Population by Longitude

Diverging bar charts showing world population distribution by longitude, with configurable alignment latitude and regional groupings.

Example Output

The default output shows population stacked by geographic region, with the equator as the horizontal axis. Northern hemisphere extends upward, southern hemisphere downward.

See tables/ for CSV data and graphs/ for visualizations.

Requirements

Python 3.8+ with:

pip install numpy matplotlib colorcet

For the global map visualization (global_qN.py):

pip install cartopy shapely

Quick Start

Preprocessed data is included in input/. To preview a histogram:

python3 longitude_histogram.py

To save output files, provide a name:

python3 longitude_histogram.py regions

Output: tables/regions.csv and graphs/regions.png

Options

python3 longitude_histogram.py output_name      # file stem for output
python3 longitude_histogram.py --bin-width 5    # 5-degree longitude bins
python3 longitude_histogram.py --align p50      # split at population median
python3 longitude_histogram.py --align 45N      # split at 45 degrees north
python3 longitude_histogram.py --no-regions     # hemisphere mode (north/south only)
python3 longitude_histogram.py --markers        # add markers from settings/place_markers.csv

See docs/longitude_histogram.md for full documentation.

Place Markers

Add labeled geographic reference points with --markers:

# Use baseline markers from settings/place_markers.csv
python3 longitude_histogram.py equator_1deg --markers

# Override specific markers for different alignments
python3 longitude_histogram.py 90s_1deg --align 90s --markers settings/custom_markers_90s.csv
python3 longitude_histogram.py p80_1deg --align p80 --markers settings/custom_markers_p80.csv

Override files merge with the baseline: same-name entries replace baseline entries, new names are added, and empty coordinates omit a marker. Format:

# Name,Latitude,Longitude,Symbol,Direction (0-360, clockwise from top)
Helsinki,60.1699,24.9384,◇,0
Réunion,,                      

Custom Regions

Override country-to-region assignments with --regions:

python3 longitude_histogram.py output --regions settings/custom_regions.csv

Override files merge with settings/countries_regions.csv. Example:

# Separate Belarus and Ukraine from "Rest of Europe"
Belarus,Belarus
Ukraine,Ukraine

Examples

./longitude_histogram.py baseline
# Default: alignment at equator. Save as graphs/baseline.png, tables/baseline.csv

./longitude_histogram.py p50_2deg --bin-width 2 --align p50 --markers --svg
# Split at the median latitude of world population. Bin-width 2 degrees of longitude

./longitude_histogram.py 90s_2deg --bin-width 2 --align 90s --markers settings/custom_markers_90s.csv --svg
# Trivial split at 90 degrees South = ordinary bottom-aligned histogram. Custom marker label placements to prevent label overlap

./longitude_histogram.py p80_1deg --align p80 --regions settings/custom_regions.csv --markers settings/custom_markers_p80.csv
# Split at 80th percentile latitude of world population. Custom regions: Belarus, Ukraine.

./longitude_histogram.py q50_1deg --align q50 --markers settings/custom_markers_1deg.csv
# Split at each longitude-specific population median

./global_qN.py tables/q50_1deg.csv graphs/map_q50_1deg.png
# World map with longitude-specific population medians

./longitude_histogram.py hemispheres_60n --no-regions --bin-width 5 --align 60n
# North/South instead of regions, split at 60 degrees North

Data Source

Population data from NASA SEDAC Gridded Population of the World (GPW) v4.11:

• Resolution: 30 arc-seconds (~1 km at equator)
• Year: 2020
• Aggregated to 15 arc-minutes (0.25 degrees) for visualization

Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Count Adjusted to Match 2015 Revision of UN WPP Country Totals, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4PN93PB

Preprocessing (Optional)

The input/ directory contains preprocessed data ready to use. To regenerate from raw GPW tiles:

1. Download GPW v4.11 data from https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-adjusted-to-2015-unwpp-country-totals-rev11

   • Population Count (30 arc-second), ASCII format
   • National Identifier Grid (30 arc-second), ASCII format

2. Extract to data/ directory

3. Run preprocessing (~ 2 minutes):

   python3 preprocess_gpw.py
   

See docs/preprocessing.md for details.

Project Structure

longitude_histogram.py  # Main visualization script
global_qN.py            # Map visualization for per-bin percentile splits
preprocess_gpw.py       # Data preprocessing (optional)

input/                  # Preprocessed data (included)
settings/               # Region mappings, colors, markers
docs/                   # Documentation
tables/                 # Output CSV files
graphs/                 # Output image files

License

Code: MIT License

Output visualizations: CC BY-SA 4.0

Data: Subject to SEDAC terms of use (see Data Source above)