Spatial Autoregressive Analysis of Land Use Change ─ case study of Ankeng, Taipei County, Taiwan
Date Issued
2009
Date
2009
Author(s)
Yu, Chia-Yuan
Abstract
Land use change models often involve substantial amounts of data with spatial characteristics. The problem is that these methods assume the data to be statistically independent and identically distributed (iid). But, spatial data have the tendency to be dependent, and the error term in a regression model tends to be spatially correlated. Therefore, models that explicitly deal with spatial autocorrelation are widely available and applicable.patial data are usually organized as polygons or grid cells. Through spatial autocorrelation analysis, the relationship between cells is always assumed to be independent. However, considering the real development situation, it does not obey the presumption of independent. As a result, a purpose of the study is to compare the results of spatial autocorrelation with the different definitions of cells.n the aspect of spatial weights, assuming that cells with the same distance, the different quantity may also result in different values of spatial weight. This paper use the concept of gravity model to acknowledge that cells are related to each other not only by the distance between them but also the quantity, namely ratio of coverage of land uses in each cell, and further compare the goodness-of-fit and evaluation between every model. The result is also compared with those of conventional regression model and data mining to find out which type of model has the best evaluation power.he results show that the definition of cell has an impact on the results of spatial autocorrelation. Comparing the evaluation of every model, models that deal explicitly with spatial effects are better than conventional regression models in the goodness-of-fit. The comparative analysis shows that the model which considering built-up area coverage and surrounding slope has advantages over the other models for our specific application. This finding shows that spatial proximity is essential in obtaining a better fit.
Subjects
Land Use Change
Spatial Autoregressive Model
Spatial Weights Matrix
Gravity model
SDGs
Type
thesis
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