Points Unknown: Cartographic Narratives
In this course students will explore new forms of site analysis through pairing geospatial analysis, mapping, and storytelling.
   
Course Description

Society is increasingly dependent on data and computation, a dependence that often evolves invisibly, without any critical assessment or accountability. In New York City, with its mandate to make data public, students have an opportunity to learn how to question data through journalistic lines of inquiry: What data are made public? What do they say about life in the city? How are neighborhoods rendered in data and what are the consequences of those representations? What undiscovered stories can be found in the data?

The architectural design process is coming to rely increasingly on complex, large, spatial datasets for urban analysis. Literacy with spatial data analysis, mapping and storytelling have yet to become an integral part of the design process. In pairing spatial training with journalistic approaches, this course will serve as the missing “integrator” of data and the real world.  

Points Unknown: Cartographic Narratives will focus on rethinking how we characterize a place, an important first step for architectural design, traditionally represented by a basic site plan. In this course, students will explore new forms of site analysis. Through pairing the processes of architecture and some of the skills of journalism, this course will explore four sites in the New York City/Hudson Valley Region—from the proposed redesign of Penn Station to the completion of the Third Water Tunnel.

The full syllabus is available here

Students will work in groups under the direction of an editor to explore a select site, spending the semester researching and constructing a geospatial narrative. Students will learn how to investigate their identified site and will select a particular issue to address. Students will research the site, conduct interviews, perform exploratory data analysis, and learn various geospatial visualization techniques to produce a comprehensive narrative.

The final output will come in the form of a presentation that successfully highlights an identified problem of the site, posits evidence through novel implementations of data, and provides a comprehensive narrative through geospatial representations. In addition, the research will surface recommendations for site intervention.

Points Unknown—designed jointly by faculty at the Graduate School of Journalism and the Graduate School of Architecture, Planning, and Preservation—is funded by Collaboratory, a new university-wide program led by the Data Science Institute and Columbia Entrepreneurship to catalyze interdisciplinary curricular collaboration.

 
Apply for Summer 2017 Student Positions

The Center for Spatial Research is seeking student candidates for both full-time and part-time positions during Summer 2017.

Students will be responsible for data analysis, visualization, map design, and research on projects dealing with our current research focus: conflict urbanism. Students will work extensively with spatial data including mining and analyzing data, processing and collecting data, and/or visualizing data in compelling and innovative ways.  Working in close collaboration with principal investigators, students will produce work for inclusion in papers, multi-media projects, and exhibitions.

Candidates must have experience with GIS and Adobe Creative Suite. In addition, a working knowledge of some of the following tools is a plus: Processing, Python, D3, R, APIs, Microsoft Access, SQL, Stata/SPSS, HTML, CSS, and Javascript.

We are seeking candidates who have experience with computational tools but are also eager to acquire additional skills through the course of their internship. CSR researchers will mentor successful candidates and match them with projects which help them build additional fluencies with computational methods.

Full-time positions are 35 hours per week for up to twelve weeks. Part-time work will be negotiated by student/project. All positions are $15/hour. Please note positions are only available for continuing students at Columbia University. 

Please send a letter of interest, CV, and relevant work examples to info@c4sr.columbia.edu

Applications will be reviewed in the order there are received. 

News Archive
Title Initiative Category Date
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Project
Access to Taxicabs for Unbanked Households: An Exploratory Analysis in New York City
person role
Author(s): 
Juan Francisco Saldarriaga, David A. King
Publication date: 
Friday, January 27, 2017
Publication name, page number: 
Journal of Public Transportation
Description (optional): 
Taxicabs are critical complements to public transit systems. In New York City, ubiquitous yellow cabs are as iconic as the city’s subway system, and the city recently added green taxicabs to improve taxi service in areas outside of the Central Business Districts and airports. In this paper, we used multiple datasets to explore taxicab fare payments by neighborhood and examine how paid taxicab fares are associated with use of conventional banking services. There are clear spatial dimensions of the propensity of riders to pay cash, and we found that both immigrant status and being “unbanked” are strong predictors of cash transactions. These results have implications for local regulations of the for-hire vehicle industry, particularly in the context of the rapid growth of services that require credit cards to use. At the very least, existing and new providers of transit services must consider access to mainstream financial products as part of their equity analyses.
Intro text (homepage): 
In this paper, we used multiple datasets to explore taxicab fare payments by neighborhood and examine how paid taxicab fares are associated with use of conventional banking services. There are clear spatial dimensions of the propensity of riders to pay cash, and we found that both immigrant status and being “unbanked” are strong predictors of cash transactions. These results have implications for local regulations of the for-hire vehicle industry, particularly in the context of the rapid growth of services that require credit cards to use.
Lead image: 
Author Last Names for table: 
King, Saldarriaga
Publication short title (carousel): 
Access to Taxicabs for Unbanked Households
Is Website?: 
no
dashboard_sort_date: 
Friday, January 27, 2017
(currently rendering default node template)
 
Just Published: Access to Taxicabs for Unbanked Households

"Access to Taxicabs for Unbanked Households: An Exploratory Analysis in New York City," by David King and CSR Research Scholar, Juan Francisco Saldarriaga has been published in the Journal of Public Transportation. In this paper, we used multiple datasets to explore taxicab fare payments by neighborhood and examine how paid taxicab fares are associated with use of conventional banking services

Abstract: Taxicabs are critical complements to public transit systems. In New York City, ubiquitous yellow cabs are as iconic as the city’s subway system, and the city recently added green taxicabs to improve taxi service in areas outside of the Central Business Districts and airports. In this paper, we used multiple datasets to explore taxicab fare payments by neighborhood and examine how paid taxicab fares are associated with use of conventional banking services. There are clear spatial dimensions of the propensity of riders to pay cash, and we found that both immigrant status and being “unbanked” are strong predictors of cash transactions. These results have implications for local regulations of the for-hire vehicle industry, particularly in the context of the rapid growth of services that require credit cards to use. At the very least, existing and new providers of transit services must consider access to mainstream financial products as part of their equity analyses.

Download the full article here

 

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Data Visualization for Architecture, Urbanism and the Humanities
An introduction to data visualization theory and methods for students entirely new to the fields of computation and information design
   
Description

Through a series of in-class exercises and take-home assignments, students will learn how to critically engage and produce interactive data visualization pieces that can serve as exploratory and analytical tools. The course will be centered around a semester long data visualization group project, through which the students will learn the basics of data visualization, data analysis, data collection, programming and version control. However, even though the course will teach specific visualization tools, the main conceptual thread will be centered around how to work with data, both in the humanities and in architecture and urbanism. Students will define their final projects around their own interests, and will bring their own datasets into their final projects.

View the syllabus and all course materials here

 
Science Surveyor Demo Launched at the Brown Media Innovation Showcase at Stanford University
Prof. Marguerite Holloway and Prof. Dan Jurafsky presenting the Science Surveyor project

Science Surveyor Demo was launched at the first annual Brown Institute Media Innovation Showcase at Stanford University. Science Surveyor is a tool developed for science journalists that uses cutting-edge algorithms to characterize the scientific literature on a selected topic. Using the abstract and citations of a peer-reviewed paper, Surveyor provides journalists context about that paper in several easy-to-read visualizations.

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CSR Researcher Juan Francisco Saldarriaga to Speak at Bloomberg – Data for Good Exchange
Sep 15, 2016 — Spatial Information Design Lab

Juan Francisco Saldarriaga will be presenting his recent paper ‘Access to Taxicabs for Unbanked Households’ during the Data for Good Exchange yearly conference at Bloomberg. The conference will take place on September 25th, 2016. Here’s a description of the presentation: Taxicabs are a critical aspect of the public transit system in New York City. Ubiquitous yellow cabs are as iconic as the city’s subway system, and the city recently added green taxicabs to improve taxi service in areas outside of the central business districts and airports. In this paper we use multiple datasets to explore taxicab fare payments by neighborhood and examine how paid taxicab fares are associated with use of conventional banking services. There are clear spatial dimensions of the propensity of riders to pay cash, and we find that both immigrant status and being ‘unbanked’ are strong predictors of cash transactions. These results have implications for local regulations of the for-hire vehicle industry, particularly in the context of the rapid growth of services that require credit cards. At the very least, existing and new providers of transit services must consider access to mainstream financial products as part of their equity analyses.

News Archive
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Columbia Data Science Society Lecture - Next Level Data Visualization

Juan Francisco Saldarriaga will be presenting multiple center projects emphasizing process and code for the Data Science Society at Columbia University. He will describe in detail how to gather data from public APIs and how to use different visualization tools to produce compelling graphics.

Read more.

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Project
Jumping the Great Firewall
person role
Author(s): 
Dan Taeyoung
Publication date: 
Tuesday, April 8, 2014
Publication name, page number: 
Spatial Information Design Lab
Description (optional): 
This project visualizes a relatively new phenomenon: online free expression in China. It examines some innovative strategies employed by users of Weibo, a Twitter–like micro–blogging platform, in order to avoid government censorship bloggers post images as text. Images are much more difficult for automated search programs to analyze, which allows image-based content to spread more widely before it is detected and removed. Taking advantage of this, some users now turning writing into images, taking screenshots of their own and others' controversial posts before they're removed, then posting and re–posting them. The project visualizes Weibo posts that were posted and deleted between September 8th to November 13th, in 2013.
Intro text (homepage): 
This project visualizes a relatively new phenomenon: online free expression in China. It examines some innovative strategies employed by users of Weibo, a Twitter–like micro–blogging platform, in order to avoid government censorship bloggers post images as text. Images are much more difficult for automated search programs to analyze, which allows image-based content to spread more widely before it is detected and removed. Taking advantage of this, some users now turning writing into images, taking screenshots of their own and others' controversial posts before they're removed, then posting and re–posting them. The project visualizes Weibo posts that were posted and deleted between September 8th to November 13th, in 2013.
Lead image: 
Author C4SR: 
Author Last Names for table: 
Project Lead: Dan Taeyoung
Publication short title (carousel): 
Jumping the Great Firewall
Is Website?: 
yes
dashboard_sort_date: 
Tuesday, April 8, 2014
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Science Surveyor
Algorithm-based method to help science journalists rapidly and effectively characterize the rich literature for any topic they might cover, as a way to inform and assist news judgment and reporting.
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One of the biggest challenges facing science journalists is the ability to quickly contextualize journal articles they are reporting on deadline. A science reporter must rapidly get a sense of what has come before in the field, understand whether the new paper represents a significant advance or not, and establish whether this finding is an outlier or part of the field’s consensus. Doing all that within a matter of hours or a few days is often impossible. The consequences of these limitations are serious and well documented. Science journalists are often overly dependent on expert sources, which encourages investigative complacency; they become vulnerable to presenting false balance and to covering articles that will be retracted; they sensationalize. As a consequence, the public often receives a mistaken view of science. Many people see science as a series of great new “discoveries” accompanied by a lot of hype; few understand its incremental character, its complexity, its nuance. 

We propose designing a tool that can help science journalists and others to rapidly and effectively characterize the scientific literature for any topic they might cover, as a way to inform and assist news judgment and reporting. Science Surveyor seeks to do this through intuitive, clear interactive graphics that visualize trends in the scientific literature. It aims to show the centrality (or not) of the ideas in the new study, temporal patterns of publishing in the field, and social relationships (the networks of authors and institutions involved in related research).

We see the development of such a tool as having the potential to greatly improve science news coverage, making it more independent, contextualized, and investigative. If successful, the tool will have wider use as well. Scientists and other researchers would use it to improve communication about their fields and practice within their fields. Members of the public would use it to engage with the specialized literature in new ways.

In September 2015 Science Surveyor began its second year of research with an extremely generous Magic Grant from the Helen Gurley Brown Institute for Media Innovation. Science Surveyor was chosen as the Brown Institute’s first Flagship Project. The expanded bi-coastal team aims to develop a methodology and to then test that methodology in interactive visualizations of several case studies from climate science and neuroscience.

Project Team
Name Project Role
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