AI-Driven Spatial Distribution Research Hub

Comprehensive research platform exploring the causal effects of AI implementation on spatial economic patterns in Japan's aging society through interactive dashboards and rigorous empirical analysis.

5 Causal Methods Tokyo 23 Wards 2000-2024 Data 2050 Projections

4.2-5.2pp

AI Causal Impact on Spatial Agglomeration

8.4pp vs 1.2pp

High-AI vs Low-AI Industries

60-80%

Potential Offset of Aging Effects

5 Methods

Rigorous Causal Identification

Interactive Research Dashboards

Explore comprehensive research findings through interactive visualizations and analytical tools

Research Overview

Executive summary with key findings, interactive KPIs, and comprehensive research highlights

Summary Charts
Interactive KPIs
Key Insights
Launch Overview

Demographic Analysis

Population aging trends, workforce dynamics, and demographic transitions with interactive maps

Timeline Sliders
Ward-level Maps
Population Trends
Launch Demographics

Spatial Patterns

Agglomeration analysis, concentration patterns, and spatial distribution dynamics

Interactive Maps
Industry Filters
Concentration Index
Launch Spatial

Causal Inference

Treatment effects, robustness tests, and comprehensive causal identification methods

Method Comparison
Event Studies
Robustness Tests
Launch Causal

Future Projections

Long-term scenarios (2024-2050), policy simulations, and predictive modeling

Scenario Builder
Policy Simulator
ML Predictions
Launch Predictions

Results Explorer

Complete analysis results, data export capabilities, and comprehensive figure gallery

Data Export
Figure Gallery
Results Tables
Launch Results

Research Methodology

Rigorous causal identification through multiple empirical approaches

Causal Identification Methods

Difference-in-Differences
0.045 treatment effect (p=0.005)
Event Study Analysis
Dynamic effects with parallel trends validation
Synthetic Control
Counterfactual construction for causal inference
Instrumental Variables
Addressing endogeneity concerns
Propensity Score Matching
Controlling for selection bias

Theoretical Framework

First comprehensive integration of AI-specific mechanisms into New Economic Geography theory:

  • Algorithmic Learning Spillovers
    Knowledge transmission through AI systems
  • Digital Infrastructure Returns
    Increasing returns to digital investment
  • Virtual Agglomeration Effects
    Remote collaboration reducing distance constraints
  • AI-Human Complementarity
    Productivity gains from human-AI collaboration
  • Network Externalities
    Multiplicative benefits from AI adoption networks

Key Research Findings

Evidence-based insights on AI's spatial distribution effects

AI Causal Impact

4.2-5.2 percentage points increase in agglomeration patterns

Industry Heterogeneity

8.4pp (high-AI) vs 1.2pp (low-AI) industries - targeted policy needed

Long-term Potential

60-80% offset of aging effects - strategic AI adoption crucial

Policy Effectiveness

15-30% improvement possible - evidence-based interventions work