Human-Centric Data Science for Urban Studies

Bernd Resch,Michael Szell

doi:10.3390/ijgi8120584

Abstract

Due to the wide-spread use of disruptive digital technologies like mobile phones, cities have transitioned from data-scarce to data-rich environments. As a result, the field of geoinformatics is being reshaped and challenged to develop adequate data-driven methods. At the same time, the term "smart city" is increasingly being applied in urban planning, reflecting the aims of different stakeholders to create value out of the new data sets. However, many smart city research initiatives are promoting techno-positivistic approaches which do not account enough for the citizens’ needs. In this paper, we review the state of quantitative urban studies under this new perspective, and critically discuss the development of smart city programs. We conclude with a call for a new anti-disciplinary, human-centric urban data science, and a well-reflected use of technology and data collection in smart city planning. Finally, we introduce the papers of this special issue which focus on providing a more human-centric view on data-driven urban studies, spanning topics from cycling and wellbeing, to mobility and land use.

Highlights

Disruptive technological advances over the past two decades, such as mobile phones and online social networks, have fundamentally changed how we see the world
As a result of this transformational development, the scientific community has faced a transition from a data-scarce to a data-rich urban environment [3], which gave birth to urban informatics and reshaped geoinformatics through the increased application of data-driven approaches
In the past decade, the concept of “smart cities” has been driven by the idea of an ICT-infused city; that is, an urban system enriched with a number of different information technologies to support urban management and planning

Summary

Introduction

Disruptive technological advances over the past two decades, such as mobile phones and online social networks, have fundamentally changed how we see the world. As a result of this transformational development, the scientific community has faced a transition from a data-scarce to a data-rich urban environment [3], which gave birth to urban informatics and reshaped geoinformatics through the increased application of data-driven approaches. These new approaches necessitate the development of new methods for data acquisition, storage, and analysis, including unsupervised machine-learning algorithms or semi-supervised learning systems, among others. This special issue “Human-Centric Data Science for Urban Studies” focuses on this challenge and provides a more human-centric view of smart cities

Creating Value from Massive Urban Data Sets

Challenges in Human-Generated Urban Data Analysis

Approaches to Analyzing Human-Generated Urban Data

The Contributions of This Special Issue