Research Projects

Project Guidelines

These guidelines are designed to help you conduct your research projects using the spatial analysis techniques learned in this course. Each project will follow a similar structure, allowing you to apply the concepts and methods consistently.

General Steps for Each Project:

  1. Operationalize the Outcome Variable:
    • Clearly define your outcome variable (the problem you are investigating).
    • Explain how you will measure this variable using the available data.
    • Provide the specific variable name(s) from the dataset that you will use.
  2. Conceptualize Covariates:
    • Identify the explanatory variables (covariates, factors, or determinants) that you will use in your analysis.
    • Provide a theoretical or literature-based justification for why these variables are relevant.
    • Cite leading paper(s) that support your choice of covariates.
    • Specify the variable names from the dataset that you will use.
  3. Data Selection and Cleaning:
    • Data Source: Specify the dataset you will use (e.g., DHS, Integrated Household Budget Survey, Labour Force Survey).
    • Spatial Data: Identify and load the appropriate shapefile for your analysis.
    • Multilevel Data: Load the survey data and prepare it for analysis.
    • Data Cleaning:
      • Handle missing values appropriately.
      • Recode variables as needed.
      • Ensure all necessary variables are present and correctly formatted.
      • Create new variables as needed.
  4. Spatial Analysis:
    • Confirmation (Statistical Tests):
      • Calculate Global Moran’s I and its p-value to determine if spatial analysis is appropriate for your outcome variable.
      • Interpret the results.
    • Spatial Autocorrelation:
      • Global Moran’s I: Assess the overall spatial clustering of your outcome variable.
      • Local Moran’s I: Identify regions/districts that are correlated in terms of your outcome variable.
    • Spatial Distribution:
      • If the p-value of Global Moran’s I is less than 0.05, visualize the spatial distribution of your outcome variable (proportion).
    • Hot Spot and Cold Spot Analysis:
      • Calculate and interpret the Getis-Ord Gi* statistic to identify hot spots and cold spots.
  5. Univariate Analysis:
    • Categorical Variables: Calculate frequencies and percentages.
    • Continuous Variables: Calculate means and standard deviations.
  6. Bivariate Analysis:
    • Association: Use chi-square tests for categorical independent and dependent variables.
    • Effects: Use ANOVA for categorical independent variables and continuous dependent variables.
    • Dependency: Use covariance to assess the relationship between continuous variables.
  7. Multivariable Analysis:
    • Regression Models: Use only variables that were significant in the bivariate analysis.
    • Specify the type of regression model used based on the outcome variable (e.g., linear, logistic, Poisson).
  8. Survey: Hierarchical Data Sets: Multilevel Analysis:
    • Model 0: Include only the dependent variable.
    • Model 1: Include the dependent variable and individual-level factors.
    • Model 2: Include the dependent variable and household-level factors.
    • Model 3: Include the dependent variable and community-level factors.
    • Model 4: Include the dependent variable and all levels.
  9. Model Comparison:
    • Model Adequacy:
      • Calculate the Intra-class Correlation (ICC). If ICC > 0%, continue to multilevel analysis.
    • Model Evaluation:
      • Use AIC, BIC, LRT, or other appropriate model comparison metrics.

Project Assignments:

R Markdown Template:

Sample of Your Document

Introduction

[Provide a brief introduction to your research topic, its importance, and the research questions you will address. Briefly mention the data source and methods.]

Data and Methods

Libraries

# Load necessary R libraries
library(sf)
library(ggplot2)
library(spdep)
library(tmap)
library(haven)
library(survey)
library(RColorBrewer)

Data Loading and Cleaning

# Load shapefile
# Specify the file path to your shapefile
#filepath <- "path/to/your/shapefile.shp"
#shapefile <- st_read(filepath)

# Load survey data
# Specify the file path to your survey data (.dta file)
#surveydata <- read_dta("path/to/your/surveydata.dta")

# Data cleaning and pre-processing steps
# ... (add your data cleaning code here)

Operationalizing Outcome Variable

[Clearly define your outcome variable and how you will measure it. Provide the variable name(s) from the dataset.]

Conceptualizing Covariates

[Identify your explanatory variables, provide a theoretical justification, cite leading papers, and specify variable names.]

Spatial Analysis

Spatial Weights Matrix

# Create spatial weights matrix
# ... (add your code to create spatial weights matrix)

Global Moran’s I

# Calculate Global Moran's I
# ... (add your code to calculate Global Moran's I)

Local Moran’s I

# Calculate Local Moran's I
# ... (add your code to calculate Local Moran's I)

Getis-Ord Gi*

# Calculate Getis-Ord Gi*
# ... (add your code to calculate Getis-Ord Gi*)

Spatial Visualization

# Create maps using tmap or ggplot2
# ... (add your code to create spatial maps)

Statistical Analysis

Univariate Analysis

# Perform univariate analysis
# ... (add your code for univariate analysis)

Bivariate Analysis

# Perform bivariate analysis
# ... (add your code for bivariate analysis)

Multivariable Analysis

# Perform multivariable analysis
# ... (add your code for multivariable analysis)

Multilevel Analysis

# Perform multilevel analysis
# ... (add your code for multilevel analysis)

Model Comparison

# Perform model comparison
# ... (add your code for model comparison)

Conclusion

[Summarize your findings, discuss their implications, and suggest future research directions.]

References

[Include a list of references cited in your report.] ```

How to Use These Guidelines:

  1. Choose Your Project: Select the project assigned to your campus.
  2. Review the Guidelines: Carefully read the general steps and the specific requirements for your project.
  3. Use the R Markdown Template: Copy the R Markdown template and start filling in the sections with your analysis, code, and interpretations.
  4. Follow the Steps: Work through each step of the guidelines, ensuring that you address all the required components.
  5. Write Up Your Results: Write a clear and concise report that explains your methods, results, and conclusions.
  6. Prepare for Presentation: Be ready to present your findings to the class.

These guidelines and the template should provide a solid foundation for your research projects. If you have any questions, please ask!