🌍 OpenGeoDemand

OpenGeoDemand is a production-grade Geospatial Demand Simulation & Logistics Optimization engine.

It combines OpenStreetMap (OSM) building footprints with Government Economic Census Data to generate hyper-realistic, spatially accurate demand patterns for any Indian city. It goes beyond simple random generation by applying economic elasticity, temporal logic, and agent-based modeling.

Status: v0.2.0 (Stable) | License: MIT

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📦 Installation

OpenGeoDemand comes with all necessary economic datasets bundled. You do not need to download external files.

pip install opengeodemand

Requirements: Python 3.8+

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🚀 Quick Start (30 Seconds)

Simulate a Food Delivery network in Surat, optimize 5 dark store locations, and generate an analytics dashboard.

from opengeodemand import GeoDemandModel, StoreOptimizer, DemandAnalyzer

# 1. Initialize & Load Data (Auto-fetches OSM & Bundled Econ Data)
city = "Surat, Gujarat, India"
model = GeoDemandModel(city)
model.load_data()

# 2. Enrich Data (Spatial Join + Sampling for speed)
# We sample 2000 buildings for a quick demo. Remove argument for full city.
model.sample_buildings(2000)
model.enrich_data()

# 3. Simulate Demand (7 Days of Food Orders)
# This generates thousands of orders with timestamps, values, and locations
orders = model.simulate(days=7, category="food")
print(f"Generated {len(orders)} orders.")

# 4. Optimize Supply Chain
# Find 5 optimal store locations, keeping them at least 2km apart
optimizer = StoreOptimizer(orders)
stores = optimizer.optimize_locations(n_stores=5, min_spacing_km=2.0)

# 5. Generate Dashboard (HTML Map + Charts)
analyzer = DemandAnalyzer(orders, output_dir="my_results", city_name=city)
analyzer.generate_report(stores_df=stores)

print("Check 'my_results/maps/dashboard.html'!")

Visual Output

Temporal Analysis of Order by Hour and Day

Economy Analysis

Simulation Diagnosis

Projected Revenue Per Optimized Store Location

Order Dashboard

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⚙️ How It Works (The Core Engine)

The library operates in 4 Stages:

1. Geospatial Extraction (load_data)

• Fetches the city boundary and building polygons live from OpenStreetMap.
• Loads the bundled Economic Census & HCES dataset (District-level wealth/consumption data).

2. Data Enrichment (enrich_data)

• Performs a Spatial Join to link every building to its specific administrative district.
• Household Estimation: Calculates potential households based on building footprint area and levels.
• Economic Proxy: Assigns a "Wealth Score" to every building based on its district's consumption expenditure.

3. Simulation Engine (simulate)

Demand is calculated using a weighted ensemble of four stochastic models:

1. Poisson Process: Baseline demand based on household density.
2. Gravity Model: Demand gravitates towards economic centroids.
3. Agent-Based: Individual household probability (Bernoulli trials).
4. Self-Exciting: Orders trigger bursts of local activity (clustering).

The Math: $$ \text{Orders} = \text{BaseRate} \times (\text{Households}) \times (\text{Wealth})^{\text{Sensitivity}} \times (\text{TimeFactor}) $$

4. Optimization (optimize_locations)

Uses Revenue-Weighted K-Means Clustering to find centroids of high demand. It then iteratively merges clusters that violate the min_store_spacing_km constraint to ensure realistic coverage.

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🎛️ Customization & Scenarios

You don't need to know the math. You can use Smart Profiles or override parameters manually.

1. Industry Profiles

The library comes with pre-tuned behaviors:

Category	Behavior Description
"food"	High volume, low value. Peaks at Lunch/Dinner. Moderate weekend boost (1.3x).
"grocery"	Moderate volume. High weekend spike (1.5x). Spatially clustered.
"electronics"	Very low volume, high value. High Wealth Sensitivity (only rich areas buy).
"fashion"	High price sensitivity. Daytime peaks.

2. Custom Parameters (custom_params)

You can pass a dictionary to simulate() to override any logic:

# Example: Simulating a "Luxury Furniture Sale"
orders = model.simulate(
    days=7,
    category="custom",
    custom_params={
        "base_rate": 0.5,            # Very rare orders
        "order_value_mean": 25000,   # High Ticket Size
        "wealth_sensitivity": 2.5,   # Power Law: Rich districts buy 5x-10x more
        "weekend_multiplier": 2.0,   # Sale is on weekend
        "time_curve": "daytime"      # People don't buy sofas at 2 AM
    }
)

Parameter Reference

Parameter	Default	Description
base_rate	20	Daily orders per "average" district unit. Controls total volume.
max_orders_per_bldg	50	Hard cap to prevent outliers in skyscrapers.
wealth_sensitivity	1.0	Elasticity: >1.0 means rich areas demand disproportionately more. 0.0 means wealth doesn't affect volume.
price_sensitivity	1.0	Elasticity: >1.0 means rich areas pay higher prices. 0.0 means fixed price everywhere.
weekend_multiplier	1.0	Multiplier for Saturday/Sunday volume (e.g., 1.5 = +50%).
min_store_spacing_km	2.0	Optimization: Minimum distance allowed between two stores.
time_curve	"food"	"food" (1pm/8pm peaks), "daytime" (9am-6pm), or "night" (8pm-2am).

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📂 Output Files

The library automatically organizes outputs into a structured directory (e.g., my_results/):

1. maps/dashboard.html 🌟

An interactive LeafletJS map containing:

• Heatmap: Visualizes demand density.
• Store Markers: Optimized locations with projected revenue popup.
• Summary Card: Total revenue, order count, and duration.

2. plots/ (Static Analytics)

• temporal_analysis.png: Daily order trend + Hourly distribution bar chart.
• economic_analysis.png: Order value histogram + Cumulative revenue curve.
• store_performance.png: Revenue comparison bar chart for optimized stores.
• simulation_diagnostics.png: Demand concentration analysis (Pareto).

3. orders/ (Raw Data)

• orders.csv: The raw transactional log.
  • Columns: building_idx, timestamp, lat, lon, order_value, method.

4. optimization/

• optimal_stores.csv: Lat/Lon and projected metrics for the deployed stores.

5. data/ (Infrastructure)

• buildings_enriched.geojson: The OSM polygons with calculated household and economic attributes. Useful for QGIS visualization.
• districts.geojson: The intersected economic districts.

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💡 Use Cases

1. Q-Commerce / Dark Stores: Determine where to open the next 5 warehouses to guarantee 10-minute delivery in high-demand zones.
2. Retail Expansion: Compare "Electronics" vs "Fashion" demand to decide which store format fits a specific neighborhood.
3. Logistics Stress Testing: Generate 100,000 orders to test if your routing algorithm can handle the load.
4. Academic Research: Generate realistic synthetic datasets for Smart City projects where real private data is unavailable.

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Data Sources:

• Map Data © OpenStreetMap contributors.
• Economic Data: Aggregated from Public Indian Economic Census & HCES reports.