A Framework for Voting Behavior Prediction Using Spatial Data
DOI:
https://doi.org/10.55524/Keywords:
Spatial Analysis, Voter Turnout, Data Analytics, ARIMA Model, Recurrent Neural Network (RNN), Time Series ForecastingAbstract
The greatest method to anticipate the future is to look at what has happened in the past. We shall present important election behavioral predictions in this paper. This study article will focus on the data offered by Present age wise voting statistics, voter demographics, votes cast, and spatial correlation among surrounding states in order to validate that a place's exit poll data. The major goals of our paper are to first encourage voting among different age groups based on projected circumstances, and then to understand the influence of a state's neighbours. Conclusively studying the entire voting scenario of previous years, which will aid in the forecast of citizens' voting behavior in the approaching years, as well as recognizing the root cause of the weaker portions and improving upon the flaws for a better future. Our main goal is to use some current voting data from a region to train and determine the major voting population in the various states of the United States based on their geographical influence. This will aid in the analysis of the current situation as well as assisting the government in creating awareness in places where it is missing.
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Michaud, J., Mäkinen, I.H., Szilva, A. et al. “A spatial analysis of parliamentary elections in Sweden 1985–2018”, Appl Netw Sci 6, 67, 2021
Verba S, Nie NH. Participation in America: political democracy and social equality. University of Chicago Press ed. Chicago: University of Chicago Press; 1987
Dalton RJ. The potential of big data for the cross-national study of political behavior. Int J Sociol. , 46: 8–20, 2016 [4] Franch F. (Wisdom of the Crowds)2: 2010 UK Election Prediction with Social Media. J Inf Technol Polit., 10: 57–71, 2013
Mavragani A, Tsagarakis KP. YES or NO: Predicting the 2015 GReferendum results using Google Trends. Technol Forecast Soc Change., 109: 1–5, 2016
Ceron A, Curini L, Iacus SM. iSA: A fast, scalable and accurate algorithm for sentiment analysis of social media content. Inf Sci.; 367–368:, pp 105–124, 2016
Barclay FP, Pichandy C, Venkat A, Sudhakaran S. India 2014: Facebook “Like” as a Predictor of Elec- tion Outcomes. Asian J Polit Sci., 23: 134–160, 2015
Andersen R, Tilley T and Heath AF, “Political Knowledge and Enlightened Preferences: Party Choice Through the Electoral Cycle”, British Journal of Political Science , 35: 285-302, 2005
Tsakalidis Adam, et al., predicting elections for multiple countries using twitter and polls, IEEE 2015 [10] Hans Ulrich Buhl, 2011, From Revolution to Participation: SocialMedia and the Democratic Decision-Making Process, BISE Editorial, 2011
Unankard, X. Li, M. Sharaf, J. Zhong, and X. Li, “Predicting elections from social networks based on sub-event detection and sentiment analysis,” Web Information Systems Engineering WISE 2014, vol. 8787, pp. 1–16, 2014
Yusuf Perwej, Kashiful Haq, Firoj Parwej, M. M. Mohamed Hassan ,“ The Internet of Things (IoT) and its Application Domains”, International Journal of Computer Applications (IJCA) ,USA , ISSN 0975 – 8887, Volume 182, No.49, Pages 36- 49, 2019, DOI: 10.5120/ijca2019918763
Fotheringham, S. A., Charlton, M. & Demšar, U., Looking for a Relationship? Try GWR. In: H. J. Miller & J. Han, eds.
Geographic Data Mining and Knowledge Discovery. s.l.:CRC Press, pp. 227-252, 2009
Anselin, L., Thirty years of spatial econometrics. Papers in Regional Science, 89(1), pp. 3-25, 2010
Fazekas Z and Méder ZZ,”Proximity and directional theory compared: Taking discriminant positions seriously in multi party systems”, Working Paper, 2012
Tapp, A. F.,. Areal Interpolation and Dasymetric Mapping Methods Using Local Ancillary Data Sources. Cartography and Geographic Information Science, pp. 215-228, 2010
Makazhanov A, Rafiei D, Waqar M. Predicting political preference of Twitter users. Soc Netw Anal Min. 4: 1–15, 2014
Ceron A, Curini L, Iacus S. Using social media to fore-cast electoral results: A review of state-of-the-art. Ital J Appl Stat., 25, pp 237–259, 2015
David E, Zhitomirsky-Geffet M, Koppel M, Uzan H. Utilizing Facebook pages of the political parties to automatically predict the political orientation of Facebook users. Online Inf Rev. 40, pp 610–623, 2016 [20] Shira Fano and Debora Slanzi , Using Twitter Data to Monitor Political Campaigns and Predict Election Results. Springer international publishing AG, 2017
Volkova S, Bachrach Y, Armstrong M, Sharma V. Inferring Latent User Properties from Texts Published in Social Media. AAAI., pp. 4296–4297, 2015
Suarez Hernandez A, et al., predicting political mood tendencies based on twitter data, 2017
X. Zhang, X. Zhu, B. She and S. Bao, "The spatial data integration and analysis with China Geo-Explorer", 17th International Conference on Geoinformatics Geoinformatics 2009 Conference, 2009
L. Anselin, Y. W. Kim and I. Syabri, "Web-Based Analytical Tools for the Exploration of Spatial Data", Journal of Geographical Systems, vol. 6, pp. 197-218, 2004
Yusuf Perwej, “An Experiential Study of the Big Data”, International Transaction of Electrical and Computer Engineers System (ITECES), USA, Science and Education Publishing, Volume 4, No. 1, Pages 14-25, 2017, DOI: 10.12691/iteces-4-1-3
Yusuf Perwej, Asif Perwej, Firoj Parwej, “An Adaptive Watermarking Technique for the copyright of digital images and Digital Image Protection”, International journal of Multimedia & Its Applications (IJMA), USA , Volume 4, No.2, Pages 21- 38, 2012, DOI: 10.5121/ijma.2012.4202
S. Deepajothi and S. Selvarajan:, "A Comparative Study of Classification Techniques On Adult Data Set", International Journal of Engineering Research Technology (IJERT), vol. 1, no. 8, 2012
Yusuf Perwej, “An Evaluation of Deep Learning Miniature Concerning in Soft Computing”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), Volume 4, Issue 2, Pages 10 - 16, 2015, DOI: 10.17148/IJARCCE.2015.4203
Z. Wang, W. Yan and T. Oates, "Time series classification from scratch with deep neural networks: A strong baseline", Proc. Int. Jt. Conf. Neural Networks, vol. 2017-May, pp. 1578-1585, 2017
S. Yao, S. Hu, Y. Zhao, A. Zhang and T. Abdelzaher, "DeepSense: A unified deep learning framework for time series mobile sensing data processing", 26th Int. World Wide Web Conf. WWW 2017, pp. 351-360, 2017
G. Zhang, Time series forecasting using a hybrid arima and neural network model, Neuro-computing, vol. 50, pp. 159- 175, 2003
A.M. Alonso and C. Garcia-Martos, "Time Series Analysis - Forecasting with ARIMA models", Universidad Carlos III de Madrid Universidad Politecnica de Madrid, 2012
S. Atique, S. Noureen, V. Roy, V. H. Subburaj, S. Bayne and J. Macfie, "Forecasting of total daily solar energy generation using ARIMA: a case study", 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) (IEEE CCWC 2019), Jan. 2019
Yusuf Perwej, “Recurrent Neural Network Method in Arabic Words Recognition System”, International Journal of Computer Science and Telecommunications (IJCST), UK, London Volume 3, Issue 11, Pages 43-48, 2012
Yusuf Perwej, “The Bidirectional Long-Short-Term Memory Neural Network based Word Retrieval for Arabic Documents”, Transactions on Machine Learning and Artificial Intelligence, (UK), ISSN 2054-7390, Volume 3, Issue 1, Pages 16 - 27, 2015, DOI: 10.14738/tmlai.31.863
I. Moghram and S. Rahman, "Analysis and evaluation of five short-term load forecasting techniques", IEEE Transactions on Power Systems, vol. 4, no. 4, pp. 1484-1491, Nov 1989
P. J. Brockwell and R. A. Davis, Time Series: Theory and Methods, Springer, 2009
Akhtar N,” Perceptual evaluation for software project cost estimation using ant colony system”, Int J Comp Appl 81(14):23–30, 2013
Tadayoshi Kohno, Adam Stubblefield, Aviel D. Rubin, Dan S. Wallach,"Analysis of an Electronic Voting System", Johns Hopkins University Information Security Institute Technical Report, TR-2003-19, July 23, 2003
A. Pajala, A. Jakulin, and W. Buntine, "Parliamentary group and individual voting behavior in Finnish Parliament in year 2003: A group cohesion and voting similarity analysis, " 2004
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