The Challenges of Urban Data Ethics: Big Data and Intelligent Decision Support Systems

Magalhães, L. A.

doi:10.2139/ssrn.4441713

Cited by 1 publication

(1 citation statement)

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“…For street-scale activities, the existing big data is often inaccurate enough to be more closely integrated with the space in detail. Moreover, most of urban big data is controlled by Internet companies and mobile operators, which is not affordable and accessible (Magalhaes, 2021).…”

Section: Introductionmentioning

confidence: 99%

Understanding street-level urban vibrancy via spatial-temporal Wi-Fi data analytics: Case LivingLine Shanghai

Zhang,

Li,

et al. 2023

Environment and Planning B: Urban Analytics and City Science

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Urban vibrancy is a topic of great concern in the field of urban design and planning. However, the definition and measurement of urban vibrancy have not been consistently and clearly followed. With the development of technologies such as big data and machine learning, urban planners have adopted new methods that enable better quantitative evaluation of urban performance. This research attempts to quantify the impact on the urban vibrancy of the urban interventions introduced by the LivingLine project in a residential neighborhood renovation made in Siping Street, Shanghai. We use Wi-Fi probes to process collected mobile phone data and segment people into different categories according to commuting patterns analysis. We use a pre-trained random forest model to determine the specific locations of each person. Subsequently, we analyze the behavior patterns of people from stay points detection and trajectory analysis. Through statistical models, we apply multi-linear regression and find that urban intervention (well-curated and defined lab events deployed in the street) and people’s behavior are positively correlated, which helps us to prove the impact of urban intervention on street dynamics. The research proposes a novel, evidence-based, low-cost methodology for studying granular behavior patterns on a street level without compromising users’ data privacy.

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Section: Introductionmentioning

confidence: 99%