Impact of COVID-19 on Urban Everyday Life in Greece. Perceptions, Experiences and Practices of the Active Population

Abstract

The COVID-19 pandemic that erupted in early 2020 has affected the everyday life, the practices, and the perceptions of the global population significantly. The aim of this paper is to investigate the perceptions, experiences, and practices of the active population regarding everyday urban life (working conditions, mobility, urban space, etc.) in the context of the first wave of the pandemic, by focusing on people who live and work in Greek cities. The data were collected using a structured questionnaire addressed to an active population. The majority of respondents declared that their personal and family lives were affected by the lockdown, with significant effects on their professional life and economic activities. Stress and fear were two feelings that increased. As far as mobility was concerned, the majority of respondents were unwilling to change their commuting practices. Although most of our findings presented a homogenous distribution in our sample, some changes in perceptions, experiences, and practices were correlated with the gender, age, and family status of the respondents. The results of the analysis provide useful information to policy makers and show that it is necessary to radically rethink aspects of urban space and urban mobility, in relation to the multiple problems and dynamics of the active population.

1. Introduction

The COVID-19 pandemic that erupted at the end of 2019 has significantly and in multiple ways affected the everyday life, the practices, and the perceptions of the global population [1,2]. In order to limit the spread of the virus, national governments, often under the guidance of international organizations like the World Health Organization (WHO), have adopted a series of emergency measures to protect citizens and public health, measures that vary from country to country and from city to city [3,4]. These measures focus on social distancing, suspension of economic, educational and cultural activities, as well as restrictions on citizens’ mobility.

Lockdown measures have created new conditions for the cities and everyday life [3] and led to a widening of social inequalities related to access to housing, the health care system, technology, and labor; these inequalities make women, people with disabilities, precarious workers, poor people, and ethnic minorities even more vulnerable [5,6]. The increase in social inequalities is a fact of special importance for the active population [7]. Working conditions have changed significantly for a large part of the population, mainly by expanding and deepening trends that existed before the COVID-19 crisis [8]. Strict sanitary measures have been applied in workplaces; teleworking and the use of technology have been boosted; many workers lost their jobs or found themselves unemployed; flexibility, insecurity, and precariousness have increased; employees in occupations critical for securing public health and supplying essentials for living (doctors, nurses, couriers, employees at food stores, etc.) have been overloaded with work. These changes in working conditions, in combination with changes in family routines [9], urban mobility [10,11], and social activities [12] form a new unprecedented condition in the cities [13].

Obviously, changes in working conditions do not affect everyone in the same way. As the COVID-19 crisis expands and deepens, the dominant discourse about the effects on labor is more and more consolidated in the direction of focusing almost exclusively on macroeconomic arguments and concerns; the result is financial recession, the rise of unemployment, and the adoption of flexible forms of labor [14,15,16]. However, gender, age, marital status, as well as the type and the status of work are—among others factors—associated with differentiated perceptions, experiences, and practices of the active population during the lockdown [6,14], a fact that is ignored or underestimated in the dominant academic, social, and political discourse.

The outbreak of COVID-19 has also had a profound impact on transport and mobility. Transformations of public space are directly related to the changes in urban mobility [17]. Different social distance measures implemented in many cities to lessen transmission risks have significant effects on restricting travel and activity participation. Because of confinement, the changes in working conditions, and the reduced public activities, travel demand has decreased in many cities. Public transport use, road traffic, and everyday mobility have collapsed to very low levels due to curfew measures [18,19]. Travel restrictions have discouraged the use of public transport. Public transport operators have sought to minimize risks through employing sanitization and physical distance policies for passengers (backdoor boarding, cashless operation) [20]. People with access to a private car might be inclined to drive more as they are alone in their vehicle and, thus, more protected than in public transport. In cases of short trips, walking and cycling have increased, since social contact can easily be avoided during active travel. On the other hand, significant improvement in air quality and reductions in carbon emissions (CO₂), nitrogen dioxide (NO₂), and particulate matter (PM10) resulting from the decrease in transport activity have been recorded [20,21]. However, these are short-term gains, and air pollution and emissions are expected to rise again once the situation is resolved. Moreover, the benefits of reduced transport during lockdowns have included significantly decreased road crash deaths and injuries [22,23].

The first case of COVID-19 in Greece was detected on 28 February 2020, and the first restrictive measures were taken at the beginning of March 2020. Gradually, during March 2020 and as the cases increased, the measures for the protection of public health were escalated. Schools and universities were closed; commercial, leisure, entertainment, and cultural activities were suspended (excluding grocery stores, banks, and pharmacies); teleworking was imposed in public and private sectors; and access to public parks and beaches was banned. Strict traffic restrictions have been in force since 23 March 2020, with few exceptions. The first wave of the COVID-19 pandemic in Greece was over in the beginning of May 2020. Since 4 May 2020 the measures have been relaxed and economic activity has been restarted.

The public discourse about the impact of COVID-19 and the measures to deal with it in Greek cities during the first wave of the pandemic is particularly intense. Emphasis has been placed on the effects of the shrinking of everyday life within the domestic sphere and on the inequalities that have emerged in relation to this [24]. Along with the impacts in the private space, the role, the function, and the surveillance of the public space has been seriously transformed. Public space is a field of special interest that became, in the context of the pandemic, not only an area of restrictions and exclusions, but also a place of relaxation and experimentation [13]. The curfew has created new conditions for mobility in the city and has affected the modal split of the Greek cities [25]. Moreover, institutional arrangements for emergency traffic measures and relatively ad hoc traffic interventions have opened the debate on how to make decisions, enhance citizens’ participation, and activate urban regeneration processes. Finally, the impact on the urban economy and local labor markets is a topic that has attracted much interest and raised questions about developing policies for mitigating financial recession and unemployment [26,27,28].

The aim of this paper is to investigate the perceptions, experiences, and practices of the active population regarding everyday life in the context of the first wave of the COVID-19 pandemic, by focusing on people who live and work in Greek cities. The data were collected using a structured questionnaire addressed to the adult active population. The paper intends to identify impacts on social life, emotional state, everyday activities, working conditions, mobility, as well as on perceptions about urban space and urban policies, and to correlate the findings with the social profile of the respondents.

The paper argues that, apart from obtaining a big picture of the multiple impacts of COVID-19 on urban life, emphasis on individual perceptions, experiences, and practices reveals areas of knowledge that would otherwise remain in the dark and enriches our understanding with more complex and more flexible variables. The ultimate purpose of this article is to provide useful information to policy makers to design more effective urban policies not only in Greek cities, but also elsewhere.

The paper is structured as follows: an introduction of the problem, namely, the effects of COVID-19 on the perceptions, the experiences, and the practices of everyday life appears in the beginning of the paper. The next section describes the methodological approach used and data collected. Then, the statistical analysis of the data is shown. Finally, based on the results of the analysis, we discuss how policy makers and stakeholders can exploit the analysis of this work.

2. Materials and Methods

2.1. Questionnaire

The analysis of this paper was based on the data collected using a structured questionnaire, addressed to the active population of Greek cities. This questionnaire consisted of five parts. The first part included introductory questions regarding respondents’ opinions on the COVID-19 pandemic and whether this pandemic affected them in their everyday life, both emotionally and in their relationships with their family and their friends. The second part examined the impact of the pandemic on the working conditions of the respondents, the third one examined the impacts on their mobility, and the fourth one explored their perceptions about urban space. Finally, the fifth part recorded the socio-demographic data of the respondents.

The survey questionnaire consisted of closed-ended questions and the majority of the responses were measured using a five-point Likert scale, ranging from 1 to 5: 1: not at all, 2: little, 3: moderately, 4: much, 5: very much; or 1: much less, 2: little less, 3: the same, 4: little more, 5: much more.

2.2. Sample and Collection Method

The survey was conducted just after the lockdown from 6 to 27 May 2020 in Greek cities. The questionnaire was part of the work of the students of the MSc Environmental Design Program of the Hellenic Open University. Each one of the students received the questionnaire electronically, and a certain number of questionnaires, depending upon the demographics of gender, age, and educational distribution, according to the data retrieved from the internet site of the Hellenic Statistical Authority, were distributed [29]. Only active respondents, aged over 18 years old and living in urban areas of continental Greece (excluding the islands) participated. The students collected the questionnaire in their city of residence; we excluded the members of their family, their close relatives, and their colleagues and distributed the questionnaires randomly in different places in the city at different times of the day and on different days of the week, depending upon the previous demographics. Considering that the students of the Hellenic Open University were spread all over Greece, the collected questionnaires were also spread across the country, and thus we received responses from the capital cities of 26 of the 39 prefectures in continental Greece. In particular, 39% of the respondents lived in the metropolitan area of Athens, 13% lived in the metropolitan area of Thessaloniki, and the rest in medium-sized cities. This distribution reflected the distribution of the population in urban areas, as 35% of the national population lives in the metropolitan area of Athens and 10% in the metropolitan area of Thessaloniki.

The sample size, in the case of the finite population, was calculated using the following equation [30]:

$$n=\frac{z^2\ast p\ast \left(1-p\right)\ast N}{M{E}^2\ast \left(N-1\right)+z^2\ast p\ast \left(1-p\right)},$$

where:

• n is the sample size,
• ME is the desired margin of error (for desired reliability, the acceptable maximum error is 0.05, with an associated 95% confidence interval),
• N is the population size (adult population of Greece: 8,926,161 inhabitants),
• p is the preliminary estimate of the proportion in the population (as the value of p was not known, the maximum value of 0.50 was assumed),
• z is the two-tailed value of the standardized normal deviate associated with the desired level of confidence (for 95% confidence interval the value of z was equal to 1.96).

In our case, the desired margin of error of 5% resulted in 384 questionnaires. We decided to double this number to decrease even more the margin of error. For that, 740 questionnaires were initially collected. Because of the absence of relevant information, 10 questionnaires were considered invalid and excluded from the analysis. Finally, 730 questionnaires were considered valid and analyzed. The desired margin of error of this sample was only 3.63%.

2.3. Data Analysis

The perceptions, experiences, and practices of the active population that lives and works in Greek cities, regarding everyday life in the context of the first wave of the COVID-19 pandemic, were statistically evaluated. Quantitative variables were indicated as mean ± standard deviation. Frequency analysis, percentages, cross tabulation, and chi-squared tests of independence were calculated for the categorical variables. The frequencies of observed and expected values were analyzed by means of cross tabulations. These frequencies revealed the relationships between cross-tabulated variables. The chi-square test for independence was used to determine whether the variables corresponding to the questions of the first two sections of the questionnaire were statistically related to the socio-demographic characteristics of the respondents. A 2-sided p-value less than 0.05 was considered to be significant.

For the construction of Binary Logistic Regression models, the following procedure was followed. The independent variables of the models were established using a stepwise method. Initially, all variables were included in each model, and then all variables having p-values less than 0.05 were eliminated. Then, all variables were added, one by one, to test if they were statistically significant, and also if they increased the predictive adequacy of the model. The independent values having a p-value < 0.05, with the entire model statistical data, are shown in the tables in the corresponding sections.

3. Results

3.1. Analysis of the Socio-Demographic Characteristics of the Sample

Table 1. Socio-demographic profile of the respondents.

Variables	Value	% (Sample)	% (Census 2011)
Gender	Male	47	48.2
	Female	53	51.8
Age group	18–29	19.9	18.36
	30–39	26.2	19.52
	40–49	18.4	18.24
	50–59	22.6	15.86
	60–69	11.1	12.19
	> 70	1.7	15.83
Marital status	Married	49.9	50.3
	Single	43	39.1
	Divorced	5.5	3.1
	Widowed	1.6	7.6
Education	Primary/Secondary school	17.3	42.1
	High school	26.6	31.7
	Higher education	50.4	24
	Master Diploma/PhD	5.8	2.2
Occupation	Civil servant	24.3	16.2
	Private employee	43.9	39.2
	Freelancer	22.5	25.8
	Unemployed	9.4	18.7
Family income, EUR	0–5000	4.9
	5001–10,000	22.4
	10,001–20,000	43.8
	20,001–30,000	17.4
	30,001–40,000	9
	40,001–50,000	1.5
	50,001–60,000	0.2
	>60,000	0.9

shows the frequency distribution of the socio-demographic profiles of the respondents. The sample reflected the gender, the age distribution, the marital status, the education, and the occupation of the corresponding official data of the Hellenic Statistical Authority, as recorded in the census of 2011. Table 1 shows that the sample consisted of 47.0% males and 53.0% females, the average age of both genders was 47.6 years. As very often occurs in this kind of questionnaire, the percentage of old people (over 70 years old) was lower in the sample than in the population, and this was due to the difficulties of reaching these people and to their low willingness to answer this questionnaire. Moreover, our target group consisted of active people, and the percentage of people over 65 years old remaining active was very low. Also, almost half (49.9%) of the respondents were married, half (50.4%) of the respondents had a university education, while 42.9% had a secondary or high school education. Regarding occupation, almost half of the sample consisted of private employees (43.9%), 24.3% were civil servants and 22.5% were freelancers. Finally, the annual total family income corresponded to EUR 10,001–20,000 for 43.8% of the respondents, while 88.5% had an annual total family income lower than EUR 30,000.

The representativeness of the sample was tested according to the procedure proposed by Parke (2013) [31]. According to this procedure, our sample represented the adult Greek population in terms of gender, age distribution, marital status, and occupation.

3.2. Descriptive Analysis

3.2.1. Data Analysis

The 730 valid questionnaires were collected and analyzed using Statistical Package for the Social Sciences (version 24.0).

Table 2. Description of variables.

How COVID-19 Has Affected Daily Life
Question/Variable	Not at all (%)	Slightly (%)	Moderately (%)	Much (%)	Very much (%)	Mean	S.D.
Restrictions due to COVID-19 have an impact on personal life	9.9	20.0	29.8	28.5	11.8	3.12	±1.342
Restrictions due to COVID-19 have an impact on family life	14.2	22.8	27.4	26.5	9.1	2.94	±1.423
Restrictions due to COVID-19 have an impact on the life of children	12.5	12.2	20.8	35.2	19.3	3.36	±1.616
Restrictions due to COVID-19 have an impact on professional life	11.3	11.7	23.8	25.8	27.4	3.46	±1.709
Restrictions due to COVID-19 have an impact on financial activities	10.1	16.6	28.6	26.3	18.4	3.26	±1.225
Restrictions due to COVID-19 have an impact on physical health	27.8	20.8	27.6	17.5	6.4	2.54	±1.240
Restrictions due to COVID-19 have an impact on mental health	18.7	24.1	27.4	20.5	9.2	2.77	±1.231
Restrictions due to COVID-19 have an impact on educational activities	50.2	13.6	18.0	12.3	5.8	2.10	±1.302
Restrictions due to COVID-19 have an impact on recreation activities	5.4	10.4	22.3	33.1	28.8	3.69	±1.151
Restrictions due to COVID-19 have an impact on mobility	5.8	9.8	22.5	31.8	30.1	3.71	±1.164
Discovery of the neighborhood due to COVID-19	36.9	17.3	27.4	13.8	4.6	2.32	±1.228
Discovery of the city due to COVID-19	44.2	15.7	24.5	11.0	4.6	2.16	±1.231
Question/Variable	Much less (%)	Less (%)	The same (%)	More (%)	Much more (%)	Mean	S.D.
Nervous due to the restrictions of COVID-19	7.5	7.2	46.0	30.8	8.5	3.26	±0.977
Stressed due to the restrictions of COVID-19	7.6	7.6	30.9	38.4	15.5	3.47	±1.081
Anxious due to the restrictions of COVID-19	6.3	9.4	48.6	27.9	7.8	3.22	±0.943
Rushed due to the restrictions of COVID-19	10.7	12.6	55.0	17.9	3.9	2.92	±0.940
Phobic due to the restrictions of COVID-19	6.0	5.1	41.0	36.3	11.7	3.43	±0.970
Lonely due to the restrictions of COVID-19	8.3	7.5	49.7	27.4	7.1	3.17	±0.968
Calm due to the restrictions of COVID-19	12.7	28.3	43.5	12.5	3.1	2.65	±0.957
Happy due to the restrictions of COVID-19	17.1	34.8	41.7	5.3	1.0	2.38	±0.863
Optimistic due to the restrictions of COVID-19	22.1	34.7	36.6	5.9	0.7	2.28	±0.898
Tired due to the restrictions of COVID-19	14.8	17.2	39.3	20.2	8.5	2.91	±1.139
Willingness for action (working, reading, etc.) due to the restrictions of COVID-19	11.6	20.2	41.0	18.3	8.9	2.93	±1.096
Active due to the restrictions of COVID-19	12.7	25.1	43.3	14.3	4.6	2.73	±1.007
Hungry due to the restrictions of COVID-19	5.7	7.5	50.2	25.5	11.0	3.29	±0.959
Intensity of family ties due to COVID-19	3/9	3.3	50.8	32.3	9.7	3.41	±0.857
Intensity of family activities due to COVID-19	7.6	6.1	42.5	31.4	12.4	3.35	±1.028
Appreciation of family due to COVID-19	2.1	0.8	67.9	18.6	10.6	3.35	±0.762
Intensity of friends ties due to COVID-19	12.2	15.5	53.7	14.4	4.2	2.83	±0.962
Increased activities with friends due to COVID-19	38.5	18.2	30.2	10.5	2.6	2.21	±1.141
Appreciation of friends due to COVID-19	3.2	4.3	65.6	18.1	8.8	3.25	±0.801
Mobility in neighborhood due to COVID-19	8.0	7.2	42.4	27.6	14.8	3.34	±1.070
Mobility for local markets due to COVID-19	4.6	3.3	57.4	22.0	12.7	3.35	±0.907
Liking neighborhood due to COVID-19	2.1	3.0	72.2	15.5	7.2	3.23	±0.711
Communication with neighbors due to COVID-19	7.2	7.6	59.4	19.9	5.9	3.10	±0.889
Liking neighbors due to COVID-19	4.7	6.9	75.1	9.6	3.7	3.01	±0.710
The impact of COVID-19 on the working conditions
Question/Variable	Yes (%)		No (%)	Suspended (%)		Mean	S.D.
Job loss due to COVID-19	8.9		72.6	18.6		2.10	±0.515
Question/Variable	Yes (%)		No (%)	No work (%)		Mean	S.D.
Work from home due to COVID-19	35.5		34.4	30.5		1.96	±0.806
Question/Variable	Much less (%)	Less (%)	The same (%)	More (%)	Much more (%)	Mean	S.D.
Working hours before COVID-19	19.9	22.2	38.7	12.5	6.7	2.64	±1.134
Productive working before COVID-19	17.2	18.3	50.9	9.1	4.5	2.65	±1.011
Question/Variable	Not at all (%)	Slightly (%)	Moderately (%)	Much (%)	Very much (%)	Mean	S.D.
Willingness for remote work	52.1	13.9	19.5	7.9	6.7	2.03	±1.279
Willingness for remote education and online courses	53.1	12.9	20.8	9.4	3.9	1.98	±1.211
The impact of COVID-19 on mobility
Question/Variable	Public transport	Car	Car-sharing	Taxi	Motorcycle	Bicycle	Walking
Main travel mode before COVID-19	19.7	57.4	2.2	0.7	6.2	1.3	12.4
Question/Variable	Much less (%)	Less (%)	The same (%)	More (%)	Much more (%)	Mean	S.D.
Travel to work during the lockdown	48.1	18.0	31.4	1.7	0.8	1.89	±0.962
Question/Variable	1–4 times/week (%)	5 times (%)	6 times (%)	>6 times (%)		Mean	S.D.
Frequency of commuting	9.4	57.9	15.1	17.6		5.78	±2.36
Question/Variable	<30min (%)	30–60 (%)	60–90 min (%)	> 90 min (%)		Mean	S.D.
Travel time	50	32.7	10.5	6.9		1.74	±0.90
where 1 = 30 min, 2 = 30–60 min, 3 = 60–90 min, 4 = more than 90 min
Question/Variable	Yes (%)		No (%)			Mean	S.D.
Change mode of transportation for work during the lockdown	9.7		90.3			1.90	±0.296
Willingness to change mode of transportation for work after lockdown	7.6		92.4			1.92	±0.265
Urban space
Question/Variable	Not at all (%)	Slightly (%)	Moderately (%)	Much (%)	Very much (%)	Mean	S.D.
Need of intervention in urban space	4.2	9.3	21.0	41.6	23.8	3.72	±1.058
Need of intervention in public transport	6.6	5.8	18.6	37.9	31.1	3.81	±1.137
Need of intervention in walking infrastructure	5.1	5.8	14.5	41.6	33.1	3.92	±1.076
Need of intervention in cycling infrastructure	4.6	3.9	14.7	37.6	39.1	4.03	±1.055
Need of intervention in public space	2.9	5.2	18.7	37.7	35.5	3.98	±1.007
Need of intervention in density of commercial uses	17.9	13.4	32.5	21.7	14.5	3.01	±1.285
Need of intervention in telecommunication infrastructure	11.4	15.4	31.8	25.1	16.3	3.19	±1.215

shows the description of the questions, the frequency distribution of the answers, and the results of the descriptive statistics (mean ± standard deviation).

3.2.2. Impacts on Everyday Activities

In the survey, the respondents were asked to evaluate the impact of the lockdown and the restrictions against the spread of COVID-19 on their everyday life (Table 2). Specifically, 70.1% and 63.0% of the respondents declared that their personal life and family life, respectively, were affected from “moderately” to “very much.” Similarly, 75.3% of the respondents stated that the life of their children was affected from “moderately” to “very much.” Impacts on professional life seemed to be more intense, as 77.0% of the participants declared that they were affected “moderately” to “very much.” Freelancers and unemployed people were more affected than employees (sum of “moderately” to “very much” = 63.2% in the first category, 49.5% in the second one, p-value = 0.01 < 0.05). Quite the same percentage (73.3%) of the sample declared that the restrictions had serious financial impacts. Half of the respondents (51.5%) declared that there was a “moderately” to “very much” of an impact on their physical health, while the other half (48.5%) declared “not at all” or “slightly.” Similar were the findings regarding mental health: slightly more than the half of the respondents (57.1%) declared “moderately” to “very much” of an impact on mental health. The impact on educational activities was the lowest one (only 36.1% of the respondents were “moderately” to “very much” impacted). However, this low percentage was due to the fact that only people in continuing education and those with children in school age were impacted. On the other hand, the impact of measures on recreation was very intense, as 84.2% of the respondents stated “moderately” to “very much” regarding changes in recreation activities. A chi-squared test for independence indicated a slight association between age and recreation activities, as the pandemic has affected the younger more than the older respondents (sum of “moderately” to “very much” = 66.7% in the first category, 57.5% in the second one, p-value = 0.013 < 0.05). Similar results were recorded in the case of mobility: 84.44% of the respondents stated “moderately” to “very much” regarding changes in mobility. A chi-squared test for independence indicated a strong association between gender and impacts on mobility practices, as women were more affected by the lockdown than men (sum of “moderately” to “very much” = 65.8% in the first category, 57.5% in the second one, p-value = 0.021 < 0.05).

3.2.3. Impacts on Emotional State

The respondents were asked to evaluate the changes in their emotional state during the lockdown (Table 2). In all these situations, a number of the respondents corresponding to 30–60% of the sample declared that there was no change in their emotional state. However, a significant number declared a generally negative change, and only a minority declared a positive one. More specifically, 39.3% of the sample stated that they were more nervous than before the pandemic (46% declared no change); more women than men (sum of “more” to “much more” = 44.9% in the first category, 32.9% in the second one, p-value = 0.001 < 0.05) and more married respondents than non-married ones (sum of “more” to “much more” = 44.5% in the first category, 32.4% in the second one, p-value = 0.001 < 0.05). Stress increased for 53.9% of the respondents, while 30.9% declared no change. This increase was mainly observed in the case of older rather than younger people (sum of “more” to “much more” = 58.6% in the first category, 48.3% in the second one, p-value = 0.007 < 0.05), women rather than men (sum of “more” to “much more” = 58.4% in the first category, 48.8% in the second one, p-value = 0.010 < 0.05), and married rather than unmarried respondents (sum of “more” to “much more” = 59.6% in the first category, 46.3% in the second one, p-value < 0.001). Moreover, anxiety increased for 35.7% of the participants (48.6% declared no change) and 21.8% of the respondents felt more rushed than before the lockdown (55% declared no change and 23.3% felt less rushed). Fear was a feeling that increased for many participants: 48% declared an increase, while 41% declared no change. This was more intense in the case of women than men (sum of “more” to “much more” = 56.5% in the first category, 38.4% in the second one, p-value < 0.001), older people than young ones (sum of “more” to “much more” = 51.9% in the first category, 43.3% in the second one, p-value = 0.025 < 0.05), and married than unmarried respondents (sum of “more” to “much more” = 53.4% in the first category, 40.7% in the second one, p-value = 0.001 < 0.05). As far as loneliness was concerned, 34.5% of the respondents felt lonelier (49.7% declared no change), with more singles than married ones (sum of “more” to “much more” = 38.9% in the first category, 31.0% in the second one, p-value = 0.029 < 0.05). Calm, happiness, and optimism were three feelings that were affected significantly. Of the respondents, 41.0% stated that they were less calm than in the past (43.0% declared no change); 51.09% and 56.8% of the respondents felt less happy and optimistic, respectively (41.7% and 36.6%, respectively, declared no change), a finding that was close to the results of the survey conducted by Chtouris and Zissi [10]. Concerning tiredness, willingness for action and activity, 28.7% of the respondents felt more tired, 31.8% had decreased willingness for action, and 37.8% felt less active (39.3%, 41%, and 43.3% of the respondents declared no change in the above three feelings). Finally, hunger increased for 36.5% of the research sample, possibly because eating became an activity that replaced habits that were restricted and improved mood [32] (50.2% felt as hungry as before the lockdown).

3.2.4. Impacts on the Relations with Urban Space

As imposed restrictions deeply affected everyday life and activities, the responses indicated that the perceptions of a significant number of the respondents about their city and their neighborhood, as well as practices in urban space, remained mainly the same as before the COVID-19 crisis. However, another significant part of the sample declared that they had a more positive view of their neighborhood. In particular, 45.8% and 40.1% (sum of “moderately” to “much more”) of the respondents declared that they expanded their knowledge and experience regarding their neighborhood and their city, respectively, during the lockdown. Similarly, a significant number of the respondents (42.4%) stated that, during the lockdown, they moved around in their neighborhood more than before the COVID-19 crisis (42.4% declared no change), 34.7% used local shops more than in the past (57.4% declared no change), and 22.7% changed positively their perception about their neighborhood (72.2% declared no change). Moreover, we found that women moved around in their neighborhood and liked their neighborhood more than men (sum of “more” to “much more” = 47.4% in the case of women, 36.8% in the case of men, p-value = 0.04 < 0.05, and sum of “more” to “much more” = 27.3% in the case of women, 17.5% in the case of men, p-value = 0.002 < 0.05, respectively), a fact that revealed gendered aspects of the COVID-19 crisis in urban space.

3.2.5. Impacts on Social Life

The survey revealed that social activities were affected by the lockdown. This also had an impact on social relations in terms of quality. In particular, the intensity of family ties increased for 42.0% of the respondents (50.4% declared no change). Ties with friends increased for 18.6% of the respondents, but the confinement resulted in a decrease of those ties in 27.7% of the sample (53.7% declared no change). Moreover, 29.2% and 26.9% of the participants, respectively, respected relatives and friends more than before the COVID-19 crisis (67.9% and 65.6% declared no change). Similarly, one fourth of the participants (25.8%) argued that they communicated more with their neighbors than before the lockdown, while the perceptions of the vast majority of the respondents (75.1%) about their neighbors did not change (13.3% had a more positive opinion and 11.6% a more negative one). However, in terms of social activities, restrictions in mobility and the shrinking of everyday day life in domestic space increased activities with other family members (for 43.8%, while 42.5% declared no change) and reduced activities with friends (for 56.7%, while 30.2% declared no change), as was also stated in other surveys [9]. These facts were declared mainly by respondents who were over 40 years old (59.0%) and married (60.6%). However, younger people stated that they met with friends more than older respondents (48.3% of the sum “more” to “much more,” against 40.2% for the older people, p-value = 0.037 < 0.05), while the chi-squared test indicated some association between family status and activities within the family, as married respondents became closer to their family and did more activities with other family members (52.2% of the sum “more” to “much more” for the married people against 32.8% for the unmarried ones, p-value = 0.000 < 0.05).

3.2.6. Impacts on Working Conditions

The pandemic and the lockdown measures had severe impacts on employment and working conditions and pushed more and more workers to working from home. The survey revealed that although 72.6% of the respondents did not have any change in their working position due to the pandemic, 8.9% of the respondents lost their jobs, and 18.6% of the respondents were suspended. In total, 27.5% of the respondents were severely affected by the pandemic. Also, one third of the respondents (34.4%) experienced teleworking. The chi-square test revealed again a statistically significant difference for gender (p-value = 0.010 < 0.05), as women experienced teleworking more than men did (56.7% of woman against 43.3% of men). The pandemic had a significant impact on working hours and productivity. Specifically, during the lockdown period, 42.1% of the respondents worked fewer hours per day than before the pandemic, while 19.2% worked more. Women, also, worked more hours than men (53.7% for woman and 46.3% for men, p-value = 0.041 < 0.05). Of the respondents 35.5% declared that their productivity was lower than before the pandemic, and only 13.6% of the respondents declared that they were more productive. The evaluation of location and time-schedule flexibility of teleworking was examined in the survey. Only 13.1% of respondents expressed that they warmly accepted teleworking, 17.5% of the respondents expressed “moderately,” while almost seven out of ten (66.0%) refused teleworking as an alternative form of work. Women had more extreme reactions to teleworking than men (51.8% selecting “not at all” for women, against 48.2% for men). Moreover, married respondents preferred teleworking more than singles (49.2% for the sum “much” to “very much” for singles and 50.8% for married respondents, p-value = 0.003 < 0.05). The pandemic has also affected educational systems worldwide, requiring educators to develop effective strategies for online courses and remote teaching. From the survey it arose that only 13.3% of respondents declared that they warmly wanted to attend online courses, 20.8% of the respondents declared moderately, while a large percentage (66.0%) did not accept remote teaching and online courses. A chi-squared test for independence indicated an association between the level of education and the acceptance of online courses (p-value = 0.035 < 0.05). A higher level of education was more likely to refuse online courses (65.4% for the higher level of education against 34.6% for the lower level).

3.2.7. Impacts on Mobility

The outbreak of the pandemic had a profound impact on transport and mobility in Greek cities [17]. In the survey, respondents were asked about the modes of transport they used for commuting before the lockdown and how frequently they did so. About 60% (57.4%) of the respondents used a car for commuting, while the percentage in favor of public transport for commuting was quite low (19.7%). The shares of other modes of transport split during the lockdown: 6.2% for motorcycles, 2.2% for car-sharing, 0.7% for taxi, 1.3% for bicycle, and 12.4% for walking. Almost six out of ten respondents (57.9%) declared that they commuted 5 times per week, 9.4% of respondents declared that they commuted 1–4 times per week, 15.1% declared 6 times per week, while 17.6% of the participants declared that they commuted more than 6 times per week. The frequency of commuting depended on the type of job and the location of residence. Travel time to work before the pandemic ranged from 30 min (50% of the participants), 30–60 min (32.7% of the participants), 60–90 min (10.5%) to more than 90 min (6.9%). During the lockdown, only 31.4% of the respondents kept the same travel behavior for work. Most of the respondents (66.1%) reduced the travel frequency; this was obvious due to teleworking and job losses or suspended jobs, and only a small percentage of the respondents (2.5%) increased the frequency of commuting. The high reduction in mobility to and from workplaces was also reflected in available data on the national level (38% decrease), as well as in the metropolitan area of Athens (42% decrease) [17]. Moreover, there was very little change in the transport mode, as 90.3% of our respondents used the same mode as before the enforcement of the curfew. However, it should be noted that there was a small increase in cycling and walking. As far as intentions for the future, 92.4% of the respondents declared that they did not intend to change their modal choice for work after the rise of mobility restrictions, while the rest intended to use public transportation more. However, the intentions of the respondents were not always realized, as a survey conducted in the city of Thessaloniki presented a 25% reduction in the use of public transport after the lockdown [25].

3.2.8. Concerns About Urban Policies

The lockdown and the measures against the spread of COVID-19 deeply affected everyday life in the city and thus raised serious concerns about urban space [2,4]. This was directly reflected in the responses we received regarding the adequacy of urban infrastructures and urban policies. The need for interventions in urban space was reported as a serious issue by 86.4% of the respondents. Interventions in urban mobility and public space were recorded as an important issue for urban space for about nine out of 10 of the respondents: 87.6% of the respondents prioritized interventions in public transport, 89.2% prioritized interventions for improving walking conditions, 91.4% prioritized infrastructure for facilitating cycling, and 91.9% prioritized interventions for improving public space. Concerns about the quality of public space and walking conditions were higher among older respondents (41.0% of the older against 31.2% of the younger respondents, p-value = 0.008 < 0.05, for the public space; and 37.1% of the older against 29.9% of the younger respondents, p-value = 0.048 < 0.05, for the walking conditions) and women than men (39.5% of women against 31.1% of men, p-value = 0.019 < 0.05, for the public space; and 34.8% of women against 31.0% of men, p-value = 0.04 < 0.05, for the walking conditions), while interest in cycling facilities was slightly higher among younger respondents (44.7% of the younger against 35.7% of the older respondents, p-value = 0.016 < 0.05). Issues related to the density of commercial uses and the quality of telecommunication infrastructure seemed to be less important than public spaces, but still considered as important to seven out of 10 of the respondents. Of the respondents 68.7% claimed that the density of commercial shops, restaurants, cafes, hotels, etc., was a major issue in the context of the COVID-19 crisis, and 73.2% of the responses stressed the significance of improving telecommunication services, in line with the importance both fields acquired in the public discourse about urban life [3].

3.3. Regression Analysis

A regression analysis was applied to detect probable relationships between the variables used here. The application of Binary Logistic Regression revealed five regression models. Standardized regression coefficients (β) were presented in a linear regression model, along with their significant levels. All statistical tests were two-tailed, and differences were considered to be statistically significant at a p-value equal to or less than 0.05. Also, at the end of the tables, the –2 Log-Likelihood, the Chi-Square, the Significance level, and the Overall Percentage of the model are given. These data showed the predictive adequacy of the models.

3.3.1. Model M1

The M1 model correlated the impact of COVID-19 restrictions on personal life with the other variables.

Table 3. Model M1.

Variables	Restrictions Due to COVID-19 Have an Impact on Personal Life
	B	Sig.	Exp(B)
Restrictions due to COVID-19 have an impact on family life	2.529	0.000	12.538
Restrictions due to COVID-19 have an impact on the life of children	0.753	0.017	2.124
Restrictions due to COVID-19 have an impact on mental health	1.027	0.000	2.791
Restrictions due to COVID-19 have an impact on recreation activities	0.859	0.005	2.361
Restrictions due to COVID-19 have an impact on mobility	0.756	0.014	2.130
Marital status	−0.911	0.018	0.402
−2 Log-Likelihood	330.672
Chi-Square	175.107
Sig.	0.000
Overall percentage	78.70%

shows the variables found to be statistically significant to the M1 model.

This model showed that the respondents who declared that their personal life was affected by the restrictions due to COVID-19 were those whose family life (B = 2.529, sig. = 0.000), children’s lives (B = 0.753, sig. = 0.017), mental health (B = 1.027, sig. = 0.017), recreation activities (B = 0.859, sig.0.005), and mobility (B = 0.756, sig.0.014) were also affected. There was also an impact of family status (B = −0.911, sig. = 0.000), with singles more affected than married people by the restrictions due to COVID-19. Binary Logistic Regression showed that the model M1 was significant at a statistical significance level of 0.01 (sig. = 0.000) and the overall forecast success was 78.70%.

3.3.2. Model M2

The M2 model correlated the nervousness that one felt due to COVID-19 restrictions with the other variables.

Table 4. Model M2.

Variables	Nervous Due to the Restrictions of COVID-19
	B	Sig.	Exp(B)
Stressed due to the restrictions of COVID-19	1.960	0.000	7.102
Anxious due to the restrictions of COVID-19	1.070	0.000	2.915
Rushed due to the restrictions of COVID-19	0.704	0.007	2.023
Calm due to the restrictions of COVID-19	−1.068	0.000	0.344
Willingness for action (working, reading, etc.) due to the restrictions of COVID-19	−0.535	0.012	0.585
Marital status	0.559	0.007	1.749
−2 Log-Likelihood	631.306
Chi-Square	309.912
Sig.	0.000
Overall percentage	79.5%

shows the variables found to be statistically significant to the M2 model.

The respondents who declared that the restrictions due to COVID-19 made them more nervous were the same ones who said that they became more stressed (B = 1.960, sig. = 0.000), more anxious (B = 1.070, sig. = 0.000), more rushed (B = 0.704, sig. = 0.007), less calm (B= −1.068, sig. = 0.000) and did not have a willingness for action (working, reading, etc.) (B = −0.535, sig. = 0.012). Family status also had an impact (B = 0.559, sig. = 0.007), with married more affected than singles by the restrictions due to COVID−19. Binary Logistic Regression showed that the model M2 was significant at a statistical significance level of 0.01 (sig. = 0.000) and the overall forecast success was 79.5%.

3.3.3. Model M3

The M3 model correlated the discovery of the neighborhood due to COVID-19 with the other variables.

Table 5. Model M3.

Variables	Discovery of the Neighborhood Due to COVID-19
	B	Sig.	Exp(B)
Discovery of the city due to COVID-19	3.978	0.000	53.435
Mobility in neighborhood due to COVID-19	0.977	0.000	2.656
Liking neighborhood due to COVID-19	1.083	0.001	2.954
Communication with neighbors due to COVID-19	0.726	0.015	2.067
Need for intervention in public transport	0.816	0.002	2.262
−2 Log-Likelihood	483.928
Chi-Square	481.069
Sig.	0.000
Overall percentage	86.4%

shows the variables found to be statistically significant to the M3 model.

The respondents who discovered their neighborhood due to COVID-19 also discovered their city (B = 3.978, sig. = 0.000), they moved around in their neighborhood (B = 0.977, sig. = 0.000), and they declared that they liked their neighborhood (B = 1.083, sig. = 0.001) and communicated with their neighbors (B = 0.726, sig. = 0.015). In addition, they believed that there was a need for intervention in the organization of urban space in terms of public transport (B = 0.816, sig. = 0.002). Binary Logistic Regression showed that the model M3 was significant at a statistical significance level of 0.01 (sig. = 0.000) and the overall forecast success was 86.40%.

3.3.4. Model M4

The M4 model correlated the work from home due to COVID-19 with the other variables.

Table 6. Model M4.

Variables	Work from Home Due to COVID-19
	B	Sig.	Exp(B)
Working hours before COVID-19	−0.770	0.000	0.463
Productive working before COVID-19	−1.160	0.000	0.313
Willingness for remote work	1.299	0.000	3.667
−2 Log-Likelihood	683.177
Chi-Square	102.730
Sig.	0.000
Overall percentage	71.60%

shows the variables found to be statistically significant to the M4 model.

The respondents who worked from home due to COVID-19 used to work fewer hours (B= −0.770, sig. 0.000) and were less productive (B= −1.160, sig. = 0.000). Also, the respondents who worked from home wanted to continue working from home after the restrictions (B = 1.299, sig. = 0.000). Binary Logistic Regression showed that the model M4 was significant at a statistical significance level of 0.01 (sig. = 0.000) and the overall forecast success was 71.60%.

3.3.5. Model M5

The M5 model correlated the opinions of the respondents regarding the necessary interventions in their city, in the context of COVID-19, with the other variables.

Table 7. Model M5.

Variables	Need for Interventions in Urban Space
	B	Sig.	Exp(B)
Need for intervention in public transport	1.780	0.000	5.931
Need for intervention in walking infrastructure	0.870	0.002	2.388
Need for intervention in cycling infrastructure	0.630	0.008	1.878
Need for intervention in density of commercial uses	0.657	0.003	1.929
−2 Log-Likelihood	668.668
Chi-Square	203.188
Sig.	0.000
Overall percentage	78.6%

shows the variables found to be statistically significant to the M5 model.

The respondents who believed that some interventions needed to be made in their city, in the context of COVID-19, were the same ones who declared that there was a need to make interventions in the organization of urban space in terms of public transport (B = 1.780, sig. = 0.000), in terms of walking infrastructure (B = 0.870, sig. = 0.002), in terms of cycling infrastructure (B = 0.630, sig. = 0.008), and in terms of the density of commercial uses (B = 0.657, sig. = 0.003). Binary Logistic Regression showed that the model M5 was significant at a statistical significance level of 0.01 (sig. = 0.000) and the overall forecast success was 78.6%.

4. Discussion

During the first wave of the COVID-19 pandemic major changes took place concerning the perceptions, experiences, and practices of the active population in the Greek cities. Lockdown destabilized people’s lives and affected their social activities, their emotional state, their everyday activities, their working conditions, their mobility, as well as their perceptions about urban space and urban policies in various ways.

The majority or a significant number of the respondents declared that their personal and family life was affected by the lockdown, but also that there were significant effects on their professional life and economic activities. According to the findings of our survey, emotional state was affected for many of the respondents. Stress and fear were two feelings that increased for a large part of our sample. Social activity was affected in two main ways: activities within the family increased and activities with friends were reduced. The impacts of the pandemic on working conditions presented multiple features: most respondents were still working, while many of them lost their jobs and many were in a “suspension” status; some respondents shifted to teleworking and others did not; some worked less and others worked more; some were more productive and others less. Urban mobility was a sector that was deeply affected by the COVID-19 crisis, and our respondents stated major changes in their mobility practices, especially in commuting. In particular, most of the respondents reduced the frequency of commuting per week. Also, although the vast majority of the respondents used the same means of transport as before the pandemic, there was a slight but significant increase in cycling and walking. This is a positive sign and it can significantly contribute to sustainable mobility if these people retain this new habit. Finally, the relationship with the city and the neighborhood was radically changed for a significant percentage of the respondents. Additionally, concerns about urban space increased, especially as far as the quality of public space, walking conditions, and cycling facilities.

Although most of our findings presented a homogenous distribution in our sample, some changes in perceptions, experiences and practices were slightly or strongly correlated with the social profile of the respondents. For example, in many cases gender was a crucial factor, and women seemed to be more vulnerable to the new conditions, a finding that was in line with the outcomes of other research projects, surveys, and policy reports (e.g., [14,24]). In particular, women felt more nervous, stressed, and scared; worked more; were affected more by teleworking; changed mobility practices more; and had stronger concerns about their neighborhood, walking facilities, and the quality of urban space than men. Moreover, age was another crucial factor that differentiated impacts on perceptions, experiences, and practices, as it was also found by other surveys [9]. Younger respondents experienced a shrinking in their recreational activities, met their friends more often, and expressed their concern about cycling facilities, while older respondents felt more stress and fear, were more active within the family circle, and were more interested in the improvement of public space and walking conditions. Finally, family status seemed to differentiate experiences of the COVID-19 crisis. Most married respondents declared that they became closer to their family and did more activities with other family members. Also, married respondents felt more nervous, stressed, and scared than unmarried respondents, a fact that might be related to their concern about responding to family needs within an uncertain and fluid socioeconomic context. At the same time, singles felt lonelier, a fact that highlighted the importance of the family in social life in Greece.

The research approach of the paper gave us the chance to rethink aspects of urban space and mobility in the city in relation to the multiple needs, problems, and dynamics of employees. The lessons learned from the first wave of the COVID-19 pandemic were very useful for coping with the impacts of the coming waves, as well as with the impacts of future pandemics or crises on Greek cities. In this context, what is critical for the improvement of the resilience of Greek cities is to develop policies on urban mobility, public space, and urban infrastructure that take into account the different needs, problems, and dynamics of employees and the population in general and prevent the expansion of existing and the rise of new inequalities within urban spaces.

Challenges regarding mobility and public transport that emerged due to COVID-19 and especially the benefits of active mobility (walking, cycling) provide new impetus to transport and urban planners to rethink forms of mobility and urban planning. As the International Transport Forum highlights [18], cities have to meet the triple challenge of “react, reboot, rethink” to continue providing creative social and economic activity, despite new health imperatives. In the medium and long run, it would be wiser to plan small and medium-sized cities and new or regenerated urban districts based on public transport and active mobility [20]. Until now, integrated urban and transport planning is not a priority in many cities. Policy makers should concentrate on establishing new speed limits and ensuring larger spaces for cycling and pedestrians in order to enlarge distances between users in order to both safeguard an enhanced level of road safety and prevent COVID-19 spread. It is important to seize on the future opportunities in order to rethink cities with safer road traffic and no accidents in the aftermath of the pandemic [22].

In closing, we claim that this paper contributes to the academic discourse about everyday life in the urban space, not only in Greece but also worldwide, in multiple ways. First, the academic elaborations that have been done so far are based mainly on empirical data and do not reveal quantitative aspects of the phenomena. Moreover, research on perceptions, experiences, and practices of urban life during the lockdown is still quite limited. Finally, investigating the experience of urban space and urban mobility in the context of the pandemic through the interrelation of urban space and labor enriches a discourse that has developed in recent years with new questions and concerns.