Map Club, Session 02 — SVG Maps with Kartograph


This week, Map Club embarked upon its second session by experimenting with Kartograph, a lightweight framework for building interactive map applications without an external mapping service. Kartograph is comprised of two libraries: the first is a Python library,, which generates compact, Illustrator-friendly SVG maps from shapefiles and PostGIS; the second, Kartograph.js, is a JavaScript library for creating interactive SVG maps based on SVG maps.

After installing the framework and its dependencies, attendees experimented with the to transform shapefiles into web-ready SVG maps. Jingying accessed NYC OpenData to generate a static map of galleries in New York City:


Natural Earth provided a map of the European railway system to refine in Adobe Illustrator:


Next week, we’ll take a more workshop-like approach to learning QGIS, a free and open source geographic information system. See you then!

For this session’s resources and materials, visit the Map Club Github repository.

Map Club, Session 01 — Exploring CARTO & CARTO.js


Map Club kicked off its first summer session with a dive into CARTO.js, a unified JavaScript library that interacts with the CARTO (formerly CartoDB) web mapping and visualization engine.

After setting up CARTO accounts and local testing servers, attendees prepared datasets for upload to the CARTO interface, explored different geovisualization methods provided by the platform, and experimented with interactive capabilities offered by the CARTO.js library.


Next week we’ll explore Kartograph, a framework for building interactive map applications. See you then!

For this session’s resources and materials, visit the Map Club Github repository.

Map Club!

Map-Logo-Resized-Small-201606230120Come join us for the launch of Map Club with five fast-paced hack sessions geared toward the rapid acquisition of skills in geospatial technology held in the Lehman Library, room 215.

Led by DSSC Spatial Research Intern, Emily Fuhrman, each session provides an informal and fun opportunity for the exploration of a web-based library or framework. Sessions will be loosely divided into three phases: background and setup, self-paced making, and sharing.

All sessions are 2-4pm.

7/12 → Interactive Mapping with CartoDB.js [RSVP]

7/19 → Interactive Mapping with Kartograph [RSVP]

7/26 → GIS Data Processing with QGIS  [RSVP]

8/2 → Interactive Mapping with Mapzen  [RSVP]

8/9 → Geographic Projections with D3.js  [RSVP]

Intersession and Summer Hours

Congratulations for all of you graduating this year! You have worked hard and we are proud of you!

For those who will be here with us for a little while longer, fear not! We are here to help you during the summer months. Our hours will be as follows:

May 13-22nd, 9am-5pm M-F, Closed Saturday and Sunday.

From May 23rd-August 5th:
Monday-Thursday: 9am-9pm
Friday: 9am-5pm
Saturday: 10am-6pm
Sunday: Closed

We will be closed May 30th for Memorial Day and July 4th for Independence Day. We can’t wait to see you in the summer months!

Digital Centers Intern Showcase, May 5th

Don’t miss the Digital Centers Intern Showcase on May 5 from 12pm – 2pm in the Studio@Butler!DCIP Intern Showcase Slide (2)

This program, held every year, is the chance to see the fruits of the labors of the interns in the Digital Centers Internship Program (

Our interns will share their projects and answer questions about their work and their experiences collaborating with the staff of the Libraries. We at the Digital Centers are very proud of the work that our interns do, and we are excited to share all of their accomplishments with you.

Using R with ArcGIS

With a successful collaboration between DSSC and ESRI, a hands-on workshop on ESRI R plugin was presented by Shaun Walbridge, a senior developer from ESRI, on Wednesday, April 20. Shaun provided an in-depth tutorial on how to use R in ESRI, and answered questions from students and librarians.

Our audiences were from a broad background: librarians from Columbia and other institutions in NYC, PhD students, Master’s students, people from administrations, etc.


Presentation Highlights:

SP data types in R
  • 0D: SpatialPoints
  • 1D: SpatialLines
  • 2D: SpatialPolygons
  • 3D: Solid
  • 4D: Space-time
R — ArcGIS Bridge
  • Store your data in ArcGIS, access it quickly in R, return R objects back to ArcGIS native data types (e.g. geodatabase feature classes).
  • Knows how to convert spatial data to sp objects.
  • Package Documentation
  • Upcoming: Conda for managing R environments

For more information about this workshop, please visit

Thank you again for joining us!



Extended Hours and Study Hall

Can you believe it is already getting close to finals? We can’t either! To help you in your mad rush to the end of the year the library will begin extended hours and study hall starting April 17, 2016. The hours will be as follows:

Sunday: 11am-Midnight, Study Hall Midnight-4am
Monday-Thursday: 9am-Midnight, Study Hall 8am-9am, Midnight-4am
Friday:9am-10pm, Study Hall 8am-9am, 10pm-4am
Saturday: 10am-10pm, Study Hall 10pm-4am

Service desks will be closed Sunday-Thursday at 11:45PM and Friday & Saturday at 9:45PM from 4/18-5/12 during study hall hours. It will remain open as a study hall space from 4/18 midnight until 4AM and 4/18-4/11 8am-9am, midnight until 4am. We will close at Midnight on 5/12 and begin winter intersession hours on 5/13.

Happy Studying!

R Open Labs: mi

Last week, Professor Ben Goodrich stopped by R Open Labs to show us how to use the mi package, which he worked on with Professor Andrew Gelman.

As the name ‘mi’ suggests, this package does multiple imputations to help you predict missing values in your data sets.


Screen capture of mi demo by Benjamin Goodrich





Check out the demo if you’d like to give the mi package a try!

R Open Labs continues every Weds, 10 am -12pm, through April 20th.  Drop in anytime! 



R Open Labs: QQPlot

Post by Ellie Ransom, Research Services Coordinator, Science and Engineering Libraries

At last week’s R Open Lab, we visualized our data to test our assumptions of normality using two of of R’s native commands, qqnorm and qqline.

We practiced using the built in dataset, trees, and looked at the variables Height and Volume separately.

Notice how the Height variable is basically normal, but the Volume variable appears to be skewed.


Inline image 2


Inline image 3

It might make sense to transform the skewed data before analyzing it!


Inline image 4

Share your thoughts and suggestions with us here, and we’ll see you next week!