Пример #1
0
def calc_probability_LD_migration_zvoleff(person, time):
    """
    Calculates the probability of local-distant migration for an agent, using 
    the results of Alex Zvoleff's empirical analysis of the CVFS data, 
    following the results of the analysis conducted by Massey et al. (2010).
    """
    #########################################################################
    # Intercept
    inner = rcParams['migration.ld.zv.coef.intercept']

    if person.is_in_school():
        inner += rcParams['migration.ld.zv.coef.in_school']

    inner += person.get_years_schooling() * rcParams['migration.ld.zv.coef.years_schooling']

    #######################################################################
    # Household level variables
    household = person.get_parent_agent()
    inner += rcParams['migration.ld.zv.coef.own_farmland'] * household._own_land

    #######################################################################
    # Neighborhood level variables
    neighborhood = household.get_parent_agent()
    inner += rcParams['migration.ld.zv.coef.log_market_min_ft'] * np.log(neighborhood.NFOs['market_min_ft'] + 1)

    #########################################################################
    # Other controls
    if person.get_sex() == "female":
        inner += rcParams['migration.ld.zv.coef.female']

    ethnicity = person.get_ethnicity()
    assert ethnicity!=None, "Ethnicity must be defined"
    if ethnicity == "HighHindu":
        # This was the reference level
        pass
    elif ethnicity == "HillTibeto":
        inner += rcParams['migration.ld.zv.coef.ethnicHillTibeto']
    elif ethnicity == "LowHindu":
        inner += rcParams['migration.ld.zv.coef.ethnicLowHindu']
    elif ethnicity == "Newar":
        inner += rcParams['migration.ld.zv.coef.ethnicNewar']
    elif ethnicity == "TeraiTibeto":
        inner += rcParams['migration.ld.zv.coef.ethnicTeraiTibeto']

    age = person.get_age_years()
    if (age >= 15) & (age <= 24):
        inner += rcParams['migration.ld.zv.coef.age15-24']
    elif (age > 24) & (age <= 34):
        inner += rcParams['migration.ld.zv.coef.age24-34']
    elif (age > 34) & (age <= 44):
        inner += rcParams['migration.ld.zv.coef.age34-44']
    elif (age > 44) & (age <= 55):
        inner += rcParams['migration.ld.zv.coef.age45-55']
    elif (age > 55):
        # Reference class
        pass

    prob = 1./(1 + np.exp(-inner))
    if rcParams['log_stats_probabilities']:
        logger.debug("Person %s local-distant migration probability %.6f (age: %s)"%(person.get_ID(), prob, person.get_age_years()))
    return prob
Пример #2
0
def calc_probability_marriage_zvoleff(person, time):
    """
    Calculates the probability of marriage for an agent, using the results of 
    Alex Zvoleff's empirical analysis of the CVFS data, following the results 
    of the analysis conducted by Yabiku (2006).
    """
    inner = rcParams['marrtime.zv.coef.(Intercept)']

    ethnicity = person.get_ethnicity()
    assert ethnicity!=None, "Ethnicity must be defined"
    if ethnicity == "HighHindu":
        # This was the reference level
        pass
    elif ethnicity == "HillTibeto":
        inner += rcParams['marrtime.zv.coef.ethnicHillTibeto']
    elif ethnicity == "LowHindu":
        inner += rcParams['marrtime.zv.coef.ethnicLowHindu']
    elif ethnicity == "Newar":
        inner += rcParams['marrtime.zv.coef.ethnicNewar']
    elif ethnicity == "TeraiTibeto":
        inner += rcParams['marrtime.zv.coef.ethnicTeraiTibeto']

    # Gender
    if person.get_sex() == "female":
        inner += rcParams['marrtime.zv.coef.genderfemale']

    age = person.get_age_years()
    inner += rcParams['marrtime.zv.coef.age'] * age
    inner += rcParams['marrtime.zv.coef.I(age^2)'] * (age ** 2)

    # Neighborhood characteristics
    neighborhood = person.get_parent_agent().get_parent_agent()
    log_percent_agveg = np.log((neighborhood._land_agveg / neighborhood._land_total)*100 + 1)
    inner += rcParams['marrtime.zv.coef.interp_logpercagveg'] * log_percent_agveg

    inner += rcParams['marrtime.zv.coef.SCHLFT'] * neighborhood.NFOs['school_min_ft']
    inner += rcParams['marrtime.zv.coef.HLTHFT'] * neighborhood.NFOs['health_min_ft']
    inner += rcParams['marrtime.zv.coef.BUSFT'] * neighborhood.NFOs['bus_min_ft']
    inner += rcParams['marrtime.zv.coef.MARFT'] * neighborhood.NFOs['market_min_ft']
    inner += rcParams['marrtime.zv.coef.EMPFT'] * neighborhood.NFOs['employer_min_ft']

    # Schooling
    inner += rcParams['marrtime.zv.coef.schooling_yrs'] * person.get_years_schooling()
    if person.is_in_school():
        inner += rcParams['marrtime.zv.coef.in_school']

    # Account for monthly differences in marriage rates - some days (and 
    # months) are more auspicious for marriage than others.
    month_num = int(np.mod(np.round(time*12, 0), 12) + 1)
    if month_num == 1:
        # This was the reference level
        pass
    elif month_num == 2:
        inner += rcParams['marrtime.zv.coef.month2']
    elif month_num == 3:
        inner += rcParams['marrtime.zv.coef.month3']
    elif month_num == 4:
        inner += rcParams['marrtime.zv.coef.month4']
    elif month_num == 5:
        inner += rcParams['marrtime.zv.coef.month5']
    elif month_num == 6:
        inner += rcParams['marrtime.zv.coef.month6']
    elif month_num == 7:
        inner += rcParams['marrtime.zv.coef.month7']
    elif month_num == 8:
        inner += rcParams['marrtime.zv.coef.month8']
    elif month_num == 9:
        inner += rcParams['marrtime.zv.coef.month9']
    elif month_num == 10:
        inner += rcParams['marrtime.zv.coef.month10']
    elif month_num == 11:
        inner += rcParams['marrtime.zv.coef.month11']
    elif month_num == 12:
        inner += rcParams['marrtime.zv.coef.month12']
    
    prob = 1./(1 + np.exp(-inner))
    if rcParams['log_stats_probabilities']:
        logger.debug("Person %s marriage probability %.6f (age: %s)"%(person.get_ID(), prob, person.get_age_years()))
    return prob