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CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures
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50%; open Wong, S. L., K. K. W. Wan, and T. N. T. Lam (2010) Find in CUMINCAD Artificial Neural Networks for Energy Analysis of Office Buildings with Daylighting , Applied Energy 87 (2): 551–57. doi:10.1016/j.apenergy.2009.06.028
50%; open Wong, S., Wan, K. and Lam, T. (2010) Find in CUMINCAD Artificial neural networks for energy analysis of office buildings with daylighting. , Applied Energy, 87(2), pp. 551-557
50%; open Wu, X., Lu, Y., Lin, Y. & Yang, Y. (2019) Find in CUMINCAD Measuring the Destination Accessibility of Cycling Transfer Trips in Metro Station Areas: A Big Data Approach , Int. J. Environ. Res. Public Health, 16(15), 2641. http://doi.org/10.3390/ijerph16152641Wang, H., & Noland, R. (2021, January 26). Changes in the pattern of bikeshare usage due to the COVID-19 pandemic. Findings. Retrieved May 16, 2021, from https://findingspress.org/article/18728Zafri, N.M., Khan, A., Jamal, S., & Alam, B.M. (2021). Impacts of the COVID-19 Pandemic on Active Travel Mode Choice in Bangladesh: A Study from the Perspective of Sustainability and New Normal Situation. Sustainability, 13(12), 6975. http://doi.org/10.3390/su13126975Zhang, Y., & Mi, Z. (2018). Environmental benefits of bike sharing: a bigdata-based analysis. Applied Energy, 220, 296-301. https://doi.org/10.1016/j.apenergy.2018.03.101
50%; open Zhang, T, Huang, X, Wen, D and Li, J (2017) Find in CUMINCAD Urban Building Density Estimation From High-Resolution Imagery Using Multiple Features and Support Vector Regression , IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, pp. 3265-3280
50%; open Zhang, Y., Cheng, S., Mei, W., Jiang, L., Jia, Z., Cheng, Z., Sun, J. & Wang, Q. (2023) Find in CUMINCAD Understanding of thermal runaway mechanism of LiFePO4 battery in-depth by three-level analysis , Applied Energy, 336,120695. https://doi.org/10.1016/j.apenergy.2023.120695
50%; open Zhang, Y., Zhang, Q., Zhao, Y., Deng, Y., & Zheng, H. (2022) Find in CUMINCAD Urban Spatial Risk Prediction and Optimization Analysis of Poi Based on Deep Learning from the Perspective of an Epidemic , International Journal of Applied Earth Observation and Geoinformation, 112, 12942. Available at: https://doi.org/1.116/j.jag.222.12942
50%; open An, R., Wu, Z., Tong, Z., et al. (2022) Find in CUMINCAD How the built environment promotes public transportation in Wuhan: A multiscale geographically weighted regression analysis , Travel Behaviour and Society, 29: 186-199
50%; open Asadi, S, Amiri, SS and Mottahedi, M (2014) Find in CUMINCAD On the Development of Multi-Linear Regression Analysis to Assess Energy Consumption in the Early Stages of Building Design , Energy & Buildings, 85, pp. 246-255
50%; open Chatterjee S, Hadi A and Price B (2000) Find in CUMINCAD Regression analysis by example , New York, NY: John Wiley and Sons
50%; open Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., & Mosavi, A. (2019) Find in CUMINCAD An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines , The Science of the total environment, 2019-02, Vol.651 (Pt 2), p.2087-2096"
50%; open Dietrich von, Rosen (eds) (2018) Find in CUMINCAD Bilinear Regression Analysis , Springer International Publishing
50%; open Fei, M. A., Fr, A., Kfy, B., Yg, A., Cz, A., Dan, G. A. (2019) Find in CUMINCAD The spatial coupling effect between urban public transport and commercial complexes: A network centrality perspective - ScienceDirect , Sustainable Cities and Society, 50, 101645-101645.Fotheringham, A. S., Yang, W., Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.Harris, P., Fotheringham, A. S., Crespo, R., Charlton, M. (2018). Inference in multi-scale geographically weighted regression. WILEY, 43(3), 399.Jia, J., Zhang, X. (2021). A human-scale investigation into economic benefits of urban green and blue infrastructure based on big data and machine learning: A case study of Wuhan. Journal of Cleaner Production, 316, 128321.Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., Fotheringham, A. S. (2019). MGWR: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale. International Journal of Geo-Information, 8(6), 269.Rifkin, J. (2013). The Third Industrial Revolution. International Study Reference, 6(1), 8-11.Yu H, Fotheringham A S, Li Z, et al. (2019). Inference in multi-scale geographically weighted regression. Geographical Analysis, 52(1), 87-106
50%; open Fotheringham, A. S., Charlton, M. E., & Brunsdon, C. (1998) Find in CUMINCAD Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis , Environment and Planning A: Economy and Space, 30(11), 1905-1927
50%; open Fotheringham, A., Brunsdon, C. & Charlton, M. (2002) Find in CUMINCAD Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , Wiley
50%; open Green, SB (1991) Find in CUMINCAD How Many Subjects Does It Take To Do A Regression Analysis , Multivariate Behav Res., 26(3), pp. 499-510
50%; open Hu, Ling (2014) Find in CUMINCAD Research on the Application of Regression Analysis Method in Data Classification , Journal of Networks, 1276, pp. 3151-3157
50%; open Krackhardt (1988) Find in CUMINCAD Predicting with networks-Nonparametric multiple regression analysis of dyadic data , Social Network. 10: 359-381
50%; open Nishii, K., M. Tanahashi, T. Doi, and T. Kiuchi (1996) Find in CUMINCAD The LOGMAP modelling for structural analysis of railroad line area image: An empirical study on the attribute – regression model , Infrastructure Planning Review, 13, p.49-56
50%; open Rosen, D. v. (2018) Find in CUMINCAD Bilinear Regression Analysis , Springer International Publishing
50%; open Tso, G.K.F. and Yau, K.K.W. (2007) Find in CUMINCAD Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks , Energy 32(9), pp. 1761-1768. doi: 10.1016/j.energy.2006.11.010

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