Python Tutorial: Geospatial data

Key Takeaways

welcome to the course and choose spatial data in this course you will discover how to integrate geospatial data in your Python workflow for data science I am your s fandom bozo and my co-instructor is done the octopus battle but before we dive into the fascinating world of manipulating analyzing spatial data let us pause for a second and define what in particular is geospatial data geospatial data are data for which specific location is associated with each record first of all it is data a lot of the operations we will be doing with geospatial data are very similar to those we would do with non spatial data but with geospatial data every observation has a location and can be put on a map this allows us to look at spatial relationships between the data the real power of geospatial data however is the ability to combine both the data themselves and their location unlocking several opportunities for sophisticated analysis spatial data comes in all shapes and sizes a typical example of traditional geospatial data are government governmental census data here we see a picture of the population density in the United States but nowadays there is an increasing availability of new sources of spatial data for example here Danny tracked a bike ride with a smartphone resulting in spatial data in geographic information sciences there are two models for how we record the world the first model is a raster which encodes the world as a continuous surface represented by a grid such as the pixels of an image prominent examples includes altitude data or satellite images the other model is to represent the world as a collection of discrete objects using points lines and polygons this is called vector data here is a real world example of the two data models of the same area on the left you see a thermal satellite image showing the heat loss of buildings on the right you see a visualization of vector data of the same area discrete features where buildings are represented as polygons and roads as lines in this course will focus on vector data so let's take a deeper look into it vector features are made up of three different types of geometries to start with a point geometry a single location with XY coordinates next a line is a group of connected points in the code you will notice that it is called a line string finally a polygon is formed by a closed line to the circles an area additionally one feature can also consist of multiple geometries such as a multi polygon let's give a real-world example illustrating those types of extra data you can represent the countries of the world as polygons shown here on this figure now we add the locations of megacities as point features finally we add some of the largest rivers of the world as lines a last important concept of this first video are the feature attributes typically we will have information about our vector features using the country polygons as example we could have information about the name of the country its capital population number etc when we have a collection of such features for example all the countries in the world combined with its attributes we end up with the table this is a kind of data that will be used in this course for the exercises we assume that you have basic knowledge of the panelists package to work with tabular data and matter clip for visualization let's do some first exercises