Spatial Analysis Assignments Repository

This repository showcases a collection of class projects completed during GIS courses completed at the University of Toronto Scarborough and contains a variety of projects that cover a range of statistical methods and spatial data analysis techniques. These projects were part of our coursework, designed to enhance our skills in data analysis and visualization.

Explanation of Statistical Methods

Here are concise explanations of each statistical method used:

Simple Linear Regression

• Models the relationship between two continuous variables.
• Seeks to find a linear equation that best fits the data.

Multiple Linear Regression

• Extends simple linear regression to include multiple predictor variables.
• Examines how different factors simultaneously influence the dependent variable.

Binary Logistic Regression

• Used when the outcome is binary (yes/no, 1/0).
• Models the relationship between a binary dependent variable and independent variables.

Multinomial Logistic Regression

• Appropriate when the outcome has more than two categories.
• Models the probability of each category relative to a reference category.

Principal Component Analysis (PCA)

• Reduces the dimensionality of data while preserving important information.
• Creates uncorrelated variables known as principal components.

Factor Analysis

• Identifies latent factors explaining correlations among observed variables.
• Simplifies complex datasets by representing them with a smaller number of factors.

Cluster Analysis

• Groups similar data points together based on characteristics.
• Useful for discovering natural groupings within datasets.

Spatial Autocorrelation

• Assesses the similarity of data points in geographic space.
• Helps identify spatial patterns and dependencies.

Spatial Econometrics

• Combines economic models with spatial analysis techniques.
• Accounts for spatial dependencies and effects in economic data.

Decision Trees

• Machine learning technique for classification and regression tasks.
• Creates a tree-like model of decisions and their consequences.

Results

Here are some projects that showcase the application of these statistical methods:

• GGRC30 Staten Island Areal Weighting Choropleth Map.pdf - Visualize geographical data using choropleth maps.

• GGRC30 Staten Island Location-Allocation Map.pdf - Explore optimized locations and allocations on Staten Island with an interactive map.

• GGRC30 Staten Island Service Areas Map.pdf - Examine service areas on Staten Island using a detailed map.

• GGRC30 Staten Island Suitability Analysis Map.pdf - Evaluate the suitability of different areas on Staten Island for various purposes.

• GGRC30 Staten Island EV Charging Stations Presentation.pdf - Gain insights into Staten Island's EV charging stations through a presentation.