Exploring the Spatial Distribution of Mosques and Malay-Muslim Population in Post-Pandemic Singapore

02.522 Urban Data & Methods II: Computational Urban Analysis Group Project Report

Exploring the Spatial Distribution of Mosques and Malay-Muslim Population in Post-Pandemic Singapore Group 4: Gian Jian Xiang (1005684) Khalishah Nadhirah Bte Abu Bakar (1000668) Ng Qi Rong Rachel (1002409)

Introduction The current COVID-19 pandemic has affected more than 150 nations worldwide (Fong et al., 2020). Due to its worldwide impact, its effects were felt across many domains, including sectors on economic (Chudik et al., 2020), social (Saladino et al., 2020), academia (Harper et al., 2020) and religion (Quadri, 2020).

In light of COVID-19, governments worldwide had introduced various local interventions such as lockdowns and safe distancing measures. Reducing congregational capacities of event spaces, workplaces and religious gatherings were some examples of the collective local attempt to abide by the safe distancing measures (Haug et al., 2020).

This introduced drastic changes in the geospatial movements of the population with regards to their routine activities. Specifically, these changes posed threats to the feasibility of achieving basic hygiene in areas of extreme poverty (Gibson & Rush, 2020), threats to the allocation of healthcare facilities and resources for the ageing population (Lakhani, 2021) and threats to political influences stemming from media and communication (Allcott, 2020). Our study aims to contribute to this broader COVID-19 literature of understanding the underlying drivers behind geospatial movements of the population in the context of religion.

Therefore, the study aims to explore the impact of COVID-19 on the spatial distribution of mosque’s occupancy in Singapore, and in doing so, recommend where more mosques could be built to better match a potentially altered spatial demand with mosques capacities.

Majority of male Muslims tend to attend Friday prayers at mosques which they can most conveniently access that Friday afternoon. Considering work from home arrangements have become a norm in the ongoing COVID-19 pandemic, the spatial distribution of congregants to mosques could potentially have been impacted. Coupled with the reduced capacities imposed on places of worships to curb the spread of the virus, these provide an impetus for the study.

Background and Motivation Singapore is a multicultural city-state. To cater to the religious diversity of the Singapore population with impartiality, the government has safeguarded land parcels for the various religious groups to conduct their religious activities (Chan & Siddique, 2019). Thus, places of worship like temples, mosques and churches can be found in every town in Singapore.

Figure 1: Religious Composition of major races in Singapore

Figure 1 shows the religious composition of the major races in Singapore (Chan & Siddique, 2019). According to the 2015 General Household Survey, majority of the Chinese population either practise Buddhism or has no religion, with about 21% belonging to the Catholic or Other Christians categories. On the other hand, while most Indians are Hindu, many among the Indian population are either Muslims or Christians. Perhaps the most interesting composition is that of the Malays, who almost exclusively belong to the Islam category.

Why the interest in mosques and Islam?

All Muslims pray five times daily, which can be performed anywhere. However, there are other mandatory prayer times that must be performed in the mosques, which provide spaces for congregational worship services. One notable example is the Friday prayers. While it is optional for women, the Quran necessitates that all men must attend these prayers physically at the mosques (Quadri, 2020). In addition, they tend to visit mosques closest to them for the Friday prayer afternoon, regardless of if they are at work or at home. On the contrary, Buddhist and Taoist worshippers are known to visit temples not as frequently and not at regular times. Christians, on the other hand, tend to attend specific churches every Sunday regardless of where they live or work. Thus, compared with the other religions, the unique and routine practices of Islam contribute to making the religion a particular interest to the study.

How might COVID-19 have affected the spatial distribution of mosques to congregants?

Since June 2020, capacity restrictions have been placed on places of worships to curb the spread of the COVID-19. Out of the 67 mosques in Singapore, only five have been allowed to expand their capacities to 250 in December 2020 (CNA, 2020). This is achieved by segregating the mosques into five zones, each zone only having up to 50 people. All the other mosques can only accommodate up to 100 visitors per prayer session. Even if we assume all mosques have a capacity of 250, these figures are still significantly lower than the original capacities for most mosques (see Figure 2 below).

Figure 2: Comparison of current COVID-19 restricted occupancy against the original full mosque occupancy

To increase the number of prayer slots to ensure all male Muslims can perform the Friday prayers without affecting safe distancing measures, three sessions have been provided for every mosque every Friday. The first slot starts at 12.45pm and the third slot ends at 3.15pm.

Each slot takes 30 minutes and there is a buffer of 30 minutes between slots. This improvement essentially triples the capacities by up to 750 for certain mosques.

Besides the restricted capacities, many people, including the male Muslim population, have been working from home since the Circuit Breaker measure was implemented in April 2020.

Coupled with the tendency for them to attend the congregational worship sessions closest to their current locations on Fridays, it is believed that the spatial distribution of mosques to congregants could have been altered because of COVID-19, and thus exploring this issue will be the objective of the study.

Why the need for a map-view interface?

To facilitate crowd control with the reduced capacities, the Islamic Religious Council of Singapore (MUIS) has adopted the safe management guidelines set out by the Ministry of Culture, Community and Youth (MCCY) by introducing an online booking system that allows users to view the availability of all mosques in Singapore and book their slots in advance. The booking system (see Figure 2 below for user interface) allows Muslims to book their preferred choice of mosques to visit and the time slots starting from Tuesdays at 10am onwards prior to the Friday on the same week (MUIS, 2020).

However, the information is presented in table form and difficult to navigate, especially if one has to search the availability of the next closest mosque when the closest one is full.

Therefore, by presenting the information of mosques capacities in a map view, not only could this feature be integrated into the existing booking system as an improvement, but the

visualization could also aid in exploring the spatial distribution of mosques to the MalayMuslim population in Singapore.

Figure 3: Prayer Booking Form by MUIS

Data Sources The team acquired data from the following sources.

1. The “Master-plan-2014-subzone-boundary-no-sea” shapefile (.shp) was used as the base map with subzone information, taken from data.gov.sg.

2. From the same website, the male Muslim population per subzone was derived from the 2015 population census titled “Resident Population by Planning Area/Subzone, Ethnic Group and Sex”. We filtered Ethnic Group = Malays, Sex = Males.

Note: As almost all Malay population in Singapore practise Islam, we can use the figures for the race as a proxy for the religion.

3. The location of all mosques in Singapore was retrieved from a user-created map hosted on Google Maps platform Mosques in Singapore. (n.d.). The .kml file was then converted into a .shp file.

4. Mosque occupancy data for one Friday in the month of April 2021 was scraped using Python from the MUIS Prayer Booking Form (n.d.) on Tuesdays prior to the Friday prayers.

5. Accessibility factors on transport networks to the mosques were retrieved from Land Transport Authority (LTA)’s DataMall (2021) through an API call over Python. This includes data on Bus Routes, Bus Stops, MRT station exits.

6. The “Master-plan-2014-Land-Use.shp” was used to retrieve the residential areas within the subzones, taken from data.gov.sg.

Methodology GIS: Choropleth Map of Malay Muslim Population Density Firstly, a choropleth thematic map of the population density of male Muslims by subzones (Figure 3) was created in ArcGIS using the “Master-plan-2014-subzone-boundary-no-sea” shapefile, joined with the male Muslim population per subzone data. The population density per subzone was derived by normalizing the number of male Muslims with the subzone area using the following formulae: 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 (𝑀𝑢𝑠𝑙𝑖𝑚 ) 𝑀𝑎𝑙𝑒𝑠 𝑝𝑒𝑟 𝑆𝑢𝑏𝑧𝑜𝑛𝑒 =

𝑁𝑜. 𝑜𝑓 (𝑀𝑢𝑠𝑙𝑖𝑚) 𝑀𝑎𝑙𝑒𝑠 𝑝𝑒𝑟 𝑆𝑢𝑏𝑧𝑜𝑛𝑒 𝑆𝑢𝑏𝑧𝑜𝑛𝑒 𝐴𝑟𝑒𝑎

Figure 4: Base map - male Muslim population density in Singapore

GIS: Occupancy Percentages of Mosques Next, one Friday’s worth of mosque occupancy figures was scrapped from MUIS’s online booking portal. As there was a lack of information on the exact restricted capacities for every mosque, and to simplify calculations, the team assumed the maximum capacity of 250 congregants for all mosques per prayer time slot. As mentioned, MUIS had increased the number of prayer time slots to three per Friday. This means there would be a total of 750 slots for a Friday per mosque.

The occupancy percentage per mosque can thus be calculated with the following formulae: % 𝑜𝑐𝑐𝑢𝑝𝑖𝑒𝑑 =

(750 − 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦) × 100% 750

The mosques’ locations and occupancy percentages were then overlaid onto the base map to produce the following map:

` Figure 5: Population density of male Muslims and Mosque Occupancy

In the map, the occupancy percentages were broken down into five quantiles scales, with the lowest occupancy rate being 39.2% and the highest being fully occupied. A diverging colour scale was used, where red indicates high occupancy percentages and blue indicates relatively low occupancy percentages.

From the map, the following observations can be gleaned: 1. Corresponding to the regions with a high population density of male Muslims, the mosques with high occupancy percentages are generally located along the Eastern side of Singapore (from Geylang to Tampines). Additionally, there are a few popular mosques located in the Northern region of the island as well, and which also corresponds to the high male Muslim population density in that subzone.

2. Conversely, the mosques with low occupancy percentages are in the central regions of Singapore, where the male Muslim population density tends to be lower too.

3. However, counterintuitively, there are two mosques with high occupancy located in the Western region of Singapore, which are away from the populous residential areas.

As that area is known to an industrial zone, one deduction could be that these mosques are visited by people working there.

Based on the visualisation, the team next built a linear regression model to verify if there is indeed any relationship between the occupancy percentages of the mosques and the population density of the subzone, among other potentially relevant factors.

Statistical Analysis: Supervised Learning - Linear Regression Model Linear regression was built to find the relationship between the mosque occupancy percentages and the following factors: 𝑦 = 𝛽0 + 𝛽1 ∗ 𝑥1 + 𝛽2 ∗ 𝑥2 + 𝛽3 ∗ 𝑥3 + 𝛽4 ∗ 𝑥4 + 𝛽5 ∗ 𝑥5 𝑤ℎ𝑒𝑟𝑒, 𝑦 = 𝑀𝑜𝑠𝑞𝑢𝑒 𝑂𝑐𝑐𝑢𝑝𝑎𝑛𝑦 𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑥1 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑛𝑒𝑎𝑟𝑏𝑦 𝐵𝑢𝑠 𝑆𝑡𝑜𝑝𝑠 𝑤𝑖𝑡ℎ𝑖𝑛 400𝑚 𝑟𝑎𝑑𝑖𝑢𝑠 𝑜𝑓 𝑚𝑜𝑠𝑞𝑢𝑒 (𝑁𝑜𝑜𝑓𝑁𝑒𝑎𝑟𝑏𝑦𝐵𝑢𝑠𝑆𝑡𝑜𝑝𝑠) 𝑥2 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐵𝑢𝑠 𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑠 𝑤𝑖𝑡ℎ𝑖𝑛 400𝑚 𝑟𝑎𝑑𝑖𝑢𝑠 𝑜𝑓 𝑚𝑜𝑠𝑞𝑢𝑒 (𝑁𝑜𝑜𝑓𝐵𝑢𝑠𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑠) 𝑥3 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑛𝑒𝑎𝑟𝑏𝑦 𝑀𝑅𝑇𝑠 𝑤𝑖𝑡ℎ𝑖𝑛 400𝑚 𝑟𝑎𝑑𝑖𝑢𝑠 𝑜𝑓 𝑚𝑜𝑠𝑞𝑢𝑒 (𝑁𝑜𝑜𝑓𝑁𝑒𝑎𝑟𝑏𝑦𝑀𝑅𝑇𝑠) 𝑥4 = 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝑀𝑎𝑙𝑎𝑦 𝑀𝑎𝑙𝑒𝑠 𝑜𝑓 𝑐𝑜𝑟𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑖𝑛𝑔 𝑠𝑢𝑏𝑧𝑜𝑛𝑒 (𝑀𝑎𝑙𝑎𝑦𝑀𝑎𝑙𝑒𝑠𝐷𝑒𝑛𝑠𝑖𝑡𝑦) 𝑥5 = 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑜𝑓 𝑛𝑒𝑎𝑟𝑒𝑠𝑡 𝑟𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙 𝑎𝑟𝑒𝑎𝑠 (𝐴𝑣𝑔𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒)

Number of nearby Bus Stops (within 400m buffer) - 𝑥1 The team leveraged on LTA DataMall through an API call to obtain ‘Bus Stops’ points in Python.

These points were overlaid against the 400m buffer radius of mosques created using the Proximity Tool in ArcGIS. Next, using ArcGIS’s Overlay Tool, an intersection of buffer polygons and the Bus Stop points was executed to limit to the nearest bus stops within the 400m radius of the mosques (see Figure 6 below). A count of bus stops, aggregated at mosque level, is calculated to obtain this variable, NoofNearbyBusStops.

Number of Bus Services (within 400m Buffer) - 𝑥2 Similarly, the team leveraged on LTA DataMall through an API call to obtain ‘Bus Routes’ points in Python. From this Bus Routes data, the unique count of bus services per bus stop is aggregated. This data is joined with Bus Stops (seen in the previous paragraph) using a unique identifier signifying the Bus Stop ID. This joined shapefile was overlaid against the 400m buffer radius of mosques created using the Proximity Tool in ArcGIS. Next, using ArcGIS’s Overlay Tool, an intersection of buffer polygons and the joined shapefile was executed to limit to the nearest bus stops within the 400m radius of the mosques (see Figure 6 below). A sum of bus services is aggregated at the mosque level to obtain this variable, NoofBusServices.

Figure 6: Intersection of Bus Stops within 400m Radius of Mosque

Number of nearby MRTs (within 400m Buffer) - 𝑥3 Similarly, the team leveraged on LTA DataMall through an API call in Python again to obtain unique MRT points from ‘Passenger Volume by Train Stations’ data. These points were overlaid against the 400m buffer radius of mosques created using the Proximity tool in ArcGIS. Next, using ArcGIS’s Overlay tool, an intersection of buffer polygons and the MRT points was executed to limit to the nearest MRTs within the 400m radius of the mosques (see Figure 7 below). A count of MRTs, aggregated at the mosque level, is calculated to obtain this variable, NoofNearbyMRTs.

Figure 7: Intersection of MRT points within 400m Radius of Mosque

Population Density of Male Muslims in its corresponding subzone - 𝑥4 As described in section “GIS: Occupancy Percentages of Mosque” above.

Average distance of nearest residential areas - 𝑥5 A separate map was created in ArcGIS to calculate the values. Instead of the subzone boundary shapefile, the “Master-Plan-2014-Land-Use” shapefile was used instead. Areas, where the land use belongs to the “RESIDENTIAL” category, were filtered as a separate layer.

The centroids of each of these residential areas were calculated.

Using ArcGIS’s Generate Near Table tool, each residential area was assigned the nearest mosque based on the Euclidean distances calculated from its centroid to all the mosques. Per mosque, the distances of all corresponding residential areas were averaged to create this variable. Note that by using this method to calculate the average residential distances, there is an underlying assumption that congregants visit the mosques closest to their homes.

For additional information, out of the 67 mosques, three did not have any residential areas assigned to them because they were not the nearest to any residential area compared to the other mosques. To circumvent this issue, the Generate Near Tool was used again to calculate and assign the top three nearest mosques per residential area instead. Two of these three

mosques had several residential areas listed as their second closest, whose corresponding distances were then used to calculate the average distance for each of these mosques.

Rather surprisingly, one of them has residential areas listed only as their third closest. This mosque (see Figure 8 below) was located away from the residential areas and had two other mosques nearby that “cannibalized” it. For this mosque, the corresponding distances of the group of residential areas that were assigned to it as their third closest were used to calculate its value for this variable.

Figure 8: Location of Mosque #37 w.r.t other mosques and residential areas nearby

Regression Analysis Looking at the p-values (see Figure 9 below), the team observes that the NoOfNearbyMRTs factor is significant at a p-value threshold of 5%. This means that the occupancy percentages can increase by roughly 8% with a unit increase in the number of nearby MRT stations within a 400m radius of the mosque. If a p-value threshold of 10% were used, the NoOfBusServices factor could be considered a significant factor as well. The remaining two factors, MalayMaleDensity and AvgResidentialDistance, are not significant regardless of the p-value threshold.

The R-squared value is also low, suggesting that there might not be a strong relationship between the dependent and independent variables. However, this could also be because of the small sample size and high variances as seen in the independent variables, which negates the normal distribution assumption required to be satisfied by linear regression.

Figure 9: Linear Regression OLS Results

The team conducted clustering analysis to investigate the factors influencing mosque occupancy further.

Statistical Analysis: Unsupervised Learning - K-means Clustering The team conducted K-means clustering (k = 2) of the mosques. Note that the team had tried k = 3 but there was no discernible pattern from the clustering results. The team also removed the fully occupied mosques (i.e., 100%) as it would skew the results. Boxplots (see Figure 10 below) was then created to compare the distributions of each factor between the two resulting clusters.

Figure 10: Comparing the distribution of factors between the 2 clusters with boxplots

Figure 10 (see above) reveals that cluster #1 mosques tend to have higher occupancy percentages. Correspondingly, they also tend to have more bus services and MRT stations within a 400m radius (i.e., better accessibility) and a lower average residential distance (i.e., nearer to people’s homes). The only counterintuitive finding was the population density of the corresponding subzones. To understand why this was the case, the clusters were mapped onto the same base map in ArcGIS for better visualization.

Figure 11: Spatial distribution of clusters of mosques from K-means vs population density of male Muslims

From Figure 11 (see above), we can observe that the mosques in cluster #1 (orange) tend to agglomerate around the Southern part (i.e., Central Business District) of Singapore, with a few exceptional outliers. On the other hand, cluster #0 (light blue) contains more mosques, which are spread out across Singapore. This might explain its wider range of values, especially for the factor of population density per subzone.

The fact that cluster #1 mosques tend to lie within the central parts of Singapore could explain the lower distribution of values population density per subzone since fewer male Muslims live in those areas. However, despite this, their high occupancy percentages seem to suggest that there are male Muslims who are still working from the offices in those areas (and thus attend those mosques in the vicinity).

Findings and Discussion Many people may likely not be working from home.

While the GIS visualisation showed that there may be a relationship between the occupancy of the mosques and the male Muslim population density per subzone, the regression model dispels the relationship with that factor’s high p-value.

The GIS visualisation also showed that there are mosques that experienced high occupancy despite their distances to residential areas, suggesting that some people do not have the luxury of working from home, likely due to the nature of their work.

Finally, the clustering results suggest there could be people attending mosques near their workplaces in the CBD, as evinced by those mosques’ high occupancy percentages and the corresponding subzones’ low male Muslim population densities. This might also be due to the current transitioning phase in which the government has eased workplace capacity restrictions and people have started returning to their workplaces since March 2021, a month before the occupancy data was scrapped (Lim, 2021).

Based on these findings, we can surmise that our initial assumption, that most male Muslims are working from home and hence attending mosques near their houses, may likely not be valid. However, our findings do tell us something else regarding mosques capacities and their spatial distributions.

Building more smaller mosques is more equitable than having fewer larger mosques.

While controlled capacities of mosques at 750 slots have proven to be successful safedistancing measures, it has altered the spatial distributions of male Muslims during the period of the obligatory Friday prayers. This study is an attempt to understand these distributions and highlight the inadequacies of the supply of mosques available in Singapore in contrast to the demand of the male Muslim population.

Despite the poor regression results, the team notes that there are indeed certain subzones with both a high male Muslim population and mosques with high occupancy percentages.

Further inspection is focused on Tampines, a planning area comprising of five subzones which includes two subzones that have two of the highest male Muslim populations in Singapore (i.e., Tampines East and Tampines West subzones). From Figure 5 (see above), Tampines’ only mosque, Darul Ghufran Mosque, which also happens to be Singapore’s largest, is fully occupied. This indicates its high popularity. However, in Figure 2 (see above), even with three slots a day, the restricted capacity on Darul Ghufran Mosque is less than 20% of the mosque’s original capacity of 5500.

Similarly, another planning area, Woodlands, is also home to both a high male Muslim population and mosques with high occupancy percentages. It is served by two mosques – Yusok Ishak Mosque (100% occupancy) at Woodlands South subzone and An-Nur Mosque (98% occupancy) at Woodlands West subzone. Note that from Figure 2, the original capacities of these mosques are 4500 and 2800 respectively.

Moving forward in a post-pandemic world, it is unlikely that the mosques’ original capacities could be reinstated any time soon without jeopardizing the health and safety of the congregants. Therefore, based on the preliminary findings, the team argues that in order to meet the demands of the large male Muslim population in Tampines and Woodlands, smaller mosques relative to the current large ones, with capacities of 500 – 1000 each, could be built across these planning areas. The capacity of 750 Friday prayer slots (i.e., 250 slots over three sessions) could still be applied to them, with buffer for expansion as the government progressively relaxes the capacity restrictions in the future. Such a move could ensure that congregants living in those areas would be better served by more (albeit smaller) mosques as compared to an unnecessarily huge one at a single location.

Buffers of 400m radius were created for every mosque on ArcGIS to ascertain where these new smaller-sized mosques could be built so that they would not be too near to existing ones.

The team recommends them to be located around the labelled red crosses (see Figure 12 below), one in Woodlands East subzone and the other in Tampines East subzone, to aid with the capacity constraints.

Figure 11: Mosques with 400m radius buffer with recommended new mosque locations (in red)

Limitations and Next Steps Supplement geographical visualisation with data of where people work To find out if male Muslims are attending mosques from their workplaces, the number of people working in each subzone should ideally be collected through a nationwide census and this variable should be included in the regression model. This would better inform us of the movement of male Muslims to the mosques (whether they could be coming from home or work) during the period of Friday prayers.

Include more past occupancy data In this study, the team only scrapped one Friday’s worth of mosque occupancy data in Singapore. Further work could involve scrapping data from over a longer period (i.e., past few months) to accurately determine the mosques’ long-run average occupancy percentages. This would also provide a more representative sample that would become normally distributed from the large sample size, thereby lending better validation to the regression model.

Include more independent variables To strengthen the regression model in predicting the occupancy percentages of mosques as well as determining more factors that affect them, the study could include other independent variables such as traffic data on roads surrounding the mosques and the pedestrian footpath density around them to better capture the mosques’ accessibility.

An improvement could involve conducting surveys to understand the factors influencing the movements of the male Muslims during the period of Friday prayers. Analysis from a reasonable survey sample size could inform us on the proportion of the male Muslim population’s mode of transport to these mosques. These proportions will then be introduced into the regression model as forms of weightage percentage points on independent variables such as NoofNearbyBusStops, NoofNearbyMRTs and pedestrian footpath density. Such analysis will assign more importance to the actual movements of the male Muslims during Friday prayers, thus rendering a close representation to reality.

Integrate map into MUIS online booking portal As mentioned, the availability data of each mosque is displayed in a tabular format on the current version of the online booking portal. As the team has demonstrated, this information could be displayed in a map form to improve the intuitiveness in interpreting the information.

By integrating the map with the users’ geolocation information, users can find the closest mosque based on their current positions and check its corresponding availability to book the prayer slot quickly. Visualizing the data in a graphical map view can also allow users to identify the alternative mosque choices if the first choice is fully booked.

Conclusion COVID-19 has created waves of impact that indirectly alters spatial movements of the population worldwide. Our study aims to contribute to the understanding of these spatial movements in the context of religion, particularly focusing on male Muslims in Singapore. The obligatory Friday prayers for male Muslims, which are usually performed in physical congregation, must be revisited in light of COVID-19's safe distancing measures.

The association of COVID-19 capacity restrictions in mosques and the geospatial movements of male Muslims during the period of Friday prayers is an important consideration in this study. Our initial assumptions on these movements were based on the work-from-home arrangements arising from the pandemic. Conversely, our empirical findings have proven our assumptions otherwise. Indeed, many male Muslims may likely not be working from home but rather from their workplaces.

Due to the complex interactions between mosque capacity and the geospatial movement of the working Muslim male population, the findings from this study support the proposal of building more smaller mosques instead of larger mosques, especially in areas with a higher working male Muslim population. This is to allow for more equal spatial distribution of the population during Friday prayers without compromising on safe distancing measures and contain the spread of COVID-19.

While the study yields a strong stance in support of the weekly Friday prayers, the team acknowledges that its impact can be extrapolated into addressing mosques’ capacity for other congregational prayer times such as the Terawih prayers during the month of Ramadan. In fact, the MUIS online booking portal is also used to book slots for the Terawih prayers currently (MUIS, 2021).

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