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Intro to Geospatial Machine Learning, Part 1
3.1 Machine learning as a planning
The descriptive analytics in the first two chapters provide context to non-technical decision-makers. The predictive analytics in this and subsequent chapters help convert those insights into actionable intelligence.
Prediction is not new to Planners. Throughout history, Planners have made ill-fated forecasts into an uncertain future. The 1925 Plan for Cincinnati is one such example. Cincinnati, delighted with the prosperity it had achieved up to 1925, set out to plan for the next hundred years by understanding future demand for land, housing, employment, and more.
Population forecasting, the author wrote, is the ‘obvious…basis ...
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