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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/html4/loose.dtd">
<html>
<head>
<title>Pratt Institute - Mining the Web Class Project Page</title>
<link rel="stylesheet" href="style.css" type="text/css" />
</head>
<body>
<a href="#top"></a>
<div id='header'>
<h2>Pratt Institute Spatial Analysis and Visualization Initiative<br>
Mining the Web<br>
Project Page</h2>
<h3>Guidelines for assignment submission</h3>
<p>We will be using this page to submit assignments. Write up your visualization critiques using the "vis-critique" class and "project" class for your CartoDB visualizations, following the examples below. </p>
</div>
<h2>Spring 2016</h2>
<a href="#intro"><h3>Introductions</h3></a>
<a href="#crit1"><h3>Visualization Critique 1</h3></a>
<a href="#vis1"><h3>Visualization Project 1</h3></a>
<a href="#crit2"><h3>Visualization Critique 2</h3></a>
<a href="#vis2"><h3>Visualization Project 2</h3></a>
<a href="#crit3"><h3>Visualization Critique 3</h3></a>
<a href="#vis3"><h3>Visualization Project 3</h3></a>
<a href="#crit4"><h3>Visualization Critique 4</h3></a>
<a href="shn"><h3>Save Harlem Now! Hackathon</h3></a>
<a href="final"><h3>Final Projects</h3></a>
<hr></hr>
<h2 id="final">Final Projects</h2>
<h3> Final Project - Sami Noor</h3>
<div class='project'>
<p>From working with the Worcester Alliance Against Sexual Exploitation (WAASE), I have accumulated data from the taskforce and local law enforcement in order to find strategic ways to further look into the growing problem of sexual exploitation. The goal of my final project was to use arrest data from the Worcester Police Department, and looking at poverty rates for the city.</p>
<iframe width="100%" height="520" frameborder="0" src="https://snoor101.cartodb.com/viz/7485169e-1716-11e6-9118-0e8c56e2ffdb/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>I successfully created a map that outlines the city of Worcester, and it's census tracts. This shapefile set was then merged with data from the Census, more specifically, the percent of population living below the poverty line in the year 2013. This layer was then visualized to show the intensity of the percentages of the population living below the poverty line. The second layer includes the main roads and highways in Worcester, which is highlighted to allow viewers to see the close proximity of roads and the location of arrests. The third layer are all the arrests for the year of 2013, which was visualized with the heat intensity feature and viewable via time. The dataset initially did not have the longitude, latitude, and altitude. Therfore a kml file was created and cleaned with OpenRefine and combined with the arrests records.</p>
<p>This map now allows us to view the population within Worcester living below the poverty line by census track, with amplified roads, with a cumulative animated heat map of arrests. This map is specific for the one year 2013, but definitely tells us a story. It can really show the connection between poverty and the need to resort to sex work as a means to survive. This would have been powerful to show th poverty over time with the arrests records for a longer time period.</p>
</div>
<div class="project">
<h3>Class Project - Zak Accuardi</h3>
<p>BUSES in New York City are currently a second-tier transit option. The primary layer in this map is bus reliability by census block group, as measured by a performance metric called "excess wait time", which shows how much <em>more</em> time riders wait for the bus compared to the schedule. I added a torque layer of 511 bus service alerts, which is fairly limited in that it's only <em>reported</em> incidents and thus not necessarily reflective of broader system problems. The tweet layer uses CartoDB's "bubble" method to plot tweets according to the 'impact' or reach of the tweet-er, as measured by their follower count.</p>
<p>The tweets themselves are a little disappointing, because they're mostly Foursquare check-ins. With a longer time window of collected tweets, it would be good to filter these out -- but as it stands now the map would be a bit too barren without them. I added a subway layer in which the subway lines are buffered to 400m as a means of visually de-emphasizing the areas where subway service exists. In these neighborhoods, poor bus service reliability is still important but less so than areas where subway service is absent.</p>
<iframe width="100%" height="520" frameborder="0" src="https://zaccuardi.cartodb.com/viz/1abc1d8c-d5a9-11e5-8d2e-0e3ff518bd15/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>DATA in my map comes from several different sources. Twitter data is via the Twitter API, via the CartoDB-Twitter integration. I got NY State block groups from TIGER, clipped them to the NYC boundary, and joined them to both ACS population data (via American Fact Finder) and bus reliability data. The bus reliability data ("excess wait time") was calculated from Bus Time data via the <a href="http://bus-data-nyc.github.io/">Bus Data NYC</a> database (there's also an API to interface with this data, for anyone interested). I got the MTA subway shapefile from the CUNY mapping center. Finally, I downloaded the 511 bus service alerts directly from NY State's (socrata-based) open data platform.</p>
<p>The TOOLS required to get this map to work were CartoDB, SQL, Python, and QGIS. The map itself is obviously in CartoDB, and I used SQL both to pull down bus reliability data from a MySQL server and within CartoDB to buffer subway routes, clip the tweet data to within the City of New York boundary, and join the reliability data with the block group layer. I used python to interface with the Twitter API... but then had an encoding error so I've stuck with just the tweets pulled via CartoDB's twitter integration.</p>
</div>
<div class='project'>
<h3>Final Project - Oliver Mika</h3>
<p>Relevance was the theme for my final project, I wanted to cover an issue in our urban areas not talked about so much; food deserts. Income inequality and gentrification are both pressing issues that are finally being discussed to no end amongst all walks of life. However, why are we not talking about the current state of our lower income neighborhoods? Living in Bushwick, I see the tidal wave of oncoming gentrification, however what about affordability? What about the families that have lived here, and other surrounding neighborhoods for generations who can not afford to buy organic or even heathy to begin with? What about the areas that do not even have a proper grocery store within close range of low income housing, such as Brownsville, much of Bed-Stuy, or around the Bushwick homes? </p>
<p>Quick and simple solutions are needed to address the injustices of our lower income population's lack of access to fresh food, and one such solution has already come to fruition; urban gardening. With this handy tool, not only are low income families able to grow their own produce, but even buy it from other growers at an affordable price without having to commute to a grocery store. My maps illustrate the current state of the urban gardening movement using data from Greenthumb, an organization put together by the city. With these maps I've provided, not only will we look at urban gardedning now, but its future potential for expansion and easy acccess for all.</p>
<h4>Neighborhood Poverty rate</h4>
<iframe width="100%" height="520" frameborder="0" src="https://oliverarmandomika.cartodb.com/viz/a0647c84-17c8-11e6-907b-0e5db1731f59/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe></iframe>
<p>Above are the poverty rates for the strip of neighborhoods spanning from Green Point to Canarsie. Choosing neighborhoods as case studies was an interesting decision I had to make, because it would simply be too large of an undertaking to cover the entire borough. I felt the PUMA's I've chosen (with the exception of Bed-stuy due to a lack of data) were appropriate because all were either formerly or currently poverty stricken, and (with the exception of Williamsburg) have inconsistencies with supermarket distribution.</p>
<h4>GreenThumb</h4>
<iframe width="100%" height="520" frameborder="0" src="https://oliverarmandomika.cartodb.com/viz/8190448a-17ca-11e6-8fbf-0e674067d321/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>GreenThumb is a community garden initiave put together by the city of New York. Their sizes range from small, quarter-lot sized patches to large, communal gardens. When observing the map above, it is easy to tell that these gardens tend to clump together into concentrated areas. What is particularly satisfying is that these dense areas tend to be in the vicinity of larger NYCHA facilities, which tend to be deviod of any type of normal businesses the farther away from the East river you go.</p>
<h4>NYCHA // Brownsville</h4>
<iframe width="100%" height="520" frameborder="0" src="https://oliverarmandomika.cartodb.com/viz/5e2f37de-17cb-11e6-9b1a-0ecfd53eb7d3/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>Taking a closer look at the greater Brownsville area; If we are judging based on section 8 alone, we notice quite a bit of concentrated poverty in what appears to be a more spread out neighborhood. These blocks hold large population sets, so while it may look like there are enough gardens in the area currently, that is not at all the case.</p>
<h4>NYCHA // Bushwick, Bedstuy, Greater Williamsburg</h4>
<iframe width="100%" height="520" frameborder="0" src="https://oliverarmandomika.cartodb.com/viz/c6f734f6-17cb-11e6-9ad9-0e674067d321/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>This area is deceiving. One the one hand, it looks as if there is a severe shortage of community gardens, which is true. On the other hand, although there is a lack of this specific data, since i live and walk around this entire area on a daily basis; I can tell you that there are plenty of grocery stores. However there are a few issues that this data is keeping from us. First of all, although gentrifying, Bushwick still has a fairly high poverty rate. Secondly, much of Bed-Stuy's poverty, although no accurate data is available, is spread across the neighborhood rather than being more concentrated in NYCHA housing. A third thing to keep in mind; population density is higher than that of Brownsville. In summary there are not nearly enough community gardens here and they are not evenly distributed between the two neighborhoods. Note how the middle of Bed-Stuy has nothing while most of Bushwick is lacking as well. These are areas that desperately need more even distribution.</p>
<h4>Accessibility</h4>
<iframe width="100%" height="520" frameborder="0" src="https://oliverarmandomika.cartodb.com/viz/0456808a-62dd-11e5-b948-0e5e07bb5d8a/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>It makes a world of difference when living in a big city with an incredible transportation system. Although we could all write a novel on our personal issues with the MTA, NYC is in a much better position than almost all other American cities. No part of our population benefits more than those who fall under the lower-middle class or below. Thanks to current infrastructure, whether it be trains or bus (which was not included due to a restriction on layers for the free version of CartoDB), it is already easy for many people in our study area to reach these community gardens.</p>
<h4>Potential Garden Sites // Brownsville</h4>
<iframe width="100%" height="520" frameborder="0" src="https://oliverarmandomika.cartodb.com/viz/20f62ffc-17cc-11e6-8b6e-0ecd1babdde5/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>Aside from our schools, public spaces such as parks can be ideal spots to open up new gardens, and free up more space for families, businesses and individuals alike to claim their own patches.</p>
<h4>Potential Garden Sites // Bushwick, Bedstuy, Greater Williamsburg</h4>
<iframe width="100%" height="520" frameborder="0" src="https://oliverarmandomika.cartodb.com/viz/4ed432fc-17cc-11e6-8ab3-0ea31932ec1d/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
</div>
<div class='project'>
<h3> Final Project - Matthew Caruso</h3>
<h3> 311 Complaints during Hurricane Sandy in the Rockaways</h3>
<iframe width="100%" height="520" frameborder="0" src="https://mcaru546.cartodb.com/viz/457d609e-177b-11e6-86c5-0ecd1babdde5/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<h3> 311 Complaints after Hurricane Sandy</h3>
<iframe width="100%" height="520" frameborder="0" src="https://mcaru546.cartodb.com/viz/00873802-17c6-11e6-a926-0e3ff518bd15/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p> For my final project, I looked into the complaints called into 311 during and after the week Hurricane Sandy hit throughout the Rockaways in Queens. For this visualization I found my data sets from the NYC open data source and filtered through the data collected for 311 complaints during 2012. Next I applied another filter so that only the complaints from the time Hurricane Sandy hit and the week after Hurricane Sandy hit. Lastly I wanted to show a correlation between these different types of complaints compared to the areas in the Rockaways that were damaged because of flooding, so I imported a shapefile of a data set I found from the NYC open data source that visualized the areas in NYC that was affected from flooding. </p>
<p> To visualize the different types of complaints, I color coated them on the map. According to my visualization during the time Hurricane Sandy hit the Rockaway area a majority of the complaints were water quality complaints and traffic complaints. After Hurricane Sandy hit a there were a ton of complaints being brought to 311's attention and the biggest complaints were damaged trees, water systems and complaints dealing with traffic conditions. These visualizations matcheed up very well with what I expected my correlation to be that in fact a lot of people that lived in the Rockaway areas during this disaster couldn't drink the water and also had many problems traveling because of a traffic light not working or construction that was taking place to fix the damages that were happening and had to deal with these problems weeks after the super storm hit. </p>
</div>
<div class='project'>
<h3> Final Project - Jonah Bleckner</h3>
<p>Throughout this class, I have slowly accumulated data and feedback about a project that I found myself very interested in exploring: the aging foreign-born population in New York City. For my final project, I fine-tuned the previous maps I had drafted so that a narrative about NYC and its aging population began to emerge.<p>
<h3>Like many industrialized metropolises, New York City is aging</h3>
<iframe width='100%' height='520' frameborder='0' src='https://jobleckner.cartodb.com/viz/07d5199e-148d-11e6-8913-0ecfd53eb7d3/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p> This map simply illustrates the sheer size of the senior population accross New York City. This major demographic shift is an issue that policymakers and urban planners must anticipate. As Christian Gonzalez-Rivera from the Center for an Urban Future writes, "In the next two decades, demographers expect the number of city residents 65 and older to increase by 35 percent, going from approximately 998,000 today to 1.3 million in 2030."</p>
<h3>Foreign born seniors are a substantial but often overlooked segment of the aging NYC population</h3>
<iframe width='100%' height='520' frameborder='0' src='https://jobleckner.cartodb.com/viz/d4803ab0-0e47-11e6-8292-0e31c9be1b51/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>Senior imigrants make up 46% of New York City's older population. Many foreign born seniors reside in Brooklyn and Queens, in neighborhoods like Flushing, Jackson Heights, Bensonhurst and Coney Island. According to the Center for an Urban Suture, 65 percent of all older immigrants live in Brooklyn and Queens. Areas in Manhattan and Staten Island have a lot of older residents, but few are immigrants.</p>
<h3>Many foreign born seniors have Limited English Proficiency (LEP)</h3>
<iframe width='100%' height='520' frameborder='0' src='https://jobleckner.cartodb.com/viz/4c2456c4-0ff5-11e6-a113-0e3ff518bd15/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>LEP is one of the proxy indicators that can be used to estimate the age in which a foreign-born senior immigrated to the States. As a result of language barriers, many older immigrants have trouble finding out about existing support services that New York City offers, such as tax and entitlement programs, Medicare, Medicaid and the Supplemental Nutirition Assistance Program, as well as services delivered through community-based organizations.</p>
<h3>Foreign born seniors are more likely to live in poverty than their Native-born counterparts</h3>
<iframe width='100%' height='520' frameborder='0' src='https://jobleckner.cartodb.com/viz/dc6c0d90-15a3-11e6-87b7-0e31c9be1b51/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>The distribution of Seniors living below the poverty line aligns with those neighborhoods that have large senior immigrant communities. In fact, 24% of older immigrants live below the poverty line, while only 15 % of their native born seniors do.</p>
<h3>Naturally Occuring Retirment Community (NORCS) social services are unevely distributed</h3>
<iframe width='100%' height='520' frameborder='0' src='https://jobleckner.cartodb.com/viz/3b4448c4-162a-11e6-b328-0e674067d321/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>NORCS are a comprehensive program through which the state funds the provision of social services to facilitate senior communities to age in-place. In order to qualify for thse sevices, a housing development must be composed of either 50% seniors or over 2,500 seniors, who must predominantly have low to moderate income levels. Although NORCs are an effective policy tool to deliver a wide range of service to an aging population, these NORCS do not align well with where foreign born seniors reside: less dense residential areas where NORCs can't be established. Furthermore, foreign-born seniors are less likely to live in large public housing developments where these NORCs tend to be because of NYCHA's difficult application process and long waiting lists. For this reason, NNORCs, a similar model for neighborhoods that pass the threshold of containing a large population of seniors with moderate incomes, are better for targeting sernior immigrants. However, there are very few NNORCs in New York City where immmigrant seniors currently reside.</p>
<h3>Senior Centers have the capacity to deliver culturally competent care, but are not evenly distributed</h3>
<iframe width='100%' height='520' frameborder='0' src='https://jobleckner.cartodb.com/viz/b290d28e-152c-11e6-9363-0ea31932ec1d/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>According to Gonzalez-Rivera, "Cultural barriers are a crucial and often overlooked part of why immigrant seniors are less likely to avail themselves of existing community services. Different cultural groups have different ways of socializing their elders and different attitudes around seeking government services. Because of this, service providers must be creative in finding culturally sensitive ways to reach populations in need." Senior Centers have the capacity to be more culturally-attuned to its surrounding community. However, there is very little standardization of senior center funding and services; one senior center could be a basement that organizes programs like Bingo, while another may provide a services like meals on wheels and health care services. Furthermore, the map above shows that senior centers are also not equitably distributed geographically. For example, in Bay Side, Queens, there is only one senior center for a population of nearly 11,000 foreign born seniors.
<h3>The public branch libraries is an underleveraged infrastructure for senior support that is equitably distributed</h3>
<iframe width='100%' height='520' frameborder='0' src='https://jobleckner.cartodb.com/viz/1b8decce-1247-11e6-81fd-0e98b61680bf/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>The public branch libraries in NYC form an infrastructural network in NYC that is evenly distributed. Policymakers should take advantage of this already existing network to provide more substantial in-place elderly care support accross the five boroughs.</p>
</div>
<a href="#top"><h4>Back to Top</h4></a>
<hr></hr>
<h2 id="shn">Save Harlem Now! Hackathon</h2>
<div class='project'>
<h3> Save Harlem Now! Data - Zak Accuardi </h3>
<a href="https://zaccuardi.cartodb.com/viz/51d71b40-164b-11e6-ba80-0ef7f98ade21/public_map"> PLUTO zoning data for Harlem + landmarked NYC buildings </a>
<iframe width="100%" height="520" frameborder="0" src="https://zaccuardi.cartodb.com/viz/51d71b40-164b-11e6-ba80-0ef7f98ade21/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>This map includes data on NYC building lots from the PLUTO database and landmark data from the NYC Landmarks Preservation Commission (LPC). The former was downloaded from Chris Whong's <a href="http://chriswhong.github.io/plutoplus/">Pluto Data Downloader</a>, and the latter scraped from the LPC's <a href="http://nyclpc.maps.arcgis.com/apps/webappviewer/index.html?id=93a88691cace4067828b1eede432022b">Discover NYC Landmarks</a> map app.</p>
<p>What's currently visualized in the PLUTO layer is the ratio of total assessed value to assessed land value -- high values providing some indication that the development on that lot is closer to maximizing the development potential in terms of the plot's *value*.The landmarks data is visualized here only to show relative age of properties.</p>
<p>It would be great to explore land and property value as a predictor of development -- unbuilt FAR is one indicator of likely development sites, but properties with low whole property value relative to the assessed land value could also be strong candidates for development. Combining this information with FAR could make this analysis even stronger. Even more simply, it would be interesting to look at assessed land value per land area. Besides basic name, address, and age information, the landmark dataset includes links to photographs, building types (though these don't seem to be consistent with the zoning designations), and architects.</p>
</div>
<a href="#top"><h4>Back to Top</h4></a>
<hr></hr>
<h2 id="crit4">Visualization Critique 4</h2>
<div class="vis-critique">
<h3> Visualization Critique 4 - Jonah Bleckner </h3>
<a href="http://www.nytimes.com/interactive/2015/10/31/upshot/who-still-doesnt-have-health-insurance-obamacare.html"> "We Mapped the uninsured. You'll Notice a Pattern." </a>
<p>Two years after Obamacare was instituted, the Upshot, the data journalism arm of the New York Times, released a very intriguing map of the percentage of Americans who are uninsured per county. The visualization clearly communicates a regional pattern, namely that the remaining uninsured Americans predomninetly reside in the South and Southwest, and demographically, they tend to be poorer Americans. The explanation that the article gives for this incredible disparity is that there is now a medicaid gap in which more than three million people in 19 states are "...too poor to qualify for subsidies in the new marketplaces, but unable to get into a government program" as a result of many states' rejecting the expansion of medicaid elligibility.</p>
<p>The tryptych that is included in the article is very effective in illustrating that expansion of insurance coverage over the past couple of years. The methodology behind this map was very painstaking and interesting to read about. The process started with a cell phone and landline survey of 12,000 adults in either English or Spanish. Based on these survey results and publically available data, the researchers created a model using 30 variables to predict whether a person is insured or not. These prediction were then validated by canvassing neighborhoods and partnering with local community-based organizations. The description behind the methodlogy made me think about how pollers are using big data and modelling to get more accurate predictions than simply utilizing a landline poll.</p>
</div>
<div class="vis-critique">
<h3> Visualization Critique 4 - Zak Accuardi </h3>
<a href="http://218consultants.com/transportation-tweet-map/"> 218 Consultants' "Transportation Tweet Map" </a>
<iframe width='100%' height='520' frameborder='0' src='https://aspevack.cartodb.com/viz/e3b5dfb2-907a-11e5-be80-0ea31932ec1d/embed_map' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>This map, built by a group of planning and transportation grad students at UC Berkeley achieves something spiritually similar to what I was envisioning with NYC tweets. As part of a larger class project, they've mapped a few hundred tweets in Oakland related to transportation, and color coded them according to "tweet sentiment", having processed the data using an algorithm that can guess at whether the tweet is positive or negative in nature. This sentiment map is contained in a separate layer. The map's interactivity comes from being able to read the tweet text when hovering or clicking on tweets. When the user is zoomed out, tweets are grouped together to make the map more legible.</p>
<p>Without any context presented about the actual transportation network, it's hard to draw a direct connection between the tweets and actual transportation infrastructure, which is presumably the goal. There are a couple heavy geographic clusters of tweets -- are these BART stations, perhaps, where many people tweet immediately upon exiting? Are they just busy places? Are they places of interest where lots of people post Instagram photos (and tweet about them)? It would also be nice to have some more context about the tweets themselves, at least when clicked (Twitter @handle and time of tweet, e.g.).</p>
<p>There are some valuable lessons though; I had been considering sentiment analysis beyond the scope of this class project but it may be worth looking into briefly, or at least considering for a future upgrade. The coloration by sentiment does provide a level of visual intuition that will be missing if the user is left only to read the tweet's details.</p>
</div>
<div class='vis-critique'>
<h3> Visualization Critique 4 - Oliver Mika</h3>
<p>
<a href="http://flowingdata.com/2016/04/20/parent-work-hours/">Shifting Parent Work hours</a></p>
<p>For my third installment of my visual critiques, I stuck to the theme of statistics from last class and found a visual using the standard bar graph to depict hours spent between sexes at work or home. Spanning the year 1965 all the way to 2014, we can spot the dramatic difference between gender roles. In the sixties and through much of the nineties, most women spent very few hours in the workplace and dedicated their time to raising their families while them men were bringing home the money. Now, that couldn't be any more opposite.</p>
<p>First and foremost, what I enjoy about the usage of bar graphs is how cool the data looks from this representation. At first, we see long tail distributions; the data is skewed towards men being at work and women... not. but as time progresses and females gain more independence and stray away from traditional gender roles, these graphs become messy, or technically, bi-modal. Sure, stay at home dads are still relatively uncommon in 2014, but we witness the beginning of a major social shift through these not so subtle changes in the data distribution. Any other graphics need not apply here with the exception of a few labels, the way everything is presented is perfect.</p>
</div>
<div class= 'vis-critique'>
<h3> Visualization Critique 4 - Matthew Caruso</h3>
<a href='http://hint.fm/wind/gallery/oct-30.js.html'> Wind Speeds during Hurricane Sandy </a>
<p> For my visualization critique 4, I found an interesting map of the United States of diffrent wind speeds that occurred during Hurricane Sandy from October 29th and 30th 2012. This map shows different winds speeds that occurred during these two days 1 mph all the way up to 30 mph. There is also a legend on the side of the map that shows a visualization of how fast each wind speed looks like on the map and states how many miles per hour that certain wind speed actually is.</p>
<p>I found this visualization to be very interesting, because for my final project I would like to go into more detail about the different parts in New York City that Hurricane Sandy had the greatest impact on. One thing that really stands out in this visualization is that the highest wind speeds are happening on the map around New York City, and can kind of give people a general sense of how dangerous it must have been throughout these two days for people living in New York City's impact area.</p>
</div>
<div class='vis-critique'>
<h3>Visualization Critique 4 - Sami Noor</h3>
<a href="http://15years.morizbuesing.com/"> 15 Years Fortress Europe</a>
<p>This visualization was created by <q>The Migrant Files</q> which is a consortium of journalists from over 15 European countries and coordinated by Journalism++ (an agency for data-driven storytelling). The map titled <q>15 Years Fortress Europe</q> is an interactive map of migrant and refugee deaths on the way to Europe, or trying to stay in Europe starting from the year 2000. The map is powered by the collection of articles specifically from, United for Intercultural Action, a non-profit whose network comprises over 550 organizations across Europe, and Fortress Europe, founded by the journalist and author Gabriele Del Grande, which also monitors the deaths and disappearances of migrants to Europe.</p>
<p>The map itself is very dynamic and is an accurate representation of migrant deaths in Europe, as the dataset they created via open source data has been cleaned thoroughly with OpenRefine. The Migrants' Files database of emigrant deaths now structures the data according to name, age, gender and nationality. Every fatal incident is recorded with its date, latitude, longitude, number of dead and/or missing as well as the cause. The journalists have also included a section about <q>Margins of Error</q>, detailing out their methodology for accuracy. For example, duplicates may pose a problem and the geolocation of certain events can be inaccurate.</p>
<p>This visualization has great potential in mapping out the refugee crisis within Europe and can allow policy makers and NGOs to better assist migrants in having safe passage to Europe. The map could be further improved by having a map that is much more vivid in color, and allowing users to visualize events within specific countries, date ranges, and narrowing down by different causes of death. It would also be very effective to have a running timeline of world events on the side, just to provide some background to what is happening in the world which ultimately affects migration.
</div>
<div class='vis-critique'>
<h3>Visualization Critique 4 - Nayana</h3>
<img src="https://www.nycedc.com/sites/default/files/images/infographic/Final_Extent1.gif" style='height:300px;'>
<p>
<a href="https://www.nycedc.com/sites/default/files/images/infographic/Final_Extent1.gif">Alt link</a>
</p>
<p>
I found this data visualization on the NYCEDC website based on the number of closed incidents reported by the Graffiti Free NYC cleanup initiative. It's a torque-cat that cycles through the years 2008-2013 in a Gif format similar to what I had been trying to achieve. I think it's successful in showing the overall spread and giving a top level idea of saturated areas/ distribution and changes between years.
However, it clumps the data together into irregular intervals going from increments of 2 to 4 and then 9 & 49 which creates a skewed representiation.
</p>
</div>
<a href="#top"><h4>Back to Top</h4></a>
<hr></hr>
<h2 id="vis3">Visualization Project 3</h2>
<div class='project'>
<h3>Project 3 - Nayana</h3>
<iframe width="100%" height="520" frameborder="0" src="https://nayana23.cartodb.com/viz/addccf5e-10bd-11e6-aedc-0e98b61680bf/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>
For this visualization I wanted to continue exploring exploring "public art".
This map represents graffiti complaints from 311 calls from December 2010 to April 2016.
I decideded to use a cumulative torque to represent the clusters of areas that accumulated the most graffiti over the period of time.
Ideally I'd like each year to be represented in its own layer/category to show changes/movement in popular centers.
This could be possible by cleaning up the data, adding a "year" column and using the torque cat in CartoDB.
I think it might also be useful to see 5 different maps (one for each year) side-by-side and break up the data to possibly identify trends associated with seasonal events at different times of the year.
</p>
<p> In terms of the data itself, since it comes from 311 calls, it tends to be lower Manhattan heavy. I'm not sure if the calls report graffiti in action or just illegal tags/street art after the fact. Finding data from the Graffiti-free NYC cleanups and locations might be a more accurate representation and eliminate some redundacy in reporting.
</p>
</div>
<div class='project'>
<h3> Project 3 - Matthew Caruso </h3>
<iframe width="100%" height="520" frameborder="0" src="https://mcaru546.cartodb.com/viz/29e0b6ae-10b6-11e6-9ba8-0e31c9be1b51/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p> For our third visualization, I decided to use the NYC Open Data source to develop a map that shows water quality compliants in NYC from 2009 through 2016. I decided to create a Heat Map for this visualization to get a good sense how dense the amount of compliants where in certain parts of the five boroughs. The density is measured by the latitude and longitude of the reported incidents are and the dates that the complaints were created. I changed my data column into a date data column set instead of a string data set in order for CartoDB would be able to run a time lapse to show the density of complaints throughout the course of the years. </p>
<p> After looking at the results of my maps the majority of the complaints were generated in either Manhattan and in Brooklyn. In areas such as Queens and Staten Island there seemed to be barely any complaints were created. Something that I found very shoking considering how clean New York water claims to be in the there were about 6957 water quality complaints made from 2009. </p>
</div>
<div class='project'>
<h3> Project 3 - Jonah Bleckner </h3>
<iframe width="100%" height="520" frameborder="0" src="https://jobleckner.cartodb.com/viz/4c2456c4-0ff5-11e6-a113-0e3ff518bd15/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>For our third visualization project, I decided to continue to develop my final project about mapping the demographics of seniors in New York City. In this particular map, I visualized the portion of the senior population in NYC that have limited to english proficiency. To this end, I used the 2010 American Census Survey and merged it with public use microdata area shape files in order to illustrate clearly the neighborhoods that have large populations of seniors that have limited english proficiency. In order to add another dimension to the map, I also pulled the locations of senior centers from the Department of the Aging's directory. Since the data didn't include longitude and latitude, I used a google sheets script to geocode the addresses.</p>
<p>The resulting map highlights the specific neighborhoods where there are large populations of seniors that do not speak english well but have less access to senior center resources. For example, in Bayside / Little Neck, there is only one senior center that covers approximately 6200 seniors with limited english proficiency.</p>
</div>
<div class='project'>
<h3>Visualization 3 - Oliver Mika</h3>
<h3><a href="https://oliverarmandomika.cartodb.com/viz/1595c250-6708-11e5-b2c8-0e76aec36da9/public_map"> Green Thumb Garden Density w/in BK</a></h3>
<iframe width="100%" height="520" frameborder="0" src="https://oliverarmandomika.cartodb.com/viz/1595c250-6708-11e5-b2c8-0e76aec36da9/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p> For my third visualization, I chose the theme of urban gardening within Brooklyn. I wanted to compare the neighborhoods containing the most densely concentrated areas of public gareining patches with their average income levels. My reasoning for merging these two data sets together is to confirm the general purpose of public gardening; serving the public. As this emerging trend of sourcing locally has been blowing up the past couple years, many public gardens have become overrun with growers and buyers with more money and self-interest (i.e. restaurants, food caterers) and has left less produce or open patches for lower income families. </p>
<p> What I particularly like about my map is that it is relatively easy to understand the few points i am trying to get across. Looking at the select Brooklyn neighborhoods with the highest concentration of public gardens, one can see they lie within a corridor or historically lower income neighborhoods (Bed-stuy, Crown Heights, and Brownsville/Canarsie in particular). This visual data is important when noting that these are areas that would otherwise be known as food deserts. So, although we do not have more in-depth data for produce sales/ wait lists of the people uilizing these public gardens, what we do know is a majority of these access points to fresh food is definitely made readily available to individuals and families alike from lower income communities.
</p>
</div>
<a href="#top"><h4>Back to Top</h4></a>
<hr></hr>
<h2 id="crit3">Visualization Critique 3</h2>
<div class= 'vis-critique'>
<h3> Visualization Critique 3 - Matthew Caruso</h3>
<a href='https://public.tableau.com/s/gallery/endangered-safari'> Endangered Wildlife in Africa </a>
<p> For my visualization critique 3, I found a visualization that shows a graphic created by RJ Andrews of Info We Trust about the amount of endangered species in Africa. I found this visualization to be very appealing to me and spoke a very important message. The visualization is presented with icons of each of the different animals you will find within the continent of Africa. </p>
<p> Each animal is color coated, red being very endangered and green being less concerned. Also another technique I thought was very interesting but not very effective if you were only to see the visualization without any narrative was that animals facing towards the left are all the animals in Africa that their populations are decreasing and all the animals that are facing towards the right are all the animals in Africa that are making a come back and are slowly increasing their population. </p>
</div>
<div class="vis-critique">
<h3> Visualization Critique 3 - Zak Accuardi </h3>
<a href="http://www.arcgis.com/apps/OnePane/azuretwitter/index.html?appid=b0528926daa24df1a749f6832c127422"> Tweets by Chicago Transit Authority (CTA) Riders </a>
<img src="https://lh3.googleusercontent.com/aMNZF5qE-O57j-fqX17694xehBG7iRYooSY1matUhiRebDBV5fR5XaLaJ7kdfOMYrU_gN3YKipVE5-eITJfd9sS2EFPH5Jc6BsnJpQKxGfsDEVnJuVpRtNKAGCD1tN2UPE-md_ucaNcYUevUHld9ERqwldPmz15J9qogdeLuK8ke7fcFj8Cw1Oxb9WMqnlu7tsD0k7Dpw3NXmqYwtLGWjDc69-DQpHzC3XQiKZntn1aTTkpEbSrp_iLhqDTpdj7WuCP3LrI3ivds3tr2vFpV8R0gkqEo174DiIibtNh2OwgsUJgvUwKj-UajBTB3uTYtiLpkrfqFx9uWZIsUSUEx7e3AqqUwOAPxqe6O3Oef2SQnWQ8LmTeocWPeYiN25Q5j3riyGpfWbarCUpmq9CkUyk7PtxnV0Ox4QYdpTSgdUfFz8JUo1dFr0wvbfxG7Z3VYI8j2IShPllRiFJZGggJL1vm_b--Ob_UEN3QkByVeMUYECZNUq2RD9SYQYvUwleuEcYsL1Ns5QgRwoNEGxR97dJCHusj5vOw7pjmXoPsSIlh2u4VV0MsJ3-YbJ_t6cflp9yLe6A=w958-h523-no" style="width:100%;height:520;"> </img>
<p>This fairly straightforward visualization--produced by a Chicago-based design consultant--shows recent tweets about any topic of the user's choosing, by means of entering text within a dropdown search box (only possible after the user signs in to Twitter). Rail transit lines are displayed with stops indicated by very literal train icons. The city's wards are also indicated by their respective numbers on the map. Both transit stops and wards show some additional metadata when clicked. The choice of train icons to indicate L stops is clunky, especially downtown within Chicago's "loop", where stations are so dense that they obscure the transit lines themselves.</p>
<p>"Tweets by CTA Riders" was the only map I could find that seems to be trying to accomplish something comparable to what I'm proposing for my project. I don't think they really succeed; indeed, it's possible this map was never quite finished. The requirement for the user to login to Twitter and need to actually search for the term "CTA" manually makes the map's title feel wrong. It's unclear in the context of this map why exactly wards are highlighted on the map; there's no context provided to understand why this is included. The twets show up easily on the map and they've succeeded at enabling them to show up quickly with customized search keywords, which actually seems more advanced than might have been necessary to make the map. The map itself is built on Esri's online platform, which seems to be doing okay but isn't as slick as CartoDB.</p>
</div>
<div class="vis-critique">
<h3>Visualization Critique 3 - Jonah Bleckner</h3>
<p>For this project I chose a more static map entitled, <a href="http://www.landscapesofprofit.com
"> Landscapes of Profits: Measuring real estate "flips" for community benefit in New York City.</a> This map was created as a way to substantiate a policy proposal to redistribute a portion of the wealth that is being accumulated in NEw York CIty through flip salse. A flip sale, for the purpose of this study is a residential unit that is bought, often renovated, and resold within two years. According to this report, in 2014 there was accumlatively $3.4 billion in real estate sales on properties that were owned for less than two years. As a result, the authors of this report argue that a 1% tax should be levied on properties that are flipped within two years of initial ownership in order to fund the deBlasio administration's affordable housing initative. This modest tax would bring in nearly $40 million annually to put towards community benefit projects.</p>
<p>In terms of the methodology, the authors behind this map pulled data from the Automated City Register Information System (ACRIS) of the Department of Finance, PLUTO, and the NYC Department of PLanning. In order to avoid counting the property sales that only cover the cost of renovation, the data was filtered to exclude any sales with under $100,000 in profit. Furthermore, the map excludes Staten Island because there are no Department of FInance records for the borough. It would be cool to add complementary visualizations to this project that illustrated what the sale prices was for some of the individual properties that were sold in each neighborhood.</p>
</div>
<div class='vis-critique'>
<h3> Visualization Critique 3 - Oliver Mika</h3>
<h3>Blame the Weather</h3>
<a href='https://public.tableau.com/s/gallery/blame-weather-us-flight-delayed-precipitation'>Blame Weather US Flight Delayed</a>
<p>
For my latest visualization critique, I decided to use one of the
resources provided in the syllabus. This is a serios of maps showing air
travel affected by rain at several airports across the Unites States.
Coming from Seattle, I found this map amusing because i had no idea it
was possible to delay a flight due to rainfall. The map is measured
almost as a cloropleth. There is a Weather Delay Minutes per inch of
Precipitation (WDMIP) score which is measured my color to illustrate how
many or few delays affect each airport.</p>
<p>Visually speaking, i think this map does a pretty good job of getting
its point across. It is legible, easy to understand, and is a topic that
is very precise, but whose data can be used for a variety of different
studies. Color coding helps represent the data well enough to let the
viewer know that the airports in wetter climates actually have fewer
delays than those in drier ones! Also, the colors and design make the
map(s) visually compelling so they are also pleasant to look at.</P>
</div>
<div class='vis-critique'>
<h3>Visualization Critique 3 - Sami Noor</h3>
<a href="http://www.slate.com/articles/life/the_history_of_american_slavery/2015/06/animated_interactive_of_the_history_of_the_atlantic_slave_trade.html"> The Atlantic Slave Trade in Two Minutes</a>
<p> This interactive visualization, was designed and built by Andrew Kahn, and posted along with the Slate article on the Trans-Atlantic Slave Trade. The interactive animates more than 20,000 voyages which were cataloged in the Trans-Atlantic Slave Trade Database. The dots—which represent individual slave ships—also correspond to the size of each voyage. The larger the dot, the more enslaved people on board. And if you pause the map and click on a dot, you’ll learn about the ship’s: origin point, destination, and history in the slave trade. The graph at the bottom accumulates statistics based on the raw data used in the interactive and, again, only represents a portion of the actual slave trade.</p>
<p> The visualization itself was very powerful because when you learn about history - there is so much you can visualize. This amplified the extent of the Trans-Atlantic Slave trade and allows us to see the data that has been cataloged. The proper map and tools were used in generating the map, though I believe there should be a zoom feature and a second layer with the most/least used routes within the slave trade.</p>
</div>
<div class="vis-critique">
<h3> Visualizatio Critique 3 - Nayana</h3>
<a href="http://music.columbia.edu/~luke/perfect/index.shtml"> A More Perfect Union by Luke Dubois</a>
<p>
This visualization shows the occurrence of keywords in dating profiles sorted by zipcode. The data represent about 19 Million profiles from 21 dating sites. The artist talks about it as part "romantic census" and digital portraiture with data.
</p>
<p>The maps themselves have been visualized in two different way- one, as chloropleths with the occurence of each word represented in a different color for male and female. This version of the maps works well in communicating the overall frequency and tone in specific states. However, the color choice makes it difficult to always discern the male:female ratio of use except in cases where one largely outweighs the other. <a href="http://music.columbia.edu/~luke/perfect/01AK.shtml">The second iteration of the map</a> replaces cities with the most frequently used words. It's less visual in the information it conveys but still interesting to think aobut on a more granular level. The second map uses a hover zoom effect which breaks up the view of the base map on inspeciton.</p>
</div>
<a href="#top"><h4>Back to Top</h4></a>
<hr></hr>
<h2 id="vis2">Visualization Project 2</h2>
<div class='project'>
<h3> Project 2 - Matthew Caruso </h3>
<iframe width="100%" height="520" frameborder="0" src="https://mcaru546.cartodb.com/viz/8f607e4c-0cc4-11e6-addd-0e3ff518bd15/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p> For project 2, I used yelp's api console to generate a API response and create a map for people who searched stores in San Francisco with a radius of 1000 meters. The was this map is set up is by the ratings that the yelp users give each of the places they visit. For this places on the map the lowest rating a store received was a 4.5 and the highest rating a store received was a 5. </p>
<p> It was interesting to see where people gave a rating of five and then a rating a four and a half. From the looks not to many places in this 1000 meter radius received a rating of 5, a majority of the places received a four and a half. Creating a visualization like this one can be useful for people you use apps like yelp so that they can get a general sense of how good the stores are in a certain area and which ones they should stay away from because of a low rating. </p>
</div>
<div class='project'>
<h3>Project 2 - Zak Accuardi</h3>
<a href "http://openpaths.cc"> Open Paths API Practice </a>
<iframe width="100%" height="520" frameborder="0" src="https://zaccuardi.cartodb.com/viz/95982748-0b10-11e6-82f5-0ecfd53eb7d3/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>I have visualized data from OpenPaths, an app/location tracking service built by the data science team at the NY Times. I've had the app enabled on my phone for some time (though it was off for the past few months), so this seemed like a good opportunity to see what I've been doing of late. The geographic scope of the map is not ideal; it will be nicer to limit the geography to the NYC-area or thereabouts using SQL after class this week! The map has only the most basic styling for now, and was a good opportunity to use the Torque functionality in CartoDB (since the data is literally a time series of my location). The one thing I tweaked is that the indicator changes color to reflect when I got a new cell phone.</p>
<p>OpenPaths is designed to be user-friendly, but also to protect users' data should they desire to have it protected. I pulled some example code from the OpenPaths website, which is why my code is slightly more sophisticated than my actual Python skills should reflect. This was necessary to navigate the Oauth process, I think, and resulted in a more customizable framework for creating the actual queries.</p>
<p>Getting the data was relatively straightforward once I had figured out how to install packages in python -- for software developers' impressive capacity to dumb down instructions to a level of machine-readability, the ones who give advice on Stack Exchange typically don't write text to a level of lay-person-readability. Once I worked out a few kinks though, it was pretty smooth sailing with Anaconda, OpenRefine, and CartoDB.</p>
</div>
<div class='project'>
<h3>Visualization 2 - Jonah Bleckner</h3>
<h3>The growth of the LinkNYC Network</h3>
<iframe width="100%" height="520" frameborder="0" src="https://jobleckner.cartodb.com/viz/3c5b620c-0a49-11e6-a8e9-0e5db1731f59/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>For my second visualization project, I chose to use the Open Data NYC API endpoint to pull the data on the existing network of <a href= "https://www.link.nyc">LinkNYC</a> links as of March 2016. Since this is a small dataset, less than 150 links have been constructed, the script that I wrote on python was very simple. Besides the longitude and latitude coordinates, this dataset also includes the date in which the construction for each link was completed. These construction completion dates allowed me to illustrate the growth of this infrastructural network over the past year.</p>
<p>The rhetoric around this highly publicized infrastructure project revolves around the idea that this is an opportunity to address the widening digital divide in NYC. Each of these links provides free wifi, along with other features like a USB charging port, a tablet to browse the web and a source of ad revenue for the city. In light of the language of egalitarianism that has been used to promote LinkNYC, it is interesting to see that the network has only been extended along 3rd and 8th Avenue in Manhattan, and has not reach any of the other boroughs since construction started nearly a year ago. In order to add a bit more complexity to this torque map, I also added shape files for the subway lines (courtesy of CUNY Mapping Service at the Center for Urban Research) and public schools in the city.</p>
</div>
<a href="#top"><h4>Back to Top</h4></a>
<hr></hr>
<h2 id="crit2">Visualization Critique 2</h2>
<div class='vis-critique'>
<h3> Visualization Critique 2 - Sami Noor</h3>
<h3>The Strength of Nations</h3>
<p>The visualization I chose is called <a href="https://tedideas.files.wordpress.com/2015/09/03_viscom_0172a.jpg?w=1550&h=1314">The Strengths of Nations</a> which was found via Ted - <a href="http://ideas.ted.com/gallery-how-networks-help-us-understand-the-world/">Gorgeous networks that help us understand the world</a>. This was developed to visualize and allow one to see the variations in how different nations pursue science. This visualization analyzes 23 scientific in over 10 nations - USA, United Kingdom, France, China, Australia, Germany, Taiwan, Canada, Spain and Japan.</p>
<p>Here are some patterns noted from the site:</p>
<p><q>The two European nations with the most scientific publications (France and Germany) excel in physics and chemistry. France's network emphasizes engineering applications. Germany emphasizes the more theoretical aspects of chemistry and physics. Spain on the other hand, focuses on science that links to agriculture. The two pacific rim nations with the most similar profile are China and Taiwan. Both strongly address applied mathematics: from computer science towards engineering and applied physics. Japan explore more the medical areas and physical chemistry.</q></p>
<p>This is a great visualization but should have been placed over a clean basemap outlining the world map. This would make the data more powerful than it already is. Another feature that would have been quite useful would be enabling the zoom functionality and allowing viewers to see further into the visualization and which scientific areas are least focused on in the different nations analyzed.</p>
</div>
<div class='vis-critique'>
<h3>Visualization Critique 2 - Zak Accuardi</h3>
<a href="http://www.npr.org/2016/04/18/474256366/why-americas-schools-have-a-money-problem">Why America's Schools Have a Money Problem</a>
<iframe src="http://apps.npr.org/dailygraphics/graphics/school-funding-map-20160408/child.html?hasGeoText=true&initialWidth=1205&childId=responsive-embed-school-funding-map-20160408&parentUrl=http%3A%2F%2Fwww.npr.org%2F2016%2F04%2F18%2F474256366%2Fwhy-americas-schools-have-a-money-problem" width="100%" scrolling="no" marginheight="0" frameborder="0" height="1111px"></iframe>
<p>I am a big sucker for data journalism, and while this is only the first of what is promised to be several posts on the topic of education funding in the US, it looks really promising. The map itself uses federal data on school district education spending. Education is primarily funded at the state level so the visualization is [subtly and intelligently] state-centric. What's really interesting to me is how clear the variation is within thes states with respect to spending per student. This shows *such* an obvious policy failure, it's really astounding</p>
<p> And there lies the map's power. There's nothing sophisticated going on here. This is maps 101, but taught as a master class -- powerful information communicated simply and clearly. They use easily differentiated colors, draw your eye to state boundaries without hitting you over the head with them, and have established the data bins to highlight clear discrepancies. The key in the bottom left corner tells you exactly what you're seeing -- this is merely a relative comparison -- and you can hover over the details for more info on the actual spending numbers. Not only that, but you're shown how each specific district stacks up against its peers *within that state*, which is the right comparison given (as mentioned above) that states are the key policy-making unit in this equation.</p>
<p> Finally (and I genuinely wish I had something critical to say about this map, but I really just think it's amazing), I want to highlight the extraordinary value added by presenting this map in the broader context of a thoughtfully reported narrative. Data here is, as it should be, the beginning of a broader conversation that is better able to capture the "Who", "Why" of (in this case) the education funding challenge in the US, as well as the "How" of what needs to be done to fix it. The map gives us important insight into the "Where" and some of the "What".</p>
</div>
<div class="vis-critique">
<h3>Visualization Critique 2 - Nayana</h3>
<h3>Adam Gilchrist at the Bat</h3>
<p>
A <a href="https://visualizeit.files.wordpress.com/2009/05/gilchrist.jpg?w=700"> visualization</a> that I've been exposed to a lot is one generated quickly and often as a tool in cricket commentary. It overlays the trajectory of the ball over a map of the field to represent where the most runs are being scored revealing to both teams where the vulnerabilities+opportunities are. It gives the team at the bat a better understanding of where gaps in fielding are and the opposing team a chance to reevaluate its bowling and defense strategy.</p><p>You lose some information from the angle of the map and it might help to add a layer representing the fielders to better visualize what’s really going on at a given time.</p>
</div>
<div class='vis-critique'>
<h3>Visualization Critique 2 - Oliver Mika</h3>
<h3>Data USA</h3>
<iframe src="http://datausa.io/map/?level=county&key=age,age_moe,age_rank"></iframe>
<p>
For my visualization critique, I decided not just to use a particular visual, but a site I have recently discovered. Data USA is a comprehensive interactive list of census & demographic data put into an extremely easy to use search based website. I enjoy using it because it effortessly compiles data into a straight forward, informative story rather than just listing it in the same fashion the census bureau does. What else is impressive is the list of sources. It's simple, meaning the page is easy to find, and each source is labeled in bold and briefly but clearly explained where it is used within the site. What else, there are also caveats listed for each data source. I particularly appriciate this because it lets the user know that not all parts of the data are going to be correct. </p>
<p> My biggest criticism of the US Census bureau are it's wild inaccuracies and the fact that one is just led to assume that all the data must be true. I tend to trust data more when it can be noted that there are some inaccuracies due to the author screening it before hand, rather than not knowing just how inaccurate it is overall. The finishing touches are subtle but difference makers as well; the cloroplethed map, the interactive aspect of clicking on each state, county, PUMA or MSA and pulling up brief population and demographic info. These all may be very basic points that anyone could whip up on CartoDB, but to me they are still difference makers.
</p>
</div>
<div class='vis-critique'>
<h3> Visualization Critique 2 - Jonah Bleckner</h3>
<h3>Accidental Skyline</h3>
<iframe width='100%' height='520' frameborder='0' src='http://masnyc.cartodb.com/viz/bdf7572a-f197-11e3-854e-0e230854a1cb/embed_map?title=false&description=false&search=false&shareable=true&cartodb_logo=false&layer_selector=true&legends=true&scrollwheel=true&fullscreen=true&sublayer_options=1%7C1%7C1%7C0%7C0%7C0&sql=SELECT%20*%2C%0A' allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>For my second visualization critique, I chose a CartoDB map that was produced by the Municipal Arts Society, entitled <a href= "http://www.mas.org/urbanplanning/accidental-skyline/"> "Accidental Skyline,"</a> in response to the proliferation of huge skyscrapers along the southern border of Central Park. This map has the potential to serve as a tool for the public to engage in the on-going and contentious debate about real estate development in the city.</p>
<p>This map successfully allows users to make six different layers visible simultaneously. The first two layers illutrate the available Floor to Area Ratio (FAR) accross the five boroughs. Regions of the city with a high level of FAR availability (i.e. underused development rights) are considered extremely ripe for developers to build huge profitable building as-of-right. The map then allows users to see the spatial relationship between the neighborhoods in NYC that are vulnerable to large scale developments, with the historic districts that are protected by the the Landmarks Preservation Committee. Other layers on this interactive map include: Parks, NYCHA's real estate portfolio, and the Subway Lines. One UX issue with this map is that you cannot easily compare the state of each borough; one is forced to look at each borough in isolation. However, the data visualization clearly communicates which areas of the city are likely to see massive redevelopments that are destructive to local communities.</p>
</div>
<div class='vis-critique'>
<h3> Visualization Critique 2 - Matthew Caruso</h3>
<a href="http://flowingdata.com/2016/04/14/every-kobe-bryant-shot-charted/"> Every Shot Ever Taken By Kobe Bryant </a>
<p> One very interesting visualization that caught my eye was the visualization for the data presented on how every shot Kobe Bryant took throughout his 20 year career in the NBA. Throughout Kobe Bryant's 20 season career as a Los Angeles Laker it was recorded that he has taken 30,699 shots, which is an absurb amount of shots. I found this visualization on FlowingData.com and had many concerns on how they presented this data. For instense, I thought it was very confusing to understand because of how much data there is and how clustered the map was. After looking into this data set in more detail, I did find it very interesting that something like this could ever been recorded properly. </p>
<p> One of the aspects of the map that I found very interesting and very useful was how exact each shot was recorded. On this map you are able to click on different spots that they have marked down and it goes crazy details like what day the shot was taken and even time that shot was taken against. You are also able to see on the map where Kobe Bryant made a majority of his shots throughout his career and it even breaks it down into percentage on how often Kobe Bryant would have made that particular shot. There are also other very interesting sets of data recorded on this made such as the very first field goal Kobe Bryant made and what day he made this shot on. Each shot is color coated to express wether he made it or not and how important certain shots were depending on the game. I could imagine that this visualization might be something the Laker's organization can use to express how valuable Kobe Bryant was to there organization and might be fun to look into for a die hard Kobe fan.</p>
</div>
<a href="#top"><h4>Back to Top</h4></a>
<hr></hr>
<h2 id="vis1">Visualization 1</h2>
<div class='project'>
<h3> Visualization 1 - Oliver Mika</h3>
<iframe width="100%" height="520" frameborder="0" src="https://oliverarmandomika.cartodb.com/viz/52e88794-057f-11e6-92c0-0ea31932ec1d/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p> Keeping up with the theme of "Save Harlem Now!", I realised that despite its rich history and reputation, I know little to nothing about the neighborhood. I wanted to get a better feel for what the actual zoning is like to understand why some new development is labeled as being out of place. I used NYC open Data to obtain a list of building heights by floor count just to get a basic idea for what the buiding codes historicaly have been. I also went ahead and included a series of points on the map which represent a variety of facilities in the neighborhood. I included this to see if perhaps there were other indicators for Harlem's gentrification aside from the cheaper price of rent and accessibility.</p>
<p>
Unfortunately, I do not feel my data and visualization gets the point across very well. My biggest issue was translating data to the map in a way that could be easy for anyone to understand. But with the Facilities I had trouble labeling each facility type so as of right now they are merely points on the map with data pertaining to literal zoning (i.e. community area numbers, facility numbers rather than type, etc. etc.). The only thing I feel went right was the very basic chloropleth of building heights, which gave me a better feel for the physical landscape of the general area and why new high rises do indeed stick out like sore thumbs. </p>
</div>
<div class='project'>
<h3> Visualization 1 - Matthew Caruso </h3>
<iframe width="100%" height="520" frameborder="0" src="https://mcaru546.cartodb.com/viz/3a41b03a-05c0-11e6-b106-0e5db1731f59/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p> This visualization shows the amount of Victorian Box Trees there are planted along the streets in San Fransico and what street they are more commonly planted on. The Victorian Box Tree is known to be a very common and a type of tree that does very well in the San Francisco environment. I decided to go with a heat map to display this visualization because it shows where the density of Victorian Box Trees are in San Francisco. </p>
<p> As you can see on this visualization, the majority of Victorian Box Trees are planted within the Northeast section of San Francisco. They are manly located in along the Mission District neighborhood and the Western Addition neighborhood. The Victorian Box Tree is also very dense along main streets in San Francisco such as Golden Gate Avenue. After analyzing the data it was very interesting to see where these types of trees where planted and could be very useful to find the reasonings behind why these trees are planted on these streets, either because they are survive better in those parts of the city or if they are simply visually appealing </p>
</div>
<div class ='project'>
<h3> Visualization 1 - Sami Noor </h3>
<h3> Kansas City 2014 Homicide Data </h3>
<p><iframe width="100%" height="520" frameborder="0" src="https://snoor101.cartodb.com/viz/0008749e-05b6-11e6-9c5e-0e8c56e2ffdb/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe></p>
<p> Using Kansas City's (KC) Open Data portal, The <a href="https://snoor101.cartodb.com/viz/4e289c2a-01a2-11e6-8231-0ef7f98ade21/public_map" target="_blank">original map</a> I created a torque map using the dataset for Crime in KC in the year of 2014. In order to use the data set, I had to remove unnecessary columns (ie. reference numbers, codes) to allow CartoDB to succesfully geocode the dataset. I also added columns, 'State', and 'Country' to easily allow CartoDB recognize the addresses for the reported crimes. I was the able to visualize the data with the 'Torque' wizard, and was able to have the map play out the different reported crimes throughout the year.</p>
<p> As the data set was quite large with many points, I decided to clean the dataset further to display crimes labeled <q>Homicide-Non Negligent</q> and created an animated cumulative heat map. Due to the fact there were no longitude or latitude points within the dataset file, the file was geocoded via <q>BatchGeo</q> and analyzed as a KML file. From the heatmap, all of the 2014 homicide incidence were better visualized and allows us to see the concentration of crime and in which region.</p>
</div>
<div class='project'>
<h3>Visualization 1 - Zak Accuardi</h3>
<a href="https://zaccuardi.cartodb.com/viz/617dcecc-0048-11e6-82e3-0ef7f98ade21/public_map">Seattle bike and ride infrastructure</a>
<iframe width="100%" height="520" frameborder="0" src="https://zaccuardi.cartodb.com/viz/617dcecc-0048-11e6-82e3-0ef7f98ade21/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>I managed to add four layers to my CartoDB map, including a heat map of Seattle's bike rack facilities overlayed on the Puget Sound bike lane network. I added park-and-ride facilities and regional 'transit centers' for good measure, to hint at places where cars are afforded an easy access point to transit networks but bikes might not be. I coded these point facilities in an intuitive way and tried to color them so that they are visually distinct. There are a few places where this doesn't happen nicely and the icons overlap. I couldn't see an easy way to ask CartoDB to avoid these overlaps but I did make one of the icons more transparent, which helps.</p>
<p>There are a couple takeaways here. First, on a basic level, bike racks don't map very strongly to bike infrastructure. It's important to have a place to lock your bike, but somewhat moot for most riders if you don't have a safe and convenient means of getting there. Second, with respect to what transit professionals refer to as "first-last mile" access to public transportation, cars in Seattle are substantially advantaged compared to bicycles. In other words, it is much more convenient to drive to a public transportation hub (either a park and ride facility or a Transit Center) than it is to ride a bicycle there in the Seattle region.</p>
<p>It's too bad that the bike rack data is only for the City of Seattle, but I suppose open data beggars can't be open data choosers. The differences in extent is one issue; a lack of current-ness is another. The bike route map looks like it hasn't been updated since 2011, for example -- which I wouldn't fault them for if it was something harder to inventory, like bike racks, but one would hope that each city in the region would have pretty up-to-date knowledge of the extents of their bike networks (and that this would be of interest to the county/region). Lastly, it would have been nice to do a torque-style cumulative map of bike rack installations, but the data wasn't of sufficient quality to make this possible. The dataset does have timestamps by bike rack but the timestamps aren't accurate enough, making most of the racks clump together when you use torque to visualize the data.</p>
</div>
<div class='project'>
<h3>Visualization 1 - Jonah Bleckner</h3>
<h3>Naturally Occurring Retirement Communities vis-a-vis Foreign-Born Senior Populations</h3>
<iframe width="100%" height="520" frameborder="0" src="https://jobleckner.cartodb.com/viz/3930d84c-0593-11e6-9de9-0ef7f98ade21/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p> According to a report published by the Center for an Urban Future (CUF), "Neighborhoods or housing developments with large concentrations of older people may be eligible to be named Naturally Occurring Retirement Communities (NORC)." The NORC programs then offer in-place supportive services to the geographically-defined areas or housing developments that have a critical threshold of residents that are 60 years of age or older and have moderate to low incomes.</p>
<p> For this project, I wanted to show that the distribution of NORCs in NYC are not aligned with the neighborhoods in which there is a high growth in immigrant seniors. In other words, the supportive services that are attached to the NORCs are not reaching immigrant senior communities as effectively as native-born senior communities. I would like to update this project by utilizing neighborhood shape files instead of the borough shape files in order to communicate this story in a finer grain.</p>
</div>
<div class='project'>
<h3>Visualization 1 - Nayana Malotra</h3>
<h3>Art in Public Spaces</h3>
<iframe width="100%" height="520" frameborder="0" src="https://nayana23.cartodb.com/viz/6788c30a-05c3-11e6-a3aa-0ef24382571b/embed_map" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe>
<p>This visualization shows public artwork overlayed with zoning areas in Charlotte, NC. I used the "simple" wizard for the artwork layer and was able to import Json vectors directly from Data.gov for the zoning categories. I added a hover to see more details/ get some more context about the type of art in each location. </p>
<p>This was where the data itself became a little problematic because of inconsistent labelling and missing datafields.</p>
<p>It was interesting to see a cluster of artwork in the center of the city which was categorized as "other" in terms of zoning- it might be useful to overlay foot traffic// bike paths to see how much exposure the pieces actually get.</p>
</div>
<a href="#top"><h4>Back to Top</h4></a>
<hr></hr>
<h2 id="crit1">Visualization Critique 1</h2>
<div class='vis-critique'>
<h3> Visualization Critique 1 - Oliver Mika</h3>
<a href="http://media.bizj.us/view/img/4471501/sarah-marijuana-scenario-c1*750xx1199-679-0-98.jpg" target="blank"> Recreational Marijuana Zoning in Seattle</a>
<p> The reason I chose this map was because of how huge of a factor the zoning was throughout the entire process of legalization. It was the key debate for opponents and supporters alike of this new industry that ws bound to thrive; where can and can't recreational shops be allowed to open?</p>
<p>
What I appreciate about this map is the careful detail to zoning, precisely showing the areas where new store are allowed to open. In addition, the points on the map which indicate both current medicinal dispensaries and others which plan on expanding into the recreational industry. However, it is the lack of other factors, other points and polugons, which represent schools, day cares, and other points or areas of interest where legal shops are forbidden from opening, which take away from this map's greatest potential. Thus, it is more two-dimensional and does not tell the complte story. If anything, I am left more confused by the zoning. </p>
</div>
<div class='vis-critique'>
<h3> Visualization Critique 1 - Zak Accuardi </h3>
<a href="http://www.dvrpc.org/webmaps/CyclePhilly/"> Cycle Philly </a>
<p> Delaware Valley Regional Planning Commission (DVRPC) is using a fun dataset gathered from the "Cycle Philly" app, which tracks users' cycling routes and prompts them to enter their trip purposes. This kind of data is valuable in transportation planning because it is hard to keep track of how many people actually use bike infrastructure, let alone why. Nonetheless this dataset is relatively incomplete just by virtue of the fact that it only represents the behaviors of 220 Philadelphia bike riders. To the mapmakers' credit, they acknowledge this clearly. </p>
<p> The map's aesthetic is nice. The black background with bright colored lines seems like an industry standard at this point. It's a nice touch to distinguish between trip density along each corridor using both line width and color -- this map is trying to communicate one thing at a time and do it really clearly. </p>
<p> The interactive functionality is modest but informative, mostly allowing users to visualize frequent bike routes by specific trip purpose (by default, all trips are aggregated). It would be nice to be able to overlay different trip types to compare them more directly, or to add some additional contextual information, e.g. census demographics or even some of the major destinations that riders might be riding to and from. </p>
</div>
<div class='vis-critique'>
<h3>Visualization Critique 1 - Jonathan Marable</h3>
<a href="http://dotearth.blogs.nytimes.com/2016/04/11/the-park-service-maps-americas-natural-and-human-made-soundscapes-and-silences/"> The Park Service Maps America's Natural and Human-Made Soundscapes and Silences </a>
<p> This article released on April 11, 2016 by the New York Times refers to a set of maps created by the National Park Service that showed amounts of ambient noise throughout the United States. The data was visualized quite well. The choropleth allowed for distinction between ranges of noise. The addition of the note distinguishing between the maps was definitely important to know in order to understand what the major takeaway should be from the maps. Though the map is missing titles, the legend helps the viewer understand what the map is showing.</p>
<p> As stated on the National Park Service's site <a href=
"http://www.nature.nps.gov/sound/soundmap.cfm"> Mapping Sound on a National Scale </a>, such maps are important because,
"http://www.nature.nps.gov/sound/soundmap.cfm"> Mapping Sound on a National Scale</a>, such maps are important because,
<blockquote>
Park visitors and wildlife interact with each other and park resources through their senses, including the sense of hearing. So, protection of natural sounds is good for both ecosystems and the quality of visitor experience. Additionally, there are laws and policies that require the agency to conserve acoustic environments “unimpaired for the enjoyment of future generations."
</blockquote>
Mapping sound was a great way to bring awareness to the presence of sound in the environment. The story told by these maps are easy enough to understand whether the reader is a park official, a policymaker, or merely a resident of the United States.
</p>
<p> The visualization reveals compelling takeaways regarding ambient noise, land use, and the natural environment. I found the amount of naturally-occurring ambient noise to be particularly surprising. Though it informs us that noise levels tend to be higher in areas with greater moisture and vegetation, it also shows the mere presence of people tends to result in greater ambient noise. The level of ambient noise only increases as the concentration of people increases, and ambient noise levels are particularly high in cities. I would be curious to see a pair of maps comparing ambient noise levels during the day to ambient noise levels at night.</p>
<p> The source data set can be found at the <a href="https://irma.nps.gov/DataStore/Reference/Profile/2217356"> NPS Data Store</a>. This location is also home to other data sets and maps pertaining to observations such as Nighttime Lights and Wind Power Potential. This source could be an important one to explore in terms of understanding and facing challenges we may face in the future regarding land management.</p>
</div>
<div class='vis-critique'>
<h3>Visualization Critique 1 - Jonah Bleckner</h3>
<h3>The Rich Live Longer Everywhere. For the Poor, Geography Matters</h3>
<p>For this assignment, I wanted to select and reflect upon a data visualization that has reached a large audience, and so for that reason I chose a recent interactive map that was published by the New York Times entitled, <a href= "http://nyti.ms/23nEYyr"> "Rich Live Longer Everywhere. For the Poor, Geography Matters." </a> (On the subreddit, "DataisBeautiful," this trending NYT map has been viewed 4,784 times.)</p>
<p>This visualization maps the life expectancy of 40-year olds with household incomes below $28,000, adjusted for race. In other words, the author of this map wanted to illustrate that beyond income, geography has a significant effect on public health. I think that this simple visualization powerfully drives this point home, especially since it is evident that life expectancy is on average higher for those individuals that live in some of the wealthier American cities along the east and west coast. From a technical standpoint, I did have some trouble figuring out what polygonal parameters were used by the author. They are vaguely identified as areas, and are too big to be census tracts or counties.</p>
<p>I also thought about how my perception about what message the map communicates transformed after reading the accompanying text. It contextualized the map within the current debates and scholarship that exists about the intersection between wealth and public health. In some ways, this map tells a nuanced story that challenges us to pose complex public policy questions.</p>
</div>
<div class= 'vis-critique'>
<h3>Visualization Critique 1 - Matthew Caruso </h3>
<iframe src="http://googletrends.github.io/iframe-scaffolder/#/s/01fJ5Q" width="100%" height="550" frameborder="0" allowfullscreen></iframe>
<a href= "http://googletrends.github.io/iframe-scaffolder/#/view?urls=Thanksgiving%202015%7Chttps:%252F%252Fgoogledataorg.cartodb.com%252Fu%252Fgoogledata%252Fviz%252Fbf595f4c-7381-11e5-9ec5-42010a14800c%252Fembed_map&active=0&sharing=1&autoplay=0&loop=1&layout=narrative&theme=red&title=A%20day%20in%20the%20life:%20US%20Thanksgiving%20on%20Google%20Flights&description=The%20day%20before%20Thanksgiving%202015%20shown%20in%20US%20domestic%20and%20international%20air%20travel%20booked%20with%20Google%20Flights">Google Thanksgiving Flights</a>
<p>One visualization that I found online was a map generated by Google to show the amount of people that were flying around the United States on Thanksgiving. The data is represented in a torque map and shows different airlanes color coded flying across the United States. I thought this was very interesting and effective why to display this data set because it shows were these flights are taking off and were they are landing.</p>
<p> A map like this can show you how diverse the United States is and how someone can have family members living across the country. This map also shows flights leaving the United States and going overseas into different countries as well and this information is very useful because it shows you what types of airlines actually travel overseas. I was very impressed with how this data was represented in this map and I thought this map made it very easy to understand where certain airline companies fly and it was also very interesting to see where their final destination will be.</p>
</div>
<div class='vis-critique'>
<h3>Visualization Critique 1 - Sami Noor</h3>
<a href="http://www.nytimes.com/interactive/2016/04/11/upshot/for-the-poor-geography-is-life-and-death.html" target="_blank"> The Rich Live Longer Everywhere. For the Poor, Geography Matters</a>
<p> This Monday, the NY Times published <q>The Rich Live Longer Everywhere. For the Poor, Geography Matters</q> and had a powerful visualization mapping out the life expectancy in relation with income. The data was supplied from the JAMA article <q>The Association Between Income and Life Expectancy in the United States, 2001-2014</q> which was published a day before According to the study design from the JAMA: Income data for the US population were obtained from 1.4 billion deidentified tax records between 1999 and 2014, mortality data were obtained from Social Security Administration death records.</p>
<p> Due to the fact that the visualization itself was interactive, you were able to see the data clearly as it mapped out life expectancies. However, it should be noted that the visualization clearly states <q>Life expectancy of 40-year-olds with household incomes below $28,000, adjusted for race</q>. Though it was seamless to look at the different life expectancies in various parts of the US on the map, it only reflected on a certain household income which could not be altered. Though, the data is then reorganized in a tabular format with more income levels (plus various correlations) and their corresponding life expectancies.</p>
</div>
<a href="#top"><h4>Back to Top</h4></a>
<hr></hr>
<h2 id="intro">Introductions</h2>
<div class='vis-critique'>
<h3>Introduction - Nayana Malhotra</h3>
<p>Hi, my name is Nayana. I'm a Creative Technologist and Ethnographer. Currently trying to learn how to play chess (well) all help is appreciated. </p>
</div>
<div class='vis-critique'>
<h3>Introduction - Jonathan Marable</h3>
<p>Hello, my name is Jonathan Marable. I am a graduate student at Pratt Institute, and I am working as a Graduate Assistant with SAVI. </p>
</div>
<div class='vis-critique'>
<h3>Introduction - Oliver Mika</h3>
</p>Hello, my name is Oliver, I love cities and maps and am interested in sustainable Urban Planning.
</p>
</div>
<div class='vis-critique'>
<h3>Introduction - Sami Noor</h3>
<p>Hi, my name is Sami Noor. I'm a Queens native, Flushing to be precise (#TheNanny). Law and Order SVU fanatic, 17 seasons and still going strong. I studied International Development with a focus on Global Health. Feel free to talk epidemiology to me - perhaps not after 6pm.</p>
</div>
<div class='vis-critique'>
<h3>Introduction - Zak Accuardi</h3>
<p>I am Zak. I grew up in Portland, OR, went to college in NYC, stayed for another year, left for a few years, and came back in June to start my current job, in which I work to improve public transportation. Otherwise, mostly I make ice cream.</p>
</div>
<div class='vis-critique'>
<h3>Introduction - Matthew Caruso</h3>
<p> Hello, my name is Matthew Caruso and I am very interested in conveying data in meaningful ways that can help people figure out different types of problems. </p>
</div>
<div class='vis-critique'>
<h3> Introduction - Jonah Bleckner</h3>
<p> Hello, my name is Jonah Bleckner, and I am interested in telling stories about the urban environment through data visualization. </p>
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<h3> Introduction - Richard Dunks</h3>
<p>Hello, my name is Richard Dunks and I'm the teacher. I'm originally from Las Vegas, NV. I've lived in NYC for 3 years. I love maps and I love data. I love sharing my experience with others.</p>
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<a href="#top"><h4>Back to Top</h4></a>
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<a href="2015_fall.html"><h2>Project Page from Fall 2015</h2></a>
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