Informatic Architecture: Urban Data Analysis for a Community Machine

Abstract

What is architecture’s response to the Information Age? The focus of this thesis project is to develop a data-rich design methodology that challenges conventional practice by using “big data.” This begins with critical examination of contemporary work that uses quantitative information: MVRDV (datascapes) and Carlo Ratti/MIT Senseable City Lab (futurecraft). Next, a methodology is developed, which uses two datasets to make informed decisions. The first selects a study city and identifies city-wide issues. These problems are then examined on a local scale in the site selection dataset. Both datasets utilize a series of evaluative tools (average, sum, subcategory frequency, percent normalization) to compare cities/sites and reach conclusions. The methodology applies “big data” to select a city (Phoenix, AZ), choose a site (undeveloped park in SW Phoenix), and formulate a program that responds to a community’s needs (community farm, STEM programs and library, community center, site and neighborhood improvements). The design project is a Community Machine, a building that reacts to shifts in data as information is added/updated. This machine abandons more conventional notions of form-making for a design that prioritizes flexibility, adaptability, and dynamism. In the end, data can democratize the design process, uncover unknowns, and expand the traditional scope of architects. Deeper data integration and public interaction/input within the conceptual framework are possible future steps for a system that is built to improve and change.