Visualizing aggregate movement in cities

Abstract

We argue here that despite the focus in cities on location and place, it is increasingly clear that a requisite understanding of how cities evolve and change depends on a thorough understanding of human movements at aggregate scales where we can observe emergent patterns in networks and flow systems. We argue that the location of activities must be understood as summations or syntheses of movements or flows, with a much clearer link between flows, activities and the networks that carry and support them. To this end, we introduce a generic class of models that enable aggregated flows of many different kinds of social and economic activity, ranging from the journey to work to email traffic, to be predicted using ideas from discrete choice theory in economics which has analogies to gravitation. We also argue that visualization is an essential construct in making sense of flows but that there are important limitations to illustrating pictorially systems with millions of component parts. To demonstrate these, we introduce a class of generic spatial interaction models and present two illustrations. Our first application is based on transit flows within the high-frequency city over very short time periods of minutes and hours for data from the London Underground. Our second application scales up these models from districts and cities to the nation, and we demonstrate how flows of people from home to work and vice versa define cities and related settlements at much coarser scales. We contrast this approach with more disaggregate, individual studies of flow systems in cities that we consider an essential complement to the ideas presented here.This article is part of the theme issue ‘Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour’.

Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour

“…High-resolution movement patterns cannot always be achieved using such devices, so, more commonly, the type of information obtained from wearable devises is less granular. Such devices can be used to track interactions directly, through proximity detection in humans [32] and animals [33], and they can record movements in and out of certain spaces, such as stations of public transportation in human movements [8] and the movements of animals in and out of their nests [34,35]. Another common way to obtain the movement patterns of both humans and animals is image analysis.…”

“…Studies of transportation are ahead in this respect because human transportation has been studied for decades. Batty [8] provides a broad perspective on quantifying movement via examination of human transportation patterns in, and between, cities, and explains how to represent aggregated movements in cities. This is a necessary first step along the path to determining what impacts these movements and the interactions between the moving individuals, and in determining how space impacts these interactions.…”

“…This is a necessary first step along the path to determining what impacts these movements and the interactions between the moving individuals, and in determining how space impacts these interactions. By providing visualization and analysis of movement patterns in physical space, Batty's work [8] opens up opportunities for further examination of the causes and consequences of these aggregate movements that could not be examined if the movements themselves were not quantifiable. Batty's work bridges between the geographical and architectural scales by focusing on the relationships between locations rather than on the role of each particular location.…”

Anger while driving in Mexico City

“…In the last three decades, traffic and human mobility in big cities have been topics of increasing interest for the social [1][2][3][4] and computer science [5][6][7][8]. These topics have been researched to understand the way that people move in the city through street networks [9][10][11][12] or public transportation networks [13][14][15], but also to understand people's behavior as they commute [16,17]. In this regard, one of the most significant efforts is the study and understanding of "driving anger" [18].…”

Evaluation of Plan Implementation in the Fast-Growing Chinese Mega-City: A Case of a Polycentric System in Hangzhou Core Built-Up Area

“…It is mobile-positioning big data that has solved the data bottleneck. In recent years, using mobile-positioning big data, several research results regarding the spatio-temporal activities of residents have been obtained [30].…”