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Spain Implementation - Complete Documentation

Overview

The Spain dataset is the first fully implemented country in the False Identity Generator system, featuring comprehensive demographic data, cultural characteristics, and realistic generation rules.

Completion Status

✅ COMPLETED - All components implemented and tested 📊 Test Success Rate: 96-100% (48-50/50 identities pass validation)

Dataset Components

1. Country Rules (data/countries/spain/rules.txt)

Demographics

finished=true
min_age=18
max_age=83
life_expectancy=83

Naming System

name_order=first+surname1+surname2
surname_count=2
surname_usage=always

Spanish naming follows patronymic tradition:

  • First surname: Father's first surname
  • Second surname: Mother's first surname
  • Example: "María García López" (García from father, López from mother)

Fertility & Family

female_fertility_min_age=15
female_fertility_max_age=45
parent_min_age_gap=18
parent_max_age_gap=45
average_children=1.2
min_children=1
max_children=3
sibling_probability=75
average_siblings=1.8

Implementation Details:

  • Mother age at birth: Strictly validated between 15-45 years
  • Sibling ages: Calculated to ensure mother could give birth to all
  • Twins/Triplets: 15% chance, max 3 siblings with same age
  • Unique names: All siblings have different names (50-attempt uniqueness check)

Death Probabilities (Age-Based)

Parents (based on identity's age):

death_prob_parents_very_young=2
death_prob_parents_young_adults=5
death_prob_parents_adults=30
death_prob_parents_middle_aged=50
death_prob_parents_older_adults=70
death_prob_parents_seniors=85
death_prob_parents_elderly=95

Realistic Implementation:

  • Identity 70+: 95% chance parents deceased, died 10-30 years ago
  • Identity 60-70: 85% chance parents deceased, died 5-20 years ago
  • Parent ages capped at life_expectancy + 0-5 years (83-88)
  • No unrealistic scenarios (parents living to 100, dying "last year" when child is 75)

Physical Characteristics

Hair Colors (realistic Spanish distribution):

hair_colors=Black:35,Dark Brown:30,Brown:20,Light Brown:8,
            Dark Blonde:3,Blonde:2,Light Blonde:1,Red:1,
            Gray:0,White:0

Eye Colors:

eye_colors=Dark Brown:45,Brown:30,Light Brown:15,Hazel:5,
           Green:3,Blue:1.5,Gray:0.5

Skin Tones:

skin_tones=Light:60,Medium:35,Olive:4,Tan:0.8,Dark:0.2

Religion

religions=Catholic:55,Atheist:25,Agnostic:12,
          Non-practicing Catholic:5,Muslim:2,
          Protestant:0.7,Other:0.3

Economic Data

Social Class Distribution:

social_classes=low:25,middle:50,upper-middle:20,high:5

Salary Ranges (Annual, EUR):

salary_low=12000,22000
salary_middle=22000,40000
salary_upper-middle=40000,75000
salary_high=75000,200000

Pension Ranges (Annual, EUR):

pension_low=8000,16000
pension_middle=15000,30000
pension_upper-middle=28000,55000
pension_high=50000,120000

Employment:

unemployment_rate=13.5
jobless_rate=2.5

Phone System

phone_country_code=+34
phone_length=9
phone_mobile_prefixes=6,7
phone_format=### ## ## ##

Implementation: ONLY mobile phones generated (no landlines) Format: +34 6XX XX XX XX or +34 7XX XX XX XX

Languages

min_languages=1
max_languages=4
available_languages=English:70,French:30,German:15,Italian:10,
                    Portuguese:12,Catalan:20,Basque:8,
                    Galician:6,Chinese:0.5,Arabic:1
language_level_basic=30
language_level_intermediate=50
language_level_advanced=20

Special Rules:

  • University graduates: MUST have B1+ English (intermediate or advanced)
  • Young people (<30): 100% have at least basic English
  • Primary education only: Max basic level languages

2. Names Database

Structure

data/countries/spain/names/{gender}/{age_bucket}.txt

Age Buckets:

  • very_young (18-23)
  • young_adults (23-34)
  • adults (34-50)
  • middle_aged (50-65)
  • older_adults (65-83)
  • seniors (beyond life expectancy)
  • elderly (oldest segment)

Example Names:

Male - very_young:

Alejandro
Hugo
Pablo
Álvaro
Adrián

Female - elderly:

María
Carmen
Josefa
Francisca
Dolores

Implementation

Age-appropriate name selection ensures realistic generational naming patterns.

3. Surnames

Located in: data/countries/spain/surnames/

Files:

  • male.txt: ~200 Spanish surnames
  • female.txt: Same surnames (Spain doesn't gender surnames)

Most Common:

García
Fernández
González
Rodríguez
López
Martínez
Sánchez
Pérez
...

4. Cities

File: data/countries/spain/cities.txt

Format: City|PostalCode|Province

Madrid|28001|Madrid
Barcelona|08001|Barcelona
Valencia|46001|Valencia
Sevilla|41001|Sevilla
Zaragoza|50001|Zaragoza
...

Total: 100+ Spanish cities with postal codes and provinces

5. Regional & Historical Characteristics

File: data/countries/spain/regional_characteristics.txt

Format: text|min_age|max_age|gender|probability

Key Characteristics:

Completed mandatory military service (Mili)|45|75|male|0.95
Grew up during Franco's dictatorship|55|999|both|1.0
Experienced the Spanish transition to democracy|50|70|both|1.0
Lived through the 1992 Barcelona Olympics era|40|999|both|0.7
Witnessed Spain joining the European Union (1986)|45|999|both|0.8
Experienced the 2008 economic crisis|25|999|both|0.85
Grew up with the Peseta currency (pre-Euro)|32|999|both|0.9
Remembers the 2004 Madrid train bombings|27|999|both|0.7
Affected by youth unemployment crisis|22|35|both|0.4

Age Calculation Logic:

  • Person must have been 5-7+ years old when event occurred to "remember" it
  • Example: 2004 bombings (min_age 27) → Person born 1998 or earlier → Was 6+ in 2004

6. Jobs Database

Structure

data/global/jobs/{gender}/{social_class}.txt

Social Classes: low, middle, upper-middle, high

Example Jobs:

Female/Middle:

Nurse
Teacher
Accountant
Office Manager
Social Worker
Pharmacy Technician
Human Resources Specialist
Marketing Coordinator
...

Male/High:

CEO
Director
Senior Engineer
Financial Manager
Surgeon
Architect
Investment Banker
...

Age Requirements

File: data/global/jobs/age_requirements.txt

Teacher:22
Nurse:22
Pharmacy Technician:21
Lawyer:24
Doctor:26
Surgeon:28
University Professor:28
CEO:30
...

Implementation:

  • Persons under minimum age CANNOT have that job
  • Persons under 18 are automatically "Student"
  • Work history validated: person doesn't start career unrealistically late

7. Hobbies

Global Hobbies by Age/Gender

Structure:

data/global/hobbies/{age_bucket}/{gender}.txt

Example - adults/male:

Playing football
Watching football matches
Playing basketball
Cycling
Hiking
Video games
Reading
Playing guitar
...

Spain-Specific Hobbies

File: data/countries/spain/hobbies.txt

Following Real Madrid
Following FC Barcelona
Following Atlético de Madrid
Playing padel
Going to terrazas in summer
Eating tapas
Watching bullfighting
Flamenco dancing
...

Selection Logic:

  1. 2 national hobbies (guaranteed)
  2. 1 class-based hobby
  3. 1 gender-based hobby
  4. 1 neutral hobby
  5. Total: 3-5 hobbies per person

8. Death Causes

File: data/global/death/causes_by_age.txt

Format: age_bucket:cause1,cause2,cause3...

very_young:Car accident,Motorcycle accident,Drowning,...
young_adults:Car accident,Drug overdose,Suicide,...
adults:Heart attack,Cancer,Stroke,Diabetes complications,...
elderly:Alzheimer's disease,Pneumonia,Heart failure,...

Special Cases:

  • Childbirth complications: 5% chance if mother died same year as child birth (identity or sibling)

9. Education System

File: data/global/education/levels.txt

No formal education
Incomplete primary education
Primary education
Secondary education
Vocational training
Bachelor's degree
Master's degree
Doctorate/PhD

Job-Education Mapping

File: data/global/education/job_education_map.txt

Nurse:bachelor|Nursing
Teacher:bachelor|Education
Lawyer:bachelor|Law
Doctor:doctorate|Medicine
Software Engineer:bachelor|Computer Science
...

10. Cultural Considerations

File: data/countries/spain/considerations.txt

Content: Detailed cultural guide covering:

  • Social behavior (kisses on cheeks, personal space, eye contact)
  • Daily life schedule (late meals, siesta culture)
  • Communication style (directness, interrupting as engagement)
  • Work culture (work-life balance, August vacations)
  • Social etiquette (greeting shopkeepers, tipping)
  • Food & drink culture (coffee, tapas, wine)
  • Regional differences (Catalonia, Basque Country, Galicia)
  • Things to avoid (Civil War discussions, bullfighting assumptions)

Generation Statistics

Family Structure

Parent Distribution (for identity age 60-80):

  • 95-98% have deceased parents
  • Death occurred 6-20 years ago (realistic timing)
  • Parent death ages: 60-88 years (not 95-100)

Sibling Probability: 75%

  • Average: 1.8 siblings
  • Twins/Triplets: 15% chance
  • All siblings have unique names

Children (for married/divorced 40+):

  • Probability: 65-85% depending on age
  • Average: 1.2 children
  • Respect mother's fertility limits

Employment

Age <18: 100% Student Age 18-23: 70% Student, 20% Employed, 10% Unemployed Age 65+: 100% Retired

Work History Validation:

  • No one starts career after age 25 without previous jobs (unless advanced degree)
  • People 40+ have previous job history (80% probability)
  • Work start dates never in the future

Language Proficiency

Spanish: 100% native English:

  • Age <30: 100% have at least basic
  • University graduates: 100% have B1+ (intermediate/advanced)
  • Primary education only: Max basic level

Implementation Features

Validation Systems

  1. Age Constraints:

    • All family member ages validated against fertility limits
    • No impossible scenarios (mother age 8 giving birth)

  2. Death Timing:

    • Realistic death ages (based on life expectancy)
    • Realistic timing (old identity → parents died long ago)
    • No future death dates

  3. Employment Coherence:

    • Age requirements respected
    • Career start ages realistic
    • Work history complete for older persons

  4. Regional Characteristics:

    • Age-filtered (person was old enough to remember event)
    • Probability-based selection

Special Cases Handled

  • ✅ Mother death in childbirth (identity or sibling birth)
  • ✅ Twins/triplets (same age, unique names, marked in display)
  • ✅ Unique sibling names (50-attempt uniqueness, fallback numbering)
  • ✅ Realistic parent ages and death timing
  • ✅ Work history gaps filled for older workers
  • ✅ University graduates have appropriate English level

Output Format

CLI Display

Box-formatted output with:

  • Personal information (name, DOB, physical characteristics)
  • Location (city, postal code, province)
  • Employment (current/previous with dates and termination reasons)
  • Economic data (salary/pension, social class)
  • Education and languages
  • Contact (phone, email with inbox URL)
  • Hobbies
  • Family details (parents, siblings, partner, children)
    • Twin/triplet markers: "(Twin)" or "(Triplet)"
    • Deceased members: death dates and causes
  • Regional characteristics
  • Cultural considerations

JSON Export

All data serialized to JSON for:

  • Storage
  • Import/export
  • API integration

Known Limitations

  1. Test Validation: Test suite has incorrect validation for "Housewife/Jobless with previous_positions" (this IS valid in reality)

  2. Single Country: Only Spain fully implemented (others planned)

  3. Binary Gender: System currently supports only male/female (could be extended)

Future Enhancements

  • More nuanced employment sectors
  • Expanded regional characteristics
  • Education field specializations
  • Hobby combinations based on personality clusters
  • Financial history (credit scores, debts)
  • Health conditions (age-appropriate)
  • Additional Spanish regions with unique characteristics

Version: 1.0 Last Updated: December 2025 Maintainer: Leucocito Status: Production-ready for Spain dataset