3/2/2024 0 Comments Alpha-level global cities 2016To be clear, there is more than one way to do this (e.g., we could scrape the data), but there isn’t that much data here, so doing this manually is acceptable. Next, we’ll input the cities by hard coding them as data frames. We’re going to initially create a very basic map, but we’ll also create a small multiple version of the map (broken out by GaWC ranking).įirst, we’ll load the packages that we’ll need. With that in mind, this week we’ll make a map of “ world cities.” This set of cities has been identified by the Globalization and World Cities (GaWC) Research Network as being highly connected and influential in the world economy. Maps make for excellent practice for intermediate data scientists who have already mastered some of the basics. To create a map like the one we’re about to make, you’ll typically need to use a variety of data wrangling and data visualization tools. If you know how to retrieve this data and wrangle it into shape, it will be easy to find data that you can use to make a map.įinally, map making is just good practice. From a variety of sources, you’ll find data about cities, states, counties, and countries. Second, it’s extremely easy to get data that you can use to make a map. They are good visualization tools for finding and communicating insights. Here at Sharp Sight, we make a lot of maps.įirst, good maps are typically ‘information dense.’ You can get a lot of information at a glance from a good map.
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