lczexplore: an R package to explore Local Climate Zone classifications

Abstract

Climate change is a growing concern for city planners as Urban Heat Islands have an impact on mortality (Clarke, 1972), health in general (Lowe, 2016) and consumption of energy for building cooling (Malys et al., 2012) among other effects. A first step towards large scale study of urban climate is to define classes based on logical division of the landscape, such as Local Climate Zones (LCZ) defined by Stewart & Oke (2012).A spatial comparison is performed by producing agreement maps between classifications, agreement statistics and a confusion matrix to help qualify and quantify the misclassifications.The lczexplore package aims at comparing different LCZ classifications, but can be used to compare any pair of classifications on geographical units. This software is available as a free and open source R package. Statement of need Comparing mapsAs stated in (Visser & De Nijs, 2006) comparing maps is an important issue in environmental research. The four main reasons to compare categorical variables on geographical units are:• to assess the differences between maps generated by models under different scenarios and assumptions, • to detect temporal changes, • to calibrate or validate models, • to perform uncertainty and sensitivity analysis. Comparing specifically LCZ mapsClimate change is a growing concern for city planners with a special focus on Urban Heat Island phenomenons. The terms rural and urban lack of a clear definition and different classifications of urban and rural landscapes were proposed. Stewart & Oke (2012) defined an approach that complies with criteria of geographical classification defined in Grigg (1965). Their Local Climate Zones (LCZ) are based on a logical segmentation of land-use and urban canopy parameters and define 10 urban types and 7 land cover types.LCZ have gained popularity in the past decade as they sum up relevant information and can help, for instance, apprehend the intensity of the Urban Heat Island (Kotharkar & Bagade, 2018).