Table 1 Summary of the model names, general formulation, section number, and equation number.

Proximity

Y(p) = X_i(p)

4.1

(4.1.1)

Land Use Regression

Y(p) = X(p)β + ε

4.2

(4.2.1)

Geographically Weighted Regression

Y(p) = X(p)β(p) + ε

4.3

(4.3.1)

Geostatical Models

Y(p) = GP(μ(p), Σ_θ(p, p_*))

4.4

(4.4.1)

Machine Learning

Y(p) = f(X(p))

4.5

(4.5.1)

Ensemble Model

\({{\bf{Y}}}(p)={\sum }_{m = 1}^{M}{w}_{m}{f}_{m}(X(p)){\sum }^{M}{w}_{m}=1\)

4.5.2

(4.5.3)

Boosting

Y_m(p) = Y_m−1(p) + νy_m(p)

4.5.2

(4.5.4)

Mechanistic

\(Y(p):= \frac{\partial {C}_{i}}{\partial t}=-{{\boldsymbol{\nabla }}}\cdot \left({{\bf{v}}}{C}_{i}\right)+{{\boldsymbol{\nabla }}}\cdot \left(D{{\boldsymbol{\nabla }}}{C}_{i}\right)+\mathop{\sum }_{j = 1}^{n}{r}_{i,j}+{G}_{i}\)

4.6

(4.6.1)