From the pros, from the Spaceflight 101 article Tiangong-1 Re-Entry, click for full size:

For a circular LEO, for $x_p$, $y_p$ in the plane of the orbit we can just write
$$x_p = \cos(\omega t) $$
$$y_p = \sin(\omega t), $$
and if it is inclined to the equator by an angle $i$, the $x$, $y$, $z$ coordinates when the $\hat{z}$ axis is parallel to the Earth's rotational axis will be
$$x = \cos(\omega t) $$
$$y = \sin(\omega t) \cos(i), $$
$$z = \sin(\omega t) \sin(i), $$
and the latitude will be
$$\lambda=\arctan\left(\frac{z}{\sqrt{x^2+y^2}} \right) $$
Here's a plot for inclinations of 20, 40, 60, and 80 degrees. Two Three things stand out.
- As inclination approaches 90 degrees and the orbit becomes polar, the plot of latitude versus time becomes more triangular. Of course at exactly 90 degrees the latitude increases or degreases purely linearly with time for a circular orbit.
- More interesting is that the U-shape of the time-binned histogram of latitude flattens out as well. For high inclination orbits, the amount of time spent per degree of latitude becomes much more even, except for the "ears" at max and min, where there it still sort-of "stalls" at the extrema before turning around again.
- Even more interesting is the time-binned histogram rescaled by $1/ \cos(\lambda)$ for surface area rather than latitude, as recommended by @Litho's comment. If you were looking for debris, or looking to avoid getting hit by debris personally, this would be the plot for you.

import numpy as np
import matplotlib.pyplot as plt
halfpi, pi, twopi = [f*np.pi for f in 0.5, 1, 2]
rads, degs = pi/180, 180/pi
omega = twopi
N = 20000
t = np.linspace(0, 1, 20000)
incs = [rads*d for d in (20, 40, 60, 80)]
lats = []
for inc in incs:
x = np.cos(omega*t)
y = np.sin(omega*t) * np.cos(inc)
z = np.sin(omega*t) * np.sin(inc)
rxy = np.sqrt(x**2 + y**2)
lat = np.arctan2(z, rxy)
lats.append(lat)
bins = np.arange(-90, 91, 1)
hists = []
for lat in lats:
latdegs = degs*lat
hists.append(np.histogram(latdegs, bins))
if True:
fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
for lat in lats:
ax1.plot(t, degs*lat)
ax1.set_ylim(-90, 90)
ax1.set_title('latitude vs time', fontsize=16)
ax2 = fig.add_subplot(3, 1, 2)
for a, b in hists:
ax2.plot(b[1:], a)
ax2.tick_params(labelleft='off')
ax2.set_xlim(-90, 90)
ax2.set_title('per unit latitude, area=1', fontsize=16)
ax3 = fig.add_subplot(3, 1, 3)
for a, b in hists:
brads = rads*b
ax3.plot(b[1:], a/np.cos(brads[1:]))
ax3.tick_params(labelleft='off')
ax3.set_xlim(-90, 90)
ax3.set_title('per unit area for @Litho, area=1', fontsize=16)
plt.show()