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.
Explaining the City
Explaining the City
Learning Objectives
By the end of this chapter students will understand the following:
- Models can be descriptive, predictive, or explanatory.
- Exploratory data analysis can help uncover meaningful patterns in data, which can in turn help guide model development.
- Regression is a flexible tool for helping to understand complex relationships within cities.
- Urban data are spatial data and these can be statistically problematic when used in a model, but there are techniques to explicitly account for spatial patterns.
Understanding the Interconnected City
The complexity of cities fosters opportunities for interaction that emerge between a diverse set of actors including households, businesses, and governments, whose interests may or may not align. Further, what we observe at any particular moment is the outcome of a myriad of processes, those ...
- Loading...
