import sys
import random
from scipy.optimize import fmin

input = sys.stdin.readline

n = int(input())

XR = []
XL = []
XS = []
YU = []
YD = []
YS = []

for _ in range(n):
    x, y, d = input().split()
    x = int(x)
    y = int(y)
    if d == "D":
        XS.append(x)
        YD.append(y)
    elif d == "U":
        XS.append(x)
        YU.append(y)
    elif d == "R":
        XR.append(x)
        YS.append(y)
    elif d == "L":
        XL.append(x)
        YS.append(y)


max_YD = max(YD + [float("-inf")])
max_YS = max(YS + [float("-inf")])
max_YU = max(YU + [float("-inf")])
min_YD = min(YD + [float("inf")])
min_YS = min(YS + [float("inf")])
min_YU = min(YU + [float("inf")])

max_XR = max(XR + [float("-inf")])
max_XL = max(XL + [float("-inf")])
max_XS = max(XS + [float("-inf")])
min_XR = min(XR + [float("inf")])
min_XL = min(XL + [float("inf")])
min_XS = min(XS + [float("inf")])


def diff_xy(t):
    diff_y = max(max_YD - t, max_YU + t, max_YS) - min(min_YD - t, min_YU + t, min_YS)
    diff_x = max(max_XL - t, max_XR + t, max_XS) - min(min_XL - t, min_XR + t, min_XS)
    return diff_x * diff_y


def binary_search(min_t, max_t):
    mid_t = (min_t + max_t) / 2
    if max_t - min_t < 10 ** (-10):
        return diff_xy(mid_t)

    if diff_xy(min_t) < diff_xy(mid_t):
        return binary_search(min_t, mid_t)
    elif diff_xy(max_t) < diff_xy(mid_t):
        return binary_search(mid_t, max_t)
    else:
        return min(binary_search(min_t, mid_t), binary_search(mid_t, max_t))


option_a = max(max_YD, max_YS, max_YU)
option_b = max(max_XR, max_XL, max_XS)
option_c = min(min_YD, min_YS, min_YU)
option_d = min(min_XR, min_XL, min_XS)

optimal_0 = fmin(diff_xy, 0, full_output=False, disp=False, xtol= 10 ** (-10))
optimal_a = fmin(diff_xy, option_a, full_output=False, disp=False, xtol= 10 ** (-10))
optimal_b = fmin(diff_xy, option_b, full_output=False, disp=False, xtol= 10 ** (-10))
optimal_c = fmin(diff_xy, option_c, full_output=False, disp=False, xtol= 10 ** (-10))
optimal_d = fmin(diff_xy, option_d, full_output=False, disp=False, xtol= 10 ** (-10))


ans = min(diff_xy(optimal_0[0]), diff_xy(optimal_a[0]),
          diff_xy(optimal_b[0]), diff_xy(optimal_c[0]),
          diff_xy(optimal_d[0]))

for i in range(100):
    randi = random.randint(0, 10 ** 8)
    randii = fmin(diff_xy, randi, full_output=False, disp=False, xtol= 10 ** (-10))
    ans = min(ans, diff_xy(randii[0]))

print(ans)


### FIXED CODE ###

import sys
from scipy.optimize import fmin

input = sys.stdin.readline

n = int(input())

XR = []
XL = []
XS = []
YU = []
YD = []
YS = []

for _ in range(n):
    x, y, d = input().split()
    x = int(x)
    y = int(y)
    if d == "D":
        XS.append(x)
        YD.append(y)
    elif d == "U":
        XS.append(x)
        YU.append(y)
    elif d == "R":
        XR.append(x)
        YS.append(y)
    elif d == "L":
        XL.append(x)
        YS.append(y)


max_YD = max(YD + [float("-inf")])
max_YS = max(YS + [float("-inf")])
max_YU = max(YU + [float("-inf")])
min_YD = min(YD + [float("inf")])
min_YS = min(YS + [float("inf")])
min_YU = min(YU + [float("inf")])

max_XR = max(XR + [float("-inf")])
max_XL = max(XL + [float("-inf")])
max_XS = max(XS + [float("-inf")])
min_XR = min(XR + [float("inf")])
min_XL = min(XL + [float("inf")])
min_XS = min(XS + [float("inf")])


def diff_xy(t):
    diff_y = max(max_YD - t, max_YU + t, max_YS) - min(min_YD - t, min_YU + t, min_YS)
    diff_x = max(max_XL - t, max_XR + t, max_XS) - min(min_XL - t, min_XR + t, min_XS)
    return diff_x * diff_y


options = [
    max_YD - max_YS,
    max_YS - max_YU,
    (max_YD - max_YU) / 2,
    min_YD - min_YS,
    (min_YD - min_YU) / 2,
    min_YS - min_YU,
    max_XL - max_XS,
    max_XS - max_XR,
    (max_XL - max_XR) / 2,
    min_XL - min_XS,
    (min_XL - min_XR) / 2,
    min_XS - min_XR]

ans = diff_xy(0)

for option in options:
    if option >= 0:
        ans = min(ans, diff_xy(option))

print(ans)