This paper investigates how vision-language models associate objects with their properties and spatial relations in multimodal tasks like image captioning and visual question answering. The research reveals that VLMs employ two concurrent mechanisms for spatial reasoning: the language model backbone represents content-independent spatial relations on visual tokens, while the vision encoder encodes object layouts that are directly utilized by the language model. The dominant spatial information originates from the vision encoder and is distributed globally across visual tokens, extending beyond object regions. These findings provide insights into the internal workings of multimodal models for spatial understanding.