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Vol. 3, No. 1, 2021, pp. 28-35 ISSN 2656-6052 (online) | 2656-1107 (print) 
 
 

      10.26555/eshr.v3i1.3629 28  
  

Research Article 
 
Spatial Analysis of Tuberculosis, Population and Housing 
Density in Yogyakarta 
 
Muthia Ardiyanti 1,*, Sulistyawati 1, Yudha Puratmaja 2 

1 Faculty of Public Health, Universitas Ahmad Dahlan, Yogyakarta, Indonesia 
2 Master of Public Health, Faculty of Public Health, Khon Kaen University, Khon Kaen, 

Thailand 
 
* Correspondence: muthia1600029089@webmail.uad.ac.id. Phone: +6282328817403 
 
Received 05 February 2021; Accepted 04 March 2021; Published 05 March 2021 
 

ABSTRACT 

Background: Tuberculosis (TB) is an infectious disease that becomes a health problem 
globally, including in Indonesia. Yogyakarta City is a district that struggle with TB; from 2017-
2018, there was an increase of TB case in this city. There was limited evidence concerning 
TB and its possible risk factors among TB case 2017-2018, mainly using GIS in Yogyakarta. 
Method: This study used an ecological study design to determine the correlation between 
population and housing density with TB incidence in Yogyakarta City in 2017-2018. 
Secondary data was obtained from the Yogyakarta City Health Profile 2018-2019.  
Spearman rank correlation test and spatial analysis using Quantum GIS software were 
employed to analyse the data. 
Results: There was a relationship between TB and population density variables (p-value = 
0.034; R = -0.568) and housing density (p-value = 0.012; R = -0.625) in Yogyakarta, 2017-
2018. 
Conclusion: This study indicates that the density of housings and population affect the 
prevalence of Tuberculosis. 

Keywords: Population density; Housing density; Spatial Analysis; Tuberculosis 

INTRODUCTION 

Tuberculosis (TB) is one of the top 10 infectious diseases - a significant health problem 
globally. Most TB bacteria attack the lungs but can also attack other organs. The transmission 
occurred through the air when a person with pulmonary TB coughs, sneezes, talks, or spits 
up. Most of the TB cases occurred in Southeast Asia (44%), Africa (24%), and the Western 
Pacific (18%). Globally, Indonesia is the third rank of TB case after India and China (1).  

Yogyakarta Special Region (DIY) is one of the provinces that still have TB problems. In 2017, 
DIY reported the total number of TB cases as many as 2,785 cases and increased in 2018; 
there were 3,237 TB cases in 2018 (2). Yogyakarta City is part of the DIY province, which still 
struggles to combat TB. In 2017, the number of TB cases in Yogyakarta City was 551 cases, 

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of which 253 were confirmed as new cases (3). In 2018, the number of TB cases in Yogyakarta 
City had increased. According to the Yogyakarta City Health Profile in 2019, the highest 
number of TB cases was in the Umbulharjo I Health Center with 74 cases of all TB cases, 
while those confirmed with positive smear TB were 41 cases (4).  

With an area of 32.50 km2, the City of Yogyakarta's population density in 2018 was 12,703 
people / km2. The densest area is in the Gondomanan sub-district, 37,866 people / km2, and 
the lowest population density is in the Kotagede sub-district with 6,038 people / km2. The 
housing density in Yogyakarta City in 2018 was 3,113 units/km2. The densest housing is in 
the Gedongtengen sub-district with 5,757 units/km², and the lowest housing density is in the 
Umbulharjo sub-district with 1,868 units / km² (4). 

Population density affects TB incidence because it increases the possibility of contact with TB 
sufferers (5). Apart from population density, housing density can also affect the spread of TB 
disease because it is related to poor sanitation, slum places, lots of garbage if it not well 
maintained. Home is a place to grow and develop both physically, spiritually, and socially. The 
situation of the housing environment affects the health environment (6). According to Nafsi, 
who overviewed the spatial analysis of TB incidence based on housing density, all pulmonary 
TB cases were found in high housing density areas (7). 

Every region has a different risk factor for TB disease. A spatial analysis approach is needed 
to help analyse risk factors, especially those related to geographic conditions, to develop the 
control measures (8). In the health sector, GIS can produce a spatial picture of health events, 
analyse the relationship between locations and the environment and disease incidence. Based 
on the existing environmental conditions, GIS can also stratify risk factors for disease (9). This 
research aimed to perform spatial analysis with the Geographic Information System (GIS) and 
risk factor analysis of population and housing density versus Tuberculosis (TB) in Yogyakarta 
City in 2017-2018.  

METHOD 

We used a quantitative study using an ecological study design with a Geographic Information 
System (GIS) approach. This research was conducted in Yogyakarta -  July 2020 by including 
independent variables: population and housing density; the dependent variable is TB 
incidence. This study's population and the sample had an aggregate number of TB cases in 
Yogyakarta City in 2017-2018 per sub-district. Data were collected from Yogyakarta City 
Health Profile 2018-2019: TB cases, population density, housing density.  

The analysis was performed in three steps: (1) univariate analysis, to determine the statistical 
distribution of each variable, including the independent variables (population density and 
housing density) and the dependent variable (TB incidence); (2) bivariate analysis, used to 
see the correlation of the independent variables (population density, housing density) with the 
dependent variable (TB incidence) by using an alternative test of Pearson's correlation, 
namely the Spearman rank test (3) spatial analysis using Quantum GIS  to produce map 
depicting the spatial relationship of the independent variables (population density and housing 
density) with the dependent variable (TB incidence). 

 

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RESULTS 

There were 551 and 564 TB cases in Yogyakarta City during 2017 and 2018, respectively, 
that spread into 14 sub-districts. In 2017, the highest TB incidence was in the Umbulharjo sub-
district with 89 cases and the lowest in Pakualaman sub-district with 4 cases. In 2018, the 
highest TB incidence occurred in the Umbulharjo sub-district with 110 cases and the lowest in 
Pakualaman sub-district with 11 cases (Table 1).  

Table 1. Number of Tuberculosis Incidents in Yogyakarta City in 2017-2018 
 

Sub-district 

Number of Cases 
(Person) 

Population Density  
(people / km2) 

Housing Density  
(units / km²) 

2017 2018 2017 2018 2017 2018 

n % n % n % n % n % n % 
Danurejan 35 6 31 5 19255 9 33703 14 3661 8 3661 8 

Gondokusuman 55 10 59 10 10598 5 6785 3 1917 4 1917 4 

Gondomanan 22 4 30 5 13437 6 37866 16 2809 6 2811 6 

Gedongtengen 32 6 34 6 21289 10 22340 9 5757 13 5757 13 

Jetis 47 9 43 8 16068 8 11692 5 3270 8 3270 8 

Kotagede 27 5 34 6 10978 5 6036 3 2364 5 2364 5 

Kraton 34 6 40 7 15749 7 19940 8 2604 6 2604 6 

Stimulating 41 7 38 7 13897 7 15284 6 2583 6 2583 6 

Mantrijeron 45 8 31 5 13541 6 8408 4 3129 7 3129 7 

Look 34 6 25 4 22704 11 18295 8 4173 10 4173 10 

Nails 4 1 11 2 17121 8 17130 7 4806 11 4806 11 

Tegalrejo 45 8 43 8 12709 6 11022 5 2155 5 2155 5 

Umbulharjo 89 16 110 20 8542 4 8565 4 1868 4 1868 4 

Wirobrajan 41 7 35 6 15861 7 19297 8 2489 6 2489 6 
 

In 2017, the highest population density was in the Ngampilan sub-district with 22,704 people 
/ km², and the lowest was in Umbulharjo sub-district with 8,542 people / km². In 2018, the 
highest population density was in the Gondomanan sub-district of 37,866 people / km², and 
the lowest was in the Kotagede sub-district of 6,036 people / km² (Table 1). Gedontengen sub-
district was ranked as the highest housing density of 5,757 units / km², and the lowest was in 
the Gondokusuman sub-district of 1,917 units / km² (Table 1). 

Bivariate analysis of population density and TB incidence in Yogyakarta City in 2017-2018, 
obtained r = - 0.568 and p-value = 0.034. There was a significant relationship between 
population density and TB with a p-value ˂ 0.05. The statistical test showed that the 
relationship between population density and TB incidence has a strong relationship (r = 0.51-
0.75) and has a negative direction where the lower the population density, the higher the TB 
incidence. There was a significant relationship between housing density and TB incidence in 
Yogyakarta City in 2017-2018 (p ˂ 0.05) r = - 0.652. The statistical tests showed that the 
relationship between housing density and TB incidence had a strong relationship (r = 0.51-
0.75) and had a negative pattern where the lower the housing density, the higher the TB 
incidence (Table 2). 

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Table 2. Bivariate analysis between TB risk factors for TB incidence 

Variable R p-value 
Population density - 0.568 0.034 
Housing density  - 0.652               0.012 

 

Figure 1 shows the distribution of TB incidence tends to the distribution of population density 
inversely. The high incidence of TB tends in areas with low to moderate population densities. 
Areas with low population density have a higher incidence of TB than regions with high 
population densities; for example, the Umbulharjo sub-district has a low population density but 
has a high TB incidence. 

 

Figure 1. Map of Population Density vs the number of TB Incidence in Yogyakarta City 
2017-2018 per sub-district 

 

Based on Figure 2, the distribution of TB incidence tends not to follow housing density 
distribution. The high incidence of TB tends to be in areas with low to moderate housing 

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densities. Areas with low housing densities have a higher incidence of TB than regions with 
high housing densities, such as Umbulharjo sub-district has a low housing density but has a 
high incidence of TB. 

  

 

Figure 2. Housing Density Map vs the number of TB Incidence in Yogyakarta City 
2017-2018 per sub-district 

DISCUSSION 

There was a statistically significant relationship between population density and TB incidence 
in Yogyakarta City in 2017-2018 (p-value ˂ 0.05). The spatial analysis shows that the 

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distribution of TB incidence in Yogyakarta City tends to distribute population density inversely. 
Wulandari, in her research, shows different results from this study - there is a significant 
relationship between population density and new cases of pulmonary TB AFB (+) in South 
Jakarta in 2006-2010 with p = 0.000, where an increase in population density is followed by 
the rise in TB cases (5). Our result is different from Achmad's research, which found no 
correlation between population density and the number of smear-positive pulmonary TB cases 
in South Jakarta in 2007-2009 with a p-value = 0.116 (10). The same finding found in other 
parts of Indonesia, which stated that there is no significant relationship between population 
density and the proportion of positive smear pulmonary TB in Kota Pariaman (p-value = 0.551), 
Bukittinggi (p-value = 0.140) and Dumai (p-value = 0.993) (11). 

Some factors trigger a disease occurrence (12). Following the research conducted by Hastuti, 
Ahmad and Ibrahim in Kendari City, there was no significant difference between high 
population density and low population density and TB positive smear in Kendari City (13). 

We found a relationship between population density and TB incidence, where the lower the 
population density, the higher the TB incidence. Spatially, the population density is not related 
to the TB incidence because the TB incidence does not follow the population density 
distribution. This finding can be seen in the Umbulharjo sub-district with the lowest population 
density among 14 sub-districts, namely 8,565 people / km², but has the highest TB incidents. 
Accordingly, the spatial pattern of population density does not affect the incidence of TB. 
Therefore, predicted other factors influence the increase in the number of TB cases in the 
Umbulharjo sub-district, such as nutritional status, socioeconomic conditions, housing’s 
occupancy, the housing floor's condition, and ventilation, lighting, humidity, and altitude. 
According to Utami, ventilation is a risk factor for TB in the working area of the Umbulharjo I 
health centre; respondents who live in housing with poor ventilation are estimated to be 7.563 
times more at risk of TB incidence compared to those who live in a large housing with proper 
ventilation (14). 

Another thing that proves that population density is not the only risk factor contributing to TB 
incidence is that Gedongtengen and Danurejan sub-districts have relatively high population 
densities. Still, an increase does not follow this in the number of TB incidents. According to 
Dotulong, Sapulete and Kandou (15), age and sex are also risk factors for TB; a productive 
age and male are more susceptible to pulmonary TB. However, this study is not following the 
existing theory, where a densely populated area will undoubtedly cause disease transmission 
with a more complex chain of spread, especially in airborne diseases such as Tuberculosis.  

Regarding the housing density, there is a statistically significant relationship between housing 
density and TB incidence in Yogyakarta City in 2017-2018 (p-value ˂ 0.05). Spatial analysis 
shows that the distribution of TB incidence in Yogyakarta City tends the housing density 
distribution inversely. Umbulharjo sub-district has a low housing density but with high TB 
cases. The study results in Wonosobo, Central Java, showed the same results as this study: 
there was a significant relationship between housing density and TB incidence. Pratiwi, 
Pramono and Junaedi mentioned that someone who lives in an area with a high density of 
housings has 5 times higher risk of being exposed to TB than those who live in a low density 
(16). This shows that the density of housings affects TB incidence because dense 
environments may also have poor sanitation, slum areas, lots of garbage, and poorly 
maintained grounds, especially in developing countries (6).  

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We found that there was a statistically significant relationship between housing density and 
TB incidence but in a negative direction - the lower the housing density, the higher the TB 
incidence, and spatially the TB incidence did not follow the distribution of housing density - the 
lowest housing density had the highest number of cases. Umbulharjo sub-district has the 
lowest housing density (1,868 units / km²) but has a high TB incidence. This shows that the 
density of housings does not have a direct effect on the incidence of TB.  

The needs for housing in the city of Yogyakarta is increasing in recent years. In Yogyakarta 
City, it was reported there were 3,304 housing is not habitable, unhealthy for habitation, spread 
unevenly in all sub-districts and villages. Housing not feasible for a living is considered slum 
areas caused by high-density settlements' environmental factors and the buildings' physical 
condition (17). The condition of the home environment has a close relationship with disease 
transmission, including TB. One of the housing conditions that can allow TB transmission is 
the density of occupancy. According to Utami, the housing density is a risk factor for TB 
incidence in the Umbulharjo Community Health Center's working area.  Respondents who live 
in high occupancy homes that do not meet the as healthy housing are estimated to be 4.375 
times more likely to be at risk of TB incidence than those who live in homes with low occupancy 
that meet healthy housing (14). 

Thus, it is necessary to increase environmental-based TB control programs and healthy living 
behaviours. The role of sanitarians or environmental health workers is needed to prevent and 
eradicate TB disease by creating a healthy environment and healthy living habits, especially 
improvements in the sufferer and people living in areas with high housing densities and an 
increased number of TB cases. Increase education about healthy homes. Besides, TB control 
can be done by immunisation to boost immunity. Immunisation coverage is essential because 
it can prevent disease. 

CONCLUSION 

The spatial analysis results show no relationship between population density and housing 
density on the incidence of TB in Yogyakarta City in 2017-2018. In contrast, the statistical 
analysis results show a significant relationship between population density and housing 
density on the incidence of TB with p <0.05 during 2017-2018 in Yogyakarta City.  

Authors' contribution 

MA was responsible for the design, data collection, analysis, and drafting of the manuscript. 
SS and YP were responsible for the design and analysis, and review of the manuscript. 

Funding 

This research has not received external funding. 

Conflict of interest 

There is no conflict of interest in this research.  

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