Articles related to practical cartography from the team that helps B2C companies build and defend revenue with AI.
This post is part of our practical cartography and data science series. The problem: you want to split up a few million U.S. address records into equally-sized chunks that retain spatial hierarchy. You want to do this without anything other than a street address (geocoding is expensive!). Maybe you
This post is part of our practical cartography series. Most American geographers will note that - as much as we'd like it to be otherwise - ZIP Codes are not polygons. Rather, they're constantly-changing lines used by the USPS to coordinate delivery in an efficient network. Many of us polygon-happy
This post is part of our practical cartography series. We just rebuilt our Argo reverse-geocoding module as a proper command-line tool. Got a pile of coordinates in a table like this? Pipe them through argo to get the context of an address assigned to each of them: npm install argo-geo
This post is part of our data science and practical cartography series. GNU parallel + ogr2ogr = happy data scientists These power tools in combination make it very easy to process lots of geodata at once, in as many parallel operations as your local machine or server can support. Reprojecting in bulk
This is part of our practical cartography and PostgreSQL series. Put a map on it! Sometimes it's a pain to open up QGIS and load a PostGIS-enabled DB. Sometimes I don't feel like writing a custom tileserver and hooking it up to Leaflet or Mapbox GL just so I can
#post-getting-bite-sized-bits-of-openstreetmap { background-image: url(/blog/content/images/2016/03/roads.svg); background-repeat: no-repeat; background-size: 100%; background-position: 50% 0; } At Faraday, we dig OSM. OpenStreetmap (OSM) is the foundation of our basemap and a model of the power of open data. It guides customers on our platform to their ideal audiences . . . . . . and