PhD Dissertation Defense: Talia Kaufmann
Date/Time: Tuesday, August 2 at 9:00am
Title: Towards Algorithmic City Planning: Data-driven indicators for policies and planning
City planning is the process of setting rules and regulations to distribute land, infrastructure and services amongst urban communities. These services and amenities provided to communities affect residents’ physical and mental well being, their social and job opportunities and overall quality of life. However, the uneven allocation of these resources is one of the main contributors to urban inequality. The current planning practice suffers from a limited understanding of the complex social and spatial relationships at play in cities while important decisions about the spatial distribution of resources are not guided by empirical analysis. Nowadays, the vast amounts of data collected from mobile devices, applications, social networks and the “Internet of Things” holds the key to developing objective spatial indicators for benchmarking across communities and cities. These indicators can unleash the potential of data-driven city planning – allowing planners to understand and address spatial and social inequalities between communities based on objective indicators.
This dissertation proposes a set of data-driven indicators for supporting city planning processes while also offering a framework for measuring urban environments at scale. It focuses on indicators to support land use planning, which provides the “blueprint” of cities determining the quantities and spatial distributions of urban amenities and services. Each chapter tackles a different aspect of the relationship between a city’s population and the amenities that serve them. Chapter 1 establishes universal scaling patterns across US cities in the quantity and distribution of urban amenities, showing how these patterns can be used to support land use planning decisions in the construction of new cities while the deviations from them to uniquely highlight each city’s strengths and deficits to guide development of growing cities. Chapter 2 offers a framework to measure inequality in access to parks across 500 cities in 6 countries, demonstrating the potential of Google Maps data for fine-grained metrics at scale. The study then exemplifies how these accessibility metrics can be leveraged to benchmark accessibility across cities and countries. Chapter 3 models amenity consumption patterns across 162 metro areas in the US in light of accessible amenities close to home. Results show that having easy access to essential amenities decreases traveled distances across all urban communities but that increased access to amenities is more valuable for low income communities.
Taken together, the fine-grained indicators proposed here, when measured in a consistent framework across countries over time on a global scale, can pave the way for illuminating the social and spatial factors contributing to urban inequality. Moreover, such objective metrics will motivate policy and planning efforts for improved accessibility to amenities and consequently residents’ well being, advancing inclusive data-driven decision making in city planning.
Dan O’Brien, Associate Professor of Public Policy and Urban Affairs and Criminology and Criminal Justice, Northeastern University
David Lazer, Distinguished Professor of Political Science and Computer Sciences, Northeastern University
Lewis Dijkstra, Head of the Economic Analysis Sector, Directorate-General for Regional and Urban Policy, European Commission; Visiting Professor, London School of Economics and Political Science
Trivik Verma, Assistant Professor of Urban Science and Policy, Multi-Actor Systems Department, TU Delft