Lessons from COVID-19 pandemic: Analysis of unequal access to food stores using the Gini coefficient

Abstract

COVID-19 has dramatically altered daily life worldwide, with some urban residents resorting to panic buying at the beginning of the pandemic. Large-scale lockdowns and restaurant closures have increased the need for grocery shopping. Such shifts in consumer patterns have altered supply–demand systems. Insufficient food store availability increases the likelihood of crowding and thus increases the probability of viral infection. People who live without easy access to food stores also face high infection risks when forced to travel long distances for grocery shopping. The COVID-19 pandemic has demonstrated the importance of the number and distribution of food stores to virus transmission. Food access is also a core factor of urban resilience during the pandemic. This study used the Gini coefficient to investigate the fairness of accessibility to food stores at the city and village levels, with Taipei City chosen as the research area. Different spatial scales were considered, and we calculated the equality of food access for older (≥65 years old) and non-older populations separately to determine whether one group faces greater inequality. At the city level, both older and non-older populations in Taipei have reasonable access (Gini coefficient between 0.3 and 0.4), with mean Gini coefficients of 0.3616 and 0.3655, respectively. This city-level analysis represents the overall degree of unequal access to food stores. At the village level, eight villages (1.8 %; total N = 456) had severe access inequality (Gini coefficient > 0.6) for older adults; they are located primarily in downtown or suburban areas. For the non-older population, only two villages (0.4 %; total N = 456) in suburban areas exhibit severe access inequality. The village-level analysis identified villages with low equality of access to food stores and revealed local problems that cannot be observed at the city level.

1. Introduction

Since the initial outbreak of COVID-19 in December 2019, approximately 635 million people have been infected worldwide, and >6.6 million people have died from COVID-19 (World Health Organization, 2022). The COVID-19 pandemic has altered people's daily lives, such as their food-purchasing habits (Martin-Neuninger & Ruby, 2020). Specifically, the demand for food increased, but purchasing opportunities decreased. This increased demand was caused by people's anxiety about the pandemic. Many consumers exhibited panic buying or hoarding behaviors (Hobbs, 2020; Islam et al., 2021). They began to purchase large quantities of food and daily necessities. The decrease in purchasing opportunities was caused by government policy. During the peak periods of the pandemic in 2020, strict regulations were enforced in most countries, and people worldwide experienced lockdowns of varying durations (Urzeala et al., 2022). People were asked to reduce their movements and could leave their homes only when necessary or in emergencies. In many countries, restaurants and street vendors were forced to close. Therefore, residents switched to purchasing ingredients to cook meals at home (Hobbs, 2020), further increasing the demand for groceries. For example, consumers in China accumulated greater food reserves than they had before the pandemic to avoid running out of food at home during lockdowns. Before the outbreak of COVID-19, consumers typically stockpiled sufficient food for 3.37 days; during the pandemic, consumers stockpiled sufficient food for 7.37 days (Wang, An, Gao, Kiprop, & Geng, 2020). These fast-changing consumption patterns disrupted supply–demand systems, particularly in urban areas, because urban residents do not typically obtain fresh food directly from farms. Stay-at-home orders altered the daily lives of the urban population. Although people could purchase food online, especially those in urban areas, people in some countries preferred grocery shopping in supermarkets and hypermarkets, even during the peak periods of the COVID-19 pandemic. For example, according to a consumer survey report, more people in Taiwan chose to purchase groceries from supermarkets (29.2 %) and hypermarkets (27.2 %) rather than from e-commerce platforms (8.1 %) in 2020 (Kantar Taiwan Branch, 2021).

Numerous studies have reported that supermarkets may be major vector habitats for virus transmission (Ying and O'Clery, 2021; Zhang et al., 2020), and citizens may also be infected in their vehicles while traveling to food stores. Despite this risk of infection, shopping was the primary reason for people leaving their homes during lockdowns because food is a basic necessity (Abdullah, Dias, Muley, & Shahin, 2020). Under the assumption of equal spatial accessibility to food stores, all people should distribute themselves evenly across different stores to prevent any individual store from being overly crowded. Ideally, no one should be forced to travel long distances to stores, which would increase their risk of infection. Therefore, providing equal access to food stores is necessary for maintaining urban resilience during peak pandemic periods. Unequal access to food stores harms the health of all citizens (Urzeala et al., 2022). This fact was rarely commented on before the pandemic; therefore, it can be regarded as a lesson from COVID-19. Specifically, the widespread distribution of food stores contributes to infection prevention.

Nevertheless, predicting of where people purchase food during a pandemic is highly challenging; therefore, we must make some reasonable assumptions. Abdullah et al. (2020) demonstrated that the COVID-19 pandemic caused people to reduce their travel distance when shopping. Thus, we may reasonably assume that citizens tend to travel to the nearest supermarket to buy food during a pandemic. When food stores are concentrated in a specific area, citizens must travel longer distances and cluster in the same stores to purchase food. Conversely, when every citizen has equal access to food stores, citizens tend to travel shorter distances and disperse evenly among different shops.

In response to COVID-19, a new method is required to investigate the equality of access to food stores. However, studies analyzing the fairness of access to food stores in terms of consumer demand are rare, and little research has been given to access to food stores in Taipei City especially. In this study, the Gini coefficient was used prospectively to quantify the equality of access to food stores at the city and village levels in Taipei. The experience of older adult citizens was also considered, and the Gini coefficients of older (over 65 years) and non-older subpopulations were investigated. This approach allowed us to obtain accurate data on Taipei as an aging society and examine whether specific groups face worse fairness than others.

2. Literature review

2.1. Methods of measuring food store access

Identifying and describing of areas with low access to food stores has been widely reported in the literature. Typically, related research is based on pure measures for calculating accessibility. We list the five most common measures for calculating accessibility and the studies that employ these methods in Table 1 . Among the measures, gravity-based measures and the E2SFCA method are commonly used to account for both the spatial distribution of stores and their distances from residential areas (Bryant & Delamater, 2019; Kar, Wan, Cova, Wang, & Lizotte, 2022; Luo & Qi, 2009; Radke & Mu, 2000). These methods allow researchers to calculate the accessibility of each administrative area.

Table 1.

Common methods for measuring food access.

Author (year)	Method catagories	Results	Accessibility measure1
Cummins and Macintyre (1999)	Calculated the number or ratio of food stores in each community	Food stores tended to be relatively evenly distributed in the study area	N/A
Smoyer-Tomic, Spence, and Amrhein (2006)	Calculated the distance to the nearest supermarket from each community	Some subsets of the population had limited access to supermarkets	Distance measures
Apparicio et al. (2007)	Calculated the average distance to the three closest different supermarkets (to calculate the variety of accessible food stores)	Those areas classified as deprived and with low accessibility to supermarkets were, on average, within 1.34 km of three supermarkets	Distance measures
Donkin, Dowler, Stevenson, and Turner (1999)	Determined an area within a certain linear/road network distance around each store or postcode with shop	The entire study area contained few areas in which a person had to walk >500 m to reach a food store	Isochrone access/cumulative opportunity measures
Larsen and Gilliland (2008)	Calculated the number of food stores within a radius of a certain linear/road network/distance from home	Spatial inequality in access to supermarkets have increased over time	Isochrone access/cumulative opportunity measures
Hamrick and Hopkins (2012)	Estimated the travel time required for grocery shopping	Individuals living in low-income areas with limited supermarket access spent significantly more time traveling for grocery shopping than the national average	Travel time budgets
Jeong and Liu (2020)	Evaluated food access using gravity-based accessibility indicators	A substantive change in food access was observed between 2003 and 2015 based on a gravity-based accessibility indicator	Gravity-based measures
Wiki et al. (2019)	Used an enhanced two-step floating catchment area (E2SFCA) to estimate the accessibility of food retailers	In general, the researchers observed increased accessibility to all food retailers in highly deprived areas	E2SFCA method

¹Please see Ingram (1971), Levinson and Wu (2020), and Siddiq et al. (2021).

Diverse accessibility measurement approaches elucidate the spatial distribution of food stores more comprehensively and provide evidence for scholars, governments, and decision-makers to inform the development of related interventions. However, these accessibility measures are unable to measure the level of fairness, which must be determined to ensure the safety and health of communities. The importance of fairness is clearly demonstrated in Fig. 1 , which shows two communities and six food stores in each community. Each community comprises 12 blocks of equal population. Although these two communities have the same accessibility value (0), on average, individuals in Community A must travel farther to reach stores, and the store located in the northern area is typically crowded. Therefore, members of Community A have a higher risk of infection. We further design a choropleth map to illustrate the fairness level of “grocery store” accessibility in a specific area. In this case, Community C appears to have greater fairness of accessibility than Community D. Consequently, the Gini coefficient method we propose allows for the determination of a precise quantitative value to highlight the differences in equality between these two communities (Fig. 2 ). Moreover, our method is advantageous in cases where the differences in equality between two communities cannot easily be observed (Fig. 3 ).

Fig. 1.

Cases of virtual communities A and B.

Fig. 2.

Cases of virtual communities C and D.

Note: The values are not necessarily reasonable in this schematic.

Fig. 3.

Cases of virtual communities E and F.

Note: The values are not necessarily reasonable in this schematic.

2.2. Gini coefficient for assessing inequality

Inequality and social segregation are widely studied by economists, sociologists, geographers, and policymakers. Although the Gini coefficient was once most frequently used in economics, it is now widely used in numerous research fields, such as spatial statistics (Rey & Smith, 2013). For example, Wu and Kim (2021) developed two indices to evaluate the inequality of access to green spaces across cities in China. Wüstemann, Kalisch, and Kolbe (2017) identified inequalities in green space provision in major German cities and determined which social indicators (e.g., income, education, and age) were significantly correlated with green space provision. Talen (2001) investigated the spatial inequalities in access to schools within each district in the US state of West Virginia. Jang, An, Yi, and Lee (2017) considered spatial equality (on the supply side) and transit-oriented development (on the demand side) to strengthen the planning of Seoul's public transportation service. Studies have also adapted the Gini coefficient to measure the equality of access to green space (Kabisch & Haase, 2014; Xu, Haase, Pribadi, & Pauleit, 2018), health care (Lai, Huang, Risser, & Kapadia, 2008; Yu et al., 2019), transportation (Delbosc & Currie, 2011; Jang et al., 2017), education (Asongu, Orim, & Nting, 2019; Thomas, Wang, & Fan, 2001), and cultural spaces (Wang, Zhang, Xu, & Yan, 2019). These studies have demonstrated that the Gini coefficient is appropriate for assessing spatial equality. The Gini coefficient has limitations, such as its inability to capture spatial dependencies. However, the Gini coefficient can be used to effectively describe inequality based on the findings of previous studies; therefore, we adopted it in this study.

3. Methods and data

3.1. Conceptual framework

This study used the Gini coefficient to explore the equality of access to food stores in Taipei City (Fig. 4 ). In the first step, we performed the geocoding and visualization of supermarkets, hypermarkets, and traditional markets using open data. In the second step, we calculated the Gini coefficients at the city and village levels to conduct comparisons. We also compared the inequality among older and non-older populations using each area level to assess whether one group experienced greater inequality than the other.

Fig. 4.

Flowchart of the research process.

3.2. Study area

Taipei City (25°N, 121°E) is located in northern Taiwan (Fig. 5 ) and is the economic and political center of the country. Composed of 12 administrative districts and 456 villages or 11,490 basic statistical areas (BSAs; Fig. 6 ), Taipei contained approximately 2.6 million people living within a 272-km² area at the end of 2021. Because of Taipei's high population density, its residents are at a high risk of viral infection. The number of confirmed cases in Taiwan sharply increased in May 2021 (Fig. 7 ). In response to the increased risk of community transmission caused by local cases with unknown sources of infection, Taiwan's Central Epidemic Command Center raised the alert level to Level 3 in Taipei City on May 15, 2021. The government implemented a soft lockdown plan, asked citizens to stay at home, and prohibited the provision of indoor catering services in restaurants. The policy resulted in a drastic reduction in the flow of people on roads and public transportation in Taipei. By contrast, supermarkets, hypermarkets, and traditional markets were crowded with citizens who were unable to maintain social distance. Despite the risk of infection, citizens feared the exhaustion of their food supplies during the soft lockdown period. This situation may have led people to rethink the importance of fair access to food stores.

Fig. 5.

Study area.

Fig. 6.

Administrative organization.

Fig. 7.

New daily COVID-19 cases in Taipei (Taiwan Centers for Disease Control, 2021).

3.3. Data set

To investigate the fairness of accessibility to food stores, we considered three types of food stores, namely hypermarkets, supermarkets, and traditional markets, to represent all food stores in Taipei. The location data of hypermarkets and supermarkets were gathered from the official websites of these stores; hypermarkets included Carrefour, Costco, RT-MART, and Far Eastern A.Mart, and supermarkets included Carrefour Market, PX Mart, Wellcome, Simple Mart, Jason's, and Farmers' Association Supermarket. The location data of traditional markets were collected from the Taipei City Market Administration Office. The collected data were classified by address, and we geocoded this information to illustrate the locations of food stores. For the number of people, we used statistical data on the level of BSAs from the Social and Economic Statistics System of the Taiwan Ministry of the Interior. Of the 11,490 BSAs in Taipei, the average area of each BSA is 2.3437 ha; each BSA had 233 residents on average, and the standard deviation was 140 (Fig. 6). To accurately measure household location, we employed land use data obtained from the National Surveying and Mapping Center of the Ministry of the Interior; these data comprised 9 categories of land use, 41 subcategories, and 103 detail items. We selected two subcategories that reflect the types of areas in which most residents live, that is, residential areas (code number: 0502) and residential areas with some industrial or commercial use (code number: 0503). For data processing, we calculated food store access for a particular BSA. First, we computed the centroid of the appropriate land use type (i.e., residential areas and residential areas with industrial or commercial use) in each BSA and assigned population data (i.e., older and non-older populations) to each centroid. Subsequently, we estimated the average location in which most residents lived; afterward, we analyzed 500-m and 1000-m buffer regions around the centroid and computed the number of food stores in each buffer region. BSAs with fewer than two residents were excluded from further calculations.

3.4. Method: Gini coefficient

In his book Variabilità e Mutabilità [Variability and Mutability] in 1912, Corrado Gini introduced the Gini coefficient as a measure of the degree to which objects differ. Specifically, the Gini coefficient can explain the mean difference among N quantities, making it suitable for calculating differences in exact measures in the social sciences, such as income scales (Ceriani & Verme, 2012). Gini proposed to quantify the Lorenz curve, proposed by Max Lorenz (1905). The curve represents income distribution, with the X and Y coordinates representing population percentages and wealth or income, respectively. When the wealth/income distribution is equitable, the Lorenz curve is a straight line with a slope of 1 connecting (0 %, 0 %) and (100 %, 100 %), perfectly representing that N% of the population holds N% of the wealth/income at each point. However, when income distribution is unfair, the cumulative N% of the population holds less than the cumulative N% of the wealth/income, and the curve sinks below the perfect equality line. In the most extreme case, when one person has all the income, the Lorenz curve becomes a broken line connecting (0 %, 0 %), (100 %, 0 %), and (100 %, 100 %) in the graph. Therefore, the more the curve deviates from 45°, the more uneven the wealth/income distribution is (Fig. 8 ). The Gini coefficient is commonly used in economics to calculate income inequality. An index equaling zero reflects perfect equality, that is, a situation in which everyone has the same wealth or income. Larger index values reflect greater inequality. In an extreme case, the Gini coefficient equals 1 when only one person has all the income. The Gini coefficient can be calculated using the following formula:

$$G=1-\sum _{i=1}^n\frac{P_i}{P}\left(B_{i-1}+B_i\right).$$ (1)

Fig. 8.

Typical Lorenz curve.

For our purposes, i is the number representing a BSA (in the whole city or a specific village) and i = 1, 2, 3, …, n; P is the total population of the area (the whole city or a specific village); P _i is the population of BSA i; and B _i is the cumulative share of food stores in BSA i. The Gini coefficient ranges from 0 to 1. When the coefficient is close to 0, food stores are evenly distributed among the population. Conversely, when the coefficient is close to 1, the spatial inequality of food stores is high.

At the city level, we used formula (1) to calculate Gini coefficient values in order to elucidate the entire city's fairness of access to food stores. We used the same formula to calculate each village's Gini coefficient to determine the village-level situation. Because a village (or the whole city) contains many BSAs and each BSA has its own parameters, we can use formula (1) to assess the similarity of the parameter from each BSA in a particular village (or the whole city). When calculated using that formula, the Gini coefficient for a village (or the whole city) is closer to 0 if every BSA value in the village (or the whole city) is similar. We considered food stores in the buffer zone of a radius of 1000 m to be accessible to young people and those in the buffer zone within 500 m of the centroid to be accessible to older adults. Therefore, each BSA has two representative accessibility parameters suitable for calculating the Gini coefficient of younger and older adults' access to food.

The calculation method is detailed in Fig. 9 . The green point features represent the locations of food stores, and the yellow polygon represents the 1-km (threshold for the non-older populations) road network distance from a BSA's centroid. In this case, the non-older populations in the BSA can access nine food stores. If a village has 10 BSAs, then that village has 10 representative accessibility parameters. If each parameter is similar, the fairness of food access in the village is higher. In other words, the formula is used to calculate whether a gap exists in the number of food stores that the non-older populations living in each BSA can access, and a Gini coefficient can be obtained to represent the entire village's fairness of access to food stores.

Fig. 9.

Schematic for calculating the Gini coefficient.

No universal standard is available for determining which Gini coefficient value indicates severe unfairness. The United Nations Human Settlement Programme guidelines set a widely accepted standard, which regards the “international alert line” as 0.4. (Moreno, Bazoglu, Mboup, & Warah, 2008). We set four thresholds (0.3, 0.4, 0.5, and 0.6) and thereby divided equality into five levels, namely, “relatively equal,” a “reasonable situation,” a “warning situation,” “high inequality,” and “severe inequality.” All values over 0.4 were considered to indicate general inequality (Table 2 ; Hsu et al., 2023).

Table 2.

Definition of each equality level in this study.

	Standard
Relatively equal	Below 0.3
Reasonable situation	0.3 to 0.4
Warning situation	0.4 to 0.5
High inequality	0.5 to 0.6
Severe inequality	Over 0.6

Source: Hsu et al. (2022).

4. Results and discussion

4.1. Overall fairness of food store accessibility in Taipei City

Fig. 10, Fig. 11 illustrate the food store locations and population density of each village, respectively. Many food stores are located in the inner city1 (i.e., southwestern Taipei). Although Da'an District had 44 markets and Datong District had 15 markets —the administrative districts with the highest and lowest food provision, respectively—Zhongshan and Beitou Districts had the highest (1.65) and lowest (1.09) levels of access to food per 10,000 residents. The number of food stores is higher in densely populated areas.

Fig. 10.

Food store locations.

Fig. 11.

Population density.

In addition to a straightforward overlay analysis of spatial distributions, we calculated our Gini coefficient at the city level to accurately measure the equality of accessibility to food for different age groups in Taipei. Fig. 12 depicts the Lorenz curves, which indicate a “reasonable situation” of equality in the distribution of food stores for the two age groups at the city level. The Gini coefficients for older and non-older populations were similar (0.3616 and 0.3655, respectively).

Fig. 12.

Lorenz curve of food stores accessibility for older and younger adult populations.

4.2. Spatial distribution of food inequality at the village level

In addition to overall city-level trends, we measured the accessibility of food stores at the village level. We further calculated the Gini coefficients for older and non-older populations at the village level; the mean Gini coefficients for these populations were 0.2721 and 0.2145, respectively, which indicate relative equality. The minimum Gini coefficients were 0.1098 and 0.0579, respectively, indicating relative equality. However, the maximum Gini coefficients were 0.9898 and 0.7884, respectively, representing severe inequality. The standard deviations of the coefficients for the two age groups were 0.1064 and 0.0781, respectively. The standard deviation of all villages' Gini coefficients for older adult populations was larger than that for non-older populations. For older adults, the Gini coefficient values of each village were considerably different, indicating greater spatial heterogeneity (Table 3 ).

Table 3.

Descriptive statistics for Gini coefficients.

	Older adult	Non-older population
Minimum	0.1098	0.0579
Maximum	0.9898	0.7884
Mean	0.2721	0.2145
Standard deviation	0.1064	0.0781

The findings are detailed in the village-level statistical maps in Fig. 13, Fig. 14 , in which the differences in equality between older and non-older populations are observable. For older adults, eight villages had severe inequality (Gini coefficient > 0.6), and these villages were primarily located in (a) downtown areas near sizable green spaces (e.g., mountains, farmlands, or large parks) or infrastructure (e.g., large stations, airports, or military fortresses) and (b) suburban areas. These environmental conditions can exacerbate the uneven distribution of food stores. For example, a village in Zhongshan District had severe inequality, probably because of its proximity to a mountain and military-restricted area (Fig. 13). As a suburban area, Beitou District had eight villages with a Gini coefficient of over 0.4, but all villages in Datong and Songshan Districts (inner city) had Gini coefficients below 0.4 (Table 4 ). For the non-older population, only two villages in suburban areas exhibit severe inequality at the village level (Fig. 14). Both were located in mountainous areas in Shilin and Beitou Districts. Villages with a Gini coefficient >0.4 for non-older populations were less than those for older adults (Table 4). Uncolored areas on the maps represent the villages without any access to markets (i.e., without any food stores in their centroid's buffer zone). Therefore, the Gini coefficients could not be calculated for these villages, and no Gini coefficient value can be shown on the maps. All such villages were suburban (Fig. 13, Fig. 14).

Fig. 13.

Older adult Gini coefficient.

Fig. 14.

Non-older populations Gini coefficient.

Table 4.

Number of villages in each district with Gini coefficients over 0.4.

Inner-city districts	Older adult	Non-older population	Suburban districts	Older Adult	Non-older population
Datong	0/25	0/25	Beitou	8/42	2/42
Wanhua	2/36	1/36	Shilin	5/51	2/51
Zhongzheng	4/31	1/31	Neihu	2/39	0/39
Zhongshan	2/42	1/42	Nangang	1/20	0/20
Da'an	1/53	1/53	Wenshan	6/43	2/43
Songshan	0/33	0/33
Xinyi	3/41	0/41

4.3. Importance of Gini coefficients for different scales

At the city level, the distributions of food stores and population were similar across Taipei. The Gini coefficients accurately reflected the degree of inequality in accessibility to food stores at the city level. In the case of Taipei, the coefficients for both older (0.3616) and non-older populations (0.3655) reflected a “reasonable situation,” with no marked differences between the two age groups. Therefore, we can assume that all Taipei citizens have equal accessibility to food stores, obviating the need to travel long distances and preventing people from crowding. Thus, the city-level Gini coefficient provided a single concrete value to describe the fairness of access to food in an entire city.

However, many local situations are not observable at the city level; therefore, we calculated data at the village level. The village-level Gini coefficients identified villages with low equality in accessibility to food stores; in these villages, policy interventions may be warranted. In the case of Taipei, the descriptive statistics revealed that the minimum, maximum, and mean coefficients for the two age groups were similar. For both groups, the minimum and mean Gini coefficients indicated relative equality, and the maximum Gini coefficients indicated severe inequality. The standard deviation of older adults' (0.1064) Gini coefficients was larger than that of the non-older population (0.0781), reflecting greater food accessibility inequality at the village level. The village-level Gini coefficient can be used to design statistical maps to illustrate the spatial distribution of inequality. Older adults (N = 8 villages) have more severe food access problems than non-older populations (N = 2 villages). In addition, the inner city (particularly Da'an District) had high equality, whereas suburban areas (particularly Beitou District) contained the most villages with coefficients over 0.4, reflecting a “warning situation”. Thus, the village- level Gini coefficient can be used to determine the location of communities that face particularly high inequality.

5. Conclusion and additional thoughts

Our findings indicated that the number and distribution of food stores are important factors affecting the equality of access to food during crises. The lessons from COVID-19 and several subsequent studies have indicated that people increase their risk of infection by traveling to and staying at stores to purchase food (Ying & O'Clery, 2021; Zhang et al., 2020). Markets are also crucial for maintaining urban resilience, but this was less evident before the pandemic. To quantify the equality of access to food stores in Taipei, we applied an index using the Gini coefficient. The following findings were obtained. (a) The city-level Gini coefficient can be used to represent the overall degree of unequal accessibility to food stores, and the traditional formula of Gini coefficients can be used to identify inequality, but not where the inequality occurs at divisions finer than the city level (Dawkins, 2006). (b) The village-level Gini coefficient can be used to directly identify places with lower equality in accessibility to food stores and to reveal local problems that cannot be observed at the city level. This index can be used as a reference for city governments to assess whether the number and distribution of stores fulfill the food-purchasing needs of citizens during crises.

This study divided citizens into two age groups because of the aging population of Taipei. Approximately 20 % of Taipei's population is over 65 years old, which is consistent with the World Health Organization's definition of a super-aged society. If we only use 1000 m as the threshold to determine whether citizens can access food stores, the results may be overestimated. In previous studies, scholars have typically used 500 m as the distance threshold for older adults walking for shopping (Nakamura, Nakamura, Okada, Ojima, & Kondo, 2017; Tani et al., 2018). Therefore, it is necessary to calculate younger and older adults' Gini coefficients in a super-aged city. We recommend that the governments of other cities with unique demographic conditions group citizens demographically and calculate their Gini coefficients separately to determine whether specific groups face greater inequality than others.

This is the first study to apply the Gini coefficient to measure the fairness of food store accessibility and to employ a cross-sectional design and single study area (Taipei). Follow-up research is underway to extend this method to broader applications. Research could assess whether or how urban structure affects equality and whether Gini Coefficient is useful for cross-city and cross-time comparisons.

Finally, a crucial limitation of the study must be reported: unequal distribution of food stores may not be important to people who are used to online shopping. This study offers an explanation in response to this situation, demonstrating that a focus on physical food stores remains crucial in the food supply chain. Although online purchases (e.g., on e-commerce platforms) may be a solution for individuals to obtain food during the pandemic, online purchases are not entirely safe. The following issues may be taken into consideration: (a) fresh food requires a complete cold chain system, and improper storage can lead to food contamination, (b) logistics companies may halt delivery services to pandemic hotspots, (c) couriers and food delivery personnel bear the risk of infection and could spread the virus to consumers, and (d) the digital gap deprives some residents of the opportunity to obtain fresh food in this manner. Therefore, many Taipei residents must travel to physical food stores to purchase food. Overall, the role of physical food stores may become more or less crucial in the post-pandemic era. Future research can assess changes in customer shopping frequency and the types of foods purchased (fresh or ultra-processed) with reference to the Gini coefficients of villages. Moreover, changes in the density and distribution of food stores and the underlying reasons for these changes must be explored.

CRediT authorship contribution statement

Chong-En Li: Conceptualization; Investigation; Data curation; Formal analysis; and Writing original draft.

Zih-Hong Lin: Methodology, Software; Validation; and Visualization.

Yi-Ya Hsu: Investigation; Data curation; and Methodology.

Nae-Wen Kuo (*Corresponding author): Conceptualization; Supervision; Methodology; and Writing – reviewing and editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.