We are living in an era where technological advances are common. According to AgingInPlace (2022), over the years, technology has revolutionized our world and daily lives. Technology plays an important role in society today. Technology’s advancements has given us brand new devices in recent decades, like smartwatches, tablets, and voice assistant devices. These devices have provided quicker ways to communicate through instant messaging apps and social media platforms. It has also made possible to do things like transfer money instantly and make purchases for everything from clothes, food delivery, groceries, furniture, and more.

Google Trends is a well-known, free tool provided by Google that allows you to analyse the popularity of top search queries on its Google search engine. In market exploration work, we often use Google Trends to get a very quick view of what behaviours, language, and general things are trending in a market. Philippe Massicotte’s developed a gtrendsR package for running Google Trends queries in R. It’s simple, you don’t need to set up API keys or anything, and it’s fairly intuitive.

The gap between scientist and decision makers has existed for decades. Despite the advance of technology in communication, scientists finds difficult to share the information that decision makers can use. Traditional scientific mode of sharing information, which is often depend on peer reviewed articles often fail to plainly communicate results to policy makers and practitioners. Recently, developing tools that support interactive exploration of results have changed the ways scientific evidence findings are communicated in better and easy way.

Introduction In this post, we explore the analysis of local spatial autocorrelation statistics, focusing on the concept and its most common implementation in the form of the Local Moran statistic. We explore how it can be utilized to discover hot spots and cold spots in the data, as well as spatial outliers. To illustrate these techniques, we will use the catch data from Deep Sea fishing authority. Moran’s I Moran’s I statistic is arguably the most commonly used indicator of global spatial autocorrelation.

The Global Forecasting System (GFS) atmospheric model is a dataset from the National Oceanic and Atmospheric Administration (NOAA) and National Centers for Environmental Prediction (NCEP). In this database, wind is stored as velocity vector components (U: eastward_wind and V: northward_wind) at 10 m above the Earth’s surface. The resolution of the data is 0.5 degrees, approximately 50 km. Wind velocities have been registered six times per day (00:00 – 03:00 – 06:00 – 09:00 – 12:00 – 15:00 – 18:00 – 21:00 (UTC)), since 6th May 2011 and is updated daily.