Executive Summary  

Research Question 

What are the key socioeconomic factors influencing Electronic Gaming Machine expenditure per capita across Local Government Areas in Victoria, and how can these insights inform targeted policy interventions to mitigate the adverse effects of gambling? 

Introduction    

 

The proliferation of Electronic Gaming Machines (EGMs) has emerged as a significant public health concern in Victoria, with substantial implications for community well-being and economic stability. EGMs, commonly found in clubs, pubs, and casinos, have been associated with adverse social outcomes, including financial hardship, mental health issues, and family disruptions. Understanding the socioeconomic factors that contribute to EGM expenditure is crucial for developing effective policies and interventions. 

 

 

Data exploration and preprocessing 

After investigating the available data sets, we selected several data points we found interesting, and thought could plausibly have a causal or predictive relationship with gambling spending. The chosen points are as follows: 

 

    LGA 

    2012 ERP age X-X, persons 

    2012 is the only data that aligns with the gambling dataset. 

    Number of Households 

    Personal income <$400/week, persons 

    Number of families 

    Female-headed lone parent families 

    Male-headed lone parent families 

    IRSD (avg) 

    A composite metric that uses data on factors such as income, education, employment status, and access to resources to measure how disadvantaged an area is compared to others. 

    Holds degree or higher, persons 

    Did not complete year 12, persons 

    Unemployed, persons 

    Aboriginal or Torres Strait Islander, persons 

    Born in non-English speaking country, persons 

 

Data prep/ Imputation 

Across the communities’ data set values less than 5 were replaced with <5 rather than their true value. This requires data imputation to avoid losing valuable data points. Here we simply replace <5 with 2.5 across the dataset to minimise the maximum deviation from the true value that could possibly occur. Given the minimum range across the chosen columns is 435 (male headed lone parent households) the maximum error is 0.0057 relative to range (and significantly less again across all other columns). This level of discrepancy is negligible compared to other sources of error and provides significantly more data points.  

 

The gambling dataset was grouped by LGA rather that town suburb as in the communities’ dataset. To allow an inner join and comparisons between the two the communities dataset was grouped by LGA. Most chosen columns were totals so could be simply added whereas IRSD (avg) had to be handled more carefully to get the weighted average across the suburbs in the LGA. Finally, the LGA names across the two datasets had to be standardised to facilitate the inner join of the two. 

 

Data manipulation/ descriptive statistics  

The cleaned and combined dataset can now be used to derive new metrics of interest, such as gambling losses per capita (the feature being predicted or explored). The total population is calculated using the separate age group data, allowing most data points to be converted into percentages. This conversion is crucial because comparing LGAs (Local Government Areas) of different sizes based on raw counts can be misleading, as larger populations naturally have higher counts. 

 

 

Table 1: Pearson correlation b/w each variable of interest and target variable (Losses per Capita) 

Variable 
	

Correlation with losses per capita 

IRSD (avg) 
	

-0.4187 

% Personal income <$400/week 
	

0.1023 

% Holds degree or higher 
	

0.0463 

% Did not complete year 12 
	

0.0231 

% Unemployed 
	

-0.1975 

% Aboriginal or Torres Strait Islander 
	

-0.1035 

% Born in non-English speaking country 
	

-0.0326 

% Female-headed lone parent families 
	

0.2022 

% Male-headed lone parent families 
	

0.1638 

 

Table 2: Descriptive statistivs 

 
	

IRSD (avg) 
	

% Personal income <$400/week 
	

% Holds degree or higher 
	

% Did not complete year 12 
	

% Unemployed 
	

% Aboriginal or Torres Strait Islander 
	

% Born in non-English speaking country 
	

% Female-headed lone parent families 
	

% Male-headed lone parent families 
	

losses per capita 

count 
	

57.0 
	

57.0 
	

57.0 
	

57.0 
	

57.0 
	

57.0 
	

57.0 
	

57.0 
	

57.0 
	

57.0 

mean 
	

1001.552 
	

30.423 
	

12.896 
	

38.754 
	

2.332 
	

0.938 
	

11.94 
	

12.609 
	

2.694 
	

470.86 

std 
	

46.007 
	

3.876 
	

7.031 
	

10.055 
	

0.574 
	

0.762 
	

11.367 
	

2.035 
	

0.384 
	

221.49 

min 
	

899.11 
	

23.162 
	

5.514 
	

11.131 
	

1.145 
	

0.098 
	

1.492 
	

8.635 
	

1.894 
	

110.126 

max 
	

1097.682 
	

41.94 
	

35.399 
	

54.984 
	

4.034 
	

3.584 
	

47.277 
	

16.628 
	

3.759 
	

1446.956 

(max - mean) / std 
	

2.089 
	

2.972 
	

3.201 
	

1.614 
	

2.962 
	

3.471 
	

3.109 
	

1.975 
	

2.77 
	

4.407 

 

 

 

Sub Subtitle 1.1  

 

 

 

 

 

 

 

 

 

 

 

 

 

Data Modelling  

 

Unsupervised K-means Clustering Model 

 

Figure 2.1. 1: K-means clustering of income < $400/week vs female headed lone parent family vs gambling expenditure.  
	

 

Figure 2.1. 2: K-means clustering of IRSD on average vs number of female headed lone parent family’s vs gambling expenditure. 

 

 

After preprocessing, and data exploration was finished on the merged communities and EGM csv files, outliers were apparent regarding the magnitude of gambling expenditure in certain LGA’. Once these values were removed, the 3 columns of interest which were compared in Figure 2.1.1 and 2.1.2, had to be normalised using MinMaxScalar to ensure k-means gave a reasonable, valid clustering to interpret.  

 

K-means clustering was used as it works well clusters of a hyper spherical structure, which was the initial assumption of the make-up of the data once plotted. An additional benefit for k-means clustering was the desire to find the centroids of the clusters, in hopes of making meaningful comparison between clusters and interpret the implication to the research question.  

 

The KMeans function was fitted to the normalised data and plotted on a 3-dimensional scatter plot. Centroids of IRSD, Education vs Gambling k = 2 [0.729 0.446 0.387] [0.358 0.112 0.510] 

Centroids of Income, Lone-headed family vs Gambling k = 2 [0.102 0.104 0.365] [0.528 0.504 0.543] 

 

Clusters with high levels of IRSD and higher numbers of individuals with degrees were categorised by lower gambling expenditure per Capita. Whereas lower IRSD scores, indicating higher relative socioeconomic disadvantage, and lower attainments of degrees were synonymous with higher values of gambling expenditure per capita. Lower number of female-headed lone-parent households and lower number of individuals with an income of less than 400 per week were seen to be indicative of lower gambling expenditure per capita. This increased in a linear fashion with increases in both x and y axis values resulted in an increase in z. The implication of the coordinates of both centroids is that increased levels of socioeconomic disadvantage, accompanied with a lower income and lower education, and the stress and impact of a single parent household is correlated with an increase in gambling.  

 

 

Elbow Method Evaluation  

 

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Figure 2.1. 3: Average distance from every k centroid to all data points for income < $400/week vs female headed lone parent family vs gambling expenditure. 
	

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Figure 2.1. 4: Average distance from every k centroid to all data points for IRSD on average vs number of female headed lone parent family’s vs gambling expenditure. 

 

 

 

Visual Assessment of clustering Tendency (VAT) Evaluation 

  

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Both VAT and the ‘Elbow’ on the SSE graph indicate that the optimal number of k clusters to be 2 or 3. The density and size of the clustering on the VAT diagram also indicates the relation of the data. 

 

 

 

 

 

Supervised Linear Regression Model 

 

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Figure 2.2.1: Linear Regression Model of Number of Female-headed lone parent families vs Gambling Expenditure per Capita 
	

 

Figure 2.2.2: Linear Regression Model of Average IRSD of LGA vs Gambling Expenditure per Capita 

 

 

A linear regression was decided to be used as a supervised machine learning model do to the continuous nature of the data we were interested in. A test-train split of our data was used in conjunction with MSE and [Equation] to evaluate the validity of the model.  

   

MSE 
	

r2 
	

coefficents 

0.0308, 
	

0.279, 
	

-0.491 

0.0471 
	

-0.104 
	

0.239.    

 

Supervised k Nearest Neighbours (k-NN) Model 

 

 

Figure 2.3.1: 
	

 

 

 

Used test train split then confusion matrix to evaluation model. Accuracy 0.5 

 

Random Forest Regressor: 

Feature Selection: (Regression) 

An initial set of 23 features was considered, encompassing a wide range of socioeconomic factors. 

Iterative Feature Selection: Recursive Feature Elimination with Cross-Validation (RFECV) was employed to systematically remove features and evaluate model performance. 

The process evaluated x iterations, each decreasing the feature count by one and recording the corresponding mean R² score. 

Optimal Feature Set: The subset of six features that maximized the model's predictive performance was selected, enhancing interpretability and reducing computational complexity. 

3.3 Handling Multicollinearity through Feature Reduction 

Multicollinearity, the presence of high correlations among predictor variables, can distort the estimated coefficients of a regression model and complicate the interpretation of individual predictors. Although Random Forest models are inherently robust to multicollinearity due to their ensemble nature, addressing it enhances model interpretability and the stability of feature importance metrics. 

Approach: 

Feature Elimination: Redundant variables were removed based on domain knowledge and their correlation with the target variable. 

Assessment of Variance Inflation Factor (VIF): VIF values were computed to quantify multicollinearity, ensuring that selected features had acceptable VIF scores (VIF < 5). 

Outcome: 

The reduction to six key predictors improved model performance and mitigated multicollinearity issues by eliminating redundant features. 

Simplification enhances interpretability, allowing for clearer insights into how each socioeconomic factor predicts EGM expenditure. 

3.4 Model Training and Evaluation 

Model Selection: 

A Random Forest Regressor was selected for its robustness, ability to handle complex, non-linear relationships, and resistance to overfitting. 

The ensemble nature of Random Forest makes it suitable for datasets with a limited number of observations relative to the number of features. 

Hyperparameter Tuning: 

GridSearchCV was utilized to perform hyperparameter tuning, exploring a parameter grid that included: 

Number of Estimators 
	

Maximum Depth 
	

Minimum Samples Split 
	

Minimum Samples Leaf 

100.0 
	

5.0 
	

2.0 
	

1.0 

200.0 
	

10.0 
	

5.0 
	

2.0 

500.0 
	

20.0 
	

10.0 
	

4.0 

 

The optimal hyperparameters were selected based on the highest mean cross-validated R² score. 

Metric 
	

Description 

Coefficient of Determination (R²) 
	

Measures the proportion of variance in the dependent variable predictable from the independent variables. 

Mean Absolute Error (MAE) 
	

Represents the average absolute difference between predicted and actual values. 

Mean Squared Error (MSE) 
	

Measures the average squared difference between predicted and actual values. 

Root Mean Squared Error (RMSE) 
	

The square root of MSE, providing error measurement in the same units as the target variable. 

 

(USEABLE FOR ALL DISCUSSIONS??) 

Factor 
	

Impact 

Low Personal Income 
	

Individuals earning less than $400 per week are more likely to engage in gambling, potentially as a coping mechanism for economic hardship. 

Unemployment Rates 
	

Higher unemployment correlates with increased EGM expenditure, indicating financial stress and higher propensity to gamble. 

Persons Aged 5-9 
	

Households with young children may face financial pressures, leading to adult gambling behaviors to supplement income. 

IRSD (avg) 
	

Lower socioeconomic status is associated with higher gambling expenditure, indicating the role of social inequality. 

Female-headed Lone Parent Families 
	

Single-parent households, particularly those headed by females, may experience increased financial and social stress, leading to higher gambling expenditures. 

Born in Non-English Speaking Countries 
	

Cultural differences may predict gambling behaviors, with some communities showing higher engagement with EGMs. 

  

 

Model Performance Insights 

Insight 
	

Description 

Explained Variance (R²) 
	

The model explains approximately 34.95% of the variance in EGM expenditure per capita. 

Variability in R² 
	

R² scores vary across folds, possibly due to unmeasured factors like accessibility, cultural attitudes, and policy differences. 

Higher Errors in Some Folds 
	

Some folds exhibit higher errors, highlighting the need for additional data or alternative models to capture the complexity of gambling behaviors. 

 

 

x. Limitations and Improvement Opportunities 

x.1 Limitations 

    Data Constraints: 

    Temporal Limitations: Reliance on historical data may not capture current trends or the impact of recent policy changes on EGM expenditure. 

    Sample Size: The limited number of data points (58 LGAs) restricts the model's ability to generalize and may affect the robustness of the findings. 

    Potential Biases: 

    Unmeasured Confounding Variables: Factors such as mental health, individual psychological factors, and specific gambling behaviors were not included. 

    Data Aggregation: Aggregation at the LGA level may obscure intra-regional disparities and localized factors predict gambling expenditure. 

x.2 Improvement Opportunities 

    Incorporating Shapefiles: 

    Utilizing geographic information system (GIS) data, such as shapefiles, can improve spatial analysis and accurately attribute suburbs to specific LGAs, enhancing the dataset's precision. 

    Expanding the Feature Set: 

    Including additional variables such as mental health indicators, education levels, housing stability, and crime rates could provide a more comprehensive understanding. 

    Alternative Modeling Techniques: 

    Exploring models like Gradient Boosting Machines, Support Vector Regression, or Neural Networks may capture complex relationships more effectively. 

    Qualitative Studies: 

    Complementing the quantitative analysis with qualitative research, such as surveys or interviews, can provide deeper insights into the motivations and behaviors driving EGM expenditure. 

    Data Augmentation: 

    Employing techniques like Gaussian noise addition or generating synthetic data using methods like SMOTE can increase the number of training data points, helping the model to generalize better. 

 

 

 

 

References  

References are optional but if you have references, they should be APA Style and Times New Roman 10, Aligned Left, Single Line, 0pt before, 6pt after, hanging 0.5 inches). 

 

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