From the course: Hands-On PostgreSQL Project: Spatial Data Science

Understanding the basics of geospatial data science

From the course: Hands-On PostgreSQL Project: Spatial Data Science

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Understanding the basics of geospatial data science

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- Geospatial data science might sound complex, but it's something that we all interact with daily. Whether it's through tracking a package, using a ride sharing app, or checking the weather. Geospatial data science is an interdisciplinary field that combines data science with geographic information systems to analyze and interpret data tied to physical places and locations. It's transforming our understanding of the world by uncovering hidden patterns and insights through spatial analysis. Many of us use geospatial technology on a daily basis. For example, we use ride sharing apps that rely on realtime location data to match us with nearby drivers. When we track a package delivery, the system uses geospatial data to provide realtime updates on its location. Fitness apps also leverage this technology to map our running or biking routes. While weather apps use it to show us local forecasts based on our location. Beyond everyday use, there are many use cases where organizations leverage geospatial technology to drive innovation, support decision making, and address complex societal challenges. This was a real-time dashboard developed by Johns Hopkins Coronavirus Resource Center during the COVID-19 pandemic, to track the spread of disease in order to manage public health resources more effectively. This is an example of how conservationists are using satellite imagery data to monitor wildlife movements and protect endangered species. This is Toronto Police Service's public safety data portal, where crime analysts and community enthusiasts are able to build maps and reports based on geospatial data to inform policy decision-making and improve transparency for public safety in Toronto. These are just a few examples of how geospatial data science impacts our daily lives, improving public health, environmental conservation, and urban living. So what is geospatial data science? Geospatial data science is a multidisciplinary field that combines geographic information system technology, data science, and statistical analysis to study and analyze spatial data. Spatial data, also known as geospatial data, is information that has a geographic aspect to it. This means the data is tied to specific locations on the Earth's surface. Common examples of geospatial data include maps, satellite imagery, GPS coordinates, and location-based data collected from various sources. The underlying concept of geospatial data science is built upon the first law of geography, which states that everything is related to everything else, but near things are more related than distant things. In the graph that you see on the screen, the orange dots closest to the red dot are more related than the orange dots that are further away. According to global market research and consulting firm, MarketsandMarkets, the geospatial analytics market is projected to grow from $78.5 billion in 2023 to $141.9 billion by 2028. To work with geospatial data, we use a variety of tools and technologies. One of the most powerful tools for managing and analyzing large spatial data is PostgreSQL, commonly known in the industry as Postgres, a relational database system with spatial extensions like Post GIS and PG routing. Throughout this course, we'll dive deep into using PostgreSQL for spatial data science projects.

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