What is geospatial data? - Python Tutorial

- In the first chapter of this course, we are going to cover the core definitions and key concepts of geospatial data science. We are going to learn what geospatial data is and where to find it, and we are going to discuss about the potential business applications as well. Additionally, we are going to overview a long list of potential use cases and real-life applications. By the end of this chapter, you will have a broad perspective on how wide ranged the applications of geospatial are, and how you could start incorporating the concepts of geospatial data in your daily work. In this section, we will start to familiarize ourselves with the concept of geospatial data and learn how to spot geospatial data when we see it. As you are here, you have already been working with data or some kind of digital information before. Now we will learn the uniqueness that separates geospatial data from any randomly picked data. Geospatial data is any kind of information or data that is linked to a specific location on earth. In other words, whatever information or data we have, it can be geospatial information as long as we can pinpoint a certain place or an area on a map that this information is describing. This can be the location of your favorite restaurant, the administrative boundaries of your city, or the traffic intensity measured in your street. Such data can come from many different sources and in various shapes and forms. However, the structure is always the same, the definition of the measured quantity, it's measured value, and the location it's describing. In other words, first we need to clarify and clearly define the metrics we want to use, such as the temperature, population level, the number of trees, or even the traffic intensity. Then once we have a clear definition, we also have to decide the most sensible locational information we use. Here we see some different geospatial data points where we can see the type of the information, its value, and a locational information as well. First comes the temperature, which we can directly measure using a thermometer and register the exact location of the measurement, such as in the form of latitude and longitude coordinates. Then we can measure traffic intensity as well. For instance, by counting the total number of vehicles that passed across the street during an hour. Here, the natural geospatial unit is not a single point, but a street or a road. Finally, we can handle two-dimensional areas such as an entire city or country as geospatial data. For instance, by attaching the value of the local time based on unified global time zones, we can create a geospatial data point that captures one country and its time zone. These three examples show how we can define immeasurable value, capture its value, and attach this value to the different types of locations. Later on, we will learn more about the technical details behind these different types of locational units. In this section, we learned that geospatial data can be any kind of information or data that is linked to a specific definitive location on earth. We also learned that these locations could be described by coordinates, lines like streets, or even two-dimensional areas such as countries.