Digital Urban Sensing: A Multi-layered Approach

TL;DR

This paper compares various digital datasets capturing human activity in New York City, revealing differences and complementarities that can enhance multi-layered urban models for zoning and socio-economic analysis.

Contribution

It provides a comprehensive comparison of diverse urban sensing datasets and demonstrates how their integration can improve understanding of urban dynamics.

Findings

Different datasets show substantially varied spatial and temporal patterns.

Limitations exist in individual datasets' ability to fully represent urban activity.

Multi-layered models leveraging multiple datasets can improve urban planning applications.

Abstract

Studies of human mobility increasingly rely on digital sensing, the large-scale recording of human activity facilitated by digital technologies. Questions of variability and population representativity, however, in patterns seen from these sources, remain major challenges for interpreting any outcomes gleaned from these records. The present research explores these questions by providing a comparison of the spatial and temporal activity distributions seen from taxi, subway and Citi Bike trips, mobile app records, geo-tagged Twitter data as well as 311 service requests in the five boroughs of New York City. The comparison reveals substantially different spatial and temporal patterns amongst these datasets, emphasizing limitations in the capacity of individual datasets to represent urban dynamics in their entirety. We further provide interpretations on these differences by decomposing the spatial distributions with working-residential balance and different propensities for demographic groups to use different activities. Nevertheless, the differences also highlight the opportunity to leverage the plurality to create multi-layered models of urban dynamics. We demonstrate the capacity of such models to advance urban zoning and socio-economic modeling - two common applications of digital urban sensing.