"How Mass Incarceration Creates ‘Million Dollar Blocks’ in Poor Neighborhoods" - Washington Post
Jul 30, 2015 — Spatial Information Design Lab

Emily Badger of the Washington Post reported the Million Dollar Blocks project "There are neighborhoods on the West Side of Chicago where nearly every block has been painted red — a sign, on the above map, that someone there was sentenced to time in an Illinois state prison between 2005 and 2009 for a nonviolent drug offense.

On several dark-red blocks [mapped here in Chicago], the missing residents are so many — or their sentences so long — that taxpayers have effectively committed more than a million dollars to incarcerate people who once lived there.

This is the perverse form that public investment takes in many poor, minority neighborhoods: "million dollar blocks," to use a bleak term first coined in New York by Laura Kurgan at Columbia University and Eric Cadora of the Justice Mapping Center. Our penchant for incarcerating people has grown so strong that, in many cities, taxpayers frequently spend more than a million dollars locking away residents of a single city block."

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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.

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TUTORIAL
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Project
Justice Atlas of Sentencing and Corrections
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Author(s): 
Kurgan, Cadora, Cummings,
Publication date: 
Saturday, April 12, 2008
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Justice Mapping Center
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The Justice Atlas of Sentencing and Corrections is an online tool for mapping the residential distribution of people involved in the criminal justice system. It uses aggregated address data to map the flow of people being removed to prison, reentering communities from prison, and the standing population concentrations of people under parole or probation supervision.
Intro text (homepage): 
The Justice Atlas of Sentencing and Corrections is an online tool for mapping the residential distribution of people involved in the criminal justice system. It uses aggregated address data to map the flow of people being removed to prison, reentering communities from prison, and the standing population concentrations of people under parole or probation supervision.
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Project Lead: Laura Kurgan
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Justice Atlas of Sentencing and Corrections
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Saturday, April 12, 2008
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The Art of Data Visualization: Activity Mapping Workshop

Juan Francisco Saldarriaga will be leading a workshop on how to download API data using Python in the context of the Art of Data Visualization conference to be held at Columbia University on April 7th. The workshop will take place at the Digital Social Science Center (215 Lehman Library) from 10:30 AM to 11:30 AM.

Here’s a description of the workshop: This workshop will introduce you to basic Python programing and to social media APIs. Students will learn how to write basic Python code to import data, query API's and extract information, and export the results in formats that can be used for analysis or mapping.

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The Art of Data Visualization: Activity Mapping Lecture

Juan Francisco Saldarriaga presented two recent center projects in his talk ‘Activity Mapping’ during the Art of Data Visualization conference held at Columbia University on April 6th. The talk took place at the Davis Auditorium (room 412 Schapiro CEPSR) at 10:50 AM.

Here's a video of the talk:

Here’s a description of the talk: Foursquare check-ins? Citibike rides? Open data can tell us a lot about our cities and how we use them: what we think of them, how we feel about them and how we live in them. In this talk we present two research projects that use this data to explore and understand how people live in New York. We analyze check-in data from Foursquare and Facebook to examine how social media activity relates to socio-economic factors and what this kind of data can tell us about how people feel about the modern city. We also analyze Citibike ride data visualizing the imbalance problems the system faces. All of this, while also exploring multiple ways of representing spatial data.

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The Banking Divide For Taxi Access: Evidence From New York City
In this project we use multiple datasets to explore taxicab fare payments by neighborhood and examine how access to taxicab services is associated with use of conventional banking services.
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Since 2008 yellow taxis have been able to process fare payments with credit cards, and credits cards are a growing share of total fare payments. However, the use of credit cards to pay for taxi fares varies widely across neighborhoods, and there are strong correlations between cash payments for taxi fares, cash payments for transit fares and the presence of unbanked or underbanked populations. In this paper we use multiple datasets to explore taxicab fare payments by neighborhood and examine how access to taxicab services is associated with use of conventional banking services.

Green Cab Origins
Green Cab Origins

Taxicabs are a critical aspect of the public transit system in New York City. The yellow cabs that are ubiquitous in Manhattan are as iconic as the city’s subway system, and in recent years green taxicabs were introduced by the city to improve taxi service in areas outside of the central business districts and airports. Approximately 500,000 taxi trips are taken daily, carrying about 800,000 passengers, and not including other livery firms such as Uber, Lyft or Carmel. Since 2008 yellow taxis have been able to process fare payments with credit cards, and credits cards are a growing share of total fare payments. However, the use of credit cards to pay for taxi fares varies widely across neighborhoods, and there are strong correlations between cash payments for taxi fares, cash payments for transit fares and the presence of unbanked or underbanked populations.

These issues are of concern for policymakers as approximately ten percent of households in the city are unbanked, and in some neighborhoods the share of unbanked households is over 50 percent. In this paper we use multiple datasets to explore taxicab fare payments by neighborhood and examine how access to taxicab services is associated with use of conventional banking services. There is a clear spatial dimension to the propensity of riders to pay cash, and we find that both immigrant status and being ‘unbanked’ are strong predictors of cash transactions for taxicabs. 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. Without some type of cash-based payment option taxi services will isolate certain neighborhoods. At the very least, existing and new providers of transit services must consider access to mainstream financial products as part of their equity analyses.

Overall there are observable differences for cash payments by taxi type, location, trip origin and trip destination. It is impossible to know what characteristics differ between a typical yellow cab passenger and a typical green cab passenger, but something leads green cab passengers to use cash far more often than yellow cab passengers. The results shown on the maps suggest that there is a spatial factor in play.

In all maps there are stark lines that demarcate where riders predominately use cash (shown in yellow) and where they use credit (shown in blue). The areas marked with yellow are the places where cash is king. With the exception of a credit card hotspot surrounding Columbia University in Morningside Heights Manhattan payment types divide cleanly along income lines, where wealthy neighborhoods flanking Central Park (the empty white rectangle in the middle of the map surrounded by blue to the south and yellow to the north) on the Upper West Side and Upper East Side pay for taxi trips mostly with credit cards and poorer neighborhoods to the north in Spanish and Central Harlem are dominated by cash. One interesting aspect is that the socio-demographic characteristics of neighborhoods seemingly play a large role in determining payment type. It is likely that the cash or credit choice is a function of access to a bank account, for which these spatial data are a good proxy. Another takeaway is that much of the city still does not produce a lot of taxi trips and there is not enough data to present primary payment types at all.

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Launch Preview of Conflict Urbanism Aleppo on November 17th, 2015
Nov 18, 2015 — Dare Brawley

On November 17th, 2015 the Center for Spatial Research, in collaboration with the Tow Center for Digital Journalism at Columbia University, hosted a launch preview of Conflict Urbanism: Aleppo.

Laura Kurgan and Madeeha Merchant were joined by collaborator Jamon Van Der Hoek in presenting the research and development that has led to the interactive map of the city of Aleppo, Syria.

The event also featured presentations by Josh Lyons of Human Rights Watch, Tyler Radford of Humanitarian Open Street Maps, and Timothy Wallace of The New York Times about how their work might ineract with a project like Conflict Urbanism: Aleppo. 

Acknowledging that conflict zones are information rich and analytically poor, we hope to begin an interdisciplinary discussion about the potential of detecting the effects of urban conflict through satellite imagery analysis.

Focusing on the current catastrophe in Syria, the Conflict Urbanism: Aleppo project began with an attempt to link eyes in the sky with algorithms and ears on the ground. Towards these ends, we have created an open-source web platform that allows users to navigate maps and satellite images of the city of Aleppo, at the neighborhood scale, across multiple data sets. Working with data from Human Rights Watch, UNOSAT, and the Violations Documentation Center, the platform combines our algorithmically-derived damage identification with their expertise.

We discussed what the correlation of human rights data with satellite imagery analysis tells us about the conflict in Syria, and Aleppo in particular. What possibilities do machine learning and remote sensing algorithms promise for damage detection?  Can and should we use crowd-sourcing and citizen science to better train our algorithms?  We invite advocates and researchers from human rights organizations, humanitarian and development agencies, the academy, and the news media to join us in exploring potential uses of the platform and our tools.

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