Creating Floor Plan Heatmaps
Heatmaps are powerful tools in architecture for visualizing spatial data directly on a floor plan. They allow you to quickly identify patterns such as occupancy density, thermal comfort, or lighting levels across different zones of a building. By using color gradients to represent numerical values, heatmaps make it easy to see which areas are more heavily used, warmer, or otherwise distinct, providing actionable insights for design improvements or energy optimization.
12345678910111213141516import seaborn as sns import matplotlib.pyplot as plt # Hardcoded occupancy levels for 5 zones (rows: rooms, columns: zones) occupancy_data = [ [3, 5, 2, 4, 1], [2, 8, 6, 5, 2], [1, 3, 7, 8, 4], [0, 2, 5, 7, 6], [1, 1, 2, 4, 8] ] plt.figure(figsize=(6, 5)) sns.heatmap(occupancy_data) plt.title("Occupancy Levels Across Floor Plan Zones") plt.show()
In this heatmap, each cell represents a specific zone within the floor plan, with the color intensity indicating the level of occupancy. Lighter colors typically show lower occupancy, while darker or more saturated colors reveal areas with higher usage. By examining the spatial arrangement of the colors, you can quickly identify which rooms or zones are most frequently occupied and which remain underutilized. The heatmap's color scale, mapped directly from the occupancy values in the code, provides a visual summary that is much easier to interpret than a table of numbers.
1234567891011121314151617181920import numpy as np zone_labels = ["Zone A", "Zone B", "Zone C", "Zone D", "Zone E"] room_labels = ["Room 1", "Room 2", "Room 3", "Room 4", "Room 5"] plt.figure(figsize=(6, 5)) ax = sns.heatmap( occupancy_data, annot=True, fmt="d", cmap="YlOrRd", xticklabels=zone_labels, yticklabels=room_labels, cbar_kws={'label': 'Occupancy Level'} ) plt.title("Labeled Occupancy Heatmap") plt.xlabel("Zones") plt.ylabel("Rooms") plt.tight_layout() plt.show()
1. What architectural insights can be gained from a floor plan heatmap?
2. Which seaborn function is used to create a heatmap?
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Heatmaps are powerful tools in architecture for visualizing spatial data directly on a floor plan. They allow you to quickly identify patterns such as occupancy density, thermal comfort, or lighting levels across different zones of a building. By using color gradients to represent numerical values, heatmaps make it easy to see which areas are more heavily used, warmer, or otherwise distinct, providing actionable insights for design improvements or energy optimization.
12345678910111213141516import seaborn as sns import matplotlib.pyplot as plt # Hardcoded occupancy levels for 5 zones (rows: rooms, columns: zones) occupancy_data = [ [3, 5, 2, 4, 1], [2, 8, 6, 5, 2], [1, 3, 7, 8, 4], [0, 2, 5, 7, 6], [1, 1, 2, 4, 8] ] plt.figure(figsize=(6, 5)) sns.heatmap(occupancy_data) plt.title("Occupancy Levels Across Floor Plan Zones") plt.show()
In this heatmap, each cell represents a specific zone within the floor plan, with the color intensity indicating the level of occupancy. Lighter colors typically show lower occupancy, while darker or more saturated colors reveal areas with higher usage. By examining the spatial arrangement of the colors, you can quickly identify which rooms or zones are most frequently occupied and which remain underutilized. The heatmap's color scale, mapped directly from the occupancy values in the code, provides a visual summary that is much easier to interpret than a table of numbers.
1234567891011121314151617181920import numpy as np zone_labels = ["Zone A", "Zone B", "Zone C", "Zone D", "Zone E"] room_labels = ["Room 1", "Room 2", "Room 3", "Room 4", "Room 5"] plt.figure(figsize=(6, 5)) ax = sns.heatmap( occupancy_data, annot=True, fmt="d", cmap="YlOrRd", xticklabels=zone_labels, yticklabels=room_labels, cbar_kws={'label': 'Occupancy Level'} ) plt.title("Labeled Occupancy Heatmap") plt.xlabel("Zones") plt.ylabel("Rooms") plt.tight_layout() plt.show()
1. What architectural insights can be gained from a floor plan heatmap?
2. Which seaborn function is used to create a heatmap?
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