AI for Location Intelligence: Geospatial Analytics, POI Recommendation, and Site Selection

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## TL;DR
AI location intelligence uses geospatial representations to make coordinates, movement traces, imagery, POIs, and geo-text usable for prediction, search, recommendation, and planning. The strongest current work treats location as structured spatial data, not just another column in a tabular model.

## Core Explanation
Location intelligence AI stack: (1) Data -- remote sensing imagery, street-level observations, GPS trajectories, POI records, coordinates, demographic layers, and geo-text; (2) Representation -- spatial features, learned embeddings, graph structure, and multi-scale encodings that preserve distance, neighborhood, and scale effects; (3) Models -- supervised predictors, graph neural networks, sequence models, and geospatial foundation or language-model workflows; (4) Applications -- trade-area analysis, site selection support, mobility modeling, urban analytics, logistics planning, and POI recommendation.

## Detailed Analysis
The main engineering issue is that space has structure: nearby observations are correlated, patterns change by scale, and train/test splits can leak geography if they ignore spatial blocks. Location embeddings such as Space2Vec address part of that problem by encoding absolute position and spatial relationships at multiple scales, so downstream models can learn place patterns without treating latitude and longitude as ordinary independent numeric fields. Practical systems still need privacy controls, careful provenance for mobility or POI data, and evaluation that separates true geographic generalization from memorizing nearby examples.

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