1.3 - The Current Condition of Cleveland's Surburbs
Feb 27, 2015
1.2 - Cleveland's Surburban History
Feb 20, 2015
1.1 - Background on Race and the Suburbs
Feb 13, 2015
The Data Action project will develop and prototype examples of how data visualization can be used to influence debates on civic issues and ultimately affect policy. The unprecedented growth of data has generated excitement in popular media and press for its ability using it to reshape the dynamics of everyday life. However, big data does not act on its own and will not change the world unless it is collated and synthesized into tools that people can acquire and use. The Data Action project will develop data visualizations on current events to provide examples for how anyone can use data to to expose hidden patterns and ideologies to audiences inside and outside the policy arena to encourage civic change.
Who We Are
We are research assistants and collaborators at the Civic Data Design Lab.
Wenfei is currently a masters student in architecture and urban planning at MIT. She is interested in the intersection of urban spatial analysis and data visualization.
GIS and Visualization Specialist
Mike is a GIS/Data Visualization Specialist with DUSP, focusing on high level data visualization, spatial analysis, and cartographic techniques.
Director - Civic Data Design Lab
Sarah Williams is currently an Assistant Professor of Urban Planning and the Director of the Civic Data Design Lab at the Massachusetts Institute of Technology’s School of Architecture and Planning.
We want to kick off our first blog post by trying to understand the landscape of data visualizations out there.
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Date: Feb 2015
Background On Race and the Suburbs
Feb 13, 2015
The racial diversity of the suburbs is growing. A Brookings Institute study from 2011 titled “Melting Pot Cities and Suburbs” looked at census data from 1990, 2000, and 2010 and discovered that half of American cities are majority non-white and that more than half all minority groups living in metro areas are now living in suburbs. Not only is the US becoming more racially diverse in general, but also as more and more previous residents of the urban areas have the opportunity to head for the suburbs often in search of more opportunities and better resources, we see a change in the racial mix of the suburban ring around the central city.
The same Brookings study attributes these changes to three factors: 1) the relatively higher growth of minority populations in contrast to an aging white population, 2) a more diverse child population, especially in the suburbs, and 3) “black flight” of African American populations from cities. The Urban Institute made a series of very informative maps that show racial change between different census decades from 1980 to 2010. Going back to the events of Ferguson and St. Louis, which was what initially motivated us to look at this topic, we can clearly see that the black population in the central city of St. Louis lessened while the northern and southern suburbs of the city grew in their minority population.
What are the real-life implications of this change and how does this impact the way people live? Do people have more amenities and better education but perhaps less political power because of their dispersal? Or, perhaps, moving to the suburbs does not necessarily offer the improved opportunities and education that drove people to move there in the first place. An article by Lawrence Levy in the New York Times during the 2008 elections suggests that minorities are still segregated the suburbs; he cited that 70% of Long Island black elementary and secondary school students attended only 10% of 124 school districts, with legal action towards more equitable districting being difficult due to the numerous number of school districts in the region. A recent study by John R. Logan at Brown University reaffirms this idea and shows that blacks and Hispanics actually live in the least desirable suburban neighborhoods and only have access to the lowest performing schools, thus losing out again on the opportunities that were supposed offered to them in a suburban environment.
In the next few weeks, we plan to investigate one consequence or aspect of this trend through a map or visualization of these trends. Stay tuned!
Module 1 - Cleveland's Suburban History
Feb 20, 2015
We decided to investigate the effects suburban living racial dynamics within the context of Cleveland, Ohio in order to get a more nuanced and spatial understanding. We chose Cleveland because its surburban history is as long as the city itself, and it exemplifies a city which saw a housing and industrial boom during and after World War II.
Before the 1850s, there were several independent townships and villages within Cuyahoga county, which contains the city of Cleveland. The residents of these settlements generally tended to stay with in their communities due to lack of easy transportation. With the development of the horse-drawn streetcar, which allowed for development up to a 3 mile radius around downtown, more and more communities began to arise near the city in order to take advantage of the city's superior public infrastructure and amenities. The invention of the electrified increased the radius of development to 10 miles and increased the pace of village incorporation even further. After the slowdown in suburban development during the Depression, the city saw a second wave of growth as a consequence of the World War II. Employment had increased 34% in 1944 from 1940 due to wartime production. Because there were building restrictions and workers coming to the city during the war, the housing had reached 0.5% by March 1943 . Temporary housing units even had to be built to accomodate the need for housing. Due to the poorer quality housing stock in the inner city, large space requirements for construction plants, and the high demand for housing, the number of suburban communities increased quickly after the end of the War in August 1945.
After the War, with the push from the GI Bill, which provided government-guaranteed mortgages to new veterans, and the rest of the labor market following along the trend, the expansion of the suburbs around Cleveland grew dramatically. The city's older inner ring suburbs saw the most growth. For instance in 1950, Parma, a south-western suburb of the city, nearly doubled its population from 14,000 in 1931. By the 1970s, at the peak of the city's suburban population boom, the city of Cleveland had lost 127,457 residents while its suburbs gained 631,042. Post-1970s, with the economic depression and the signs of age in Cleveland's inner ring suburbs, we see in the city a trend that appears throughout the country: population growth stagnates or declines while simulataneously getting older, the demand for better social programs increases, and physical infrastructure of the suburbs deteriorates.
An important element to highlight in the racially segregated manner in which Cleveland's suburbs grew is the practice of redlining, which was established with the National Housing Act of 1934 and the creation of the Federal Housing Authority (FHA). Although the history of racially-charged segregation policies in the US dates back to the 19th century, most notably in the case of Yick Wo v. Hopkins , wherein the Supreme Court declared that policies preventing Chinese laundry owners in San Francisco from receiving a permit to operate a laundry in a wooden building, the practice became official with creation of the FHA. These policies would delineate areas of desirability for banks' lending purposes and were often based on assumptions about the community.
Below we show images from Cleveland's Federal Home Owners' Loan Corporation's redlining maps.
In the next post, we will look at the Cleveland today and the potential impact that these redlining policies have had on the city's suburbs.
Module 1 - The Current Condition of Cleveland's Surburbs
Feb 27, 2015
Module 2 - Pollution Through Chinese Eyes
July 23, 2015
Social media is widely used in China and many users leverage the medium to discuss important social issues. One of the most controversial topics in China is the status of environmental pollution, most notably air pollution caused by its vast factories and industrial spaces. The Chinese government’s tight control of information makes it hard to know how the Chinese react to adverse environmental conditions. Discussions on social media are not always captured by government controls and can provide a lens into Chinese public opinion.
Pollution Through China’s Own Lens is a web-based application that exposes how the Chinese public responds to pollution by visualizing posts on Weibo, the Chinese version of Twitter. The results illustrate that pollution is widespread. Pictures and posts of the Chinese landscape immersed amongst haze show how it might feel to be on ground in some China’s most polluted cities. Visitors to the application view social media postings through categorical filters generated from Weibo hashtags about pollution. The map interface allows user to observe spatial trends in the pollutions posts.
When users first navigate to the application, a window will lead them to a map that displays data clustered by location across China and the world. Users can click on a cluster of posts to zoom in to a specific locations. Individual posts are represented by a camera. At the bottom of the map, there is a single row of images. These images represent the posts that are within the current extent of the map. Hovering over a single camera icon will locate the post in the row of images. Users can click on an image to see it at a higher resolution. In the upper left of the map, Filter by Category can be used to see different types of posts.
The gallery component operates in a similar fashion to the mapping application. To visit the gallery, click on an image in the map, then click View Gallery. Users can also click the Gallery button at the upper right of the map. The gallery allows for filtering by category, just as on the map. Click on each image to read more on the post and get a detailed view of the post. In this detailed view, clicking View on Map will take you to the location of the post in the map component.
The Data-Gathering Process
On a recent trip to China a few weeks ago, our research team saw first-hand the prevalence of social media use that includes online shopping, pay for services (such as a class or tickets to a museum), event registration, voice chat, amongst other applications. Social media apps such as Wechat, QQ, and Weibo (Chinese version fo Twitter) are ubiquitous and essential to life in China. Even Apple CEO Tim Cook has joined Weibo, a move that not only reveals the importance of China to the western economy, but also the centrality of social media in China. Despite its prevalence, or perhaps because of it, the Chinese government controls what is posted on social media.
The research team decided to use Weibo, a Twitter-like microblogging platform, to gather images of pollution that people captured and posted to the app. This was partially out of convenience (they have an API) and partially due to the fact that we know Weibo monitors and censors certain topics such as pollution, making us curious what we'd find. Unfortunately, Weibo recently disabled their API functionality that allowed searching based on hashtags, such as pollution. The team therefore decided to scrape the weibo website instead, looking for images with specific hashtags.For those of you unfamiliar with web-scraping, it is a technique of gathering data straight from the html code that creates a website.
The hashtags we chose are based on issues we know to be pressing in China. For instance "Yellow Dust" refers to the seasonal dust storms in East Asia, which brings sand from the deserts of Mongolia. It has become a bigger issue in the past few years due to the pollutants contained in the dust. Another category, "PM2.5", stands for Particulate Matter 2.5, an air pollutant that can come from automotive exhaust as well as other operations that require the burning of fuel. In Beijing, a city known for its high levels of air pollution, the city measures the concentration of PM 2.5 daily as an indication of the air quality outside. Some of the categories that we scraped, such as "Pollution" and "Air Pollution" didn't get great results - the Weibo site had only a handful of images for each month. Other categories such as "Haze" and "Take Pollution Pics", got us dozens of images DAILY. When we asked our Chinese friends about this, they said sometimes Chinese censors don't catch certain phrases and therefore do not remove them from Weibo. Furthermore, sometimes people explicitly use slang to communicate about sensitive issues. This article from The Wire explains a little more about this phenomenon, explaining how slang is used to escape censorship.
After we scraped the Weibo website, when then kept only the data that were attached to a user ID in order to connect the image to information about the user himself through the Weibo API. This gave us information such as the user's sex, age, city, followers, etc. We used the "city" category to map the photos. As such, the photos are a representation of where the users are from, and not necessarily where they were taken. Nevertheless, through this process, we were able to create a dataset that reflected a collection of images of how (or sometimes not!) Chinese people understand pollution through pictures. Many images are face mask selfies, many are cityscapes where you can barely see the buildings because of the pollution cover. Relative to other countries, and because of censorship, Chinese social media does not tell us too much, but even the small amount of information that it does reveal tells a powerful story.
Click to view the application and explore the visualization to see the social media posts on pollution in China and investigate the impact of pollution on the ground in China and in everyday life for Chinese citizens using social media as our lens.
Big thanks to Alan Jones for working on a large part of data-scraping process and helping us navigate the giant firehose that is Weibo's stream of posts!