The Global Religious Infrastructure Database (OC) [OC] Statistics

What This Visualization Shows

This data visualization displays "The Global Religious Infrastructure Database (OC) [OC]" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on Data sources: Overture Maps (1.56M holy sites), IRS nonprofit filings, ChurchUnion directory, denominational scrapers (SBC, UMC, Catholic dioceses, LDS), country-specific Wikidata imports, and 30+ other sources.

Tools: Python, SQLite (2.7 GB database, 52 columns), BigQuery, Census geocoding API, Shapely/STRtree for spatial validation.

Method: Each point is a geocoded place of worship with lat/lon coordinates. Faith classification comes from source metadata, denominational cross-referencing, and name-based ML classification for unlabeled entries. The image was rendered by rasterizing all 2.15M geocoded points into a 4800×2400 pixel grid using numpy/PIL — each pixel's color is the weighted average of all points falling in that cell. No matplotlib scatter (which would take hours) — this rendered in 18 seconds.

Validation: All 2.15M coordinates were checked against Natural Earth world border polygons using Shapely STRtree spatial indexing. Results:

97.73% on land (2,105,575 points)

2.27% in ocean (48,878), but 44,890 of those are within 5km of simplified Natural Earth coastlines

Validation completed in 171 seconds for all 2.15M points

Database stats:

2.23M total entries (2.15M geocoded, 96.7%)

311 distinct denominations classified

244 countries represented

543K websites, 97K phone numbers, 29K emails

100% provenance coverage — every record has an auditable source trail

Why? Religious infrastructure is one of the most geographically ubiquitous but least systematically mapped categories of human activity. This database supports research in sociology, urban planning, disaster response, and religious demography.

Happy to answer questions about the pipeline, specific faiths, or any region.

|Color|Faith|Count| |:-|:-|:-| |🟡 Gold|Christian|1,713,881| |🟢 Green|Islam|134,002| |🔴 Orange-Red|Hindu|140,669| |🩷 Pink|Buddhist|119,763| |🔵 Royal Blue|Jewish|33,546| |🩷 Deep Pink|Shinto|5,766| |🤎 Brown|Sikh|4,697| |🟣 Purple|Baháʼí|1,803| |⚫ Gray|Other|326|, which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

This data visualization represents a sophisticated analysis of complex information patterns that provide valuable insights into underlying trends and relationships. Data visualization serves as a bridge between raw numerical data and human understanding, transforming abstract statistics into comprehensible visual narratives.

The power of data visualization lies in its ability to reveal patterns, outliers, and correlations that might not be apparent in traditional tabular formats. Through careful selection of chart types, color schemes, and interactive elements, effective visualizations can communicate complex information quickly and accurately to diverse audiences.

Modern data visualization combines statistical analysis with design principles to create compelling visual stories. This interdisciplinary approach requires understanding both the underlying data and the cognitive processes involved in visual perception. The result is more effective communication of quantitative insights that can inform decision-making and drive positive change.

Data Analysis and Insights

The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.

Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.

The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.

Significance and Applications

This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.

For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.

The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.