While large language models (LLMs) are dominating the field of natural language processing, it remains an open question how well these models can perform spatial reasoning. Contrary to recent studies suggesting that LLMs struggle with spatial reasoning tasks, we demonstrate in this paper that a novel prompting technique, termed Patient Visualization of Thought (Patient-VoT), can boost LLMs’ spatial reasoning abilities. The core idea behind Patient-VoT is to explicitly integrate *bullet lists, coordinates, and visualizations* into the reasoning process. By applying Patient-VoT, we achieve a significant boost in spatial reasoning performance compared to prior prompting techniques. We also show that integrating bullet lists into reasoning is effective in planning tasks, highlighting its general effectiveness across different applications.