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http://dx.doi.org/10.13161/kibim.2019.9.1.022

Machine Learning Based Architecture and Urban Data Analysis - Construction of Floating Population Model Using Deep Learning -  

Shin, Dong-Youn (단국대학교 건축학과)
Publication Information
Journal of KIBIM / v.9, no.1, 2019 , pp. 22-31 More about this Journal
Abstract
In this paper, we construct a prototype model for city data prediction by using time series data of floating population, and use machine learning to analyze urban data of complex structure. A correlation prediction model was constructed using three of the 10 data (total flow population, male flow population, and Monday flow population), and the result was compared with the actual data. The results of the accuracy were evaluated. The results of this study show that the predicted model of the floating population predicts the correlation between the predicted floating population and the current state of commerce. It is expected that it will help efficient and objective design in the planning stages of architecture, landscape, and urban areas such as tree environment design and layout of trails. Also, it is expected that the dynamic population prediction using multivariate time series data and collected location data will be able to perform integrated simulation with time series data of various fields.
Keywords
Machine learning; Deep learning; ANN; Urban planning;
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1 Lee, T. H., Jeon, M. J. (2018). Prediction of Seoul House Price Index Using Deep Learning Algorithms with Multivariate Time Series Data. SH Urban Research & Insight 8(2), pp. 39-56.   DOI
2 Touchette, P. E., MacDonald, R. F., Langer, S. N. (1985). A scatter plot for identifying stimulus control of problem behavior. Journal of applied behavior analysis 18(4), pp. 343-351.   DOI
3 Bae, S. W., Yu, J. S. (2017). Predicting the Real Estate Price Index Using Deep Learning. Korea Real Estate Research Institute 27(3), pp. 71-86.
4 Batty, M. (2013). Big data, smart cities and city planning. Dialogues in Human Geography, 3(3), pp. 274-279.   DOI
5 Blais, P. (2011). Perverse cities: hidden subsidies, wonky policy, and urban sprawl, UBC Press.
6 Deren, L., Qingquan, L. (1997). Study on a hybrid data structure in 3D GIS, Acta Geodaetica et Cartographica Sinica 2.
7 Brockwell, P. J., Davis, R. A., Calder, M. V. (2002). Introduction to time series and forecasting, Springer.
8 Brownlee, J. (2017). Long Short-Term Memory Networks With Python. Machine Learning Mastery.
9 Curwell, S., M. Deakin, I. Cooper, K. Paskaleva-Shapira, Ravetz, J., Babicki, D. (2005). "Citizens' expectations of information cities: implications for urban planning and design." Building Research & Information 33(1), pp. 55-66.   DOI
10 Ding, M., Bressler, S. L., Yang, W., Liang, H. (2000). Short-window spectral analysis of cortical eventrelated potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biological cybernetics 83(1), pp. 35-45.   DOI
11 Goldberg, D. E., Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning 3(2), pp. 95-99.   DOI
12 Gulli, A., Pal, S. (2017). Deep Learning with Keras, Packt Publishing Ltd.
13 Ha, J. H., Lee, Y. H., Kim, Y. H. (2016). Forecasting the precipitation of the next day using deep learning. Journal of Korean Institute of Intelligent Systems 26(2), pp. 93-98.   DOI
14 Kotsiantis, S. B., Zaharakis, I., Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering 160, pp. 3-24.
15 Haykin, S. S. (2009). Neural networks and learning machines, Pearson Upper Saddle River.
16 Healey, P. (2006). Urban complexity and spatial strategies: Towards a relational planning for our times, Routledge.
17 Kim, M. K., Hong, C. (2016). The Artificial Neural Network based Electric Power Demand Forecast using a Season and Weather Informations. Journal of the Institute of Electronics and Information Engineers 53(1), pp. 71-78.   DOI
18 Kim, M. Y. The Development of Visualization Indicators for Case-study of Urban Geo-Spatial Information Visualization. Journal of The Korean Digital Architecture and Interior Association 12.
19 Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal 79(1), pp. 1-14.   DOI
20 Lafarge, F., Mallet, C. (2011). Building large urban environments from unstructured point data. Iccv.
21 Nicholson, D. G., King, J. C. (1997). Method and apparatus for producing a hybrid data structure for displaying a raster image, Google Patents.
22 Park, J. J., Kim, H. K., Bae, Y. J. (2015). An Analysis on the Changes in Publicly Noticed Value of Real Estate Price (PNV) on Household's Property Tax. Real Estate Research 25(3), pp. 27-39.
23 Rathore, M. M., Ahmad, A., Paul, A., Rho, S. (2016). Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks 101, pp. 63-80.   DOI
24 Scholten, H. J., Stillwell, J. (2013). Geographical information systems for urban and regional planning, Springer Science & Business Media.
25 Wilson, T. D. (2000). Human information behavior. Informing science 3(2), pp. 49-56.   DOI
26 Shin, D., Aliaga, D., Tun er, B., Arisona, S. M., Kim, S., Z nd, D., Schmitt, G. (2015). Urban sensing: Using smartphones for transportation mode classification. Computers, Environment and Urban Systems 53, pp. 76-86.   DOI
27 Sutton, R. S., Barto, A.G. (1998). Introduction to reinforcement learning, MIT press Cambridge.