PostgreSql is already the second most loved database according to the 2018 StackOverflow survey and it’s the fastest growing in popularity . It’s billed as the “world’s most advanced open source relational database”, and with its comprehensive adherence to SQL standards compliance, it’s easy to see why users love it. But when you add the spatial […]
Continue reading →For years we have used ArcGIS’ Network Analyst to compute network distances and route-based service areas. In the past we found open source options to be lacking. We experimented, for example, with pgRouting in 2014 and our rough assessment was that it did not provide an adequate alternative. Details are in our blog post on […]
Continue reading →We have moved many of our geographic analyses to QGIS from ESRI’s ArcGIS but we have yet to find an adequate replacement for ESRI’s Network Analyst extension. Network Analyst, itself, can be clunky and tricky to use (not to mention expensive!), but it has enabled us to compute network buffers and minimum network distance fairly […]
Continue reading →PostgreSQL allows you to easily dump tables for backup purposes into plain text files using code such as: pg_dump mytable > mytable_backup.sql But this process is not very flexible and can create very large output files. As an alternative, PostgreSQL has a nice little feature that allows users to export to what is called a […]
Continue reading →Although many data science-related projects can be completed with a single software tool we often find that decisions about what tool to use for a project involve weighing a combination of what tool would be “best” for the job, what tools we're most familiar with and whether we already have scripts we can use. As […]
Continue reading →dplyrThe R package dplyr, written by Hadley Wickham is only a few months old but has already become an important part of our data analysis/manipulation workflow, replacing functions that we have used for years. There are several reasons why dplyr is such a valuable tool but most important from my perspective are the following: Speed. […]
Continue reading →We use PostgreSQL/PostGIS to manage a lot of our tabular and geographic data from the US Census. In terms of workflow we will either download a shapefile manually from ftp://ftp.census.gov/geo/tiger/ or, if we’re dealing with more than one file (block groups or blocks for example), we will do this from within R (using the download.file() […]
Continue reading →