Leticia Serrano-Estrada (p), Pablo Martí-Ciriquián, Mariana Huskinson (p), Álvaro Bernabeu-Bautista

Gustavo Romanillos

The main motivation of this research is the identified gap in the literature that focuses on the contrasting diagnosis of urban and economic activity before and after the pandemic at the neighbourhood level. In this regard, this study contributes to the understanding of the transformations of cities caused by the pandemic and the monitoring of changes over time. Furthermore, it is situated within the framework of geographical and spatial analysis research that addresses the consequences of the crisis in cities by implementing GIS software and big data technologies to provide urban data and evidence-based knowledge as one of the Sustainable Development Goal 11 proposed aspects for pandemic response plans. For this purpose, Google Places and Twitter are adopted as the main sources of information. Precisely, data from these location-based have demonstrated to be a valuable resource in recognizing and studying the interrelationships and patterns between human behaviour and its geographic context, enabling the evaluation of a wide range of urban phenomena. Specifically, a mixed qualitative and quantitative methodology is proposed to assess the increase and decrease of economic activity (Google Places) and human presence (Twitter) in urban spaces across two periods: pre- and post-pandemic. Two areas of distinctive socio-economic backgrounds in three cities located in countries that applied various pandemic restriction measures are selected as case studies. The results obtained can provide beneficial insights for understanding the changes that have occurred in urban settings and for informing decision making for urban regeneration that is prepared for potential disruptive situations in the future.

Carlos Marigil (p), Gustavo Romanillos (p), Juan Carlos García-Palomares, Jorge Mallo, Raquel Sánchez-Cauce, Oliva Cantu-Ros

Leticia Serrano-Estrada

The COVID-19 pandemic triggered remarkable residential migrations all around the globe, as a result of the new preferences of an important number of citizens who looked for a new place to settle down. The increasing desire for housing with more open space caused a migration flow from dense city centers to less dense sub-urban areas as well as to small towns, villages, and the countryside. This phenomenon is subject of enormous interest for urban planners and policy makers, who need to find answers to important questions: Who has migrated? When and for how long? What has been the evolution of this residential migration over time? Has it been consolidated three years after the pandemic given the new opportunities that teleworking offers to some citizen groups? Where have citizens migrated to? And from what particular urban areas?

In an attempt to answer some of these questions, this phenomenon has been recently studied from different perspectives. Most studies have offered a general picture of this new migration, valuable but with certain limitations, given that they were commonly based on census data, registered with a low spatiotemporal resolution, and more importantly, leaving aside migrants that may have not officially changed their residence, given the uncertain scenario they were facing.

The objective of this study is to conduct research on the residential migrations that have taken place with origin in Madrid metropolitan area since the COVID-19 pandemic, based on the analysis of mobile network data. The methodology followed allowed us to infer population residence from 2019 to 2022, and then estimate residential migrations between 2019-2020, 2020-2021 and 2021-2022. The study has been conducted with a high spatial resolution, making it possible to identify migrations from the different neighborhoods of the city to other neighborhoods, municipalities, neighboring provinces, or regions within Spain. The mobile network data also provided information about the sociodemographic profile of migrants, such as age, sex, or average income.

The results offer valuable insights into the dynamics of residential migration. Clear patterns have been identified based on the migrant’s original residence, age, and income. The results also show how migration has evolved over time, providing some possible answers to relevant questions such as the impact of teleworking. The results are also compared to the migration figures of official census data, highlighting what we consider is an improvement in the measure of real residential migration.

Anna Temel (p), Sanne Kruse-Becher, Björn Helm

Gustavo Romanillos

The COVID-19 pandemic and its accompanying policies have fundamentally changed everyday
life and exposed people to severe stress factors such as fear of the illness itself, but
also its indirect effects: social isolation, shortfalls in childcare facilities and schools, and
higher unemployment risk. Hereby, some groups were more affected by the pandemic than
others: women and older people were found to suffer more from isolation and the more insecure
situation on the job market was more difficult for already financially disadvantaged
people. Many studies using wastewater samples from European cities found that drug
consumption has changed substantially during lockdowns compared to earlier years or the
post-lockdown period. The effects differed across substances and cities. Previous studies
only observe changes at the city level. However, not only the consumption of different
drugs is affected differently by the lockdown, but also drug consumers react heterogeneously
across socio-demographic groups. We estimate the impact of the first lockdown on
the consumption patterns of illegal and legal drugs of different socio-demographic groups in
five big German cities: Berlin, Hamburg, Frankfurt am Main, Saarbrücken, and Dresden.
This study uses wastewater samples covering different time periods in the pre-COVID years
2017-2020. By applying a difference-in-differences (DiD) framework this study estimates
the effect of the lockdown on the consumption of illegal and legal drugs and examines
heterogeneous effects across socio-demographic characteristics. According to the existing
literature on heterogeneous drug use patterns across socio-demographic groups, we expect
the effects of the lockdown on drug use to vary substantially.

Enrique Santiago-Iglesias (p), Gustavo Romanillos (p), Juan Carlos García-Palomares, Wenzhe Sun, Jan-Dirk Schmöcker, Jorge Mallo, Raquel Sánchez-Cauce, Oliva G. Cantu-Ros

Anna Temel

With a wide variety of forms and intensity, nightlife plays an important role in cities, at many different levels. Nightlife supports a significant part of our social interactions, enhancing social wellbeing and community-building dynamics. Furthermore, nightlife is not only relevant for the social dimension of urban life, but also for the cultural and economic ones, with evening and night activities accounting for an important share of the leisure industry.

COVID-19 pandemic has affected all kinds of human activities, but the impact on nightlife has been particularly dramatic and expanded over time, given the difficulty of most nightlife spaces to adapt to the physical distancing and air quality measures implemented by most countries. Although this is a relevant topic, very few studies have analyzed the impact of the COVID-19 pandemic on nightlife, offering insights into the effect on specific sectors, or more general but limited explorations, usually based on interviews.

The objective of this study is to examine the post-pandemic recovery of nightlife in the cities whose urban nightlife was heavily impacted, with a comparison between countries and cultures. The analysis is conducted in the cities of Madrid (Spain) and Kyoto (Japan) using mobile network data. More specifically, the research conducts a detailed spatiotemporal analysis of the nightlife activity in both cities, considering three temporal scenarios: a pre-pandemic scenario, a scenario after the lockdown but affected by pandemic-related restrictions, and a post-pandemic or "new normal" scenario, with no restrictions. The study is based on the estimation of the hourly presence of people over the course of the day, with a particular focus on evening and night hours. Regarding the spatial dimension, the presence of people has been estimated according to similar grids in both cities, for an area defined with the same criteria.

Madrid and Kyoto are two large cities with cultural similarities e.g. renowned tourism, and differences e.g. different COVID-19 restrictions. The results unveil their diverse spatiotemporal patterns of nightlife recovery. The temporal analysis reveals the different levels of recovery in working days and weekends, as well as the shifts of the peak and valley hours. The spatial analysis shows the hot spot urban areas in terms of nightlife, and how these areas have evolved in the three different scenarios. Finally, the spatiotemporal analysis unveils the existence of spatial clusters with similar temporal profiles in terms of the presence of people, for the three different scenarios, in both cities.