The daily urban dynamic indicator: Gauging the urban dynamic in Porto during the COVID-19 pandemic

Abstract

The SARS-CoV-2 outbreak motivated the development of a myriad of weekly and daily indicators that track economic activity to estimate and predict the consequences of the pandemic. With some exceptions, these indicators are calculated at the country level and are mainly focused on tracking economic factors, disregarding local urban phenomena. To address this, we present the Urban Dynamic Indicator (UDI), a novel composite indicator designed to measure a city's daily urban dynamic. The UDI is applied to Porto municipality, in Portugal, and it corresponds to a latent factor obtained through a factor analysis over seasonal adjusted daily data regarding traffic intensity, public transportation usage, internet usage in public buses, NO 2 emissions and noise level. The UDI's values show that, by the end of 2020, despite the approach of economic activity to its pre-pandemic values, as suggested by the Portuguese Daily Economic Indicator (DEI), Porto urban dynamic did not recover completely. The UDI enriches the information available for Porto city planners and policymakers to respond to crisis situations and to gauge the application of local policies that contribute to urban sustainable planning. Furthermore, the methodology defined in this work can be followed for the development of daily urban dynamic indicators elsewhere.

An Innovative Index for Evaluating Urban Vulnerability on Pandemic Using LambdaMART Algorithm

“…On the one hand, the pressure brought on by the pandemic will be transmitted to all aspects of urban life through various paths [15], making it more challenging to identify the possible vulnerable link. On the other hand, the pandemic gradually causes damage as it spreads, which is dynamic and occurs over a relatively long period [16]. This means that the risk of exposure and damage also dynamically changes along with the pandemic spread and the city's reaction.…”

“…However, in the fourth wave in June 2021, Okinawa, which performed relatively better in the last wave, had the highest number of infections and was short of medical resources [18]. This situation indicates that it is not enough to only focus on the inherent factors of cities, which led to the examination of dynamic urban indicators [16]. Practical methods that can investigate dynamic variables such as urban vitality and prevention measures such as social distancing are needed when conducting an urban vulnerability assessment.…”

“…[26][27][28][29][30]. Specific to pandemic vulnerability, these variables can roughly be divided into two groups: statistic variables that describe the static conditions of a city over a relatively long period, such as population, industrial structure, medical facilities; and dynamic variables that describe the dynamic status of a city during the pandemic, such as the interim policies, urban vitality, and disease prevalence [12,13,16,19].…”

Estimating pandemic effects in urban mass transportation systems: An approach based on visibility graphs and network similarity

“…Traffic zone analysis was performed by [9] which tried to address the same problem addressed in [8] , and both authors suggest that the scope (bike sharing data in the first case) and the granularity (analysis at a regional level) are limitations of their studies. In fact, while the availability of daily mobility data is growing more and more, it is not always possible to use traditional time series methods to explore human movement, as stated in [10] , which studied the problem of quantifying the impacts of pandemics on mobility by applying time series decomposition, and pointed out that traditional econometric methods are harder to use on daily time series with missing values, seasonality, and calendar effects.…”

Sustainable urban mobility: Flexible bus service network design in the post-pandemic era

“…The study of bus operation optimization remains one of the most popular topics in the transportation field, including fleet allocation, network design, passenger behavior analysis, and timetable optimization ( Ceder and Wilson, 1986 , Ibarra-Rojas et al, 2015 , Shao et al, 2022 ). But in recent years, the urban public transportation system has become one of the main mediums for the spread of the epidemic because of dynamic passenger boarding and alighting and uncertain in-vehicle movement ( Borjigin et al, 2023 , Jardim et al, 2022 , Qian and Ukkusuri, 2021 ). Public transportation has been significantly affected by the coronavirus disease, and ridership in major cities worldwide has dropped about 60% to 95% ( Fernandez Pozo et al, 2022 ), which has caused a dramatic reduction in fare income and considerable budget deficits.…”