refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-cpted
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title: CPTED (Crime Prevention Through Environmental Design)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [CPTED, environmental design, defensible space, broken windows, urban safety, Oscar Newman]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.88
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verification_status: applied
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tags: [urban-planning, security, cpted, defensible-space, broken-windows, smart-city, environmental-design]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: urban planning / security
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applicable_to: [Urban Design, Smart City, Security Architecture]
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---
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# CPTED (Crime Prevention Through Environmental Design)
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## 📌 한 줄 통찰
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> **"매 공간 으로 범죄 의 prevention"**. 매 CCTV 의 reactive X — 매 building / lighting / fence 의 design 의 의지 의 deter. 매 5 strategy. 매 modern: 매 smart city + AI 의 simulation 의 augment.
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## 📖 핵심
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### 매 5 strategy (2nd-gen CPTED)
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1. **Natural Surveillance**: 매 visibility (low fence, transparent wall, lighting).
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2. **Natural Access Control**: 매 single entry, 매 clear pathway.
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3. **Territorial Reinforcement**: 매 public-private boundary 의 clear.
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4. **Activity Support**: 매 people 의 traffic.
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5. **Maintenance** (Image): 매 broken window 의 fix.
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### 매 historical
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- **Jane Jacobs** (1961): "The Death and Life of Great American Cities" — 매 "eyes on the street".
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- **Oscar Newman** (1972): "Defensible Space".
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- **C. Ray Jeffery** (1971): 매 CPTED term.
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- **Wilson & Kelling** (1982): 매 broken windows theory.
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### 매 1st vs 2nd vs 3rd generation
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| 세대 | 강조 |
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|---|---|
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| 1st | 매 physical (Jeffery, Newman) |
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| 2nd | 매 social cohesion (community) |
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| 3rd | 매 sustainability + tech |
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### 매 application 예
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- **Park**: 매 sightline + lighting + 매 wide path.
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- **Apartment**: 매 lobby visibility + 매 single entry + 매 maintained.
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- **Parking lot**: 매 lighting + 매 emergency phone + 매 cctv.
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- **School**: 매 layered security + 매 visibility + 매 community.
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- **ATM**: 매 lighting + 매 visibility + 매 mirror.
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- **Transit station**: 매 sightline + 매 staff presence.
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### 매 modern (smart city)
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- **AI surveillance**: 매 abnormal pattern detection.
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- **Adaptive lighting**: 매 motion-triggered.
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- **Crowd flow analytics**: 매 design feedback.
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- **Predictive crime mapping**: 매 high-risk area focus.
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- **Citizen reporting app**: 매 311 / SeeClickFix.
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### 매 limitation / critique
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- **Displacement**: 매 crime 의 다른 area 의 move.
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- **Surveillance**: 매 privacy concern.
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- **Equity**: 매 wealthy area 의 over-invest.
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- **False sense**: 매 design 의 omnipotent X.
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- **Broken windows critique**: 매 racial bias.
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### 매 Korea CPTED
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- 매 2014 의 시범 도시.
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- 매 경찰청 의 cooperation.
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- 매 Salt Path / 안심 귀가 길.
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- 매 mural / lighting / mirror.
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### 매 design checklist
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1. 매 sightline 의 unobstructed?
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2. 매 lighting 의 0.5+ lux 의 every spot?
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3. 매 access route 의 single + clear?
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4. 매 dead-end / hidden alcove?
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5. 매 maintenance 의 < 24h response?
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6. 매 territoriality (sign, paint, fence)?
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7. 매 activity (cafe, store) 의 generator?
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## 💻 패턴 (응용 — design checklist + sim)
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### CPTED audit checklist (programmatic)
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```python
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def cpted_audit(location):
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return {
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'natural_surveillance': {
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'sightline_coverage': measure_sightlines(location), # 매 % visible
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'avg_lux_at_night': measure_lighting(location),
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'window_facing_ratio': building_facade_ratio(location),
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},
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'access_control': {
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'entry_count': count_entries(location),
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'pathway_clarity': measure_path_clarity(location),
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},
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'territoriality': {
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'boundary_markers': count_boundary_signs(location),
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'private_public_clarity': assess_boundary(location),
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},
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'activity': {
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'foot_traffic_per_hour': pedestrian_count(location),
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'commercial_density': commerce_per_sqm(location),
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},
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'maintenance': {
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'graffiti_density': count_graffiti(location),
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'broken_lighting_pct': pct_broken_lights(location),
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'litter_score': litter_density(location),
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},
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}
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```
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### Crime risk simulation
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```python
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def predict_crime_risk(area, design_params):
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"""매 simple model 의 risk score."""
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risk = 0
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risk -= design_params['lux_avg'] * 0.3
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risk -= design_params['sightline_pct'] * 0.5
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risk += design_params['hidden_alcoves'] * 2
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risk -= design_params['foot_traffic_per_hr'] * 0.01
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risk += design_params['litter_score'] * 0.5
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return max(0, risk)
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# 매 design alternative 의 비교
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baseline = predict_crime_risk(area, current_design)
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improved = predict_crime_risk(area, {**current_design, 'lux_avg': 5, 'hidden_alcoves': 0})
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print(f'Risk reduction: {baseline - improved:.1f}')
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```
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### Adaptive lighting (smart city)
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```python
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class AdaptiveStreetlight:
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def __init__(self, motion_sensor, schedule):
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self.sensor = motion_sensor
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self.schedule = schedule
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def update(self):
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time = datetime.now().time()
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# 매 base level
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base_level = self.schedule.level_for(time)
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# 매 motion 시 의 brighten
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if self.sensor.motion_detected_recently(seconds=30):
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self.set_brightness(min(100, base_level + 50))
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else:
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self.set_brightness(base_level)
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```
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### 311 / citizen report integration
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```python
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def cpted_response_pipeline(report):
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"""매 citizen report → 매 prioritize."""
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if report.type == 'broken_streetlight':
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priority = 'high' if report.area.crime_rate > MEDIAN else 'medium'
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target_response = 24 if priority == 'high' else 72 # hours
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elif report.type == 'graffiti':
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priority = 'medium'
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target_response = 48
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elif report.type == 'overgrown_bush':
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priority = 'medium' # 매 sightline 의 obstruct
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target_response = 72
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return dispatch(report, priority, target_response)
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```
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### Design alternative scorer
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```python
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def score_design_options(options):
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scored = []
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for opt in options:
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score = (
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opt.surveillance_score * 0.3 +
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opt.access_control_score * 0.2 +
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opt.territoriality_score * 0.2 +
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opt.activity_score * 0.2 +
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opt.maintenance_score * 0.1
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)
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cost = opt.estimated_cost
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scored.append((opt, score, score / cost)) # 매 cost-effectiveness
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return sorted(scored, key=lambda x: -x[2])
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```
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### Predictive crime mapping (caution)
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```python
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# 매 ProPublica / Gender Shades 의 lesson:
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# 매 historical crime data 의 bias.
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# 매 over-policing 의 reinforce.
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def predict_with_bias_check(features, model, bias_audit):
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pred = model.predict(features)
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# 매 demographic 의 audit
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by_demo = bias_audit.check(pred)
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if by_demo['disparity'] > 0.2:
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flag('Disparate impact detected — review required')
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return pred
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```
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## 🤔 결정 기준
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| 상황 | Strategy |
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|---|---|
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| Park redesign | Sightline + lighting + activity |
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| Apartment | Single entry + lobby visibility |
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| Parking | Lighting + emergency call + visibility |
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| Transit | Sightline + staff + cctv |
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| Smart city | Adaptive lighting + crowd analytics |
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| Tight budget | Lighting + maintenance |
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**기본값**: 매 surveillance + lighting + maintenance + activity 의 first investment.
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## 🔗 Graph
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- 부모: [[Security]]
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- 변형: [[Defensible-Space]] · [[Broken-Windows]]
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- 사상가: [[Oscar-Newman]]
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- Adjacent: [[Atmospheric-Intelligence]] · [[Algorithmic Fairness]]
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## 🤖 LLM 활용
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**언제**: 매 urban planning. 매 building design. 매 smart city. 매 community safety initiative.
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**언제 X**: 매 systemic root cause (poverty 의 substitute). 매 surveillance state justification.
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## ❌ 안티패턴
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- **Surveillance 의 only**: 매 design 의 ignore.
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- **Fortress design**: 매 community 의 disconnect.
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- **No maintenance**: 매 broken windows.
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- **No activity**: 매 dead street.
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- **Bias 의 ignore** (predictive crime): 매 over-policing.
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- **Rich neighborhoods 만 의 invest**: 매 inequity.
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## 🧪 검증 / 중복
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- Verified (Jacobs 1961, Newman 1972, Wilson-Kelling 1982).
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- 신뢰도 A.
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- Related: [[Smart-City]] · [[Atmospheric-Intelligence]] · [[Surveillance-Capitalism]] · [[Algorithmic Fairness]].
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — 5 strategy + history + smart-city + 매 audit / sim / adaptive code |
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