Summary
Contents
Subject index
The economic and political situation of cities has shifted in recent years in light of rapid growth amidst infrastructure decline, the suburbanization of poverty and inner city revitalization. At the same time, the way that data are used to understand urban systems has changed dramatically. Urban Analytics offers a field-defining look at the challenges and opportunities of using new and emerging data to study contemporary and future cities through methods including GIS, Remote Sensing, Big Data and Geodemographics. Written in an accessible style and packed with illustrations and interviews from key urban analysts, this is a groundbreaking new textbook for students of urban planning, urban design, geography, and the information sciences.
Generative Urban Systems
Generative Urban Systems
Learning Objectives
By the end of this chapter students will understand the following:
- Differences between aggregate and disaggregate modeling frameworks.
- The main features of cellular automata, microsimulation, and agent-based modeling.
- What agents are, and how they function within an agent-based model.
- Those features of a complex system.
- Constraints on the operational use of agent-based models.
Modeling Urban Systems as a Collection of Interacting Parts
Cities are often studied at the aggregate level. That is, we examine the average traffic volume on a street, the median family income in a neighborhood, or the total population of a metropolitan area. All of these scenarios describe aggregations (cars, families, and people, respectively), however it also possible to consider cities as collections of interacting entities. Rather than considering average household income, ...
- Loading...
