Analysis of the dependency among neighbouring land use, gross floor area, and land surface temperature to adapt the future climate

Global warming is changing the urban climate and human society. A report from the Royal Netherlands Meteorological Institute (Albert Klein Tank et al., 2014) identified that the temperature will rise globally by 1,0 -2,3°C in 2050. This paper focuses on the micro level of urban climate change rather global scale. Eindhoven, the fifth-largest city of the Netherlands, with a population of 224,755 in 2015 (CBS, 2015), is the research area. This paper strategically describe the dependency between land use (L), energy consumption (E) and temperature (T). In Europe, buildings account for 40% of total energy use and 36% of total CO2 emission. Therefore, this L-E-T spatial model is focused on the built environment. The target is designing a decision supporting tool for optimal land use planning, including energy consumption and land surface temperature (LST) under climate change. In this paper, a GIS platform has been applied for the creating maps and results visualization, and a regression model has been used for investigating dependency of LT. In the first spatial regression model, the land use types serve as an independent variable for LST. In the second spatial regression model, the gross floor area (GFA) of building serve as an independent variable for LST. In the third part of the regression model, both land use and GFA serve as independent variables for the LST. The regression model is used to investigate the relationships in a complete city. Using the statistical analysis, this paper investigated the dependency between land use, GFA, and LST in 30-meter spatial scale. As a result, it is
concluded that, different types of land uses, and GFA in a neighbouring area have a noticeable influence on the central area regarding to the LST.