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From GeoJSON to UTM, these tools help map the world!
Whenever I use tools like Google Maps or Uber, I sometimes find myself overlooking the complexity of the challenge they aim to solve. These apps need to make highly advanced calculations all while showing you a fun visual of a map with data that is usually highly accurate. To say the least, it’s very impressive how far we’ve come with geographic data and mapping tools!
In this article, I’d like to walk you through some of the most popular data formats and coordinates that data scientists and engineers use for geographic data. I’ve you’ve ever taken a look at map projections of the world, you’ll know that there are many many ways to visualize spatial geography and while this guide does not go through ALL of the tools, it should provide a good overview of some of the more popular ones.
Geospatial Data Formats
GeoJSON
{
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [125.6, 10.1]
},
"properties": {
"name": "Dinagat Islands"
}
}
If you’ve worked with JSON data then GeoJSON data should look pretty familiar! GeoJSON data is easy to process and also simple for the user to understand. It represents geographical features with coordinates and supports geometry types like Point, LineString, and Polygon. GeoJSON is one of the more popular data formats for spatial data and easily integrates with Python, R and tools like Tableau for easy analysis.
There are also great tools available for GeoJSON data, like https://geojson.io/ which will let you test out GeoJSON data to learn more about it.
Shapefile
Shapefiles are also one of the most common formats for spatial data that can store both geometric (shape) and attribute data. A shapefile is really just a collection of files that collectively represent points, lines, and polygons along with their associated attributes. Like in the example above, each different portion of the…
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