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The role of cartography in environmentally-sustainable development is twofold: first, useful and usable maps enable sense making and decision making about environmental processes, impacts, and protections; second, maps themselves, as digital tools, can be more energy efficient in minimizing our impact on the environment. In this paper, we elevate this energy awareness as a key design consideration for maps and introduce a novel conceptual framework for considering the carbon footprint of the content, form, and use of maps. We first systematically investigate how specific map design decisions impact the energy consumption of digital devices. We then discuss the possible ways that digital maps can be greener, outlining a series of 'big questions' about the content, form, and use context of maps needing future research to realize greener maps. Finally, we assert that green cartography requires not only novel techniques to design green maps but also greater attention to shaping individual, organizational, and social attitudes towards environmentally-friendly maps. [ABSTRACT FROM AUTHOR]

The simulation of urban expansion has become an important means to assist urban development planning and ecological sustainable development. However, the spatial and temporal heterogeneities of urban expansion has been a major challenge for modelling urban expansion. This study designed three features from the perspective of spatiotemporal heterogeneity to improve the accuracy of CA model. The new features cover the trends effects of long time-series data on urban expansion, urban spatial growth intensity based on urban growth kernel estimation and allocation probability of the newly generated urban cells from global neighbourhood effects. Finally, urban expansion in Huizhou, China, was simulated and predicted. The experimental results show that the new features can effectively reduce the prediction error for the total amount of urban growth with a deviation of about 2%, and the overall accuracy of urban expansion is as high as 0.93. The features designed in this paper are shown to be effective and can be applied to urban simulations and scenario prediction with various models. [ABSTRACT FROM AUTHOR]