How current and future urban patterns respond to urban planning? An integrated cellular automata modeling approach

摘要: Abstract While many publications predict future urban scenarios, few have deliberated the impact of issued planning on scenario prediction. We propose a planning-constrained model (named PCGA-CA) that integrates cellular automata (CA) and genetic algorithm (GA) to simulate current patterns under spatial constraints planning. The regulations include three types: fully allowed area (FAA), partially (PAA), strictly prohibited (SPA), where we implementation parameter (PIP) represent stringency in PAA. Under different PIPs, apply PCGA-CA 2015 2030 2045 scenarios for Ningbo city, China. results show substantially affect simulation accuracy pattern. As become less stringent, decreases from 90.3% 89.4% pattern becomes compact. In particular, is most compact when are not imposed. predicts quantity location illegal development, identifies spatially varying growth across regulations. For same year, with PIPs illustrate substantial differences landscape metrics. simulations should help planners local authorities assess past implementations planning, while predictions can offer view by evaluating consequences