---
id: wiki-2026-0508-cpted
title: CPTED (Crime Prevention Through Environmental Design)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [CPTED, environmental design, defensible space, broken windows, urban safety, Oscar Newman]
duplicate_of: none
source_trust_level: A
confidence_score: 0.88
verification_status: applied
tags: [urban-planning, security, cpted, defensible-space, broken-windows, smart-city, environmental-design]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: urban planning / security
  applicable_to: [Urban Design, Smart City, Security Architecture]
---

# CPTED (Crime Prevention Through Environmental Design)

## 📌 한 줄 통찰
> **"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.

## 📖 핵심

### 매 5 strategy (2nd-gen CPTED)
1. **Natural Surveillance**: 매 visibility (low fence, transparent wall, lighting).
2. **Natural Access Control**: 매 single entry, 매 clear pathway.
3. **Territorial Reinforcement**: 매 public-private boundary 의 clear.
4. **Activity Support**: 매 people 의 traffic.
5. **Maintenance** (Image): 매 broken window 의 fix.

### 매 historical
- **Jane Jacobs** (1961): "The Death and Life of Great American Cities" — 매 "eyes on the street".
- **Oscar Newman** (1972): "Defensible Space".
- **C. Ray Jeffery** (1971): 매 CPTED term.
- **Wilson & Kelling** (1982): 매 broken windows theory.

### 매 1st vs 2nd vs 3rd generation
| 세대 | 강조 |
|---|---|
| 1st | 매 physical (Jeffery, Newman) |
| 2nd | 매 social cohesion (community) |
| 3rd | 매 sustainability + tech |

### 매 application 예
- **Park**: 매 sightline + lighting + 매 wide path.
- **Apartment**: 매 lobby visibility + 매 single entry + 매 maintained.
- **Parking lot**: 매 lighting + 매 emergency phone + 매 cctv.
- **School**: 매 layered security + 매 visibility + 매 community.
- **ATM**: 매 lighting + 매 visibility + 매 mirror.
- **Transit station**: 매 sightline + 매 staff presence.

### 매 modern (smart city)
- **AI surveillance**: 매 abnormal pattern detection.
- **Adaptive lighting**: 매 motion-triggered.
- **Crowd flow analytics**: 매 design feedback.
- **Predictive crime mapping**: 매 high-risk area focus.
- **Citizen reporting app**: 매 311 / SeeClickFix.

### 매 limitation / critique
- **Displacement**: 매 crime 의 다른 area 의 move.
- **Surveillance**: 매 privacy concern.
- **Equity**: 매 wealthy area 의 over-invest.
- **False sense**: 매 design 의 omnipotent X.
- **Broken windows critique**: 매 racial bias.

### 매 Korea CPTED
- 매 2014 의 시범 도시.
- 매 경찰청 의 cooperation.
- 매 Salt Path / 안심 귀가 길.
- 매 mural / lighting / mirror.

### 매 design checklist
1. 매 sightline 의 unobstructed?
2. 매 lighting 의 0.5+ lux 의 every spot?
3. 매 access route 의 single + clear?
4. 매 dead-end / hidden alcove?
5. 매 maintenance 의 < 24h response?
6. 매 territoriality (sign, paint, fence)?
7. 매 activity (cafe, store) 의 generator?

## 💻 패턴 (응용 — design checklist + sim)

### CPTED audit checklist (programmatic)
```python
def cpted_audit(location):
    return {
        'natural_surveillance': {
            'sightline_coverage': measure_sightlines(location),  # 매 % visible
            'avg_lux_at_night': measure_lighting(location),
            'window_facing_ratio': building_facade_ratio(location),
        },
        'access_control': {
            'entry_count': count_entries(location),
            'pathway_clarity': measure_path_clarity(location),
        },
        'territoriality': {
            'boundary_markers': count_boundary_signs(location),
            'private_public_clarity': assess_boundary(location),
        },
        'activity': {
            'foot_traffic_per_hour': pedestrian_count(location),
            'commercial_density': commerce_per_sqm(location),
        },
        'maintenance': {
            'graffiti_density': count_graffiti(location),
            'broken_lighting_pct': pct_broken_lights(location),
            'litter_score': litter_density(location),
        },
    }
```

### Crime risk simulation
```python
def predict_crime_risk(area, design_params):
    """매 simple model 의 risk score."""
    risk = 0
    risk -= design_params['lux_avg'] * 0.3
    risk -= design_params['sightline_pct'] * 0.5
    risk += design_params['hidden_alcoves'] * 2
    risk -= design_params['foot_traffic_per_hr'] * 0.01
    risk += design_params['litter_score'] * 0.5
    return max(0, risk)

# 매 design alternative 의 비교
baseline = predict_crime_risk(area, current_design)
improved = predict_crime_risk(area, {**current_design, 'lux_avg': 5, 'hidden_alcoves': 0})
print(f'Risk reduction: {baseline - improved:.1f}')
```

### Adaptive lighting (smart city)
```python
class AdaptiveStreetlight:
    def __init__(self, motion_sensor, schedule):
        self.sensor = motion_sensor
        self.schedule = schedule
    
    def update(self):
        time = datetime.now().time()
        # 매 base level
        base_level = self.schedule.level_for(time)
        
        # 매 motion 시 의 brighten
        if self.sensor.motion_detected_recently(seconds=30):
            self.set_brightness(min(100, base_level + 50))
        else:
            self.set_brightness(base_level)
```

### 311 / citizen report integration
```python
def cpted_response_pipeline(report):
    """매 citizen report → 매 prioritize."""
    if report.type == 'broken_streetlight':
        priority = 'high' if report.area.crime_rate > MEDIAN else 'medium'
        target_response = 24 if priority == 'high' else 72  # hours
    elif report.type == 'graffiti':
        priority = 'medium'
        target_response = 48
    elif report.type == 'overgrown_bush':
        priority = 'medium'  # 매 sightline 의 obstruct
        target_response = 72
    
    return dispatch(report, priority, target_response)
```

### Design alternative scorer
```python
def score_design_options(options):
    scored = []
    for opt in options:
        score = (
            opt.surveillance_score * 0.3 +
            opt.access_control_score * 0.2 +
            opt.territoriality_score * 0.2 +
            opt.activity_score * 0.2 +
            opt.maintenance_score * 0.1
        )
        cost = opt.estimated_cost
        scored.append((opt, score, score / cost))  # 매 cost-effectiveness
    return sorted(scored, key=lambda x: -x[2])
```

### Predictive crime mapping (caution)
```python
# 매 ProPublica / Gender Shades 의 lesson:
# 매 historical crime data 의 bias.
# 매 over-policing 의 reinforce.

def predict_with_bias_check(features, model, bias_audit):
    pred = model.predict(features)
    
    # 매 demographic 의 audit
    by_demo = bias_audit.check(pred)
    if by_demo['disparity'] > 0.2:
        flag('Disparate impact detected — review required')
    
    return pred
```

## 🤔 결정 기준
| 상황 | Strategy |
|---|---|
| Park redesign | Sightline + lighting + activity |
| Apartment | Single entry + lobby visibility |
| Parking | Lighting + emergency call + visibility |
| Transit | Sightline + staff + cctv |
| Smart city | Adaptive lighting + crowd analytics |
| Tight budget | Lighting + maintenance |

**기본값**: 매 surveillance + lighting + maintenance + activity 의 first investment.

## 🔗 Graph
- 부모: [[Security]]
- 변형: [[Defensible-Space]] · [[Broken-Windows]]
- 사상가: [[Oscar-Newman]]
- Adjacent: [[Atmospheric-Intelligence]] · [[Algorithmic Fairness]]

## 🤖 LLM 활용
**언제**: 매 urban planning. 매 building design. 매 smart city. 매 community safety initiative.
**언제 X**: 매 systemic root cause (poverty 의 substitute). 매 surveillance state justification.

## ❌ 안티패턴
- **Surveillance 의 only**: 매 design 의 ignore.
- **Fortress design**: 매 community 의 disconnect.
- **No maintenance**: 매 broken windows.
- **No activity**: 매 dead street.
- **Bias 의 ignore** (predictive crime): 매 over-policing.
- **Rich neighborhoods 만 의 invest**: 매 inequity.

## 🧪 검증 / 중복
- Verified (Jacobs 1961, Newman 1972, Wilson-Kelling 1982).
- 신뢰도 A.
- Related: [[Smart-City]] · [[Atmospheric-Intelligence]] · [[Surveillance-Capitalism]] · [[Algorithmic Fairness]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — 5 strategy + history + smart-city + 매 audit / sim / adaptive code |