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JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING https://doi.org/10.1080/13467581.2022.2047983 Spatial analysis of population density and its effects during the Covid-19 pandemic in Sanandaj, Iran a a,b c d Kayoumars Irandoost , Hooshmand Alizadeh , Zahed Yousefi and Behzad Shahmoradi a b Associate Prof, Faculty of Arts and Architecture, Department of Urban Planning and Design, University of Kurdistan; Iran and Senior Postdoc Researcher at the Institute for Urban and Regional Research, the, Austrian Academy of Sciences, Austria; Urban Planning Department, Faculty of Arts and Architecture, University of Kurdistan, Sanandaj, Iran; Department of Environmental Health Engineering, Faculty of Health, Kurdistan University of Medical Sciences, Sanandaj, Iran ABSTRACT ARTICLE HISTORY Received 29 July 2021 Cities are densely populated centers that have struggled with many issues throughout history. Accepted 21 February 2022 Undoubtedly, the Covid-19 pandemic is the newest and one of the most critical challenges, which has caused many problems related to urban functions since its start in 2020. As the most KEYWORDS vital factor in the spread and incidence of this virus relates to the contact with infected people, Population density; covid-19 increased communication and face-to-face connections between people can probably increase pandemic; infection rate; the spread of the virus. This article seeks to answer the question whether population density sanandaj city and building density in urban areas affect the spread of Covid-19 and relating incidents. Using official statistics of Covid-19 patients from the beginning of its occurrence in Sanandaj, Iran (March 2020), to the end of 2020, its relationship with the two variables of population and residential density at the neighbourhood level was examined. The results show that the correlation between infection rate and population density per hectare as well as dwellings per hectare in the neighbourhoods is significant at the 0.01 level. This indicates that, with increasing population and residential density in the urban areas of Sanandaj, the incidence of Covid-19 has also increased. 1. Introduction Health Organization, to reduce transmission and infec- tion rates. These include social distance, quarantine, The Covid-19 pandemic is one of the most challenging and personal isolation at home, all of which can be diseases of recent centuries, affecting all countries and effective in reducing the rate of disease transmission. individuals at various levels. Transmission of the virus Research and recommendations of experts and com- can occur in certain situations, particularly in indoor, petent institutions have identified handwashing, main- crowded, and inadequately ventilated environments, taining social distance, and quarantine of infected where the infected person(s) spend longer times with people at home, as well as the use of masks as the others, such as in restaurants, at choir repetitions and most effective ways to reduce the transmission of the performances, fitness classes, night parties, in offices Covid-19 disease. However, other factors can also con- and/or at places of worship (WHO 2020). Theoretically, tribute in different ways. As cities became centers of population density increases a person’s exposure to wealth, finance, and activities, people were pulled into the infection, resulting in an increase in the number urban areas since the advent of industrialization of reproductions of the virus (also called the R number (Eltarabily and Elghezanwy 2020). This resulted in the by epidemiologists), leading to more outbreaks. Due to concentration of populations and due to the density the rapid spread of the Covid-19 disease worldwide, on this scale, cities have always been vulnerable to the social distance is the first factor that is advanced as an initiation and spread of pandemic diseases. The cur- explanation of the transmissibility of pandemic viruses rently ongoing Covid-19 epidemic can be treated as an (Kadi and Khelfaoui 2020). urban incident which causes challenges throughout The Covid-19 crisis has caused irreparable harm, the world (Liu 2020). particularly in the cities of developing countries. The In terms of urban planning, spatial aspects of den- incidence and mortality rate may be lower than in sity and compactness are among the factors that can developed countries for various reasons, but the pro- play a direct as well as an indirect role in the spread of blems and consequences are very tangible. Numerous the virus, the ways in which it is transmitted, and in the factors and strategies have been proposed by interna- increase in the number of patients. Therefore, defining tional and specialized institutions, including the World the extent of the relationship between spatial factors CONTACT Kayoumars Irandoost firstname.lastname@example.org Associate Prof, Faculty of Arts and Architecture, Department of Urban Planning and Design, University of Kurdistan © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 K. IRANDOOST ET AL. and the transmission of the Covid-19 disease can help also indicate that density is not the only factor in policymakers to consider better and more conservative increasing the incidence of the disease (Hamidi and urban development patterns for future prospects of Zandiatashbar 2021; Khavarian-Garmsir, Sharifi, and the city. Experimental observations show that the Moradpour 2021). Rather, the large number of com- extent of epidemics generally is directly related to the mercial activities and the density of transportation initial density of susceptible individuals (those who did infrastructure (Li et al. 2021) as well as demographic not develop immunity) and that it increases its severity and socio-economic characteristics of urban popula- (nonlinearly) in the course of time (Li, Richmond, and tion may play a more effective role in increasing the Roehner 2018). The number of new infections is also incidence of the disease (Gargiulo et al. 2020; Li et al. strongly correlated with the number of contacts of 2021). Furthermore, through lockdown and stay-at- susceptible individuals, with significant differences in home policies, the role of population density in the epidemic size observed among populations with dif- spread of the disease will diminish (Sun et al. 2020). ferent densities (Maybery 1999). Recent studies show One study demonstrated that the correlation between that high population density in cities, as it reduces the density and infection levels is opaque; contrary to distance between people (Acuto 2020), contributed to expectations, it was observed that the denser cities the current prevalence of and mortality rate related to were also wealthier, enabling them to channel consid- the Corona virus (Coşkun, Yıldırım, and Gündüz 2021; erable resources to respond to the pandemic – and so Hong et al. 2021; Kadi and Khelfaoui 2020; Lee et al. reduce their infection rates (UN-Habitat 2021). 2021; Nakada and Urban 2020; Wheaton and Kinsella Nevertheless, population density seems to be an Thompson 2020). Population density and spatial com- essential factor in the Covid-19 epidemic in cities. The pactness can thus be influential factors causing results of a study on American cities show that the increases in epidemics. This means that densely popu- Covid-19 pandemic has led to a further decline in lated cities are far more vulnerable to the Covid-19 housing demand in densely populated neighborhoods pandemic. Therefore, it may be necessary to consider (Liu and Su 2020). Although it is too early to conclude specific criteria related to the risk of infectious diseases whether reconfiguration of housing and the environ- in the development and design of cities along with ment can be effective in controlling this pandemic common socio-spatial criteria (Gandy 1999). (Webster 2021), there has been debates about the However, the analysis of a study on the relationship need to reconsider the nature of urban spaces between density, the incidence of Covid-19, and mor- (Martínez and Short 2021) and the appropriate popula- tality rates in 913 US metropolises has shown different tion density for cities (Desai 2020). results. In denser cities, the death toll from the Covid- According to the above background, it can be con- 19 was lower, but conversely, in less densely populated cluded that population density resulting from the spa- cities, the mortality rate was higher. As the study indi- tial compactness of urban forms has caused different cates, this was more likely due to better access to responses depending on each urban context. healthcare services and the “likelihood of greater Therefore, we cannot set a general rule. It is necessary adherence to social distancing advisories or orders in to examine each urban context according to its own compact counties” (Hamidi, Ewing, and Sabouri 2020). conditions regarding its effects on the transmission of According to Littman, residential crowding (the num- and mortality rate due to the Covid-19 pandemic. ber of people per square unit of interior space) within Accordingly, this study aims to analyze the pattern of the city is more closely associated with Covid-19 than morbidity and mortality in Sanandaj spatially by iden- population density (the number of people per surface tifying their relationship with population density, spe- area). For him, the high number of confirmed cases in cifically regarding the spatial compactness of the city’s metropolitan areas such as New York, Chicago and built-up areas. Our conceptual framework was based Seattle stands in closer relation to their global connec- on the hypothesis that congestion leads to more expo- tions than to their density (Litman 2020). These cities sure to the Covid-19 virus, and more exposure will are major centers of travel, trade, tourism, and migra- increase the rate of infection, which in turn leads to tion. It means that “connectivity matters more than an increase in the mortality rate. density in the spread of the Covid-19 pandemic” (Hamidi, Sabouri, and Ewing 2020, 495). The results of 2. Data and methods Teller’s research also confirm these findings (Teller 2021). Some studies placed emphasis on slums and The study area of this research is Sanandaj city in wes- found that the highest density in such settlements is tern Iran. This city is the capital of Kurdistan province, an important factor in increasing the transmission of and most of its inhabitants are Kurds. Sanandaj located the disease due to the lack of healthcare services and at the geographical coordinates of E46.999° and due to non-compliance with health protocols (Baker N35.311° and its altitude varies from 1450 to 1540 et al., 2020; Durizzo et al. 2021; Sahasranaman and meters. According to the official census of 2017, the Jensen 2021). However, the results of some studies city has a population of 412,767 people. Its total area JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 3 comprises about 4888 hectares, of which 4282 hectares data for four separate urban sections, these areas constitute the central area of the city, and another were ignored, and only the main area of Sanandaj 606 hectares belong to four separate urban areas. considered for the study. After the removal of The data used for the analysis include spatial, demo- detached urban areas and vacant lands, the total graphic, and other statistical data relating to Covid-19 area of the city of Sanandaj reached 1905 hectares. patients. Maps and spatial data are among the second- The built-up area of each neighborhood was speci- ary data received from the Sanandaj Housing and fied and listed separately. Based on the obtained Urban Development Department based on the latest area for each neighborhood, population density per updates. Demographic data was also obtained from hectare and housing units per hectare was also the Statistics Center and data on the number of calculated. The data concerning the Covid-19 patients with the Covid-19 disease was received from patients includes official data recorded by the the Iranian Ministry of Health and Medical Education. Iranian government, which was provided to the research team in an Excel file with fields including gender, age, occupation, address, and underlying 3. Data preparation diseases of individuals. However, only the frequency and spatial distribution of patients were used in this Since the primary purpose of this study is to inves- study. tigate the relationship between population density regarding the spatial compactness of the city’s The file concerning Covid-19 patients contains 8048 built-up areas and the prevalence of the Covid-19 cases that were recorded since the beginning of the official Covid-19 tests in Sanandaj, in the period from disease, and given the importance of the role of social distance and population congestion in the March 2020 to the end of 2020. Accordingly, the data epidemic, this article attempts to test the existence has been recorded based on the addresses in the file. It was necessary to convert these addresses into spatial or non-existence of such a relationship. For analyti- statistics by specifying them on the map. In a time- cal purposes, the data was first prepared and the required information was generated from there. The consuming and accurate process, all records were clas- sified, based on their location addresses. The share of official spatial boundaries of the city’s neighbour- each of the 68 neighborhoods determined – and its hood units, including 68 neighbourhoods and four separate urban areas, form the basis of the analysis. quantity added to – the relevant attribute table. Out of However, because most neighbourhood units the total number of records, 5922 people lived in the main area of Sanandaj city and 2126 people lived in include vacant and undeveloped land, a new area is drawn for each neighbourhood to increase the four separate areas or outside Sanandaj city limits, accuracy of the results, including only built-up areas which caused them to be removed from the list. After the data screening, we calculated the number of (Figure 1). Furthermore, due to the lack of sufficient Figure 1. Sanandaj city area, division of neighborhood units, and built-up area of the city. 4 K. IRANDOOST ET AL. Table 1. The required data used in the analysis. Number of Population Covid- Cases Number of housing Area (people per Dwellings 19 per Covid-19 Cases Family Neighborhoods Population households units (hectares) hectare) per hectare Cases hectare per 1000 people size 1 . . . . . . . . . . . . . . . . . . . . . . . 68 . . . . . . . . . . Covid-19 patients in each neighborhood per 1000 peo- neighborhoods, it is less than 3, and in 7 neighbor- ple and the number of patients per hectare of the built hoods it is more than 3.5, mainly due to the presence area as well as the further data required for completion of student and military dormitories as well as prisons in of the analysis (Table 1). these urban districts. 6. Population density and the Covid-19 4. Data analysis pandemic Due to the type of data and the nature of the problem, using two spatial analysis methods, The relationship between population distribution ArcGIS 10.8 and IBM SPSS Statics 25 software patterns and density in the built-up areas on the packages were applied. First, the obtained quanti- one hand and the rate of the spread of the the ties were plotted on the map using ArcGIS software Covid-19 pandemic on the other was studied and then the correlation coefficient between the according to spatial and Pearson correlation coeffi - indices were calculated, using the Pearson correla- cient analysis. The spatial analysis results show that tion model in the SPSS software. there is not much correlation between the inci- dence rate concerning the population and the inci- dence rate per hectare of the built-up area of the 5. Population distribution pattern in the neighborhoods. Regarding the incidence rate per built-up area hectare, this rate is less than 1 in 6 neighborhoods and in 21 neighborhoods it is greater than 4 The spatial structure of the city influences the (Figure 4). pattern of population distribution in Sanandaj. The Pearson correlation analysis results indicate The city has a relatively monocentric spatial struc- a relationship of the Covid-19 incidence rate per hec- ture with a primary central core, bordered by tare with three variables: family size, population den- northern and southern parts. The southern part sity per hectare, and dwellings per hectare. This includes mainly modern neighborhoods and sub- relationship is positive for the variables of population urbs that have developed over the past three dec- density and dwellings per hectare. According to the ades. Several settlements have furthermore rapidly values obtained, the association is significant at the developed around the city during the last decade; level of 99% and the intensity of the relationship is three have been annexed to the city limits and are relatively high. Furthermore, the relationship between considered as separate urban areas of Sanandaj. As the Covid-19 incidence rate per hectare and the vari- mentioned, these separate urban areas were able of family size is significant at the level of 95% and excluded from our study due to the explained the direction of the relationship is inverse. This means reasons. As shown in Figure 2, the population that with the increase in the family size, not only has density is higher in the northern parts of the city. the incidence of this disease not increased per unit Only two neighborhoods in the southern part of area, but it has decreased (Table 2). the city have a density of over 200 people per The results concerning the relationship between the hectare due to the student dormitories in one Covid-19 incidence rate per 1000 people with the three neighborhood and high-rise apartments in the variables of family size, population density per hectare, other neighborhood. and the number of dwellings per hectare, are different. In addition to the population density per hectare, According to the findings, the number of the Covid-19 two further indicators, namely dwellings per hectare incidence rate per 1000 people has no significant rela- and family size, can also reveal a population distribu- tion to family size, but at the level of 99% confidence, tion pattern. Figure 3 shows the frequencies of these this variable related to the two variables of population two indicators on the map. The density of dwellings density and the number of dwellings per hectare. It per hectare is similar to the population distribution implies that in the neighborhoods where the popula- pattern, but the family size in the neighborhoods is tion density and the number of homes per hectare are relatively uniform. The family size in 59 of the 68 higher, the disease incidence has not necessarily neighborhoods is between 3 and 3.5. In two increased but even decreased (Table 3). JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 5 Figure 2. Population density per hectare. Figure 3. Family Size and dwellings per hectare. 6 K. IRANDOOST ET AL. Figure 4. COVID-19 incidence rate per 1000 people and per hectare. Table 2. Pearson correlation coefficient between the Covid-19 incidence rate per hectare with the variables of family size, population density per hectare, and the number of dwellings per hectare. Family size People per hectare Dwellings per hectare COVID-19 cases per hectare Correlation −.248* .652** .665** Sig. (2-tailed) .041 .000 .000 N (Neighbourhoods) 68 68 68 *.Correlation is significant at the 0.05 level (2-tailed). **.Correlation is significant at the 0.01 level (2-tailed). 6.1. Discussion and findings form and structure of cities, and many have sought to provide a model for post-Corona urban life. New Epidemic disease outbreaks generally increase paradigms such as new urbanism and the smart through people’s close interactions and through city encourage congestion, while many believe increased population density and connectivity. that overcrowding is a factor that puts cities at Cities are centers of high population density. The risk in the face of pandemics such as the Covid- latter generally also depends on the density of 19 crisis. Many cities in developing countries are buildings and the density of housing units. The struggling with rapid population growth and World Health Organization and the results of population density. many studies emphasize that, as the population Sanandaj is one of the cities in Iran that has density decreases and social distance increases, experienced rapid population growth over the past the pandemic of the Covid-19 will also decrease. few decades and has many economic, physical, and However, cities themselves are inherently centers social problems. On the one hand, natural factors, of population concentration and consequently, including topographical features, have limited the maintaining social distancing will hardly be possi- city’s physical development, but on the other hand, ble in the long run. The Covid-19 pandemic has poverty and economic problems have led to the sparked discussions about rethinking the ideal development of slums in many parts of the city, Table 3. Pearson correlation coefficient between the Covid-19 incidence rate per 1000 people with the variables of family size, population density per hectare, and the number of dwellings per hectare. Family size People per hectare Dwellings per hectare COVID-19 cases per 1000 people Correlation −.171 −.365** −.341** Sig. (2-tailed) .163 .002 .004 N (Neighbourhoods) 68 68 68 **.Correlation is significant at the 0.01 level (2-tailed). JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 7 especially in the north and northeast. These areas Moreover, we only have tried to approach the issue have the highest population and building density. from an urban planning perspective and have looked The analysis demonstrated that in Sanandaj, as the at the challenge through a different lens. This research population density per hectare and the density of was done using official Iranian data. As was mentioned, housing units per hectare increase, the incidence of the built-up area of the neighborhoods was used in the Covid-19 per hectare also increases. On the other analysis to achieve the most accurate results possible. hand, the study of the relation between the inci- Furthermore, we only used the two variables of popu- dence per 1000 people and the two variables of lation density per hectare and housing unit density per population density and housing unit density shows hectare in the analysis. The analysis did not include that a large population and the number of housing physical, economic, and social differences between units do not increase the incidence. However, with neighborhoods. Therefore, other studies should be increasing density of these two indicators per unit performed to complete this research by considering area, the incidence rate does rise. This indicates that these factors as well as indicators such as social inter- the neighborhoods’ building density and physical actions, behavioral patterns, adherence to protocols, density contributed to the epidemic of the Covid- economic status, and level of employment. 19 virus. Another noteworthy point is that the neighbor- Disclosure statement hoods with the highest incidence rates per hectare are primarily residential and are far from the city’s No potential conflict of interest was reported by the central business district. 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Journal of Asian Architecture and Building Engineering – Taylor & Francis
Published: Mar 4, 2023
Keywords: Population density; covid-19 pandemic; infection rate; sanandaj city
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