Towards Data-Informed Interventions: Opportunities and Challenges of Street-level Multimodal Sensing

TL;DR

This paper discusses how street-level multimodal sensing data can inform urban interventions, highlighting opportunities and challenges for data-driven decision-making to improve city safety and equity.

Contribution

It identifies key opportunities and challenges in leveraging multimodal sensing data for urban policy and intervention, emphasizing a data-informed framework.

Findings

Sensing localized physical interactions can inform urban safety interventions.

Opportunities exist for data-driven policymaking in urban environments.

Challenges include technical and ethical issues in data collection and analysis.

Abstract

Over the past decades, improvements in data collection hardware coupled with novel artificial intelligence algorithms have made it possible for researchers to understand urban environments at an unprecedented scale. From local interactions between actors to city-wide infrastructural problems, this new data-driven approach enables a more informed and trustworthy decision-making process aiming at transforming cities into safer and more equitable places for living. This new moment unfolded new opportunities to understand various phenomena that directly impact how accessible cities are to heterogeneous populations. Specifically, sensing localized physical interactions among actors under different scenarios can drive substantial interventions in urban environments to make them safer for all. In this manuscript, we list opportunities and associated challenges to leverage street-level multimodal sensing data to empower domain experts in making more informed decisions and, ultimately, supporting a data-informed policymaking framework. The challenges presented here can motivate research in different areas, such as computer vision and human-computer interaction, to support cities in growing more sustainably.