multilayer-sdk

Pure-Python Synchronized Multi-Panel Map Visualization, Spatial Comparison Engine & Interactive Dashboard Builder.

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🌐 Live Interactive Studio & Documentation

Explore the interactive multi-panel map simulator and comprehensive reference manual:

👉 https://geophilo.com/multilayer-sdk/

Try out real-time neon laser crosshair tracking, split-screen curtain swipe comparisons, and live choropleth classifiers directly in your browser.

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🌟 Overview

multilayer-sdk (the headless Python core behind 02Multimap) allows urban planners, spatial data scientists, and researchers to visualize, cross-analyze, and compare multiple spatial datasets side-by-side with millisecond synchronization.

Instead of toggling layers on and off in a single map, multilayer coordinates up to 8 synchronized map viewports in dynamic grids (1x2, 2x1, 1x3, 2x2, 2x3, 2x4) with:

• Bi-directional Pan & Zoom Broadcasting: Drag or zoom on any panel to coordinate all others in real time.
• Neon Laser Crosshair Cursor Tracking: Move your mouse over any panel to project neon-colored crosshairs tracking the exact geographic coordinate across all viewports.
• Graduated Thematic Choropleths: Quantiles, Equal Interval, Natural Breaks, and Standard Deviation statistical binning with scientific palettes (viridis, magma, plasma, turbo, cividis, spectral, rdylbu).
• Interactive Split-Screen Curtain Swipe: Draggable split slider for before/after temporal change detection.
• Single-File Self-Contained HTML Dashboards: Export zero-dependency interactive HTML files ready for presentations, stakeholders, or offline field audits.
• Jupyter Notebook & Google Colab Integration: Rich inline widget display via mm.show().

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🚀 Installation

pip install multilayer-sdk

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⚡ Quickstart

1. Build a 4-Panel Synchronized Workspace

import multilayer as ml

# 1. Initialize a 4-panel (2x2) synchronized map grid
mm = ml.MultiMap(grid="2x2", title="Urban Vulnerability & Land Use Assessment", basemap="carto-dark")

# 2. Panel 1: High-resolution satellite imagery with study area boundary
mm.panel(0).title = "1. Satellite Context"
mm.panel(0).set_basemap("satellite")
mm.panel(0).add_layer("study_area.geojson", stroke_color="#38bdf8", fill_opacity=0.2)

# 3. Panel 2: Thematic choropleth of population density
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"
mm.panel(1).add_layer(pop_choro)

# 4. Panel 3: Flood hazard exposure score
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. Panel 4: Future 2030 Master Zoning Plan
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. Save as standalone interactive HTML dashboard
mm.to_html("urban_assessment_dashboard.html")

# 7. Render inline in Jupyter Notebook / Google Colab
mm.show()

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2. Draggable Split-Screen Curtain Swipe Comparison

import multilayer as ml

swipe = ml.SwipeMap(
    left_layer="landcover_2010.geojson",
    right_layer="landcover_2026.geojson",
    left_title="Historical (2010)",
    right_title="Current (2026)",
    basemap="satellite"
)

swipe.to_html("deforestation_swipe.html")

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💻 Command Line Interface (CLI)

# 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

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⚙️ Supported Grid Matrices

Grid Preset	Layout Dimensions	Panel Count	Primary Cartographic Use Case
1x2	1 Row $\times$ 2 Columns	2 Panels	Before/After comparisons, Suitability vs Actual zoning
2x1	2 Rows $\times$ 1 Column	2 Panels	Vertical elevation profiles, transport corridors
1x3	1 Row $\times$ 3 Columns	3 Panels	Past $\rightarrow$ Present $\rightarrow$ Future temporal timelines
2x2	2 Rows $\times$ 2 Columns	4 Panels	4-way evaluation (Base, Demographics, Hazards, Policy)
2x3	2 Rows $\times$ 3 Columns	6 Panels	Multi-criteria evaluation (MCDA factor grids)
2x4	2 Rows $\times$ 4 Columns	8 Panels	High-density multi-scenario sensitivity snapshots

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📄 Academic Citation

If you use multilayer-sdk in scientific publications, planning projects, or research, please cite:

@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://gitlab.com/geospacephilo/multilayer-sdk}
}

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📜 License

Distributed under the MIT License. Copyright (c) 2026 Yusuf Eminoğlu.