This archive reproduces a figure titled "Figure 3.2 Boone County population distribution" from Wang and vom Hofe (2007, p.60). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses the Census API to retrieve data, reproduce the figure, and ensure reproducibility for anyone accessing this archive.The Python code was developed in Google Colaboratory, or Google Colab for short, which is an Integrated Development Environment (IDE) of JupyterLab and streamlines package installation, code collaboration, and management. The Census API is used to obtain population counts from the 2000 Decennial Census (Summary File 1, 100% data). Shapefiles are downloaded from the TIGER/Line FTP Server. All downloaded data are maintained in the notebook's temporary working directory while in use. The data and shapefiles are stored separately with this archive. The final map is also stored as an HTML file.The notebook features extensive explanations, comments, code snippets, and code output. The notebook can be viewed in a PDF format or downloaded and opened in Google Colab. References to external resources are also provided for the various functional components. The notebook features code that performs the following functions:install/import necessary Python packagesdownload the Census Tract shapefile from the TIGER/Line FTP Serverdownload Census data via CensusAPI manipulate Census tabular data merge Census data with TIGER/Line shapefileapply a coordinate reference systemcalculate land area and population densitymap and export the map to HTMLexport the map to ESRI shapefileexport the table to CSVThe notebook can be modified to perform the same operations for any county in the United States by changing the State and County FIPS code parameters for the TIGER/Line shapefile and Census API downloads. The notebook can be adapted for use in other environments (i.e., Jupyter Notebook) as well as reading and writing files to a local or shared drive, or cloud drive (i.e., Google Drive).

Description:
This dataset accompanies the empirical analysis in Legality Without Justice, a study examining the relationship between public trust in institutions and perceived governance legitimacy using data from the World Values Survey Wave 7 (2017–2022). It includes:

• WVS_Cross-National_Wave_7_csv_v6_0.csv — World Values Survey Wave 7 core data.

• GDP.csv — World Bank GDP per capita (current US$) for 2022 by country.

• denial.ipynb — Fully documented Jupyter notebook with code for data merging, exploratory statistics, and ordinal logistic regression using OrderedModel. Includes GDP as a control for institutional trust and perceived governance.

All data processing and analysis were conducted in Python using FAIR reproducibility principles and can be replicated or extended on Google Colab.

DOI: 10.5281/zenodo.16361108
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Authors: Anon Annotator
Publication date: 2025-07-23
Language: English
Version: 1.0.0
Publisher: Zenodo
Programming language: Python

🔽 How to Download and Run on Google Colab

Step 1: Open Google Colab

Go to https://colab.research.google.com

Step 2: Upload Files

Click File > Upload notebook, and upload the denial.ipynb file.
Also upload the CSVs (WVS_Cross-National_Wave_7_csv_v6_0.csv and GDP.csv) using the file browser on the left sidebar.

Step 3: Adjust File Paths (if needed)

In denial.ipynb, ensure file paths match:

Step 4: Run the Code

Execute the notebook cells from top to bottom. You may need to install required libraries:

The notebook performs:

• Data cleaning

• Merging WVS and GDP datasets

• Summary statistics

• Ordered logistic regression to test if confidence in courts/police (Q57, Q58) predicts belief that the country is governed in the interest of the people (Q183), controlling for GDP.