Abstract
Forecasting urban expansion models are a very powerful tool in the hands of urban planners in order to anticipate and mitigate future urbanization pressures. In this paper, a linear regression forecasting urban expansion model is implemented based on the annual composite night lights time series available from National Oceanic and Atmospheric Administration (NOAA). The product known as 'stable lights' is used in particular, after it has been corrected with a standard intercalibration process to reduce artificial year-to-year fluctuations as much as possible. Forecasting is done for ten years after the end of the time series. Because the method is spatially explicit the predicted expansion trends are relatively accurately mapped. Two metrics are used to validate the process. The first one is the year-to-year Sum of Lights (SoL) variation. The second is the year-to-year image correlation coefficient. Overall it is evident that the method is able to provide an insight on future urbanization pressures in order to be taken into account in planning. The trends are quantified in a clear spatial manner.
Highlights
Urbanization proceeds at a rapid pace in many parts of the globe and significantly affects the environment as well as the quality of life (Grimm et al, 2008; Seto et al 2012)
A typical example in this category is the SLEUTH model (Clarke, 2008; Pramanik and Stathakis, 2015) where several urbanization parameters are input and the model predicts the probability of each pixel being urbanized in the future
For Athens there is a clear evidence of urban pressures along the new highway as well as towards the east coastline as a result of the new infrastructure built for 2004 Olympic games
Summary
Urbanization proceeds at a rapid pace in many parts of the globe and significantly affects the environment as well as the quality of life (Grimm et al, 2008; Seto et al 2012). There are two main types of urban expansion forecasting models depending on the degree of spatial explicitness. The first and less exact one uses aggregated units, such as administrative divisions, to exploit the time series and forecast the result. The percentage of urban extent increase can be forecast per municipality in a city. The second type of models is more exact. Raster data are used as a time series and forecasting is done per pixel. A typical example in this category is the SLEUTH model (Clarke, 2008; Pramanik and Stathakis, 2015) where several urbanization parameters (slopes, evolution of past urban areas, land use etc.) are input and the model predicts the probability of each pixel being urbanized in the future
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