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Many real complex systems, such as infrastructure and the Internet, are not random but embedded in a metric space. The problem of spatial network disintegration, or critical area identification, is a fundamental research domain in network science and has received increasing attention. Typical applications include network immunization, epidemic control, and early warning signals of disintegration. Due to the computationally challenging (NP-hard) problem, they usually cannot be solved with polynomial algorithms. Here, we propose an efficient disintegration method in spatial networks through a link-based strategy. First, we introduce a regional failure model with multiple disintegration circles for the spatial network. We then calculate the sum of the specific attribute values of the links in the circle to identify the critical regions of the spatial network, which also correspond to the geographic regions where disintegration occurs. Extensive experiments on real-world networks of different types demonstrate that the strategy outperforms conventional methods in terms of solution quality.