The City Reimagined Data Science Stories

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Stamen Design uses data to reveal new insights on city life

In the past, to understand the patterns of city life and project larger urban trends, city planners, policymakers and residents had to rely on relatively small sample groups of data and educated guesswork. In the era of big data, educated guesswork is a thing of the past. Innovative sources of city data can reveal new insights on urban life. Stamen Design’s projects Cabspotting and Crimespotting are using using city data to create innovative tools that visualize unseen economic, social and cultural trends.

In the past, to understand the patterns of city life and project larger urban trends, city planners, policymakers and residents had to rely on relatively small sample groups of data and educated guesswork. In the era of big data, educated guesswork is a thing of the past. Innovative sources of city data can reveal new insights on urban life. Stamen Design’s projects Cabspotting and Crimespotting are using using city data to create innovative tools that visualize unseen economic, social and cultural trends.

Crimespotting Oakland was developed in 2007 by Stamen’s technology director Mike Migurski, who was dissatisfied with existing crime mapping applications, which offered limited functionality and relied on dated mapping applications. By scraping publicly-accessible police data, Crimespotting Oakland presents a robust interactive mapping environment that allows users to track instances of crime in their neighborhood, police beats, and examine larger trends with time-elapsed visualizations. Every day, Crimespotting pulls crime reports from the city’s Crimewatch service and imports it into the Crimespotting database, allowing the tool to track larger trends and provide user-customized services such as neighborhood-specific alerts.

Stamen has since expanded the project by creating Crimespotting San Francisco, and in the process has set the standard for interactive crime maps for urban metros. For the developers at the Stamen, the projects are both a public service and a provocation. A statement on the Crimespotting San Francisco site reads, “We need to be able to explore public information, to draw connections, and to see new possibilities for questioning. Crimespotting enables us to do more than search for the things we already know. We believe that civic data should be exposed to the public in a more open way. With these maps, we hope to inspire local governments to use this data visualization model for the public release of many different kinds of data.”

Cabspotting, one of the firm’s other projects, uses a set of data only available in recent years--the GPS coordinates of taxi cabs--to reveal long-hidden patterns of urban life. Cabspotting traces San Francisco cabs as they travel throughout the Bay Area and places the movements on an interactive city map. The data is collected from Yellow Cab’s central dispatch server, which collects GPS data on every cab in real time. Server-side processes aggregate the data into formats that can be combined into time-elasped videos of the taxi cab traces. The patterns traced by each cab show where and when residents are moving around the city. What results is a new kind of urban anthropology. The cab traces speak to political, social and economic issues by displaying where cabs go most often--and where they rarely transport fares due to low income levels and high crime rates.

As the pioneer of large-scale data warehousing and analytics solutions, Greenplum salutes innovative studios such as Stamen that present data in new and compelling ways. Since 2001, the San Francisco-based design and technology studio has operated at the intersections of data analysis, commercial design and art, working with clients ranging from news media to technology firms, financial institutions to political action committees. The studio is committed to projects that demonstrate that big data does more than serve a utility; it can reveal new ways of understanding the world around us, and even be beautiful.

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