A SELF-EXPRESSIVE TRANSIT HUB
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By 2050, roughly 66 percent of the world’s population is expected to live in cities (United Nations). As a response to this issue, many cities are redefining their urbanization processes. Today’s most popular models of city management introduce predefined management systems — black boxes — designed by famous engineering companies. The current trend of the corporate model of a “smart city”1 treats humans as flows of products and energy. Although this approach could be temporarily successful with regards to issues such as traffic, its benefits are often exaggerated by companies that offer them without any consideration of the problems. Therefore, this approach is gradually being applied through the use of public money in more cities, despite the fact that their solutions and methods do not fit every local context. The method used in these management models is highly based on gathering big data through various city-monitoring practices. This research specifically looks into the attributes of this decontextualized data gathering method.This research brings big data and small data2 together to investigate how they can be introduced into urban monitoring and data gathering practices. On one side, computer aided monitoring and urban observation techniques are growing and gather massive amounts of big data about the cities. On the other side our age’s interest in knowing the city with small data — gathered directly by humans — is decreasing. Therefore, this research asks “what are the limits and advantages of human and computer aided site observation in understanding people’s patterns of behavior in urban spaces?” In addition to a theoretical approach towards understanding these methods through a literature review, some of the precedents of both approaches for urban site observation are studied and compared with regard to their benefits and limits. With these aspects in mind, the researcher went to the real world to do site observation in a specific small urban space in Buffalo, University Station. The observations are documented and analyzed through videos, animation, photographs, drawings, and short pieces of narration. The observations are used in order to try and understand people’s behavior in different scales and types. Then, they are categorized and compared to the previously studied methods. The main outcome of a small data gathering through site observation in University Station has been a detailed classification of the station’s people and spaces with regard to their behavior, activities, relation to the station, and their kind of appearance in that particular space. This classification immediately suggests that there are many more details about citizens of a city than simply treating them as flows of similar objects.But how can a mixed approach of human and computer aided site observation and monitoring of urban spaces, inform the city about its citizens? What kinds of data would citizens like to be known about themselves? What is useful and what is not useful? Rather than trying to design a monitoring platform which answers these questions and functions to gather data with a problem solving intention, this design research aims to design an observer platform which magnifies attributes of different kinds of data by juxtaposing them. The proposed platform is a part of the city’s transportation system in University Station, which redefines the station’s user experience by knowing more about them and having a personalized communication with them. The platform tries to classify people and the space itself into the categories determined by observations, then builds a more detailed unique persona for each user, benefiting from near real-time analysis of behaviors. The communication between the “observer platform” and the user will happen through multiple objects in University Station, The Creepy Ticket Machine, The Honest Trash Can, and The Selective Chairs. The data that the platform has already gathered about each particular citizen is revealed during different stages of this process of interaction with the objects, such as the final stage during which the ticket machine dispenses the printed ticket. This platform lets the citizens and the audience of the project speculate about plausible futures of their city in various aspects and considerations of the data that will be gathered about them.--------------------------------------------------------------------------------------------------------------------------1 This term could have different meanings. Antony Townsend writes “Smart cities are places where information technology is wielded to address problems old and new”(ch. preface). However, Townzend’s description in his book is different from the trends described in this abstract.2 Small Data is a type of data that is extracted from traditional methods of data gathering and qualitative research. See Kitchin and Lauriault’s paper, and page 16 of this thesis for more.