Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment
نویسندگان
چکیده
منابع مشابه
Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment
We developed a geographic cellular automata (CA) model based on partial least squares (PLS) regression (termed PLS-CA) to simulate dynamic urban growth in a geographical information systems (GIS) environment. The PLS method extends multiple linear regression models that are used to define the unique factors driving urban growth by eliminating multicollinearity among the candidate drivers. The k...
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urban settlements and their connectivity will be the dominant driver of global change during the twenty-first century. in an attempt to assess the effects of urban growth on available land for other uses and its associated impacts on environmental parameters, we modeled the change in the extent of gorgan city, the capital of the golestan province of iran. we used landsat tm and etm+ imagery of ...
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A Cellular Automata model based on Partial Least Squares Approaches is proposed for simulating complex urban systems. The core part of Geo-CA model is the transition rules and a mass of independent spatial variables are involved in the process of creating CA model. Studies have focused on eliminating correlation using Multi-Criteria Evaluation (MCE) and Principal Component Analysis (PCA), but t...
متن کاملPartial Least Squares Regression (PLS)
Number of latents The same number of factors will be extracted for PLS responses as for PLS factors. The researcher must specify how many latents to extract (in SPSS the default is 5). There is no one criterion for deciding how many latents to employ. Common alternatives are: 1. Cross-validating the model with increasing numbers of factors, then choosing the number with minimum prediction error...
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Pls regression is a recent technique that generalizes and combines features from principal component analysis and multiple regression. It is particularly useful when we need to predict a set of dependent variables from a (very) large set of independent variables (i.e., predictors). It originated in the social sciences (specifically economy, Herman Wold 1966) but became popular first in chemomet...
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ژورنال
عنوان ژورنال: ISPRS International Journal of Geo-Information
سال: 2016
ISSN: 2220-9964
DOI: 10.3390/ijgi5120243