Lecture Notes Pdf — Urban Planning

def _show_concepts(self): print("\n🔑 KEY CONCEPTS:") for i, concept in enumerate(self.analyzer.key_concepts[:15], 1): print(f"\ni. concept['term'].upper() (appears concept['frequency']x)") if concept['context']: print(f" Context: concept['context'][0][:150]...")

def extract_text_from_pdf(self) -> str: """Extract text from PDF file""" text = "" with open(self.pdf_path, 'rb') as file: pdf_reader = PyPDF2.PdfReader(file) for page_num, page in enumerate(pdf_reader.pages): page_text = page.extract_text() self.pages_text.append( 'page_num': page_num + 1, 'text': page_text ) text += page_text + "\n" self.full_text = text return text

def _show_summary(self): summary = self.analyzer.create_summary() print("\n📊 LECTURE SUMMARY:") print(f" Pages: summary['total_pages']") print(f" Total Words: summary['total_words']:,") print(f" Case Studies: summary['case_studies_count']") print(f"\n Main Topics: ', '.join(summary['key_topics'][:10])") print(f"\n Key Sections: ', '.join(summary['main_sections'][:5])")

def extract_key_concepts(self) -> List[Dict]: """Extract and rank key urban planning concepts""" stop_words = set(stopwords.words('english')) # 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' ] # Tokenize and find frequencies words = word_tokenize(self.full_text.lower()) words = [w for w in words if w.isalpha() and w not in stop_words] # Count frequencies of planning terms concept_counts = Counter() for term in planning_terms: count = self.full_text.lower().count(term) if count > 0: concept_counts[term] = count # Extract context for each concept concepts = [] for concept, count in concept_counts.most_common(20): # Find sentences containing the concept sentences = sent_tokenize(self.full_text) context_sentences = [s for s in sentences if concept.lower() in s.lower()] context = context_sentences[:2] if context_sentences else [] concepts.append( 'term': concept, 'frequency': count, 'context': context ) self.key_concepts = concepts return concepts urban planning lecture notes pdf

import PyPDF2 import re from typing import List, Dict, Tuple import json from collections import Counter import nltk from nltk.corpus import stopwords from nltk.tokenize import sent_tokenize, word_tokenize from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity import pandas as pd import spacy Download required NLTK data nltk.download('punkt') nltk.download('stopwords') nltk.download('averaged_perceptron_tagger') Load spaCy model (run: python -m spacy download en_core_web_sm) nlp = spacy.load('en_core_web_sm')

def _show_questions(self): questions = self.analyzer.generate_study_questions() print("\n❓ STUDY QUESTIONS:") for i, q in enumerate(questions, 1): print(f"\ni. q['question']") print(f" 💡 Hint: q['hint']")

def identify_sections(self) -> Dict[str, str]: """Identify and extract major sections from lecture notes""" lines = self.full_text.split('\n') current_section = "Introduction" sections = current_section: [] # Common urban planning section headers section_patterns = [ r'(?i)^(?:chapter|section|part)\s+\d+[:.\s]+(.+)$', r'(?i)^(\d+\.\d+)\s+(.+)$', r'(?i)^([A-Z][A-Z\s]5,)$', # ALL CAPS headers r'(?i)^(introduction|background|methodology|analysis|conclusion|references)$', r'(?i)^(zoning|transportation|land use|environmental|housing|infrastructure|sustainability)', r'(?i)^(smart growth|new urbanism|urban design|public participation|economic development)' ] for line in lines: line = line.strip() if not line: continue section_found = False for pattern in section_patterns: if re.match(pattern, line): current_section = line[:50] # Limit section name length sections[current_section] = [] section_found = True break if not section_found and current_section: sections[current_section].append(line) # Convert lists to strings self.sections = k: ' '.join(v) for k, v in sections.items() if v return self.sections r'(?i)core (?:concept|principle)[s]?: (.+?)[\.\n]'

class UrbanPlanningNotesAnalyzer: def (self, pdf_path: str): self.pdf_path = pdf_path self.full_text = "" self.pages_text = [] self.sections = {} self.key_concepts = [] self.case_studies = []

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)[^.]*?\.)' ] principles = [] for pattern in principle_patterns: matches = re.findall(pattern, self.full_text) principles.extend(matches[:5]) return principles[:10]

def export_to_json(self, output_path: str): """Export all analysis results to JSON file""" output = 'metadata': 'source_file': self.pdf_path, 'total_pages': len(self.pages_text), 'total_words': len(self.full_text.split()) , 'summary': self.create_summary(), 'sections': self.sections, 'key_concepts': self.key_concepts, 'case_studies': self.case_studies, 'study_questions': self.generate_study_questions(), 'full_text_excerpt': self.full_text[:5000] # First 5000 chars with open(output_path, 'w', encoding='utf-8') as f: json.dump(output, f, indent=2, ensure_ascii=False) print(f"Analysis exported to output_path") class UrbanPlanningStudyAssistant: def init (self, analyzer: UrbanPlanningNotesAnalyzer): self.analyzer = analyzer encoding='utf-8') as f: json.dump(output

def generate_study_questions(self) -> List[Dict]: """Generate study questions based on key concepts and sections""" questions = [] # Generate questions from key concepts for concept in self.key_concepts[:10]: questions.append( 'type': 'concept', 'question': f"What are the key principles and applications of concept['term'] in urban planning?", 'related_concept': concept['term'], 'hint': f"Review section discussing concept['term'] (mentioned concept['frequency'] times)" ) # Generate questions from sections for section_name, section_text in list(self.sections.items())[:5]: if len(section_text) > 100: questions.append( 'type': 'section', 'question': f"Summarize the main arguments presented in 'section_name' regarding urban planning approaches.", 'related_section': section_name, 'hint': "Focus on the key definitions and examples provided" ) # Add comparative questions if len(self.case_studies) >= 2: questions.append( 'type': 'comparative', 'question': f"Compare and contrast the urban planning approaches in 'self.case_studies[0]['title']' vs 'self.case_studies[1]['title']'.", 'hint': "Consider differences in context, implementation, and outcomes" ) return questions

def _show_case_studies(self): print("\n📋 CASE STUDIES:") for i, case in enumerate(self.analyzer.case_studies[:5], 1): print(f"\ni. case['title']") print(f" case['description'][:200]...")

def _take_quiz(self): questions = self.analyzer.generate_study_questions()[:5] score = 0 print("\n📝 QUICK QUIZ (5 questions)") print("Answer in your own words, then press Enter for sample answer\n") for i, q in enumerate(questions, 1): print(f"\ni. q['question']") input("Press Enter to see sample answer...") print(f"\n Sample approach: q['hint']") print(" Review the relevant section for complete answer.\n") def main(): # Replace with your PDF path pdf_path = "urban_planning_lecture_notes.pdf"

def extract_case_studies(self) -> List[Dict]: """Identify and extract case studies from lecture notes""" case_patterns = [ r'(?i)case study[:]\s*(.+?)(?:\n\n|\n\s*\n|$)', r'(?i)example[:]\s*(.+?)(?:\n\n|\n\s*\n|$)', r'(?i)([A-Z][a-z]+(?:[-\s][A-Z][a-z]+)*)\s+(?:is\s+an\s+example|demonstrates|illustrates)', ] case_studies = [] sentences = sent_tokenize(self.full_text) 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]) case_studies.append( 'title': match if isinstance(match, str) else match[0], 'description': sentence, 'context': context ) self.case_studies = case_studies return case_studies

def create_summary(self) -> Dict: """Create a structured summary of the lecture notes""" summary = 'total_pages': len(self.pages_text), 'total_words': len(self.full_text.split()), 'key_topics': [c['term'] for c in self.key_concepts[:15]], 'case_studies_count': len(self.case_studies), 'main_sections': list(self.sections.keys())[:10], 'core_principles': self._extract_principles(), 'recommended_focus_areas': self._identify_focus_areas() return summary