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Write Merge Requests Like You’re Posting to Instagram
Merge requests (or pull requests) are a huge part of a team’s development process. It’s the main gatekeeper preventing developers from throwing whatever they want into the default branch. It’s also a reference to the history and understanding of the changes someone is making to an application, giving code reviewers and testers more confidence in…
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Write Merge Requests Like You're Posting to Instagram
Merge requests (or pull requests) are a huge part of a team’s development process. It’s the main gatekeeper preventing developers from throwing whatever they want into the default branch. It’s also a reference to the history and understanding of the changes someone is making to an application, giving code reviewers and testers more confidence in…
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Analyzing Spatial Patterns in Life Expectancy with Python
All around the country boundaries separate neighborhoods with high and low life expectancy. We used python to find the most extreme examples.
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Exploring Serverless Portability
With Functions as a Service (FaaS), cloud providers promise us the ability to deploy, run, and scale small pieces of application code without worrying about the underlying hardware or virtual machines. This comes with certain advantages, such as a pricing model that only charges for the amount of time the code is running, quick deployments,…
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Using Cloud-Optimized GeoTIFFs (COGs) for More Efficient Web App Architecture
Cloud-Optimized GeoTIFFs (COGs) are geoTIFFs hosted on a cloud or file server, and are optimized for remote reads. They proved useful in a recent project.
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Cloud Optimized GeoTIFF vs the Meta Raster Format
If you are working with geospatial data visualizations you have probably heard of Cloud Optimized GeoTIFFs and may also have heard of the Meta Raster Format. These formats both provide efficient access to visualization data and have similar goals. The popular GDAL library supports both. So what are the differences, and when would you choose…
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Blacksburg, VA Finds Their “TurboTax™ for Climate Vulnerability Assessments” in Temperate
This case study showcases how Temperate is uniquely poised to walk cities through the confusing process of conducting a climate vulnerability assessment.
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Scoring Philadelphia’s City Council Districts for Children’s Health and Well-being
Scoring Philadelphia City Council districts on assets and risks using a weighted spatial analysis model in R and Python.
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Raster Vision 0.9 Release Candidate
This release of Raster Vision includes bug fixes, an easier setup, improved performance, and the ability to train models off of labels in OSM.
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Analyzing Capital Bikeshare Usage Data From The Command Line
Beginning this year, I set a goal to learn more about Linux and how I can become better at using the terminal. I then stumbled on this blog piece from Netflix on analyzing Linux performance and decided to try something similar. What is my goal? Given some data, what are quick stats that I can…
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Aster Vision – A New Open Source Framework for Deep Learning on Astrospatial Imagery and Space Exploration
Aster Vision is an open source machine learning library for analyzing huge troves of astrospatial data and finding habitable planets around nearby stars.
