Nutrient loading into rivers is generally increased by human-induced land-use changes and can lead to increased surface water pollution. Understanding the extent to which land-use patterns influence nutrient loading is critical to the development of best-management practices aimed at water-quality improvement. In this study, we investigated total nitrogen (total N) concentration as a function o...

The Land Use Scanner is a spatial model that simulates future land use. Since its development in 1997 it has been applied in many policy-related land use projects. In 2005 a completely revised version became available. In this version land use can be modelled at a more detailed 100 meter resolution as opposed to the original 500 meter resolution. Furthermore the new version offers the possibili...

Modeling land-use change is a prerequisite to understanding the complexity of land-usechange patterns. This paper presents a novel method to model urban land-use change using support-vector machines (SVMs), a new generation of machine learning algorithms used in classification and regression domains. An SVM modeling framework has been developed to analyze land-use change in relation to various ...

In this paper, we present a hybrid approach, robust principal component geographically weighted regression (RPCGWR), in examining the land change as a function of both extant urban land use and the effect of social and environmental factors in the Twin Cities Metropolitan Area (TCMA) of Minnesota. We used remotely sensed data to treat urban land use via the proxy of impervious surfaces. We then...