Spatial Analysis
Beyond Market Intelligence keeps Spatial Analysis in one place: 2 stories so far. The section currently leads with “From geospatial data to graph networks, City2Graph simplifies urban analysis.” and “Mapping City Airspace with Data to Plan Smarter Vertiport Networks”. Geospatial data is messy, and City2Graph's new paper makes a clean argument for why heterogeneous graphs beat flat tables. Lagos is crowded, complicated, and constantly moving, which makes it the perfect stress test for vertiport placement. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every Spatial Analysis story on Beyond Market Intelligence, newest first.
From geospatial data to graph networks, City2Graph simplifies urban analysis.
Geospatial data is messy, and City2Graph's new paper makes a clean argument for why heterogeneous graphs beat flat tables. The library turns buildings and street segments into analysis-ready nodes and edges, then pushes them straight into PyTorch Geometric. That is a practical bridge between urban morphology and graph neural networks. It is not about hype; it is about making the workflow simpler. For anyone tired of wrestling with geometry and attributes across conversions, this feels like a step toward a more accessible, future-focused toolkit.
Mapping City Airspace with Data to Plan Smarter Vertiport Networks
Lagos is crowded, complicated, and constantly moving, which makes it the perfect stress test for vertiport placement. This case study uses geospatial machine learning to balance population density, transport access, and airspace constraints, turning a daunting urban puzzle into a reproducible workflow. It is practical, not theoretical, and that is why it works. If you are curious about how machine learning handles spatial decision-making, this walkthrough offers a clear, actionable starting point.