multilayer-sdk

Figure 1: High-level architecture of multilayer-sdk: Coordinated multi-panel layouts, bi-directional navigation broadcasting, synchronized neon laser crosshair tracking, and split-screen curtain slider comparisons.

1. Installation & Python API Quickstart

multilayer-sdk is a pure-Python library designed from the ground up for urban planners, geospatial scientists, environmental analysts, and data scientists who need to compare complex spatial scenarios, temporal changes, hazard overlays, and multi-criteria evaluations side-by-side.

Installation

pip install multilayer-sdk

Building a 4-Panel MultiMap in Python

import multilayer as ml

# 1. Create a 4-panel synchronized workspace
mm = ml.MultiMap(grid="2x2", title="Urban Hazard & Demographics Assessment", basemap="carto-dark")

# 2. Add spatial layers and custom basemaps to individual panels
mm.panel(0).title = "1. High-Resolution Satellite"
mm.panel(0).set_basemap("satellite")
mm.panel(0).add_layer("study_area.geojson", fill_color="#38bdf8", fill_opacity=0.3)

# 3. Apply graduated choropleth classification
vlayer = ml.VectorLayer.from_geojson("demographics.geojson")
pop_choro = ml.Choropleth.classify(vlayer, property_name="density_km2", method="quantiles", color_ramp="viridis")
mm.panel(1).title = "2. Population Density (Quantiles)"
mm.panel(1).add_layer(pop_choro)

# 4. Add hazard risk layer
risk_choro = ml.Choropleth.classify(vlayer, property_name="flood_risk_score", method="equal_interval", color_ramp="magma")
mm.panel(2).title = "3. Flood Hazard Exposure"
mm.panel(2).add_layer(risk_choro)

# 5. Add zoning scenario
mm.panel(3).title = "4. Future Master Plan 2030"
mm.panel(3).add_layer("zoning_plan.geojson", fill_color="#10b981", fill_opacity=0.6)

# 6. Export offline, standalone interactive HTML dashboard
mm.to_html("izmir_urban_assessment.html")

# 7. Inline display inside Jupyter Notebook / Google Colab
mm.show()

2. Grid Layout Matrices

MultiMap provides 6 standardized matrix configurations tailored for different analytical scenarios:

3. Built-In Web Map Tile Basemaps

Each panel can independently host any standard global raster tile provider:

4. Split-Screen Curtain Swipe Maps

For direct pixel-perfect comparison of before/after orthophotos, historical change detection, or model predictions vs ground truth, SwipeMap provides an interactive draggable split-screen slider:

import multilayer as ml

# Create split-screen swipe comparison
swipe = ml.SwipeMap(
    left_layer="forest_cover_2010.geojson",
    right_layer="forest_cover_2026.geojson",
    left_title="Historical Forest (2010)",
    right_title="Current Forest (2026)",
    basemap="satellite"
)

swipe.to_html("deforestation_swipe.html")

5. Command Line Interface (CLI) Master Reference

# 1. Build a 2x2 synchronized dashboard from 4 GeoJSON files
multilayer build --layers bldgs.geojson,roads.geojson,hazard.geojson,zoning.geojson --grid 2x2 --out city_dashboard.html --open

# 2. Build a 2-panel before/after split-screen swipe comparison
multilayer compare flood_2020.geojson flood_2026.geojson --left-title "2020 Flood" --right-title "2026 Flood" --out flood_swipe.html

# 3. Inspect GeoJSON feature count, properties, and bounding box
multilayer inspect study_area.geojson

# 4. List all built-in web map tile basemaps
multilayer tiles

6. Performance Benchmarks

7. Academic Citation & References

Distributed under the open-source MIT License.

@software{eminoglu2026multilayer,
  author    = {Emino{\u{g}}lu, Yusuf},
  title     = {{multilayer-sdk: Pure-Python Synchronized Multi-Panel Map Visualization, Spatial Comparison Engine, and Interactive Dashboard Builder}},
  year      = {2026},
  publisher = {PyPI - Python Package Index},
  version   = {0.1.0},
  url       = {https://github.com/YusufEminoglu/multilayer-sdk}
}