You are about to take on a practical challenge that brings together your understanding of architectural data and visualization tools. Imagine you have a floor plan divided into zones, and for each zone, you have an occupancy value representing how many people typically use that area. Your task is to create a function that takes a hardcoded 2D list of these occupancy values and then generates a heatmap to visually represent the occupancy distribution across the zones. This heatmap will help you and other architects quickly identify which areas of the floor plan are more heavily used, supporting decisions about space planning and resource allocation.

To accomplish this, you will use the seaborn and matplotlib libraries, both of which are powerful tools for data visualization in Python. The heatmap you generate should clearly label both the x-axis and y-axis to indicate the grid position of each zone. Additionally, you will include a color bar alongside the heatmap, which acts as a legend to show what occupancy values the colors on the map represent. This makes your visualization not just visually appealing but also informative and actionable.