Micromobility vehicles, such as e-scooters, Segways, skateboards, and unicycles, are increasingly adopted for short-distance travel due to their low weight and low emissions. Despite their growing popularity, we lack controlled, low-risk environments to study rider experiences and performance. While virtual reality (VR) simulators offer a promising approach by reducing safety risks and providing immersive experiences, micromobility simulators remain largely underexplored. We introduce MicroVRide, a modular 4-in-1 VR micromobility simulator that supports e-scooters, Segways, electric unicycles, and one-wheeled skateboards on a single platform. The simulator preserves vehicle-specific physical constraints and control metaphors, enabling the study of diverse riding behaviors with minimal hardware reconfiguration. We contribute the simulator design and report a preliminary within-subject study (N = 12) that demonstrates feasibility and reveals distinct experiential profiles across vehicles.

Household decisions such as adjusting lighting, configuring appliances, and managing waste often rely on abstract or invisible data, making it challenging for non-expert users to make informed choices. While immersive analytics holds promise for embedding data directly into users' environments, most current systems assume a one-size-fits-all model of data presentation. In this paper, we propose a layered immersive analytics framework designed specifically for household decision-making. Our proposed framework supports adaptive visualization across three levels of user engagement: overview (simplified summaries), detail (interactive data exploration and decision simulation), and contextual (narrative-based feedback). These layers are intentionally mapped onto appropriate immersive technologies - augmented reality (AR) for in-situ awareness, virtual reality (VR) for analytic and experiential reasoning, and mixed reality (MR) for consequence visualization and storytelling. We illustrate this design through realistic household scenarios and argue that such a cross-immersive layered system offers a compelling direction for human-factored immersive analytics.