Urban Planning Lecture Notes Pdf Portable 🎁 Trending

# Urban planning specific terminology planning_terms = [ 'zoning', 'land use', 'transportation', 'infrastructure', 'sustainability', 'urban design', 'smart growth', 'new urbanism', 'gentrification', 'affordable housing', 'public space', 'transit-oriented development', 'mixed-use', 'walkability', 'green infrastructure', 'climate resilience', 'urban renewal', 'community engagement', 'comprehensive plan', 'subdivision', 'environmental impact', 'historic preservation', 'urban sprawl', 'density', 'parking', 'complete streets', 'placemaking' ]

Rapid urbanization caused extreme overcrowding and severe public health epidemics. urban planning lecture notes pdf

except FileNotFoundError: print(f"Error: PDF file 'pdf_path' not found.") print("Please update the pdf_path variable with your file location.") except Exception as e: print(f"An error occurred: e") print("Make sure you have installed required packages: pip install PyPDF2 nltk scikit-learn pandas spacy") # Urban planning specific terminology planning_terms = [

This tool transforms static lecture notes into an interactive study system specifically designed for urban planning content! r'(?i)core (?:concept|principle)[s]?: (.+?)[\.\n]'

def _extract_principles(self) -> List[str]: """Extract core urban planning principles""" principle_patterns = [ r'(?i)principle[s]? of (.+?)[\.\n]', r'(?i)core (?:concept|principle)[s]?: (.+?)[\.\n]', r'(?i)([^.]*?(?:should|must|requires|essential|crucial|important)[^.]*?\.)' ]

for i, sentence in enumerate(sentences): for pattern in case_patterns: matches = re.findall(pattern, sentence) for match in matches: # Get surrounding context start_idx = max(0, i - 2) end_idx = min(len(sentences), i + 3) context = ' '.join(sentences[start_idx:end_idx])