginzarly, M., Pereira Roders, A., & Teller, J. (2019). Mapping historic urban landscape values through social media. Journal of Cultural Heritage, 36. https://doi.org/10.1016/j.culher.2018.10.002 google scholar
  • Girardin, F., Calabrese, F., Fiore, F. D., Ratti, C., & Blat, J. (2008). Digital Footprinting: Uncovering Tourists with User-Generated Content. IEEE Pervasive Computing, 7(4), 36-43. https://doi.org/10.1109/MPRV.2008.71 google scholar
  • Goodchild, M. F. (2007). Citizens as Sensors: The World of Volunteered Geography. GeoJournal (69), 211-221. DOI: 10.1007/s10708-007-9111-y. google scholar
  • Gordon, E., & E Silva, A. D. S. (2011). Net locality: Why location matters in a networked world. John Wiley & Sons. google scholar
  • Guo, W., Gupta, N., Pogrebna, G., & Jarvis, S. (2016). Understanding happiness in cities using Twitter:Jobs, children, and transport. 2016 IEEE International Smart Cities Conference (ISC2), 1-7. https://doi.org/10.1109/ISC2.2016.7580790 google scholar
  • Huang, J., Obracht-Prondzynska, H., Kamrowska-Zaluska, D., Sun, Y., & Li, L. (2021). The image of the City on social media: A comparative study using “Big Data” and “Small Data” methods in the Tri-City Region in Poland. Landscape and Urban Planning, 206, 103977. https://doi.org/https://doi.org/10.1016/j.landurbplan.2020.103977 google scholar
  • Jang, K. M., & Kim, Y. (2019). Crowd-sourced cognitive mapping: A new way of displaying people’s cognitive perception of urban space. PLOS ONE, 14(6), e0218590. https://doi.org/10.1371/journal.pone.0218590 google scholar
  • Jiang, S., Alves, A., Rodrigues, F., Ferreira, J., & Pereira, F. C. (2015). Mining point-of-interest data from social networks for urban land use classification and disaggregation. Computers, Environment and Urban Systems, 53, 36-46. https://doi.org/https://doi.org/10.1016/j.compenvurbsys.2014.12.001 google scholar
  • Kaisler, S., Armour, F., Espinosa, J.A., Money, W. (2013). Big data redux: Issues and challenges moving forward. 46 th Hawaii International Conference on System Services. http://dx.doi.org/10.24251/HICSS.2019.131 google scholar
  • Kisilevich, S., Keim, D., Andrienko, N., & Andrienko, G. (2013). Towards Acquisition of Semantics of Places and Events by Multi-perspective Analysis of Geotagged Photo Collections BT - Geospatial Visualisation (A. Moore & I. Drecki (eds.); pp. 211-233). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642122897_10 google scholar
  • Kitchin, R. (2017, February 1). Urban Science: A Short Primer. https://doi.org/10.31235/osf.io/sdsu2 google scholar
  • Laurini, R. (2001). Information systems for urban planning: a hypermedia cooperavite approach. Taylor and Francis. google scholar
  • Li, F., Li, F., Li, S., & Long, Y. (2020). Deciphering the recreational use of urban parks: Experiments using multi-source big data for all Chinese cities. Science of The Total Environment, 701, 134896. https://doi.org/https://doi.org/10.1016/j.scitotenv.2019.134896 google scholar
  • Lin, S., Xie, R., Xie, Q., Zhao, H., & Chen, Y. (2017). Understanding User Activity Patterns of the Swarm App: A Data-Driven Study. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, 125-128. https://doi.org/10.1145/3123024.3123086 google scholar
  • Loo B. P. Y. & Tang W. S. M. (2019). “Mapping” Smart Cities. Journal of Urban Technology, 26(2), 129-146. https://doi.org/10.1080/10630732.2019.1576467 google scholar
  • Marti, P., Serrano-Estrada L., Nolasco-Criugeda A. (2019). Social media data: Challenges, opportunities and limitations in urban studies. Computers, Environment and Urban Systems, 74 (2019), 161- 174. https://doi.org/10.1016/jxompenvurbsys.2018.11.001 google scholar
  • Martı, P., Serrano-Estrada, L., & Nolasco-Cirugeda, A. (2017). Using locative social media and urban cartographies to identify and locate successful urban plazas. Cities, 64, 66-78. https://doi.org/https://doi.org/10.1016/j.cities.2017.02.007 google scholar
  • Mattern, S. (2021). A city is not a computer: Other urban intelligence. Princeton University Press. google scholar
  • Mora, H., Perez-delHoyo, R., Paredes-Perez, J. F., & Molla-Sirvent, R. A. (2018). Analysis of Social Networking Service Data for Smart Urban Planning. In Sustainability (Vol. 10, Issue 12). https://doi.org/10.3390/su10124732 google scholar
  • Schlieder, C., & Matyas, C. (2009). Photographing a City: An Analysis of Place Concepts Based on Spatial Choices. Spatial Cognition & Computation, 9(3), 212-228. https://doi.org/10.1080/13875860903121848 google scholar
  • Sprague, R.H., Carlson, E.D. (1982). Building effective decision support systems, Prentice-Hall. google scholar
  • Sun, Y., Fan, H., Li, M., & Zipf, A. (2015). Identifying the city center using human travel flows generated from location-based social networking data. Environment and Planning B: Planning and Design, 43, 480-498. https://doi.org/10.1177/0265813515617642 google scholar
  • Thakuriah P., Tilahun N.Y., Zellner, M. (2017a). 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 (pp. 11-48). Springer. https://doi.org/10.1007/978-3-319-40902-3 google scholar
  • Townsend, M.A. (2013). Smart Cities Big data, civic hackers, and the questfor a new utopia. London: W.W. Norton & Company. google scholar
  • Tunçer, B. and You, L. (2017). Informed Design Platform. Proceedings of the 35th International Conference on Education and Research in Computer Aided Architecture and Design in Europe (eCAADe) (pp. 545-552). Rome, Italy. google scholar
  • Uzgören, G. (2018). Airbnb impact on gentrification process: the case of Rasimpaşa Neighborhood Kadıköy. Journal of Planning, 28(2), 154-170. https://doi.org/10.14744/planlama.2018.29491 google scholar
  • Van Dicyk A.G.M. (2006). The digital divide achievements and shortcomings. Poetic 34, 221-235. https://doi.org/10.1016/j.poetic.2006.05.004 google scholar
  • Van Eck, N.J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact: Methods andpractice (pp. 285-320). Springer. http://dx.doi.org/10.1007/978-3-319-10377-8_13 google scholar
  • Van Eck, N.J., Waltman, L. (2013). VOSviewer Manual. https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.15.pdf google scholar
  • VOSviewer, (2023). Welcome to VOSviewer. https://www.vosviewer.com/ google scholar
  • Wilken, R., & Goggin, G. (2017). Locative media. Routledge. google scholar
  • Wood, S. A., Guerry, A. D., Silver, J. M., & Lacayo, M. (2013). Using social media to quantify naturebased tourism and recreation. Scientific Reports, 3. https://doi.org/10.1038/srep02976 google scholar
  • Wu C, Ye X, Ren F, Wan Y, Ning P, Du Q (2016) Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China. PLoS ONE 11(10): e0164553. https://doi.org/10.1371/journal.pone.0164553 google scholar
  • Wu, C., Ye, X., Ren, F., Wan, Y., Ning, P., & Du, Q. (2016). Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China. PLOS ONE, 11(10), e0164553. https://doi.org/10.1371/journal.pone.0164553 google scholar
  • You, L., & Tunçer, B. (2016). Exploring public sentiments for livable places based on a crowd-calibrated sentiment analysis mechanism. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 693-700. https://doi.org/10.1109/ASONAM.2016.7752312 google scholar
  • Zhang, S., & Zhou, W. (2018). Recreational visits to urban parks and factors affecting park visits: Evidence from geotagged social media data. Landscape and Urban Planning, 180, 27-35. https://doi.org/https://doi.org/10.1016/j.landurbplan.2018.08.004 google scholar
  • Zhou, X., & Zhang, L. (2016). Crowdsourcing functions of the living city from Twitter and Foursquare data. Cartography and Geographic Information Science, 43(5), 393-404. https://doi.org/10.1080/15230406.2015.1128852 google scholar

A Systematic Literature Review of Big Data in Urban Studies

Year 2023, Volume: 1 Issue: 1, 64 - 83, 15.06.2023
https://doi.org/10.26650/JTADP.01.004

Abstract

The paper aims to explore the use of big data in urban studies by analyzing selected state-of-the-art studies in urban informatics that utilize big data to support urban decision-making. The study conducts exploratory research to gain insight into the association patterns of big data-related concepts. The researchers use the VOSviewer tool to analyze 30 selected references based on keyword occurrences, abstracts, and titles. The study also focuses on how the references handle decision support and examines the relationship network of decision support with other terms. The qualitative and quantitative analysis results are presented to show the association and numeric distribution of the terms. The paper finds that decision support in the selected studies is mainly provided through data-driven computational methods, spatial statistical methods, and mapping of the spatiotemporal pattern of urban phenomena. The reference studies mainly support decisions related to urban activities and functioning, user activities and movement, visiting, and urban perception. The study contributes to presenting the trend in big data studies for urban planning and decision-making.

References

  • Abbasi, A., Rashidi, T. H., Maghrebi, M., & Waller, S. T. (2015). Utilising Location Based Social Media in Travel Survey Methods: Bringing Twitter Data into the Play. Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Net-works.https://doi.org/10.1145/2830657.2830660 google scholar
  • Adelfio, M., Serrano-Estrada, L., Marti-Ciriquiân, P., Kain, J.-H., & Stenberg, J. (2020). Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data. Applied Spatial Analysis and Policy, 13(4), 985-1017. https://doi.org/10.1007/s12061-020-09338-3 google scholar
  • Balaban, O., & Tuncer, B. (2016). Visualizing Urban Sports Movement. Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe, 2, 89-94. https://doi.org/10.52842/conf.ecaade.2016.2.089 google scholar
  • Batty (2001). Foreword. In R. Laurini (ed.), Information systems for urban planning: a hypermedia cooperavite approach. Taylor and Francis google scholar
  • Batty M. (2016). Big Data and The City. Built Environment 42 (3), 321-337.https://doi.org/10.2148/benv.42.3.321 google scholar
  • Batty M., Axhausen K., Fosca G., Pozdnoukov A., Bazzani A., Wachowicz M., Ouzonunis G., Portugali Y. (2012). Smart Cities of the Future. Eur. Phys. J. Special Topics 214, pp. 481-518. https://doi.org/10.1140/epjst/e2012-01703-3 google scholar
  • Batty, M. (2013a). Urban Informatics and Big Data. London: CASA, University College London. Retrieved date: 03/05/2019. Retrieved from:http://www.spatialcomplexity.info/files/2015/07/Urban-Informatics-and-Big-Data google scholar
  • Batty, M. (2013b). The New Science of Cities. Cambridge, MA: MIT Press. google scholar
  • Berry, D. (2011). The computational turn: thinking about the digital humanities. Culture Machine, 12. https://sro.sussex.ac.uk/id/eprint/49813/ google scholar
  • Boyd, D., Crawford, K. (2012). Critical Questions about big data: Provocations for a cultural,technological, and scholarly phenomenon. Information Communication and Society, 15 (5), 662-679. https://doi.org/10.1080/1369118X.2012.678878 google scholar
  • Çağdaş G., Bacınoğlu Z. S., Çavuşoğlu Ö., (2015). Mimarlıkta Hesaplamalı Yaklaşımlar. Mimaride Sayısal Fırsatlar, 34-42. google scholar
  • Chan, A. (2015). Big data interfaces and the problem of inclusion. Media, Culture & Society, 37(7), 1078-1083. https://doi.org/10.1177/0163443715594106 google scholar
  • Cranshaw, J., Schwartz, R., Hong, J., & Sadeh, N. (2021). The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City. Proceedings of the International AAAI Conference on Web and Social Media, 6(1 SE-Full Papers), 58-65. https://doi.org/10.1609/icwsm.v6i1.14278 google scholar
  • De Choudhury, M., Feldman, M., Amer-Yahia, S., Golbandi, N., Lempel, R., & Yu, C. (2010). Automatic Construction of Travel Itineraries Using Social Breadcrumbs. Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, 35-44. https://doi.org/10.1145/1810617.1810626 google scholar
  • Deng,X., & Newsam, S. D. (2017). Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use. CoRR, abs/1711.0.1711.03641 google scholar
  • Drudzel, M.J. & Fylnn R.R. (2002). Decision support systems. In A. Kent (ed.), Encyclopedia oflibrary and information science (2 nd ed., pp. 3-12). Marcel Dekker Inc. https://sites.pitt.edu/ druzdzel/psfiles/dss.pdf google scholar
  • Ensari, E., & Kobaş, B. (2018). Web scraping and mapping urban data to support urban design decisions. A/Z ITU Journal of the Faculty of Architecture, 15(1), 5-21. https://doi.org/10.5505/itujfa.2018.40360 google scholar
  • Fredriksson, C. (2018). Big data creating new knowledge as support in decision-making: practical examples of big data use and consequences of using big data as decision support. Journal of Decision System, 27(3), 1-18. http://dx.doi.org/10.1080/12460125.2018.1459068 google scholar
  • Garcıa-Palomares, J. C., Gutierrez, J., & Mınguez, C. (2015). Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography, 63, 408-417. https://doi.org/https://doi.org/10.1016/j.apgeog.2015.08.002 google scholar
  • Ginzarly, M., Pereira Roders, A., & Teller, J. (2019). Mapping historic urban landscape values through social media. Journal of Cultural Heritage, 36. https://doi.org/10.1016/j.culher.2018.10.002 google scholar
  • Girardin, F., Calabrese, F., Fiore, F. D., Ratti, C., & Blat, J. (2008). Digital Footprinting: Uncovering Tourists with User-Generated Content. IEEE Pervasive Computing, 7(4), 36-43. https://doi.org/10.1109/MPRV.2008.71 google scholar
  • Goodchild, M. F. (2007). Citizens as Sensors: The World of Volunteered Geography. GeoJournal (69), 211-221. DOI: 10.1007/s10708-007-9111-y. google scholar
  • Gordon, E., & E Silva, A. D. S. (2011). Net locality: Why location matters in a networked world. John Wiley & Sons. google scholar
  • Guo, W., Gupta, N., Pogrebna, G., & Jarvis, S. (2016). Understanding happiness in cities using Twitter:Jobs, children, and transport. 2016 IEEE International Smart Cities Conference (ISC2), 1-7. https://doi.org/10.1109/ISC2.2016.7580790 google scholar
  • Huang, J., Obracht-Prondzynska, H., Kamrowska-Zaluska, D., Sun, Y., & Li, L. (2021). The image of the City on social media: A comparative study using “Big Data” and “Small Data” methods in the Tri-City Region in Poland. Landscape and Urban Planning, 206, 103977. https://doi.org/https://doi.org/10.1016/j.landurbplan.2020.103977 google scholar
  • Jang, K. M., & Kim, Y. (2019). Crowd-sourced cognitive mapping: A new way of displaying people’s cognitive perception of urban space. PLOS ONE, 14(6), e0218590. https://doi.org/10.1371/journal.pone.0218590 google scholar
  • Jiang, S., Alves, A., Rodrigues, F., Ferreira, J., & Pereira, F. C. (2015). Mining point-of-interest data from social networks for urban land use classification and disaggregation. Computers, Environment and Urban Systems, 53, 36-46. https://doi.org/https://doi.org/10.1016/j.compenvurbsys.2014.12.001 google scholar
  • Kaisler, S., Armour, F., Espinosa, J.A., Money, W. (2013). Big data redux: Issues and challenges moving forward. 46 th Hawaii International Conference on System Services. http://dx.doi.org/10.24251/HICSS.2019.131 google scholar
  • Kisilevich, S., Keim, D., Andrienko, N., & Andrienko, G. (2013). Towards Acquisition of Semantics of Places and Events by Multi-perspective Analysis of Geotagged Photo Collections BT - Geospatial Visualisation (A. Moore & I. Drecki (eds.); pp. 211-233). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642122897_10 google scholar
  • Kitchin, R. (2017, February 1). Urban Science: A Short Primer. https://doi.org/10.31235/osf.io/sdsu2 google scholar
  • Laurini, R. (2001). Information systems for urban planning: a hypermedia cooperavite approach. Taylor and Francis. google scholar
  • Li, F., Li, F., Li, S., & Long, Y. (2020). Deciphering the recreational use of urban parks: Experiments using multi-source big data for all Chinese cities. Science of The Total Environment, 701, 134896. https://doi.org/https://doi.org/10.1016/j.scitotenv.2019.134896 google scholar
  • Lin, S., Xie, R., Xie, Q., Zhao, H., & Chen, Y. (2017). Understanding User Activity Patterns of the Swarm App: A Data-Driven Study. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, 125-128. https://doi.org/10.1145/3123024.3123086 google scholar
  • Loo B. P. Y. & Tang W. S. M. (2019). “Mapping” Smart Cities. Journal of Urban Technology, 26(2), 129-146. https://doi.org/10.1080/10630732.2019.1576467 google scholar
  • Marti, P., Serrano-Estrada L., Nolasco-Criugeda A. (2019). Social media data: Challenges, opportunities and limitations in urban studies. Computers, Environment and Urban Systems, 74 (2019), 161- 174. https://doi.org/10.1016/jxompenvurbsys.2018.11.001 google scholar
  • Martı, P., Serrano-Estrada, L., & Nolasco-Cirugeda, A. (2017). Using locative social media and urban cartographies to identify and locate successful urban plazas. Cities, 64, 66-78. https://doi.org/https://doi.org/10.1016/j.cities.2017.02.007 google scholar
  • Mattern, S. (2021). A city is not a computer: Other urban intelligence. Princeton University Press. google scholar
  • Mora, H., Perez-delHoyo, R., Paredes-Perez, J. F., & Molla-Sirvent, R. A. (2018). Analysis of Social Networking Service Data for Smart Urban Planning. In Sustainability (Vol. 10, Issue 12). https://doi.org/10.3390/su10124732 google scholar
  • Schlieder, C., & Matyas, C. (2009). Photographing a City: An Analysis of Place Concepts Based on Spatial Choices. Spatial Cognition & Computation, 9(3), 212-228. https://doi.org/10.1080/13875860903121848 google scholar
  • Sprague, R.H., Carlson, E.D. (1982). Building effective decision support systems, Prentice-Hall. google scholar
  • Sun, Y., Fan, H., Li, M., & Zipf, A. (2015). Identifying the city center using human travel flows generated from location-based social networking data. Environment and Planning B: Planning and Design, 43, 480-498. https://doi.org/10.1177/0265813515617642 google scholar
  • Thakuriah P., Tilahun N.Y., Zellner, M. (2017a). 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 (pp. 11-48). Springer. https://doi.org/10.1007/978-3-319-40902-3 google scholar
  • Townsend, M.A. (2013). Smart Cities Big data, civic hackers, and the questfor a new utopia. London: W.W. Norton & Company. google scholar
  • Tunçer, B. and You, L. (2017). Informed Design Platform. Proceedings of the 35th International Conference on Education and Research in Computer Aided Architecture and Design in Europe (eCAADe) (pp. 545-552). Rome, Italy. google scholar
  • Uzgören, G. (2018). Airbnb impact on gentrification process: the case of Rasimpaşa Neighborhood Kadıköy. Journal of Planning, 28(2), 154-170. https://doi.org/10.14744/planlama.2018.29491 google scholar
  • Van Dicyk A.G.M. (2006). The digital divide achievements and shortcomings. Poetic 34, 221-235. https://doi.org/10.1016/j.poetic.2006.05.004 google scholar
  • Van Eck, N.J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact: Methods andpractice (pp. 285-320). Springer. http://dx.doi.org/10.1007/978-3-319-10377-8_13 google scholar
  • Van Eck, N.J., Waltman, L. (2013). VOSviewer Manual. https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.15.pdf google scholar
  • VOSviewer, (2023). Welcome to VOSviewer. https://www.vosviewer.com/ google scholar
  • Wilken, R., & Goggin, G. (2017). Locative media. Routledge. google scholar
  • Wood, S. A., Guerry, A. D., Silver, J. M., & Lacayo, M. (2013). Using social media to quantify naturebased tourism and recreation. Scientific Reports, 3. https://doi.org/10.1038/srep02976 google scholar
  • Wu C, Ye X, Ren F, Wan Y, Ning P, Du Q (2016) Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China. PLoS ONE 11(10): e0164553. https://doi.org/10.1371/journal.pone.0164553 google scholar
  • Wu, C., Ye, X., Ren, F., Wan, Y., Ning, P., & Du, Q. (2016). Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China. PLOS ONE, 11(10), e0164553. https://doi.org/10.1371/journal.pone.0164553 google scholar
  • You, L., & Tunçer, B. (2016). Exploring public sentiments for livable places based on a crowd-calibrated sentiment analysis mechanism. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 693-700. https://doi.org/10.1109/ASONAM.2016.7752312 google scholar
  • Zhang, S., & Zhou, W. (2018). Recreational visits to urban parks and factors affecting park visits: Evidence from geotagged social media data. Landscape and Urban Planning, 180, 27-35. https://doi.org/https://doi.org/10.1016/j.landurbplan.2018.08.004 google scholar
  • Zhou, X., & Zhang, L. (2016). Crowdsourcing functions of the living city from Twitter and Foursquare data. Cartography and Geographic Information Science, 43(5), 393-404. https://doi.org/10.1080/15230406.2015.1128852 google scholar
There are 56 citations in total.

Details

Primary Language English
Subjects Architecture (Other)
Journal Section Research Articles
Authors

Gülce Kırdar 0000-0002-4700-6077

Publication Date June 15, 2023
Published in Issue Year 2023 Volume: 1 Issue: 1

Cite

APA Kırdar, G. (2023). A Systematic Literature Review of Big Data in Urban Studies. Journal of Technology in Architecture, Design and Planning, 1(1), 64-83. https://doi.org/10.26650/JTADP.01.004
AMA Kırdar G. A Systematic Literature Review of Big Data in Urban Studies. JTADP. June 2023;1(1):64-83. doi:10.26650/JTADP.01.004
Chicago Kırdar, Gülce. “A Systematic Literature Review of Big Data in Urban Studies”. Journal of Technology in Architecture, Design and Planning 1, no. 1 (June 2023): 64-83. https://doi.org/10.26650/JTADP.01.004.
EndNote Kırdar G (June 1, 2023) A Systematic Literature Review of Big Data in Urban Studies. Journal of Technology in Architecture, Design and Planning 1 1 64–83.
IEEE G. Kırdar, “A Systematic Literature Review of Big Data in Urban Studies”, JTADP, vol. 1, no. 1, pp. 64–83, 2023, doi: 10.26650/JTADP.01.004.
ISNAD Kırdar, Gülce. “A Systematic Literature Review of Big Data in Urban Studies”. Journal of Technology in Architecture, Design and Planning 1/1 (June 2023), 64-83. https://doi.org/10.26650/JTADP.01.004.
JAMA Kırdar G. A Systematic Literature Review of Big Data in Urban Studies. JTADP. 2023;1:64–83.
MLA Kırdar, Gülce. “A Systematic Literature Review of Big Data in Urban Studies”. Journal of Technology in Architecture, Design and Planning, vol. 1, no. 1, 2023, pp. 64-83, doi:10.26650/JTADP.01.004.
Vancouver Kırdar G. A Systematic Literature Review of Big Data in Urban Studies. JTADP. 2023;1(1):64-83.