🏗️ Applications¶
🌟 Real-World Building Height Applications
Discover powerful ways to leverage building height data for impactful urban analysis and decision-making
🏙️ Urban Planning & Development¶
📊 Building Density Analysis¶
#### 🎯 **Floor Area Ratio (FAR) Calculation**
**Floor Area Ratio = Total Floor Area / Plot Area**
#### 📈 **Development Potential Assessment**
# 🏢 Calculate FAR using building height data
def calculate_far(buildings_gdf, plot_polygons_gdf):
"""
📊 Calculate Floor Area Ratio for urban planning
"""
results = []
for idx, plot in plot_polygons_gdf.iterrows():
# 🔍 Find buildings within this plot
buildings_in_plot = buildings_gdf[
buildings_gdf.geometry.intersects(plot.geometry)
]
if len(buildings_in_plot) > 0:
# 📐 Calculate total floor area (assuming 3m per floor)
floor_height = 3.0 # meters per floor
total_floor_area = sum(
(building['height'] / floor_height) * building.geometry.area
for _, building in buildings_in_plot.iterrows()
)
# 📊 Calculate FAR
plot_area = plot.geometry.area
far = total_floor_area / plot_area
results.append({
'plot_id': idx,
'far': far,
'building_count': len(buildings_in_plot),
'plot_area': plot_area,
'total_floor_area': total_floor_area
})
return pd.DataFrame(results)
# 🎨 Visualization categories
def categorize_far(far_value):
"""🏗️ Categorize FAR for planning purposes"""
if far_value < 0.5:
return "🌳 Low Density Residential"
elif far_value < 1.0:
return "🏘️ Medium Density Residential"
elif far_value < 2.0:
return "🏢 Commercial/Mixed Use"
elif far_value < 4.0:
return "🏬 High Density Commercial"
else:
return "🏗️ Ultra High Density"
🌳 Shadow Impact Studies¶
#### ☀️ **Solar Access Analysis**
#### 🏢 **Building Height Impact Assessment**
# ☀️ Shadow analysis for urban planning
import numpy as np
from datetime import datetime, timedelta
def calculate_building_shadows(buildings_gdf, date, latitude, longitude):
"""
🌤️ Calculate shadow patterns for building height planning
"""
shadows = []
# ☀️ Solar position calculation
solar_elevation, solar_azimuth = calculate_solar_position(
date, latitude, longitude
)
for idx, building in buildings_gdf.iterrows():
building_height = building['height']
building_geom = building.geometry
# 📐 Calculate shadow length
if solar_elevation > 0: # Sun is above horizon
shadow_length = building_height / np.tan(np.radians(solar_elevation))
# 🧭 Shadow direction (opposite to sun azimuth)
shadow_azimuth = (solar_azimuth + 180) % 360
# 📍 Create shadow polygon
shadow_polygon = create_shadow_polygon(
building_geom, shadow_length, shadow_azimuth
)
shadows.append({
'building_id': idx,
'shadow_area': shadow_polygon.area,
'shadow_geometry': shadow_polygon,
'building_height': building_height,
'shadow_length': shadow_length
})
return gpd.GeoDataFrame(shadows)
# 🎯 Critical times for shadow analysis
critical_times = [
("🌅 Morning (9 AM)", datetime(2023, 12, 21, 9, 0)), # Winter solstice
("🌞 Noon (12 PM)", datetime(2023, 12, 21, 12, 0)),
("🌇 Afternoon (3 PM)", datetime(2023, 12, 21, 15, 0)),
("🌞 Summer Solstice", datetime(2023, 6, 21, 12, 0)) # Summer solstice
]
**🎯 Key Shadow Impact Considerations:**
- **🏠 Residential Areas:** Minimum 4 hours of direct sunlight
- **🌳 Public Parks:** Full sun access during peak hours (10 AM - 3 PM)
- **🏫 Schools:** Playground areas should receive morning sunlight
- **☀️ Solar Installations:** No shadowing on renewable energy systems
- **🚗 Streets:** Ice formation prevention in winter
🚨 Emergency Response & Safety¶
🚑 Evacuation Route Planning¶
#### 🏃♂️ **Population Estimation for Emergency Planning**
#### 🌊 **Flood Impact Modeling**
# 👥 Population estimation using building heights
def estimate_population_from_buildings(buildings_gdf, population_density_per_floor=4):
"""
👨👩👧👦 Estimate population for emergency planning using building data
"""
results = []
for idx, building in buildings_gdf.iterrows():
# 🏢 Estimate number of floors (assuming 3m per floor)
floor_height = 3.0
estimated_floors = max(1, int(building['height'] / floor_height))
# 📐 Floor area estimation
building_footprint_area = building.geometry.area
# 👥 Population estimation
if building_footprint_area > 200: # Large building
# 🏢 Commercial/residential complex
population_per_floor = building_footprint_area / 25 # 25m² per person
elif building_footprint_area > 100: # Medium building
# 🏘️ Multi-family residential
population_per_floor = building_footprint_area / 20 # 20m² per person
else: # Small building
# 🏠 Single family
population_per_floor = min(6, building_footprint_area / 15)
estimated_population = estimated_floors * population_per_floor
results.append({
'building_id': idx,
'estimated_population': int(estimated_population),
'floors': estimated_floors,
'area': building_footprint_area,
'height': building['height'],
'pop_density': estimated_population / building_footprint_area
})
return pd.DataFrame(results)
# 🚨 Emergency evacuation capacity planning
def plan_evacuation_capacity(buildings_with_population, evacuation_sites):
"""
🏃♂️ Plan evacuation routes based on building populations
"""
for site in evacuation_sites:
# 🎯 Find buildings within evacuation radius
nearby_buildings = buildings_with_population[
buildings_with_population.geometry.distance(site.geometry) < 1000 # 1km radius
]
total_population = nearby_buildings['estimated_population'].sum()
site_capacity = site['capacity']
if total_population > site_capacity:
print(f"⚠️ Evacuation site {site['name']} overloaded:")
print(f" Population: {total_population:,}")
print(f" Capacity: {site_capacity:,}")
print(f" Deficit: {total_population - site_capacity:,}")
# 🌊 Flood risk assessment using building heights
def assess_flood_vulnerability(buildings_gdf, flood_depth_raster, dem_raster):
"""
🌊 Assess building vulnerability to flooding
"""
vulnerable_buildings = []
for idx, building in buildings_gdf.iterrows():
# 📍 Get building location
building_centroid = building.geometry.centroid
# 🌊 Extract flood depth at building location
flood_depth = extract_raster_value(flood_depth_raster, building_centroid)
ground_elevation = extract_raster_value(dem_raster, building_centroid)
# 🏗️ Building characteristics
building_height = building['height']
building_area = building.geometry.area
# 🎯 Vulnerability assessment
if flood_depth > 0:
flood_level = ground_elevation + flood_depth
building_top = ground_elevation + building_height
if flood_level > ground_elevation + 0.5: # Water > 0.5m above ground
vulnerability_level = "🔴 High Risk"
evacuation_priority = 1
elif flood_level > ground_elevation: # Any flooding
vulnerability_level = "🟡 Medium Risk"
evacuation_priority = 2
else:
vulnerability_level = "🟢 Low Risk"
evacuation_priority = 3
# 🏢 Multi-story buildings can serve as shelters
if building_height > 6 and building_area > 100:
shelter_potential = True
shelter_capacity = int((building_height - flood_depth) / 3 * building_area / 4)
else:
shelter_potential = False
shelter_capacity = 0
vulnerable_buildings.append({
'building_id': idx,
'flood_depth': flood_depth,
'vulnerability': vulnerability_level,
'evacuation_priority': evacuation_priority,
'shelter_potential': shelter_potential,
'shelter_capacity': shelter_capacity,
'estimated_population': building.get('estimated_population', 0)
})
return pd.DataFrame(vulnerable_buildings)
📡 Line-of-Sight Analysis¶
#### 📶 **Communication Network Planning**
# 📡 Line-of-sight analysis for emergency communications
def calculate_line_of_sight(observer_point, target_points, buildings_gdf, dem_raster):
"""
👁️ Calculate line-of-sight for emergency communication planning
"""
los_results = []
for target_id, target_point in target_points.iterrows():
# 🎯 Create line between observer and target
line = LineString([observer_point, target_point.geometry])
# 🏗️ Find buildings intersecting the line
intersecting_buildings = buildings_gdf[
buildings_gdf.geometry.intersects(line)
]
# 📏 Check if buildings block the view
is_blocked = False
blocking_height = 0
for _, building in intersecting_buildings.iterrows():
building_height = building['height']
# 📐 Calculate height at intersection point
intersection = building.geometry.intersection(line)
if not intersection.is_empty:
# 🔍 Get ground elevation at intersection
ground_elev = extract_raster_value(dem_raster, intersection.centroid)
total_height = ground_elev + building_height
if total_height > blocking_height:
blocking_height = total_height
is_blocked = True
los_results.append({
'target_id': target_id,
'line_of_sight': not is_blocked,
'blocking_height': blocking_height,
'distance': observer_point.distance(target_point.geometry),
'buildings_in_path': len(intersecting_buildings)
})
return pd.DataFrame(los_results)
# 🚨 Emergency communication tower placement
def optimize_tower_placement(buildings_gdf, coverage_requirements):
"""
📡 Optimize emergency communication tower placement
"""
# 🎯 Find optimal tower locations
# Consider: High elevation points, minimal building obstruction, maximum coverage
pass
🔬 Urban Research & Analysis¶
🌡️ Urban Heat Island Assessment¶
#### 🏙️ **Building Density vs Temperature Analysis**
# 🌡️ Urban heat island analysis using building heights
def analyze_urban_heat_correlation(buildings_gdf, temperature_raster):
"""
🔥 Analyze correlation between building density/height and temperature
"""
# 📊 Create analysis grid (e.g., 500m x 500m cells)
grid = create_analysis_grid(buildings_gdf.total_bounds, cell_size=500)
heat_analysis = []
for idx, cell in grid.iterrows():
# 🏢 Buildings within this cell
cell_buildings = buildings_gdf[
buildings_gdf.geometry.intersects(cell.geometry)
]
if len(cell_buildings) > 0:
# 📊 Calculate building metrics
total_building_area = cell_buildings.geometry.area.sum()
avg_building_height = cell_buildings['height'].mean()
max_building_height = cell_buildings['height'].max()
building_count = len(cell_buildings)
# 📐 Calculate building coverage ratio
cell_area = cell.geometry.area
building_coverage_ratio = total_building_area / cell_area
# 🏗️ Calculate floor area ratio
total_floor_area = sum(
building['height'] / 3.0 * building.geometry.area
for _, building in cell_buildings.iterrows()
)
floor_area_ratio = total_floor_area / cell_area
# 🌡️ Extract temperature data
avg_temperature = extract_raster_statistics(
temperature_raster, cell.geometry, 'mean'
)
heat_analysis.append({
'cell_id': idx,
'avg_temperature': avg_temperature,
'building_coverage': building_coverage_ratio,
'avg_height': avg_building_height,
'max_height': max_building_height,
'building_count': building_count,
'floor_area_ratio': floor_area_ratio,
'geometry': cell.geometry
})
return gpd.GeoDataFrame(heat_analysis)
# 📈 Statistical analysis and visualization
def create_heat_correlation_analysis(heat_data):
"""📊 Create correlation analysis and visualizations"""
import matplotlib.pyplot as plt
import seaborn as sns
# 🎨 Correlation matrix
correlation_vars = ['avg_temperature', 'building_coverage', 'avg_height',
'max_height', 'floor_area_ratio']
corr_matrix = heat_data[correlation_vars].corr()
plt.figure(figsize=(10, 8))
sns.heatmap(corr_matrix, annot=True, cmap='RdYlBu_r', center=0)
plt.title('🌡️ Urban Heat Island - Building Correlation Matrix')
plt.tight_layout()
plt.show()
🌱 Climate Change Impact Studies¶
#### 📈 **Building Stock Resilience Assessment**
🏘️ Informal Settlement Mapping¶
#### 🎯 **Building Pattern Recognition**
# 🏘️ Identify informal settlements using building characteristics
def identify_informal_settlements(buildings_gdf):
"""
🔍 Identify informal settlements based on building height patterns
"""
settlement_indicators = []
# 📊 Create spatial clusters for analysis
building_clusters = create_spatial_clusters(buildings_gdf, cluster_radius=100)
for cluster_id, cluster_buildings in building_clusters.groupby('cluster_id'):
if len(cluster_buildings) >= 10: # Minimum cluster size
# 📏 Calculate cluster characteristics
avg_height = cluster_buildings['height'].mean()
height_std = cluster_buildings['height'].std()
avg_area = cluster_buildings.geometry.area.mean()
area_std = cluster_buildings.geometry.area.std()
# 🏗️ Density calculations
cluster_area = cluster_buildings.geometry.unary_union.convex_hull.area
building_density = len(cluster_buildings) / (cluster_area / 10000) # buildings per hectare
# 🎯 Informal settlement indicators
informal_indicators = {
'low_height': avg_height < 4.0, # Predominantly single-story
'height_uniformity': height_std < 1.5, # Similar heights
'small_buildings': avg_area < 80, # Small footprints
'high_density': building_density > 50, # Dense packing
'irregular_pattern': calculate_pattern_irregularity(cluster_buildings)
}
# 📊 Score informal settlement likelihood
informal_score = sum(informal_indicators.values()) / len(informal_indicators)
settlement_indicators.append({
'cluster_id': cluster_id,
'building_count': len(cluster_buildings),
'avg_height': avg_height,
'avg_area': avg_area,
'density': building_density,
'informal_score': informal_score,
'likely_informal': informal_score > 0.6,
'geometry': cluster_buildings.geometry.unary_union.convex_hull
})
return gpd.GeoDataFrame(settlement_indicators)
# 🎨 Visualization of informal settlement probability
informal_settlements = identify_informal_settlements(buildings_gdf)
informal_settlements['category'] = informal_settlements['informal_score'].apply(
lambda x: '🔴 High Probability' if x > 0.7
else '🟡 Medium Probability' if x > 0.4
else '🟢 Low Probability'
)
💰 Economic & Real Estate Applications¶
🏠 Property Valuation Support¶
#### 💵 **Building Volume-Based Valuation**
# 💰 Property valuation using building characteristics
def estimate_property_values(buildings_gdf, price_per_sqm_by_height):
"""
🏠 Estimate property values using building height and area data
"""
valuations = []
for idx, building in buildings_gdf.iterrows():
building_height = building['height']
building_area = building.geometry.area
# 🏗️ Estimate number of floors
floors = max(1, int(building_height / 3.0))
# 📊 Total floor area estimation
total_floor_area = floors * building_area
# 💵 Price determination by building type
if building_height <= 5: # Single story
price_category = 'residential_low'
base_price = price_per_sqm_by_height.get(price_category, 500)
elif building_height <= 15: # Low-rise
price_category = 'residential_mid'
base_price = price_per_sqm_by_height.get(price_category, 800)
elif building_height <= 30: # Mid-rise
price_category = 'commercial_low'
base_price = price_per_sqm_by_height.get(price_category, 1200)
else: # High-rise
price_category = 'commercial_high'
base_price = price_per_sqm_by_height.get(price_category, 2000)
# 🎯 Calculate estimated value
estimated_value = total_floor_area * base_price
valuations.append({
'building_id': idx,
'floors': floors,
'total_floor_area': total_floor_area,
'price_category': price_category,
'price_per_sqm': base_price,
'estimated_value': estimated_value,
'building_height': building_height,
'footprint_area': building_area
})
return pd.DataFrame(valuations)
# 📊 Market analysis by area
def analyze_market_by_district(buildings_with_values, district_boundaries):
"""💹 Analyze real estate market by district"""
market_analysis = []
for idx, district in district_boundaries.iterrows():
district_buildings = buildings_with_values[
buildings_with_values.geometry.intersects(district.geometry)
]
if len(district_buildings) > 0:
total_value = district_buildings['estimated_value'].sum()
avg_price_per_sqm = district_buildings['price_per_sqm'].mean()
total_floor_area = district_buildings['total_floor_area'].sum()
market_analysis.append({
'district_name': district.get('name', f'District_{idx}'),
'total_market_value': total_value,
'avg_price_per_sqm': avg_price_per_sqm,
'total_floor_area': total_floor_area,
'building_count': len(district_buildings),
'value_density': total_value / district.geometry.area
})
return pd.DataFrame(market_analysis)
📊 Investment Opportunity Analysis¶
🏗️ Development Potential
- 📏 Underbuilt Areas: Low FAR vs zoning allowance
- 🔄 Redevelopment Sites: Old/low buildings in prime locations
- 📈 Growth Corridors: Areas with increasing building heights
- 🎯 Mixed-Use Opportunities: Transition zones between uses
💰 Investment Metrics
- 📊 Price per Cubic Meter: Value efficiency analysis
- 🏢 Rent Potential: Floor area vs market rates
- 📈 Value Growth: Height increase correlation
- ⚡ ROI Estimation: Development cost vs returns
🌐 Smart City Integration¶
📡 IoT Network Planning¶
#### 📶 **Sensor Placement Optimization**
# 📡 Smart city sensor network planning using building heights
def optimize_sensor_placement(buildings_gdf, coverage_requirements):
"""
📶 Optimize IoT sensor placement using building height data
"""
optimal_locations = []
# 🎯 Potential sensor locations (tall buildings, good coverage)
candidate_buildings = buildings_gdf[
(buildings_gdf['height'] > 10) & # Minimum height for good coverage
(buildings_gdf.geometry.area > 50) # Sufficient building size
].copy()
for sensor_type, requirements in coverage_requirements.items():
coverage_radius = requirements['range']
min_height = requirements.get('min_height', 5)
# 🔍 Filter suitable buildings
suitable_buildings = candidate_buildings[
candidate_buildings['height'] >= min_height
]
# 📊 Coverage optimization
selected_locations = []
uncovered_area = buildings_gdf.geometry.unary_union
while not uncovered_area.is_empty and len(suitable_buildings) > 0:
# 🎯 Find building providing maximum new coverage
best_coverage = 0
best_building_idx = None
for idx, building in suitable_buildings.iterrows():
coverage_circle = building.geometry.centroid.buffer(coverage_radius)
new_coverage = coverage_circle.intersection(uncovered_area).area
if new_coverage > best_coverage:
best_coverage = new_coverage
best_building_idx = idx
if best_building_idx is not None:
selected_building = suitable_buildings.loc[best_building_idx]
coverage_area = selected_building.geometry.centroid.buffer(coverage_radius)
selected_locations.append({
'building_id': best_building_idx,
'sensor_type': sensor_type,
'coverage_area': coverage_area.area,
'building_height': selected_building['height'],
'location': selected_building.geometry.centroid
})
# 📊 Update uncovered area
uncovered_area = uncovered_area.difference(coverage_area)
suitable_buildings = suitable_buildings.drop(best_building_idx)
optimal_locations.extend(selected_locations)
return gpd.GeoDataFrame(optimal_locations)
# 🌐 Smart city applications by sensor type
sensor_requirements = {
'📡 Air Quality': {'range': 500, 'min_height': 15},
'🔊 Noise Monitoring': {'range': 200, 'min_height': 8},
'📶 WiFi/5G': {'range': 300, 'min_height': 20},
'📹 Traffic Cameras': {'range': 150, 'min_height': 6},
'🌡️ Weather Stations': {'range': 1000, 'min_height': 25}
}
🚇 Infrastructure Planning¶
#### 🚌 **Public Transit Demand Estimation**
🌟 Applications Expert Achieved!
You now understand the vast potential of building height data across multiple domains!