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| − | == Population Growth Trend & Forecast ==
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| − | In this population we will be using the Singstat’s `[https://www.singstat.gov.sg/-/media/files/find_data/population/statistical_tables/singapore-residents-by-planning-areasubzone-age-group-sex-and-type-of-dwelling-june-20112019.zip Singapore Residents by Planning AreaSubzone, Age Group, Sex and Type of Dwelling, June 2011-2019]` data provided. There are few objectives that we want to understand from the population historical data:
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| − | * Understand the population trend for each subzone and age group classification (younger group, economic active group, and aged population) in order to facilitate basic necessities for each age group.
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| − | * Forecast the future population trend up to 2024 using the auto ARIMA model to re-evaluate the MP19.
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| − | ** [https://people.duke.edu/~rnau/411arim.htm#arima010 Interpreting the Arima model]
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| − | ** Similar application of ARIMA model in forecasting population trends:
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| − | *** Zakria, Muhammad & Muhammad, Faqir. (2009). [https://www.researchgate.net/publication/228468254_Forecasting_the_population_of_Pakistan_using_ARIMA_models Forecasting the population of Pakistan using ARIMA models.]. Agri. Sci. 46.
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| − | *** Nyonyi, T and Mutongi, C. (2019). [https://mpra.ub.uni-muenchen.de/93983/1/MPRA_paper_93983.PDF Prediction of total population in Togo using ARIMA models].
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| − | *** Lin, Bin-Shan, et al. [https://www.jstor.org/stable/23365635?seq=1#page_scan_tab_contents “Using ARIMA Models to Predict Prison Populations.”] Journal of Quantitative Criminology, vol. 2, no. 3, 1986, pp. 251–264. JSTOR, www.jstor.org/stable/23365635.
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| − | ** Make recommendations according to the population trend insights.
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| − | === Data Cleaning Methods ===
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| − | * Data is cleaned to only show Punggol PA and its subzones.
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| − | * Age group were classified into a new group with the following requirement:
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| − | ** '''Younger Population''': 0-24
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| − | ** '''Economic Active''': 25-64
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| − | ** '''Aged Population''': 65 and above
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| − | * Summation group by was performed according to each subzone and age group classification.
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| − | * Reverse data frame vector was performed to swap rows and columns formatting as it is required to perform graph visualisation in R.
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| − | ** Methods Deployed in RPubs: [http://rpubs.com/jerrytohvan/548619 Punggol Forecast Population Analysis]
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| − | ** Methods Deployed in RPubs: [http://rpubs.com/jerrytohvan/548621 Punggol Peak Hour Travel Pattern Analysis]
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| − | === Results ===
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