Modelling and predicting the spatial dispersion of skin cancer considering environmental and socio-economic factors using a digital earth approach

Almost all causative factors of diseases depend on location.The Digital Earth approach is suitable for studying diseases globally.Geospatial information systems integrated with statistical models can be used to model the relationship between a disease and its causative factors.Through modelling,the most important causative factors can be extracted and the epidemiology of the disease can be observed.In this paper,skin cancer(the most common type of cancer)has been modelled based on its causative factors,including climate factors,people’s occupations,nutrition habits,socio-economic factors,and usage of chemical fertiliser.To fit the model,a data framework was first designed,and then data were gathered and processed.Finally,the disease was modelled using Generalised Linear Models(GLM),a statistical model based on the location of the factors.The results of this study identify the most important causative factors together with their relative priority.Furthermore,a model was used to predict the change in skin cancer occurrences caused by a change in one of its causative factors.This work illustrates the ability of the model to predict disease occurrence.Thus,by using this Digital Earth approach,skincancer can be studied in all the key countries around the world.

Fundamentals of satellite remote sensing:an environmental approach

This is a very useful textbook for university teachers giving undergraduate courses in remote sensing,and for those giving courses on environmental issues.It is written by one of the best known European remote sensing professors.The book consists of nine well-structured chapters,which,together with the more than 30 pages of literature references makes it also an excellent reference for post-graduate students doing research on environmental applications of remote sensing.

An efficient built-up land expansion model using a modified U-Net

This paper introduces an improved convolutional neural network based on the conventional U-Net for simulating built-up land expansion.The proposed method hires a pixel-wise semantic segmentation approach considering the spatial drivers affecting urbanization as data cubes.Independent variables including altitude,slope,and distance from barren,crop,greenery,roads,and urban areas for 1998,2008,and 2018 were considered as covariates for the simulation of built-up land expansion in Tehran and Karaj regions in Iran.The proposed method was compared with the random forest(RF)algorithm as the baseline model.Evaluation using the area under the total operating characteristic indicated the superiority of our modified U-Net(0.87)over the RF(0.82)algorithm.Furthermore,evaluation using the percent correct metric indicated that our proposed model is capable of learning neighborhood effects effectively leading to simulate built-up land expansion accurately,independent from applying a cellular automata(CA)model.Therefore,the modified U-Net independent from the CA which can consider the neighborhood effects is recommended for the simulation of built-up land expansion precisely.