القائمة
Apply geographic location factors to CWICR estimates. Adjust costs for regional labor rates, material prices, and market conditions.
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| name | cwicr-location-factor |
| description | Apply geographic location factors to CWICR estimates. Adjust costs for regional labor rates, material prices, and market conditions. |
| homepage | https://datadrivenconstruction.io |
| metadata | {"openclaw":{"emoji":"🗄️","os":["darwin","linux","win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3"]}}} |
Construction costs vary by location:
Apply location-based cost factors to CWICR estimates, adjusting for regional differences in labor, materials, and overall market conditions.
import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
from enum import Enum
class CostComponent(Enum):
"""Cost components for factors."""
LABOR = "labor"
MATERIAL = "material"
EQUIPMENT = "equipment"
TOTAL = "total"
@dataclass
class LocationFactor:
"""Location adjustment factor."""
location_code: str
location_name: str
country: str
region: str
labor_factor: float
material_factor: float
equipment_factor: float
total_factor: float
currency: str
notes: str = ""
@dataclass
class AdjustedEstimate:
"""Estimate with location adjustment."""
base_cost: float
base_location: str
target_location: str
labor_adjustment: float
material_adjustment: float
equipment_adjustment: float
total_adjustment: float
adjusted_cost: float
adjustment_percent: float
# Location factors (relative to US national average = 1.00)
LOCATION_FACTORS = {
# USA
'US-NYC': LocationFactor('US-NYC', 'New York City', 'USA', 'Northeast', 1.35, 1.15, 1.10, 1.22, 'USD'),
'US-LA': LocationFactor('US-LA', 'Los Angeles', 'USA', 'West', 1.25, 1.10, 1.05, 1.15, 'USD'),
'US-CHI': LocationFactor('US-CHI', 'Chicago', 'USA', 'Midwest', 1.20, 1.05, 1.05, 1.12, 'USD'),
'US-HOU': LocationFactor('US-HOU', 'Houston', 'USA', 'South', 0.95, 0.98, 0.95, 0.96, 'USD'),
'US-PHX': LocationFactor('US-PHX', 'Phoenix', 'USA', 'Southwest', 0.90, 0.95, 0.95, 0.93, 'USD'),
'US-DEN': LocationFactor('US-DEN', 'Denver', 'USA', 'Mountain', 1.00, 1.02, 1.00, 1.01, 'USD'),
'US-SEA': LocationFactor('US-SEA', 'Seattle', 'USA', 'Northwest', 1.18, 1.08, 1.05, 1.12, 'USD'),
'US-MIA': LocationFactor('US-MIA', 'Miami', 'USA', 'Southeast', 0.98, 1.05, 1.00, 1.01, 'USD'),
'US-ATL': LocationFactor('US-ATL', 'Atlanta', 'USA', 'Southeast', 0.92, 0.98, 0.95, 0.95, 'USD'),
'US-NAT': LocationFactor('US-NAT', 'US National Average', 'USA', 'National', 1.00, 1.00, 1.00, 1.00, 'USD'),
# Europe
'UK-LON': LocationFactor('UK-LON', 'London', 'UK', 'Southeast', 1.45, 1.20, 1.15, 1.30, 'GBP'),
'DE-BER': LocationFactor('DE-BER', 'Berlin', 'Germany', 'East', 1.15, 1.10, 1.10, 1.12, 'EUR'),
'DE-MUN': LocationFactor('DE-MUN', 'Munich', 'Germany', 'South', 1.25, 1.15, 1.12, 1.18, 'EUR'),
'FR-PAR': LocationFactor('FR-PAR', 'Paris', 'France', 'Ile-de-France', 1.30, 1.18, 1.15, 1.22, 'EUR'),
'NL-AMS': LocationFactor('NL-AMS', 'Amsterdam', 'Netherlands', 'North Holland', 1.20, 1.12, 1.10, 1.15, 'EUR'),
# Middle East
'AE-DXB': LocationFactor('AE-DXB', 'Dubai', 'UAE', 'Dubai', 0.85, 1.25, 1.10, 1.05, 'AED'),
'SA-RIY': LocationFactor('SA-RIY', 'Riyadh', 'Saudi Arabia', 'Central', 0.80, 1.20, 1.05, 1.00, 'SAR'),
'QA-DOH': LocationFactor('QA-DOH', 'Doha', 'Qatar', 'Qatar', 0.88, 1.30, 1.12, 1.08, 'QAR'),
# Asia
'SG-SIN': LocationFactor('SG-SIN', 'Singapore', 'Singapore', 'Central', 1.10, 1.15, 1.08, 1.12, 'SGD'),
'HK-HKG': LocationFactor('HK-HKG', 'Hong Kong', 'Hong Kong', 'Hong Kong', 1.20, 1.25, 1.15, 1.20, 'HKD'),
'JP-TKY': LocationFactor('JP-TKY', 'Tokyo', 'Japan', 'Kanto', 1.35, 1.20, 1.18, 1.25, 'JPY'),
# Australia
'AU-SYD': LocationFactor('AU-SYD', 'Sydney', 'Australia', 'NSW', 1.25, 1.15, 1.12, 1.18, 'AUD'),
'AU-MEL': LocationFactor('AU-MEL', 'Melbourne', 'Australia', 'Victoria', 1.20, 1.12, 1.10, 1.15, 'AUD'),
}
class CWICRLocationFactor:
"""Apply location factors to CWICR estimates."""
def __init__(self,
cwicr_data: pd.DataFrame = None,
base_location: str = 'US-NAT'):
self.cwicr = cwicr_data
self.base_location = base_location
self._factors = LOCATION_FACTORS.copy()
if cwicr_data is not None:
self._index_cwicr()
def _index_cwicr(self):
"""Index CWICR data."""
if 'work_item_code' in self.cwicr.columns:
self._cwicr_index = self.cwicr.set_index('work_item_code')
else:
self._cwicr_index = None
def get_factor(self, location_code: str) -> Optional[LocationFactor]:
"""Get location factor."""
return self._factors.get(location_code)
def list_locations(self, country: str = None) -> List[Dict[str, Any]]:
"""List available locations."""
factors = self._factors.values()
if country:
factors = [f for f in factors if f.country.lower() == country.lower()]
return [
{
'code': f.location_code,
'name': f.location_name,
'country': f.country,
'region': f.region,
'total_factor': f.total_factor,
'currency': f.currency
}
for f in factors
]
def add_location(self, factor: LocationFactor):
"""Add custom location factor."""
self._factors[factor.location_code] = factor
def adjust_cost(self,
base_cost: float,
target_location: str,
cost_breakdown: Dict[str, float] = None) -> AdjustedEstimate:
"""Adjust cost from base to target location."""
base_factor = self._factors.get(self.base_location)
target_factor = self._factors.get(target_location)
if not base_factor or not target_factor:
return AdjustedEstimate(
base_cost=base_cost,
base_location=self.base_location,
target_location=target_location,
labor_adjustment=0,
material_adjustment=0,
equipment_adjustment=0,
total_adjustment=0,
adjusted_cost=base_cost,
adjustment_percent=0
)
if cost_breakdown is None:
# Default breakdown
cost_breakdown = {
'labor': base_cost * 0.40,
'material': base_cost * 0.45,
'equipment': base_cost * 0.15
}
# Calculate relative factors
labor_rel = target_factor.labor_factor / base_factor.labor_factor
material_rel = target_factor.material_factor / base_factor.material_factor
equipment_rel = target_factor.equipment_factor / base_factor.equipment_factor
# Apply adjustments
labor_adjusted = cost_breakdown.get('labor', 0) * labor_rel
material_adjusted = cost_breakdown.get('material', 0) * material_rel
equipment_adjusted = cost_breakdown.get('equipment', 0) * equipment_rel
adjusted_total = labor_adjusted + material_adjusted + equipment_adjusted
total_adjustment = adjusted_total - base_cost
adjustment_pct = (total_adjustment / base_cost * 100) if base_cost > 0 else 0
return AdjustedEstimate(
base_cost=round(base_cost, 2),
base_location=self.base_location,
target_location=target_location,
labor_adjustment=round(labor_adjusted - cost_breakdown.get('labor', 0), 2),
material_adjustment=round(material_adjusted - cost_breakdown.get('material', 0), 2),
equipment_adjustment=round(equipment_adjusted - cost_breakdown.get('equipment', 0), 2),
total_adjustment=round(total_adjustment, 2),
adjusted_cost=round(adjusted_total, 2),
adjustment_percent=round(adjustment_pct, 1)
)
def adjust_estimate(self,
items: List[Dict[str, Any]],
target_location: str) -> Dict[str, Any]:
"""Adjust entire estimate for location."""
adjusted_items = []
total_base = 0
total_adjusted = 0
for item in items:
code = item.get('work_item_code', item.get('code'))
qty = item.get('quantity', 0)
# Get costs from CWICR
labor = 0
material = 0
equipment = 0
if self._cwicr_index is not None and code in self._cwicr_index.index:
wi = self._cwicr_index.loc[code]
labor = float(wi.get('labor_cost', 0) or 0) * qty
material = float(wi.get('material_cost', 0) or 0) * qty
equipment = float(wi.get('equipment_cost', 0) or 0) * qty
base_cost = labor + material + equipment
breakdown = {'labor': labor, 'material': material, 'equipment': equipment}
adjustment = self.adjust_cost(base_cost, target_location, breakdown)
adjusted_items.append({
'code': code,
'quantity': qty,
'base_cost': adjustment.base_cost,
'adjusted_cost': adjustment.adjusted_cost,
'adjustment': adjustment.total_adjustment
})
total_base += base_cost
total_adjusted += adjustment.adjusted_cost
return {
'items': adjusted_items,
'base_location': self.base_location,
'target_location': target_location,
'total_base': round(total_base, 2),
'total_adjusted': round(total_adjusted, 2),
'total_adjustment': round(total_adjusted - total_base, 2),
'adjustment_percent': round((total_adjusted - total_base) / total_base * 100, 1) if total_base > 0 else 0
}
def compare_locations(self,
base_cost: float,
locations: List[str]) -> pd.DataFrame:
"""Compare cost across multiple locations."""
data = []
for loc_code in locations:
adjustment = self.adjust_cost(base_cost, loc_code)
factor = self._factors.get(loc_code)
data.append({
'Location': factor.location_name if factor else loc_code,
'Code': loc_code,
'Country': factor.country if factor else '',
'Adjusted Cost': adjustment.adjusted_cost,
'Adjustment %': adjustment.adjustment_percent,
'Labor Factor': factor.labor_factor if factor else 1.0,
'Material Factor': factor.material_factor if factor else 1.0
})
return pd.DataFrame(data).sort_values('Adjusted Cost')
def normalize_to_base(self,
cost: float,
source_location: str) -> float:
"""Normalize cost from source location to base location."""
source_factor = self._factors.get(source_location)
base_factor = self._factors.get(self.base_location)
if not source_factor or not base_factor:
return cost
relative_factor = base_factor.total_factor / source_factor.total_factor
return round(cost * relative_factor, 2)
def export_factors(self, output_path: str) -> str:
"""Export location factors to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
df = pd.DataFrame([
{
'Code': f.location_code,
'Name': f.location_name,
'Country': f.country,
'Region': f.region,
'Labor Factor': f.labor_factor,
'Material Factor': f.material_factor,
'Equipment Factor': f.equipment_factor,
'Total Factor': f.total_factor,
'Currency': f.currency
}
for f in self._factors.values()
])
df.to_excel(writer, sheet_name='Location Factors', index=False)
return output_path
# Initialize with base location
loc_factor = CWICRLocationFactor(base_location='US-NAT')
# Adjust single cost
adjustment = loc_factor.adjust_cost(
base_cost=1000000,
target_location='US-NYC'
)
print(f"Base: ${adjustment.base_cost:,.2f}")
print(f"NYC: ${adjustment.adjusted_cost:,.2f}")
print(f"Adjustment: {adjustment.adjustment_percent:+.1f}%")
comparison = loc_factor.compare_locations(
base_cost=5000000,
locations=['US-NYC', 'US-HOU', 'US-LA', 'UK-LON', 'AE-DXB']
)
print(comparison)
cwicr = pd.read_parquet("ddc_cwicr_en.parquet")
loc_factor = CWICRLocationFactor(cwicr, base_location='US-NAT')
items = [
{'work_item_code': 'CONC-001', 'quantity': 200},
{'work_item_code': 'STRL-002', 'quantity': 50}
]
dubai_estimate = loc_factor.adjust_estimate(items, 'AE-DXB')
print(f"Dubai Cost: ${dubai_estimate['total_adjusted']:,.2f}")
loc_factor.add_location(LocationFactor(
'US-REMOTE',
'Remote Alaska',
'USA',
'Alaska',
labor_factor=1.50,
material_factor=1.40,
equipment_factor=1.35,
total_factor=1.42,
currency='USD',
notes='Remote location premium'
))