Geospatial
<- Return to all blogs
-
Calculating Rural Road Access Globally Using Open Datasets
We used three open datasets to calculate the number of people in the world who do not have sufficient access to a road in an effort to move forward the conversation on the Rural Access Indicator (RAI).
-
Calculating Rural Road Access Globally Using Open Datasets
We used three open datasets to calculate the number of people in the world who do not have sufficient access to a road in an effort to move forward the conversation on the Rural Access Indicator (RAI).
-
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.
-
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.
-
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.
-
Enhancing GIS Capacity with OpenStreetMap
We’re working with the Inter-American Development Bank to improve the GIS capacity of Guyana with tools built to extract OpenStreetMap data.
-
Using ArcMap, R, and Adobe Illustrator to Create Data Driven Infographics
Learn to create a series of data-driven infographics in the context of a project that evaluates bus performance in each Philadelphia City Council District.
-
How Evictions Correspond to Past Divestment in New Orleans
We worked with Jane Place Neighborhood Sustainability Initiative as part of the 2018 Summer of Maps program to analyze evictions data in New Orleans.
-
Digitizing Maps Using Remote Sensing Techniques in ArcMap and R
Learn how to use image processing and remote sensing techniques in R and ArcGIS to digitize spatial data from image or PDF files.
-
Preparing Data for MaxEnt Species Distribution Modeling Using R
How to use R to turn disparate environmental data into clean, usable raster data that can be fed into a MaxEnt species distribution model.
-
Evaluating Machine Learning Models in R: Predicting Marine Debris
Evaluating machine learning models in R with a focus on how to handle biased and imperfect data, specifically volunteer collected marine debris data.
