Participant observation is a qualitative research method, while census data, surveys, and remote sensing are quantitative methods.

Linear regression is a statistical method used to determine the relationship between a dependent variable and one or more independent variables.

Spatial autocorrelation analysis is used to identify patterns and relationships in spatial data, such as the clustering of certain features or the existence of spatial trends.

Overlay is a GIS operation that combines multiple layers of spatial data into a single layer, allowing for the analysis of relationships between different features.

The primary goal of urban modeling is to simulate urban processes and dynamics, such as population growth, land use change, and transportation patterns.

Geographically weighted regression (GWR) is a spatial regression model that allows the relationship between variables to vary across space.

A gravity model is used to predict the flow of people or goods between two locations based on their distance and other factors.

Trend surface analysis is a global interpolation technique, while IDW, Kriging, and nearest neighbor interpolation are local interpolation techniques.

Factor analysis is used to identify patterns and relationships among a large number of variables and reduce the number of variables in a dataset.

Regression-based models are not typically used as urban land use models, while CA models, ABM, and SDM are commonly used.

Discriminant analysis is used to classify observations into different groups based on a set of predictor variables.

Regression analysis is not a spatial clustering technique, while K-means clustering, hierarchical clustering, and density-based clustering are commonly used.

A spatial lag model is used to account for spatial autocorrelation in a regression model by including the lagged values of the dependent variable as an independent variable.

Regression-based models are not typically used as urban transportation models, while gravity models, LUTI models, and ABM are commonly used.

A spatial error model is used to account for spatial autocorrelation in the error term of a regression model by including the lagged values of the error term as an independent variable.