COVID-19 Containment Policies Research Articles

Convergence behavior of sovereign bond yields in the EU and COVID-19 government responses

This paper aims to investigate the convergence pattern of sovereign bond yields in European Union (EU) countries during the Covid-19 pandemic. To this purpose we employ the Phillips and Sul convergence methodology. We further test whether the above pattern was affected by the intensity of government responses against the Covid-19 dispersion. Our findings indicate the existence of two convergence clubs. Both clubs are composed of countries that have previously been classified as either ‘core’ or ‘periphery’ countries, indicating that in this particular health crisis, this classification is too simplistic. Moreover, our results suggest that the intensity of Covid-19 containment policies had little to no effect in shaping the convergence pattern of sovereign bond yields in the EU. These conclusions are robust even after accounting for the size of the fiscal sector of each country.

Associations of U.S. state-level COVID-19 policies intensity with cannabis sharing behaviors in 2020

BackgroundCannabis use before the COVID-19 pandemic for many involved sharing prepared cannabis for inhalation, practices that were less prevalent during the pandemic. State-level COVID-19 containment policies may have influenced this decrease. This study examined the extent to which the intensity of state-level COVID-19 policies were associated with individual-level cannabis sharing. Findings have the potential to guide harm reduction policies for future respiratory pandemics and seasonal respiratory virus waves.MethodsThis study used cross-sectional individual-level data from the COVID-19 Cannabis Study, an anonymous U.S.-based web survey on cannabis use disseminated during the early phase of the pandemic (Full sample N = 1,883). We combined individual-level data with state-level policy data from Kaiser Family Foundation’s State COVID-19 Data and Policy Actions for three time-points from June to August 2020 that overlapped with the survey period. Cannabis sharing was dichotomized as any versus no sharing. We adapted a previously published coding framework to score the intensity of COVID-19 policies implemented in each U.S. state and averaged the policy score across the time period. We then used Poisson regression models to quantify the associations of the average state-level COVID-19 policy score with cannabis sharing during the pandemic.ResultsParticipants (n = 925) reporting using inhalation as a mode for cannabis use were included in this analysis. Most respondents were male (64.1%), non-Hispanic White (54.3%), with a mean age of 33.7 years (SD 8.8). A large proportion (74.9%) reported sharing cannabis during the pandemic. Those who shared cannabis more commonly lived in states with a lower average policy score (16.7, IQR 12.3–21.5) compared to those who did not share (18.6, IQR 15.3–25.3). In adjusted models, the prevalence ratio of any cannabis sharing per every 5-unit increase in the average COVID-19 policy score was 0.97 (95% CI 0.93, 1.01).ConclusionsFewer individuals shared cannabis in states with more intense COVID-19 containment policies compared to those in states with less intense policies. Individuals who use cannabis may be willing to make changes to their behavior and may further benefit from specific and directed public health messaging to avoid sharing during respiratory infection outbreaks.

Cross-neutralizing antibody against emerging Omicron subvariants of SARS-CoV-2 in infection-naïve individuals with homologous BNT162b2 or BNT162b2(WT + BA.4/5) bivalent booster vaccination

Since the emergence of SARS-CoV-2, different variants and subvariants successively emerged to dominate global virus circulation as a result of immune evasion, replication fitness or both. COVID-19 vaccines continue to be updated in response to the emergence of antigenically divergent viruses, the first being the bivalent RNA vaccines that encodes for both the Wuhan-like and Omicron BA.5 subvariant spike proteins. Repeated infections and vaccine breakthrough infections have led to complex immune landscapes in populations making it increasingly difficult to assess the intrinsic neutralizing antibody responses elicited by the vaccines. Hong Kong’s intensive COVID-19 containment policy through 2020–2021 permitted us to identify sera from a small number of infection-naïve individuals who received 3 doses of the RNA BNT162b2 vaccine encoding the Wuhan-like spike (WT) and were boosted with a fourth dose of the WT vaccine or the bivalent WT and BA.4/5 spike (WT + BA.4/5). While neutralizing antibody to wild-type virus was comparable in both vaccine groups, BNT162b2 (WT + BA.4/BA.5) bivalent vaccine elicited significantly higher plaque neutralizing antibodies to Omicron subvariants BA.5, XBB.1.5, XBB.1.16, XBB.1.9.1, XBB.2.3.2, EG.5.1, HK.3, BA.2.86 and JN.1, compared to BNT162b2 monovalent vaccine. The single amino acid substitution that differentiates the spike of JN.1 from BA.2.86 resulted in a profound antigenic change.

Affective Polarization Between Opinion-Based Groups in a Context of Low Partisan Discord: Measuring Its Prevalence and Consequences

Abstract Affective polarization has been primarily studied as a phenomenon between partisans. Recently, Hobolt, Leeper, & Tilley (2021) showed in the context of Brexit that affective polarization can also materialize around opinion-based groups, that is, political identities that form around extraordinary issues and cut through partisan lines. We generalize their findings by documenting the emergence of affective polarization around a new political conflict in a national context of low partisan discord. Specifically, we study divisions around the government response to the COVID-19 pandemic in Germany, a multiparty system where all established parties supported certain freedom restrictions to contain the pandemic. Drawing on numerous indicators and a survey experiment from a representative online panel study, we explored the (a) prevalence, (b) consequences, and (c) context of affective polarization between supporters and opponents of COVID-19 containment policies. We found that the majority of supporters and a minority of opponents showed indications of affective polarization. Affectively polarized citizens showed various group biases that make productive political and social interactions less likely. Affective polarization was stable over time and different from partisan and sociostructural conflicts. We raise questions for future research and discuss what it means for democracy when deep political rifts are not captured by main parties in a party system.

Business, as usual? The impact of organized economic interests on the stringency of Covid-19 containment policies: insights from the Italian case

Abstract How do organized economic interests affect the governance of the coronavirus disease 2019 (Covid-19) pandemic? We investigate whether the structural and instrumental power of employer organizations and unions impact upon the stringency of containment measures implemented by governing authorities to tackle the Covid-19 pandemic, focusing on Italy during the first Covid-19 wave of early 2020 as a crucial case. Using Hausman–Taylor panel regression models and original indicators of regional stringency and of unions and employers’ organizations’ efforts to exercise instrumental power via public pressure on social media, we find that the intensity of public pressure by employer organizations is negatively correlated with the stringency of the policy responses implemented by regional authorities to tackle the Covid-19 pandemic, whilst union pressures only show a limited effect. Our findings demonstrate that business pressure and the interplay of economic and class interests are constitutive of the governance of a crucial social and public health phenomenon such as the Covid-19 pandemic.

COVID-19 containment policies, digitalization and sustainable development goals: evidence from Alibaba’s administrative data

The impact of digital platforms on the implementation of the United Nations’ (UN) Sustainable Development Goals (SDGs), especially the business sustainability of micro, small and medium-sized enterprises (MSMEs), under different containment policies during and after the COVID-19 pandemic has not yet been studied in detail. Using detailed administrative data from Alibaba Group’s online on-demand food delivery platform, we found that the digital platform contributed to the food security, wellbeing, employment and business sustainability of MSMEs both during and after the pandemic. We uncovered merchants’ heterogeneous responses to the COVID-19 pandemic during and after the implementation of different containment policies in China. On the extensive margin, the period of complete lockdown left long-term scarring on online merchants by decreasing the number of entrants, especially in the cooked food industry. In contrast, on the intensive margin, chain stores, especially those with large chain networks or multiapp stores, exhibited stronger resilience than their counterparts during and after lockdown. Thus, specialization (at the outlet level) and wider coverage (at the network level) emerged as key factors that enable business sustainability under challenging economic circumstances.

Covid-19 severity, government responses and stock market reactions: a study of 14 highly affected countries

PurposeThis study undertakes a comprehensive investigation into the far-reaching repercussions of Covid-19 stimulus packages and containment policies on stock returns, meticulously examining a diverse array of 14 distinct markets.Design/methodology/approachThis study employed the Panel SVAR model to analyze the relationships between various policies and stock market performance during the Covid-19 outbreak. The sample comprises 5432 daily observations spanning from December 2020 to January 2022 for the 14 selected markets, with missing data excluded.FindingsThe findings reveal three consistent impacts across all 14 markets. Firstly, stock returns immediately reversed and decreased within a day when Governments tightened containment policies. Secondly, economic stimulus packages led to a fall in stock returns. Thirdly, an increasing death rate caused the stock return to decrease in the following two days. These findings are supported by the uniform impulse responses in all three shocks, including common, composite and idiosyncratic shocks. Furthermore, all inverse root tests satisfy the stability conditions, indicating the stability and reliability of Panel SVAR estimations.Practical implicationsOne vital implication is that all government decisions and measures taken against the shock of Covid-19 must consider economic impacts to avoid unnecessary financial losses and support the effective functioning of stock markets during similar shocks. Secondly, investors should view the decline in stock returns due to Covid-19 effects as temporary, resulting from anxiety about the outbreak. The study highlights the importance of monitoring the impact of policies on financial markets and the broader economy during crises. Overall, these insights can prove helpful for investment decisions and policymaking during future crises.Originality/valueThis study constitutes a noteworthy addition to the literature on behavioural finance and the efficient market hypothesis, offering a meticulous analysis of the multifaceted repercussions of Covid-19 on market interactions. In particular, it unveils the magnitude, duration and intricate patterns of market volatilities linked to significant shock events, encompassing a comprehensive dataset spanning 14 distinct markets.

Analyzing and Forecasting Pediatric Fever Clinic Visits in High Frequency Using Ensemble Time-Series Methods After the COVID-19 Pandemic in Hangzhou, China: Retrospective Study.

The COVID-19 pandemic has significantly altered the global health and medical landscape. In response to the outbreak, Chinese hospitals have established 24-hour fever clinics to serve patients with COVID-19. The emergence of these clinics and the impact of successive epidemics have led to a surge in visits, placing pressure on hospital resource allocation and scheduling. Therefore, accurate prediction of outpatient visits is essential for informed decision-making in hospital management. Hourly visits to fever clinics can be characterized as a long-sequence time series in high frequency, which also exhibits distinct patterns due to the particularity of pediatric treatment behavior in an epidemic context. This study aimed to build models to forecast fever clinic visit with outstanding prediction accuracy and robust generalization in forecast horizons. In addition, this study hopes to provide a research paradigm for time-series forecasting problems, which involves an exploratory analysis revealing data patterns before model development. An exploratory analysis, including graphical analysis, autocorrelation analysis, and seasonal-trend decomposition, was conducted to reveal the seasonality and structural patterns of the retrospective fever clinic visit data. The data were found to exhibit multiseasonality and nonlinearity. On the basis of these results, an ensemble of time-series analysis methods, including individual models and their combinations, was validated on the data set. Root mean square error and mean absolute error were used as accuracy metrics, with the cross-validation of rolling forecasting origin conducted across different forecast horizons. Hybrid models generally outperformed individual models across most forecast horizons. A novel model combination, the hybrid neural network autoregressive (NNAR)-seasonal and trend decomposition using Loess forecasting (STLF), was identified as the optimal model for our forecasting task, with the best performance in all accuracy metrics (root mean square error=20.1, mean absolute error=14.3) for the 15-days-ahead forecasts and an overall advantage for forecast horizons that were 1 to 30 days ahead. Although forecast accuracy tends to decline with an increasing forecast horizon, the hybrid NNAR-STLF model is applicable for short-, medium-, and long-term forecasts owing to its ability to fit multiseasonality (captured by the STLF component) and nonlinearity (captured by the NNAR component). The model identified in this study is also applicable to hospitals in other regions with similar epidemic outpatient configurations or forecasting tasks whose data conform to long-sequence time series in high frequency exhibiting multiseasonal and nonlinear patterns. However, as external variables and disruptive events were not accounted for, the model performance declined slightly following changes in the COVID-19 containment policy in China. Future work may seek to improve accuracy by incorporating external variables that characterize moving events or other factors as well as by adding data from different organizations to enhance algorithm generalization.

Convergence Between Migrant Smuggling and Trafficking of Goods: Text Analysis of Open-Source Data

There is debate about the convergence of migrant smuggling and transnational trafficking of goods, the threat it poses to society, and its evolution in response to the COVID-19 pandemic. However, empirical evidence is limited. We tested for convergence using a methodology based on natural language processing. Results show that convergence occurs, but it is rare and episodic. Exploratory results suggest that COVID-19 containment policies had no impact on the simultaneous occurrence of migrant smuggling and transnational trafficking of goods. These findings expand our understanding of migrant smuggling and provide suggestions for policymakers to combat transnational crimes.

Between lives and economy: COVID-19 containment policy in open economies

This paper studies containment policies for combating a pandemic in an open-economy context. It does so via quantitative analyses using a model that incorporates a standard epidemiological compartmental model in a general equilibrium multi-country, multi-sector Ricardian model of international trade with input–output linkages. We quantitatively evaluate the long-run welfare and real-income losses due to the short-run pandemic shocks, and we study the role of trade in these effects. We devise a novel approach to computing national optimal policies. We find that (1) the long-run welfare and real-income losses due to just two years of pandemic shocks are substantial; (2) international trade helps buffer both the welfare and real-income losses, and it also saves lives; (3) the computed optimal policies indicate that most countries should have tightened their containment measures relative to what was done; and (4) compared to the case of autarky, the optimal policy under trade is generally more stringent.

Does the pandemic have a gender? An exploratory study about the Perceived Impact of COVID-19 measures on women and men in Ukraine and Belarus

ABSTRACT This paper examines the perceived effects of COVID-19 containment policies in Ukraine and Belarus, focusing on how these policies disproportionately affected men and women due to societal gender roles and stereotypes. The study uses document analysis, interviews with gender experts and activists, and a survey of 109 respondents to explore the different quarantine behaviours of men and women in the two countries. The analysis reveals how the virus was handled differently in the two countries, with Ukraine implementing strict lockdowns while Belarus downplayed the severity of the virus. In addition, the survey analysis was based on four dimensions - economic, social, healthcare and well-being, and civic empowerment - contributing to uncovering citizens' perceptions of the implications of the pandemic in their daily lives. The paper concludes by providing policy recommendations to address the economic and social impacts of the pandemic, as well as ways to better manage future health crises by addressing gender-specific needs.

Associations between the stringency of COVID-19 containment policies and health service disruptions in 10 countries

BackgroundDisruptions in essential health services during the COVID-19 pandemic have been reported in several countries. Yet, patterns in health service disruption according to country responses remain unclear. In this paper, we investigate associations between the stringency of COVID-19 containment policies and disruptions in 31 health services in 10 low- middle- and high-income countries in 2020.MethodsUsing routine health information systems and administrative data from 10 countries (Chile, Ethiopia, Ghana, Haiti, Lao People’s Democratic Republic, Mexico, Nepal, South Africa, South Korea, and Thailand) we estimated health service disruptions for the period of April to December 2020 by dividing monthly service provision at national levels by the average service provision in the 15 months pre-COVID (January 2019-March 2020). We used the Oxford COVID-19 Government Response Tracker (OxCGRT) index and multi-level linear regression analyses to assess associations between the stringency of restrictions and health service disruptions over nine months. We extended the analysis by examining associations between 11 individual containment or closure policies and health service disruptions. Models were adjusted for COVID caseload, health service category and country GDP and included robust standard errors.FindingsChronic disease care was among the most affected services. Regression analyses revealed that a 10% increase in the mean stringency index was associated with a 3.3 percentage-point (95% CI -3.9, -2.7) reduction in relative service volumes. Among individual policies, curfews, and the presence of a state of emergency, had the largest coefficients and were associated with 14.1 (95% CI -19.6, 8.7) and 10.7 (95% CI -12.7, -8.7) percentage-point lower relative service volumes, respectively. In contrast, number of COVID-19 cases in 2020 was not associated with health service disruptions in any model.ConclusionsAlthough containment policies were crucial in reducing COVID-19 mortality in many contexts, it is important to consider the indirect effects of these restrictions. Strategies to improve the resilience of health systems should be designed to ensure that populations can continue accessing essential health care despite the presence of containment policies during future infectious disease outbreaks.

Polarized Politics: Protest Against COVID-19 Containment Policies in the USA

Abstract Protest against containment policies in U.S. states is fueled by two drivers: the stringency of containment policies and the partisan control over the governorship and legislatures in each state. In our analysis of the period from March 2020 to March 2022, we find more protest events in states fully controlled by Democrats than in Republican controlled states both in a sample consisting of all states and a balanced sample in which we constrain observations to those red and blue states with on average similarly stringent containment policies. Protest was therefore politicized, and we find that partisanship exerts a roughly equal substantive effect on the number of protest events as the stringency of containment policies. If we assume no direct effect of partisanship on protest but allow for causal heterogeneity along partisan lines in the effect of containment policies, we find that the same increase in the stringency of policies evokes a stronger protest response in blue states than in red states.

Right and yet wrong: a politico-economic perspective on Germany’s early COVID-19 policy

ABSTRACT Applying a spatio-temporal endemic–epidemic forecasting model, we evaluate different perspectives on the adequacy of COVID-19 containment policies. Using Germany’s early containment policy as an example, we show that containment policies judged as rational based on the real-time perspective of policymakers may be deemed unnecessary or ineffective in ex-post evaluations. We also demonstrate that one-size-fits-all policies implemented in Germany early in the pandemic are likely suboptimal.

COVID-19 Policies, Pandemic Disruptions, and Changes in Child Mental Health and Sleep in the United States

The adverse effects of COVID-19 containment policies disrupting child mental health and sleep have been debated. However, few current estimates correct biases of these potential effects. To determine whether financial and school disruptions related to COVID-19 containment policies and unemployment rates were separately associated with perceived stress, sadness, positive affect, COVID-19-related worry, and sleep. This cohort study was based on the Adolescent Brain Cognitive Development Study COVID-19 Rapid Response Release and used data collected 5 times between May and December 2020. Indexes of state-level COVID-19 policies (restrictive, supportive) and county-level unemployment rates were used to plausibly address confounding biases through 2-stage limited information maximum likelihood instrumental variables analyses. Data from 6030 US children aged 10 to 13 years were included. Data analysis was conducted from May 2021 to January 2023. Policy-induced financial disruptions (lost wages or work due to COVID-19 economic impact); policy-induced school disruptions (switches to online or partial in-person schooling). Perceived stress scale, National Institutes of Health (NIH)-Toolbox sadness, NIH-Toolbox positive affect, COVID-19-related worry, and sleep (latency, inertia, duration). In this study, 6030 children were included in the mental health sample (weighted median [IQR] age, 13 [12-13] years; 2947 [48.9%] females, 273 [4.5%] Asian children, 461 [7.6%] Black children, 1167 [19.4%] Hispanic children, 3783 [62.7%] White children, 347 [5.7%] children of other or multiracial ethnicity). After imputing missing data, experiencing financial disruption was associated with a 205.2% [95% CI, 52.9%-509.0%] increase in stress, a 112.1% [95% CI, 22.2%-268.1%] increase in sadness, 32.9% [95% CI, 3.5%-53.4%] decrease in positive affect, and a 73.9 [95% CI, 13.2-134.7] percentage-point increase in moderate-to-extreme COVID-19-related worry. There was no association between school disruption and mental health. Neither school disruption nor financial disruption were associated with sleep. To our knowledge, this study presents the first bias-corrected estimates linking COVID-19 policy-related financial disruptions with child mental health outcomes. School disruptions did not affect indices of children's mental health. These findings suggest public policy should consider the economic impact on families due to pandemic containment measures, in part to protect child mental health until vaccines and antiviral drugs become available.

Protest against Covid-19 containment policies in European countries

Protests against coronavirus policies have occurred in all European countries. The intensity of protest varies strongly, however. We explain this variation by strategic choices that protest organizers make to maintain the protest movement. Specifically, we argue that protest organizers pay heed to the dynamics of the pandemic in their country: the number of protest events is higher when and where mortality rates are lower and containment policies are more stringent. At the same time, the number of protest events is influenced by political factors. Despite the fact that civil liberties facilitate trust in government, these two variables exert opposite effects: while higher trust in government and public administration reduces the number of protest events, stronger civil liberties increase the number of protest events. We find evidence for these hypotheses in an analysis of the number of monthly protest events based on information from ACLED, the Armed Conflict Location & Event Data Project, in 28 European countries between March 2020 and August 2021.

The Work, Economic, and Remittance Stress and Distress of the COVID-19 Pandemic Containment Policies: The Case of Venezuelan Migrants in Argentina and Chile.

According to the social stress process model, global crises are macro-level stressors that generate physiological stress and psychological distress. However, existing research has not identified immigrants' COVID-19 containment policy stressors or examined the social stress of sending remittances amid crises. Drawing on in-depth longitudinal interviews with 46 Venezuelan immigrants-half before and half during the pandemic-in Chile and Argentina, we identified the COVID-19 containment policies' stressors. We focused on Venezuelan immigrants because they constitute one of the largest internationally displaced populations, with most migrating within South America. We found that the governmental COVID-19 containment measures in both countries generated four stressors: employment loss, income loss, devaluation of employment status, and inability to send needed remittances. Moreover, sending remittances helped some migrants cope with concerns about loved ones in Venezuela. However, sending remittances became a social stressor when immigrants struggled to simultaneously sustain their livelihoods and send financial support to relatives experiencing hardships in Venezuela. For some immigrants, these adversities generated other stressors (e.g., housing instability) and symptoms of anxiety and depression. Broadly, for immigrants, the stressors of global crises transcend international borders and generate high stress, which strains their psychological well-being.

Lights out? COVID-19 containment policies and economic activity

This paper estimates how strongly COVID-19 containment policies have impacted aggregate economic activity. We use a difference-in-differences methodology to estimate how containment zones of different severity across India impacted district-level nighttime light intensity, as well as household income and consumption. From May to July 2020, nighttime light intensity was 9.1 % lower in districts with the most severe restrictions compared with districts with the least severe restrictions, which could imply between 5.8 % and 6.6 % lower GDP. Nighttime light intensity was only 1.6 % lower in districts with intermediate restrictions. The differences were largest in May during the graded lockdown, and tapered in June and July. Lower house-hold income and consumption corresponding to zone-wise restrictions corroborate these results. Stricter containment measures had larger impacts in districts with greater population density, older residents, and more services employment. The large magnitudes of the findings suggest that governments should carefully consider the economic costs of country-wide pandemic containment policies while weighing the trade-offs against public health benefits. Keywords: Containment policies, COVID-19, Nighttime lights, India

Effects of stay-at-home orders on skill requirements in vacancy postings

The COVID-19 pandemic and containment policies have had profound economic impacts on the labor market. Stay-at-home orders (SAHOs) implemented across most of the United States changed the way of people worked. In this paper, we quantify the effect of SAHO durations on skill demands to study how firms adjust labor demand within occupation. We use skill requirement information from the 2018 to 2021 online job vacancy posting data from Burning Glass Technologies, exploit the spatial variations in the SAHO duration, and use instrumental variables to correct for the endogeneity in the policy duration related to local social and economic factors. We find that policy durations have persistent impacts on the labor demand after restrictions are lifted. Longer SAHOs motivate management style transformation from people-oriented to operation-oriented by requiring more of operational and administrative skills and less of personality and people management skills to carry out standard workflows. SAHOs also change the focus of interpersonal skill demands from specific customer services to general communication such as social and writing skills. SAHOs more thoroughly affect occupations with partial work-from-home capacity. The evidence suggests SAHOs change management structure and communication in firms.

Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic.

Exploration of dynamic human activity gives significant insights into understanding the urban environment and can help to reinforce scientific urban management strategies. Lots of studies are arising regarding the significant human activity changes in global metropolises and regions affected by COVID-19 containment policies. However, the variations of human activity dynamics amid different phases divided by the non-pharmaceutical intervention policies (e.g., stay-at-home, lockdown) have not been investigated across urban areas in space and time and discussed with the urban characteristic determinants. In this study, we aim to explore the influence of different restriction phases on dynamic human activity through sensing human activity zones (HAZs) and their dominated urban characteristics. Herein, we proposed an explainable analysis framework to explore the HAZ variations consisting of three parts, i.e., footfall detection, HAZs delineation and the identification of relationships between urban characteristics and HAZs. In our study area of Greater London, United Kingdom, we first utilised the footfall detection method to extract human activity metrics (footfalls) counted by visits/stays at space and time from the anonymous mobile phone GPS trajectories. Then, we characterised HAZs based on the homogeneity of daily human footfalls at census output areas (OAs) during the predefined restriction phases in the UK. Lastly, we examined the feature importance of explanatory variables as the metric of the relationship between human activity and urban characteristics using machine learning classifiers. The results show that dynamic human activity exhibits statistically significant differences in terms of the HAZ distributions across restriction phases and is strongly associated with urban characteristics (e.g., specific land use types) during the COVID-19 pandemic. These findings can improve the understanding of the variation of human activity patterns during the pandemic and offer insights into city management resource allocation in urban areas concerning dynamic human activity.