https://github.com/elvano22/poverty-analysis
https://github.com/elvano22/poverty-analysis
geographically-weighted-regression poverty-prediction r regression-analysis weighted-least-squares
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/elvano22/poverty-analysis
- Owner: elvano22
- Created: 2024-11-03T06:25:19.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-11-22T19:19:30.000Z (6 months ago)
- Last Synced: 2025-03-25T22:45:23.795Z (about 2 months ago)
- Topics: geographically-weighted-regression, poverty-prediction, r, regression-analysis, weighted-least-squares
- Homepage:
- Size: 225 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Poverty-Analysis
This repository contains the analysis and modeling of poverty in Indonesia using statistical methods, particularly focusing on **Geographically Weighted Regression (GWR)**. The study identifies influential factors and constructs predictive models to analyze poverty across Indonesia's 34 provinces.
## Key Insights
Two datasets were analyzed, **"Kemiskinan"** and **"Kemiskinan2"**, to determine which provides better insights into poverty modeling:
- **Kemiskinan2** outperformed **Kemiskinan** in terms of accuracy, explanatory power, and interpretability.
- All subsequent analysis and modeling use the **Kemiskinan2** dataset for its superior performance.
---
## Repository Contents
1. **`Kemiskinan.xlsx` and `Kemiskinan2.xlsx`**:
- Datasets containing poverty data across Indonesia's provinces.
- `Kemiskinan2` includes refined indicators for better model performance.
2. **`The Code.Rmd`**:
- The R Markdown script for performing the statistical analysis and regression modeling.
- Steps include data exploration, testing for multicollinearity, and building regression models (OLS, WLS, and GWR).
3. **`The Paper.docx`**:
- Detailed documentation of the analysis, including methodology, results, and discussions.
---
## Methodology
### 1. Models Used
- **Ordinary Least Squares (OLS)**: Baseline linear regression model.
- **Weighted Least Squares (WLS)**: Addresses heteroscedasticity issues in OLS.
- **Geographically Weighted Regression (GWR)**: Incorporates spatial heterogeneity to improve model precision.
### 2. Evaluation Metrics
- **R-Squared**: Measures the proportion of variance explained by the model.
- **AIC (Akaike Information Criterion)**: Evaluates model fit, penalizing for complexity.
---
## Results: Why Kemiskinan2?
| Model | Dataset | R-Squared | AIC | Remarks |
|-------|---------------|-----------|----------|-----------------------------------|
| GWR | **Kemiskinan2** | **81.95%** | **530.18** | Best fit; explains spatial variation effectively. |
| GWR | Kemiskinan | 74.03% | 537.70 | Suboptimal; lower explanatory power. |
| OLS | Kemiskinan2 | 60.34% | 559.59 | Poor fit compared to GWR. |
| OLS | Kemiskinan | 57.29% | 556.11 | Lowest fit; lacks spatial insights.|
- **Kemiskinan2** enables GWR to achieve the highest R-Squared and lowest AIC values, making it the most effective dataset for capturing spatial variability in poverty.
---
## Steps to Reproduce
1. Install necessary R packages:
```R
install.packages(c("sp", "ggplot2", "GWmodel", "car"))
```
2. Open `The Code.Rmd` in RStudio or another compatible IDE.
3. Run the script to perform:
- Data preprocessing
- Model training and evaluation
- Visualizations and result interpretation.
---
## Conclusion
The **GWR model** using **Kemiskinan2** is the most effective for analyzing poverty in Indonesia:
- Explains 81.95% of data variability (R-Squared).
- Provides actionable insights for targeted poverty reduction strategies.
- Highlights key variables such as **education completion rate**, **adequate sanitation**, and **drinking water** as critical to reducing poverty.
For further exploration, future research can incorporate advanced machine learning techniques or additional socioeconomic indicators to enhance the model.