Topic and Rationale: This project talks about This initiative focuses on acquiring and analyzing comprehensive budgeting data for programs within the City of Toronto. Sourced from official government channels and municipal websites, the dataset encompasses crucial details, including program names, detailed expenditure and revenue categories, and corresponding budget amounts. Through this project, we aim to provide valuable insights that will contribute to informed financial planning and decision-making processes for the City of Toronto.
Our Analysis: Exploratory Data Analysis (EDA): Conduct EDA to understand the characteristics of the budget data. Explore basic statistics such as mean, median, and standard deviation. Visualize the distribution of budget allocations using histograms or box plots. Identify outliers and anomalies in the data. Expenditure and Revenue Category Analysis: Categorize expenditures into relevant categories. Explore the distribution of expenditures across different categories using bar charts. Study on Sub Category Analysis Program Summary: Create a program summary that aggregates expenditures by program. Identify programs with the highest and lowest budgets. Visualize the distribution of budgets across programs using bar charts or stacked bar charts. Trend Analysis: Analyze budget trends over multiple years if historical data is available. Identify categories or programs with significant changes in budget allocations over time using line charts or time series plots. Predictive Modeling: Explore the possibility of building predictive models to forecast future budget allocations based on historical trends. Comparison with Demographic Data: Explore the relationship between budget allocations and demographic data for the City of Toronto. This could include factors such as population density, income levels, or other relevant indicators. Correlation Analysis: Investigate potential correlations between budget categories. Explore whether increased spending in one category is associated with changes in another using correlation matrices. Utilizing ETL techniques along with Flask, JavaScript (Plotly), and HTML, we developed multiple dashboards with interactive visualizations to present insights derived from the dataset.
Dataset Links: https://open.toronto.ca/dataset/city-wards/ https://www.toronto.ca/city-government/data-research-maps/neighbourhoods-communities/ward-profiles/ https://open.toronto.ca/dataset/budget-operating-budget-program-summary-by-expenditure-category/
Visualization ideas: interactive map showing wards dropdown line/bar graphs
Sketch of final design: 3+ dashboards, with each dashboard having visualizations of separate categories.