To solve this problem, we can use a linear function to model the change in population density over time. We have two data points: (1981, 59) and (2001, 75). First, we calculate the slope of the line, which represents the rate of change in population density per year. Then, we use the point-slope form of a linear equation to find the year when the population density reaches 89 people per square mile.
To find the slope of the linear function that models the population density over time, we use the formula for the slope between two points \((x_1, y_1)\) and \((x_2, y_2)\):
\[
m = \frac{y_2 - y_1}{x_2 - x_1}
\]
Substituting the given data points \((1981, 59)\) and \((2001, 75)\):
\[
m = \frac{75 - 59}{2001 - 1981} = \frac{16}{20} = 0.8
\]
We use the point-slope form of a linear equation to find the year when the population density reaches 89 people per square mile. The point-slope form is given by:
\[
y - y_1 = m(x - x_1)
\]
Substituting \(y = 89\), \(y_1 = 59\), \(m = 0.8\), and \(x_1 = 1981\):
\[
89 - 59 = 0.8(x - 1981)
\]
Solving for \(x\):
\[
30 = 0.8(x - 1981)
\]
\[
x - 1981 = \frac{30}{0.8} = 37.5
\]
\[
x = 1981 + 37.5 = 2018.5
\]
Since we need to round to the nearest whole number, the year when the population density reaches 89 people per square mile is:
\[
x = \text{round}(2018.5) = 2019
\]