Paper by Shivam Gupta in Statistics & Probability Letters: “Quality of life (QoL) is tied to the perception of ‘meaning’. The quest for meaning is central to the human condition, and we are brought in touch with a sense of meaning when we reflect on what we have created, loved, believed in or left as a legacy (Barcaccia, 2013). QoL is associated with multi-dimensional issues and features such as environmental pressure, total water management, total waste management, noise and level of air pollution (Eusuf et al., 2014). A significant amount of data is needed to understand all these dimensions. Such knowledge is necessary to realize the vision of a smart city, which involves the use of data-driven approaches to improve the quality of life of the inhabitants and city infrastructures (Degbelo et al., 2016).
Technologies such as Radio-Frequency Identification (RFID) or the Internet of Things (IoT) are producing a large volume of data. Koh et al. (2015) pointed out that approximately 2.5 quintillion bytes of data are generated every day, and 90 percent of the data in the world has been created in the past two years alone. Managing this large amount of data, and analyzing it efficiently can help making more informed decisions while solving many of the societal challenges (e.g., exposure analysis, disaster preparedness, climate change). As discussed in Goodchild (2016), the attractiveness of big data can be summarized in one word, namely spatial prediction – the prediction of both the where and when.
This article focuses on the 5Vs of big data (volume, velocity, variety, value, veracity). The challenges associated with big data in the context of environmental monitoring at a city level are briefly presented in Section 2. Section 3 discusses the use of statistical methods like Land Use Regression (LUR) and Spatial Simulated Annealing (SSA) as two promising ways of addressing the challenges of big data….(More)”.