268 
 

 

 

Building Density Level of Urban Slum Area in Jakarta 
 

Tenty Melvianti Legarias1 *, Renny Nurhasana1, Edy Irwansyah2  
1 Urban Studies Program, School of Strategic and Global Studies, Universitas Indonesia, 

 Jakarta, 10430, Indonesia 
2 School of Computer Sciences, Bina Nusantara University, Jakarta, 11480, Indonesia 

*Corresponding Author: melvi.legarias@gmail.com 
 

 

Received 10 July  2020/ Revised  6  August 2020 / Accepted 11 August 2020/ Available Online 15 August 2020 

 

 

Abstract 

Currently, the number of urban residents is increasing and some of the urban population live in 

slum areas. Therefore, identifying the characteristics of slum areas has become crucial. This 

study aimed to identify more specific slum locations in Jakarta through the pattern of building 

densities analysis between case studies of neighborhoods association (RT) in 15 hamlets (RW) 

that classified as heavy slums category. This study also attempted to determine the relation 

between building density levels in the slum area and Jakarta spatial detail planning. This study 

engaged the Cluster and Outlier Analysis (Anselin Local Moran's I) method. This study also 

observed socio-economic factors of citizen census data based the Dasawisma Census of Family 

Welfare Empowerment in 2019. The result shows that slum locations that had direct neighbors 

towards areas which was designated at spatial detail planning as industrial zones and ware 

housing areas as well as office, trade and service zones, obtained a higher level of building 

density compared to slum locations that secured neighbors to areas that were designated as 

housing zones. High economic opportunities provided attraction and affected the growth of slum 

locations. The results also reveal that slum areas were not a concentrated population with low 

income and/or low education. Applying cluster and outlier modeling of building density levels of 

urban slums in Jakarta based on RT cluster level could reveal more specific slum locations and 

could identify factors that influence the differences of building density levels. 

 

Keywords: Slums; Spatial Detail Planning; Cluster and Outlier Analysis; Geographic 

Information System 

 

 

1. Introduction   

By 2030, the cities population are projected to be increased up to 60 percent with 1 of 4 

city residents living in slums (United Nations, 2019). The phenomenon of rapid urbanization 

affects escalation of slums, increasing the number of infrastructure and inadequate services (such 

as sewage treatment facilities, availability of clean water and sanitation, roads, and 

transportation), as well as escalation of air pollution and also influences cities expansion that are 

unplanned. Some people settled and struggled in slums, sometimes those were only a few meters 

away from the upper community and commercial areas. 

GEOSFERA INDONESIA 
p-ISSN 2598-9723, e-ISSN 2614-8528  Vol.5 No. 2 (2020), 268-287, August, 2020 
https://jurnal.unej.ac.id/index.php/GEOSI 

DOI : 10.19184/geosi.v5i2.18547 
Accredited by the Ministry of Research , Technology , and Higher Education of the Republic of Indonesia, No. 30/E/KPT/2019. 

 

 

 

 

https://jurnal.unej.ac.id/index.php/GEOSI
https://drive.google.com/file/d/1rSnVAs6cuHOwHl5BJ87CL2L6K5DQz7s6/view


 

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There are several definitions and conditions to define slums. The most convenient of 

'slum' term is a dense urban area that does not meet the standard housing conditions and indicates 

poverty area (UN Habitat, 2004). This explanation contributes to the main characteristics of 

slums which are performing high density and low housing standards (structure and services), and 

poverty. In Indonesia, slum is defined as inappropriate settlement to live, due to erratically 

building, the presence of high density, low-quality buildings, facilities and amenities shortages, 

and low affordability of infrastructure(Ministry of Public Works and Indonesian Public Housing-

PUPR, 2016). There are several methods could be utilized to identify slums in a city or region 

and to determine whether the certain area could affect slums escalation. Each slum performs 

different conditions, slums may develop or new slums may emerge, both could be enhanced 

through high rural-urban migration rates or location circum stance that usually being indicated as 

inappropriate for housing (river banks, steep slopes, landfills, uninhabitated land, railroad lines, 

adjacency towards industrial areas or markets and all over river banks) (Roy et al., 2014). 

In Lagos, Nigeria, a study of slums characteristics was performed through a census using 

respondents from 120 slums as samples, and the results showed that 7.7% of respondents 

received incomes above US $ 352.94, 13.8% were graduated of formal education and 67% were 

non-native, also, 5.6% of buildings were built with wood, while 91.5% of buildings were more 

10 years (Adedayo & Malik, 2015). Research relating slum was also performed engaging 

questionnaires towards respondents, revealed that the major factors affecting slums were 

affordability, cultural, kinship and markets adjacency, and then followed by other factors 

(Badmos et al., 2020). Friesen et al. (2018) also utilized population data of slum area to indicate 

that slum development was strongly related to demographic development of a country. 

Some studies utilizing building maps and spatial methods to identify urban areas based on 

building density levels. Buildings data could be used to describe community residence or to 

describe a business district within down town compared to population data (De Bellefon et al., 

2020). High density is also one of the physical characteristics that is frequently used to describe 

slums. Furthermore, in term of physical characteristics, there are also the small roof size and 

irregular patterns (Kuffer et al., 2016). Spatial statistical method also measures spatial 

concentration including high-density buildings. Arribas-Bel et al. (2019) also engaged building 

density measurements based on machine learning algorithms, proposed to obtain the distribution 

of building groups with significant values that could reach the minimum building density limit. 

De Bellefon et al. (2020) compared building density with engaging building data in France. 

Hence, the definition of urban area is characterized by high building density. 

Few researchers combined some methods to draw slum characteristics. The Object-Based 

Slum Detections research in Jakarta by Pratomo et al. (2017) conducted Object-Based Image 



 

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Analysis (OBIA) with distance variable upon the river or railroad, building size, building 

density, roofing materials, and illegal land expansion. All of these variables were taken from 

expert agreements. Similar research employed remote sensing to identify slum areas was 

conducted in Bandung (Leonita et al., 2018) performed a method which combining remote 

sensing imagery with machine learning by gathering all the input from the experts based on slum 

indicators. 

Wijaya et al. (2019) conducted a field study of a slum located in Kelurahan Manggarai, 

South Jakarta, to calculate the usage intensity of communal space occupied by slum residents. 

The results showed the communal space usage not only in public locations that already remained 

in spatial planning, but also communal space utilization also occupied a huge of space for roads. 

In the same location, Wati (2018) revealed that due to improper housing space conditions, the 

private space engagement spread out and utilized public space. 

Slum issues and spatial detail planning are closely interrelated. This is comformable to 

the New Urban Agenda which is committed to promote the planning and replenishment of city 

expansion, includes a commitment to recover slums. Slums in Jakarta were classified using RW 

(Rukun Warga/hamlet) administrative boundaries and there was still limited information 

regarding slums in the smaller areas or certain slum locations and limited information to 

determine the effect of spatial detail planning factors on these locations. 

The previous studies concerned in identifying the characteristics of slums based on the 

population census, field study and remote sensing methods. There have been limited studies 

focused on slum areas interrelated to spatial detail planning in Jakarta using building density. 

Therefore, this study aimed to identify more specific slum locations in Jakarta through the 

pattern of building density analysis between case studies neighborhoods association (RT) in 15 

hamlets (RW) that classified as heavy slums category. This study committed to investigate the 

affiliation between building density level in slum area and Jakarta spatial detail planning. The 

current research also observed socio-economic factors based on population census data reported 

by the Dasawisma Census of Family Welfare Empowerment in 2019. 

 

2. Methods 

2.1 Study Area 

The study area employed in this study was Jakarta Municipality. Jakarta Municipality 

was the capital city which was divided into five regions (Kota Administrasi) composed of 42 

sub-district (Kecamatan) that consisted of 262 urban villages (Kelurahan) and divided into 2,718 

citizen associations (RW). Each RW was divided into 10-20 neighborhoods (RT). Jakarta is the 

capital and largest city of Indonesia that covers 662,33 square kilometers. 



 

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Jakarta Provincial Government had classified 445 RW as slums according to DKI Jakarta 

Provincial Governor Regulation Number 90/2018 related to Improvement of Quality of 

Settlements in the Context of Integrated Housing Area Regulations, which are categorized as 

heavy, medium, light and very light priority. However, 15 RW of 445 RW were categorized as 

heavy slum were selected for this study namely: West Jakarta with seven locations consisted of 

Jati Pulo urban village RW 006, RW 008, RW 009, Kapuk urban village RW 012, RW 016, 

Jembatan Besi urban village RW 003, Keagungan urban village RW 003, North Jakarta with 

four locations consisted of Kalibaru urban village RW 004, RW 007, Penjaringan urban village 

RW 008, RW 017, East Jakarta with two locations consisted of Kampung Melayu urban village 

RW 007, RW 008, South Jakarta with one location namely Manggarai urban village RW 004 

and Central Jakarta with one location namely Tanah Tinggi urban village RW 012.  

These 15 locations were considered as heavy slums category were taken as samples, since 

those were assumed to be locations with the highest slum level compared to others. It was 

necessary to analyze these fifteen (15) locations as priority locations to improve slums and the 

analysis results could be utilized as references for other categories. The distribution of fifteen 

RW locations is presented in Figure 1 with heavy slum legend. 

 

Figure 1. Jakarta Slum Maps 



 

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2.2 Data Resources 

Spatial data sources were building map, administrative boundary map, and the spatial 

detail planning map provided by Human Settlement, Spatial Planning and Land Authority 

Department of Jakarta Provincial Government, and citizen census data reported by the 

Dasawisma Census of Family Welfare Empowerment in 2019. Jakarta buildings map dataset in 

GIS format which mapped in National Geospatial Standard precision for the year 2019 was taken 

from aerial photography in 2014. The building map was engaged to generate building density 

levels by overlaying it along with administrative boundary map of RW. All maps were officially 

published online at https://jakartasatu.jakarta.go.id. 

Jakarta administrative boundary maps composed of areas boundaries (Kota Administrasi), 

sub-district (Kecamatan), urban villages (Kelurahan), hamlets (RW), and neighborhoods 

association (RT). This research employed RW and RT administrative boundary maps. A smaller 

scale of analysis unit of RW was required to determine the focus of slum locations within RW 

area, thus this research utilized RT map scale. The current study also utilized Jakarta Spatial 

Detail Planning Map, a map with detailed plans on district-level spatial planning which was 

equipped with zoning regulations that were translations for 2011-2030 Regional Spatial Plan 

with a map scale of 1: 5,000 (Jakarta Regional Regulation, 2014). Zoning is the regions division 

into zones based on original functions and characteristics or directed to the development of other 

functions. 

Other data resources were data sets reported by the Dasawisma Census of Family 

Welfare Empowerment in 2019. This data was generated from census results by Jakarta 

Provincial Family Welfare Empowerment conducted from January to May 2019. Collection data 

method used by Dasawisma was over direct interviews by Dasawisma Officers towards each 

family member then conducted input data online through both mobile applications and web 

applications. 

Data analysis employed dependent and independent variables. The dependent variable 

was building density level which was one of the physical indicators of slums (Ministry of Public 

Works and Indonesian Public Housing-PUPR, 2016). A high level of building density that 

located within metropolitan cities and large cities is > 250 units/Ha, this parameter is engaged for 

Jakarta. The parameter value was 5 if the building density was in the range of 76% - 100% of the 

indicator, the value 3 was if the building density was in the range of 51% - 75% of the indicator, 

and the value was 1 if the building density was in the range of 25% - 50 of the indicator, a value 

of 5 was categorized a high score, a value of 3 was categorized a medium score and a value of 1 

is categorized a low score. 



 

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The independent variables employed for this study were taken from two sources. The first 

source was acquired based geographical factors, namely the spatial detail planning map and the 

second source was socio-economic factor,which was according to Dasawisma data composed of 

education, level of family activity in the settlement, and income which was represented by the 

head of the family. 

2.3 Analysis Method 

The research methods used were descriptive analysis and spatial analysis to accomplish 

the research objectives that related to spatial/regional aspects. Overlay analysis is an operation of 

GIS to superimpose several layers of a dataset which representing different themes together to 

analyze or identify the relationships of each layer. Overlay analysis represents a composite map 

with a combination of various attributes and the geometry of a data set or entity. An overlay is an 

operation of comparing variables among several scopes. During analysis process with applying 

overlay method, a new map could be generated which wasa combination two or more layers of 

input maps results. This current study, overlay analysis was performed between building maps, 

administrative boundary maps, and the spatial detail planning map. 

In addition, Local Moran's I index (LMI) was applied to detect the clusters and outliers 

based on dependent variables building density level in neighborhoods (RT) administrative 

boundary maps. This spatial analysis namely Cluster and Outlier Analysis. The analysis was 

needed to be completed in order to identify the geographical distribution of slum  phenomenon 

and to determine the factors statistically whether result in dependence phenomenon on other 

regions or it was an independent phenomenon. In order to formulate statistical calculations into 

clusters and outliers, LMI calculated the Moran I value, z-score, p-value, co-type. Z-score is the 

standard deviation, and the p-value is the opportunity value of mistrust that spatial patterns are 

random. 

The Local Moran’s I statistic of spatial association (Anselin, 1995) is presented below: 

𝐼𝑖 =
𝑥𝑖−�̅�

𝑆𝑖
2

∑ 𝑊𝑖 ,𝑗 (𝑥𝑖 − �̅�)
𝑛
𝑗=1,𝑗≠𝑖  (1) 

Where 𝑥𝑖 is attribute for feature i, 𝑋 is the mean of the corresponding attribute, 𝑤𝑖,𝑗  is the spatial 

weight between feature i and j, and: 

𝑆𝑖
2 =

∑ (𝑥𝑖−�̅�)2𝑛𝑗=1,𝑗≠𝑖

𝑛−1
 (2) 

With n equating to the total numbers of features. 

The 𝑍𝐼𝑖  score for the statistics are computed as: 



 

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𝑍𝐼𝑖 =
𝐼𝑖 −𝐸[𝐼𝑖 ]

√𝑉[𝐼𝑖 ]
 (3) 

 

 

where: 

𝐸[𝐼𝑖 ] = −  
∑ 𝑤𝑖𝑗

𝑛
𝑗=1,𝑗≠𝑖

𝑛−1
 (4) 

𝑉[𝐼𝑖 ] = 𝐸[𝐼𝑖
2] − 𝐸[𝐼𝑖 ]

2 (5) 

The result I value reveals the positive or negative value of the feature. If the value of I 

was positive, it meant that the feature was part of a cluster with neighboring features that were 

equally high or low value, whereas if the value of I was negative then the feature was an outlier 

where the feature had a different value than its neighbors. In both conditions, the p-value must be 

of very small value, thus it could be considered a significant value. In the sense of this statistic, a 

maximum p-value of 0.05, could be categorized as significant was set at a 95% confidence level. 

Co-type on statistical results was an attribute that distinguished whether a high-value significant 

cluster (HH), or a low-value significant cluster (LL), and for the outlier category would 

distinguish whether a feature was surrounded by high-value features (LH) or whether a feature 

was surrounded by low value features (HL). The Co-type  statistical results of RT area conducted 

in this study is presented on Figure 2. 

 

 

 

 

Figure 2. Cluster type for each significant feature statistically 

Source: Author's Illustration. 

 



 

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3. Results and Discussion  

3.1 Distribution of 15 RW’s Building Density Level of Heavy Slums 

The initial stage was observing the distribution of the building density level of 15 RW’s 

in heavy slum which limited the scope of this study to identify the characteristics of each RW. 

Spatial overlay analysis was performed between the administrative boundary map of 15 RW and 

the building map to calculate the level of building density. The results can be seen on Table 1.  

 

Table 1. Distribution of 15 RW’s Building Density Level of Heavy Slums 

Location 
Number of 

Buildings 

RW Area 

(Hectare) 

Building 

Density Level 

(Units/ha) 

Score of 

Building 

Density 

Jati Pulo RW 009 849 5.79 146.62 Medium 

Jati Pulo RW 008 606 4.28 141.60 Medium 

Jati Pulo RW 006 600 4.40 136.44 Medium 

Kapuk RW 016 4645 42.17 110.13 Low 

Kalibaru RW 004 1273 11.63 109.42 Low 

Tanah Tinggi RW 012 204 2.09 97.79 Low 

Kampung Melayu RW 008 415 4.28 97.00 Low 

Kampung Melayu RW 007 321 3.32 96.76 Low 

Kalibaru RW 007 1523 17.38 87.64 Low 

Kapuk RW 012 3475 41.13 84.48 Low 

Jembatan Besi RW 003 677 10.14 66.76 Low 

Keagungan RW 003 185 4.11 45.10 Low 

Manggarai RW 004 305 11.21 27.20 Low 

Penjaringan RW 017 4607 181.97 25.32 Low 

Penjaringan RW 008 1904 103.25 18.44 Low 

 

The building density score of RW could not yet indicate that the RW location was a 

heavy slum area, the scores were in between low and medium, referring to the indicator of the 

building density level with the highest level was 146.62 units/ha. Considering these results, it 

was necessary to perform deeper observations upon smaller units of analysis to focus on certain 

areas within the RW boundaries which were considered slum based on building density and 

whether the neighborhood factors within the RW boundaries affect the location or not, which in 

the current study were applied using RT boundary maps which were fractions of RW boundary 

maps. The analysis was completed using Cluster and Outlier Analysis.  

 

3.2 Cluster and Outlier Analysis 

During this analysis phase, six samples were taken of 15 RW’s in heavy slum which 

gained a total of more than 1,000 buildings. The six locations were Kapuk RW 016 with 4,645 



 

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buildings, Penjaringan RW 017 with 4,607 buildings, Kapuk RW 012 with 3,475 buildings, 

Penjaringan RW 008 with 1,904 buildings, Kalibaru RW 007 with 1,523 buildings and Kalibaru 

RW 004 with total 1,273 buildings. These six locations were also the largest RW area out of 15 

RW’s within heavy slum areas. In addition, based on RT boundary maps analysis, the exact RW 

location was also presented with a spatial detail planning map that would be applied for further 

observation. 

3.2.1 Kapuk RW 016 

In Kapuk RW 16 area, 69.78% of the area was planned as a residential zone, 28.24% as a 

green open space or blue open space zone, and 1.98% as a social and government zone. The 

results of the analysis identified HH clusters from four RT areas with an average level of 

building density was 173.83 units/Ha, which was identified into medium category, and this 

cluster occupied a residential zone and then grew in the green open space zone.  LL cluster was 

also identified as a residential zone, but it had more old buildings. LH cluster was an area that 

covered the social and government zone and also green open space/blue open space zone near 

Kali Apuran Bawah river. Cluster and outlier analysis formulated map and spatial detail planning 

map for Kapuk RW 16 that could be observed on Figure 3. The Cluster and outlier analysis result 

is presented on Table 2. 

 
 

Figure 3. (a) Cluster and outlier analysis results map, (b) Spatial detail planning map 

 

 

 



 

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Table 2. Cluster and Outlier Analysis Results of Kapuk RW 016 
RT 

Number 
Area (Ha) 

Building 

Density Level 
Lmi Index Z-Score P-Value 

Co- 

Type 

Neigh- 

bors 
Spatial Lag 

001 0.78890752 131.827873 0.001508562 0.387145471 0.364 - 2 -0.004701344 

002 2.14651369 140.6932558 5.4235E-05 1.214925052 0.108 - 3 -0.018824558 

003 1.00498735 172.14147 0.033296671 1.825571949 0.052 - 4 0.029592984 

004 1.46244218 148.3819347 0.0123515 2.991055859 0.006 HH 2 0.045258697 

005 2.46784838 103.7340875 0.02905614 1.566515726 0.038 LL 2 -0.021869871 

006 2.14748691 114.5525026 0.030704491 2.340752554 0.004 LL 2 -0.032645604 

007 3.00384643 90.88347439 0.015047986 1.104568131 0.178 - 1 -0.008408863 

008 1.80339314 149.7177701 0.000924891 0.147887924 0.422 - 3 0.002882855 

009 0.9364856 134.5455812 -0.0014358 -0.455550344 0.322 - 2 0.006427158 

010 3.26639455 111.7440022 -0.007981297 -0.557024516 0.29 - 2 0.007664893 
011 1.46873319 169.5338549 0.007327559 0.688213256 0.228 - 2 0.007102963 

012 2.37001674 157.8047922 0.006688868 1.075411377 0.212 - 1 0.010949147 

013 2.08209823 142.6445668 0.000792132 0.831162656 0.208 - 3 0.011803195 

014 1.13174822 153.7444433 0.005909034 0.857355567 0.204 - 3 0.012700498 

015 1.3356222 136.266079 -0.004879763 -1.900817882 0.036 LH 4 0.0301812 

016 1.09323106 178.370343 0.043264785 2.293780083 0.016 HH 3 0.032081698 

017 1.00928455 135.7397179 -0.000103513 -0.020345199 0.428 - 1 0.000573283 

018 0.67348341 212.3289118 0.078562803 2.109136513 0.018 HH 5 0.030608937 

019 3.03095282 116.7949555 0.004076958 0.672323026 0.28 - 1 -0.004740077 

020 1.2031728 156.2535327 0.0244037 2.514767948 0.012 HH 4 0.043950037 

021 1.2412121 123.2666035 0.00242926 0.42138908 0.36 - 3 -0.003868443 
022 1.52970121 122.8998175 -0.003649544 -0.394841901 0.322 - 3 0.005692408 

023 0.93869414 173.6454862 -0.003607513 -0.100977414 0.492 - 3 -0.003059541 

024 2.377284 127.8770227 0.000913782 0.204525798 0.454 - 1 -0.001975342 

025 1.64110897 113.947339 0.013429807 1.086381045 0.134 - 2 -0.013956718 

 

3.2.2 Penjaringan RW 017 

In Penjaringan RW 17 area, 43.74% of the area was designed as an industrial and trade 

zone, 32.28% as a commercial zone, 18.89% as green open space or blue open space zone, only 

4.38% as a residential zone, and 0.71% as a social and government zone. HH cluster was an RT 

area with 100% in green open space or blue open space zone. This area had the densest 

population in Jakarta. HL cluster was also location extension occupied by residents who arrived 

into risky zone location (green open space or blue open space zone). The LH and LL locations 

were industrial zones which the edges were occupied as residences by dwellers. Cluster and 

outlier analysis results map and spatial detail planning map for Penjaringan RW 17 are presented 

on Figure 4. The map shows the impact of industrial zones and commercial zones towards slum 

areas. The Cluster and outlier analysis results are shown on Table 3. 

 



 

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Figure 4. (a) Cluster and outlier analysis results map, (b) Spatial detail planning map 

 

 

Table 3. Cluster and Outlier Analysis Results of Penjaringan RW 017 

RT Number Area (Ha) 
Building Density 

Level 
Lmi Index Z-Score P-Value 

Co- 

Type 

Neigh- 

bors 
Spatial Lag 

001 24.6804102 39.78864177 -0.002616779 -1.670152585 0.064 - 9 0.010441781 

002 4.824186568 33.99536848 -0.007855146 -2.531712022 0.01 LH 12 0.024575347 

003 2.085333118 32.60869902 -0.011999175 -2.346268865 0.028 LH 16 0.035695071 

004 2.049918123 9.756487235 -0.038284232 -3.577396182 0.004 LH 14 0.062921035 

005 0.139748327 329.1631544 0.15481202 3.271550776 0.004 HH 17 0.048418412 

006 2.118680853 34.45540177 -0.010985832 -2.55834858 0.012 LH 16 0.034969605 

007 2.618886236 45.82100525 0.000703032 0.492226613 0.308 - 23 -0.003933508 

008 3.253114738 49.18363258 -0.003363218 -1.995818093 0.04 LH 13 0.024254728 

009 2.755275956 29.03520419 -0.019206518 -3.225429746 0.002 LH 13 0.050712012 

010 2.875708737 30.25341157 -0.011723986 -2.070478514 0.034 LH 15 0.032189144 

011 0.32690227 281.429676 0.053681346 1.780152457 0.052 - 13 0.020421872 

012 0.220512094 276.628818 0.137826164 3.296947998 0.01 HH 17 0.053599316 

013 5.350504813 27.47404313 -0.013279142 -2.509172762 0.01 LH 18 0.033420202 

014 9.009023645 31.85694824 -0.005614526 -1.576171251 0.068 - 18 0.016268559 

015 8.638203131 11.11342238 -0.003542517 -0.845140368 0.204 - 11 0.005981147 

016 9.159645356 112.558943 0.007461217 1.86864637 0.044 HH 11 0.012103057 

017 2.433667166 111.3545861 -0.004304425 -0.634164665 0.278 - 24 -0.007148742 

018 1.288795694 119.4913986 -0.011370445 -1.033328876 0.128 - 22 -0.016264959 

019 5.772279279 93.5505671 -0.011904918 -1.805523987 0.02 HL 15 -0.030526746 

020 77.01502484 4.310847146 -2.69606E-05 -0.028552754 0.228 - 1 4.00404E-05 

021 5.827540297 39.46776655 0.007115157 1.770287707 0.02 LL 14 -0.027965099 

022 4.836226834 83.74297027 0.00018264 0.205037776 0.26 - 2 0.00066871 

023 0.415659292 14.43489923 0.020400431 1.416291322 0.036 LL 15 -0.036910218 

024 0.321622248 18.65542585 0.025864 1.876704738 0.012 LL 15 -0.051479327 

025 0.201663089 4.958765662 0.035010541 1.442291566 0.024 LL 15 -0.052598772 

026 0.233895101 38.47878805 0.015023239 1.734740977 0.014 LL 15 -0.056432968 

027 0.524453793 22.88094807 0.019272681 1.230817008 0.068 - 15 -0.042632365 

028 0.478334564 62.71760871 -0.00111759 -1.541090036 0.008 HL 16 -0.04945287 

030 0.561350404 1.781418509 0.037086594 1.825113834 0.01 LL 15 -0.052719193 

031 0.252174583 3.965506699 0.037865571 1.756201831 0.008 LL 16 -0.055894254 

032 0.580194553 8.617798926 0.027070382 1.549926773 0.028 LL 16 -0.043520348 

033 0.522154356 1.915142503 0.020374635 1.757167935 0.024 LL 18 -0.029028623 

034 0.608065523 1.644559611 0.009971823 1.031768244 0.124 - 20 -0.014142324 

 



 

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3.2.3 Kapuk RW 012 

In Kapuk RW 012 area, 58.81% of the area was planned as green open space or blue open 

space zone, 29.43%  as a residential zone, and 11.77% as a commercial zone. The average level 

of building density in the HH cluster was 179.39 units/ha, which categorized into medium 

category, and this cluster occupied a residential zone and then grew into the green open space 

zone. For LL clusters, based on the results during observing aerial photo maps and building 

information maps, these locations were worked for commercial zones and burial grounds. Cluster 

and outlier analysis results map and spatial detail planning map for Kapuk RW 12 can be seen on 

Figure 5. The Cluster and outlier analysis results could be seen on Table 4. 

  

Figure 5. (a) Cluster and outlier analysis results map, (b) Spatial detail planning map 

 

 

Table 4. Cluster and Outlier Analysis Results of Kapuk RW 012 
 

RT Number Area (Ha) 
Building Density 

Level 
Lmi Index Z-Score P-Value 

Co- 

Type 

Neigh- 

bors 
Spatial Lag 

001 3.050279876 63.27288244 0.013005688 1.383595715 0.066 - 1 -0.01011694 

002 1.602879138 68.62650926 0.020564999 1.855241209 0.036 LL 2 -0.017500441 

003 3.423131314 79.4594116 0.001705587 0.20036579 0.428 - 2 -0.001792192 

004 1.071603848 165.1729791 0.022594483 1.599814094 0.07 - 5 0.027681558 

005 1.01385894 215.02005 0.091155771 2.95665696 0.002 HH 5 0.049424034 

006 1.28523603 184.4019265 0.054167684 2.500547328 0.002 HH 3 0.044661859 

007 1.605481942 176.8939236 0.047054637 2.996412639 0.002 HH 4 0.044475847 

008 1.537244856 113.8400297 0.002126715 0.903418694 0.282 - 1 -0.00876811 

009 1.589758677 164.8048875 0.017564833 1.522328007 0.078 - 3 0.021721542 

010 1.328513767 188.932931 0.022516053 1.470343221 0.084 - 5 0.017236573 

011 3.339938804 49.10269607 -0.009542732 -0.506884261 0.318 - 2 0.006048102 

012 1.23188494 141.2469577 0.010045428 2.041435921 0.016 HH 4 0.031125746 

013 2.237282831 120.2351336 -0.001570291 -0.799388461 0.21 - 3 0.01419185 

014 1.932980531 68.80565937 -0.004851992 -0.415373767 0.394 - 1 0.004141982 

015 2.334479253 87.81401667 0.010465548 1.213058723 0.132 - 2 -0.013428443 

016 2.381764559 141.9115919 0.000943805 0.31907043 0.376 - 3 0.002805225 

017 3.58094228 91.87525916 0.003578863 0.797924913 0.35 - 1 -0.00514507 

018 1.573707442 156.9542047 0.012007035 1.57992754 0.082 - 3 0.018566322 

019 1.682986199 84.96801702 0.007066262 0.670539879 0.282 - 3 -0.008431714 

020 0.873744495 151.0739132 0.005892325 0.880595533 0.23 - 2 0.011214395 

021 0.841537554 147.3493362 -0.005376213 -0.792084643 0.242 - 2 -0.011984346 

022 1.528143157 101.4302877 0.006741018 1.591998624 0.052 - 1 -0.013522301 



 

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3.2.4 Penjaringan RW 008 

In Penjaringan RW 08 area, 46.96% of the area was planned as a commercial zone, 

28.99% as industrial and trade zone, only 11.16% as a residential zone, 8.06% as a social and 

government zone and 4.83%  as green open space or blue open space zone.  The average level of 

building density in the HH cluster was 241.88 units/ha, which categorized into high category. 

The area of HH cluster was an average of 81% in the residential zone, all RT’s outside this 

cluster were located within commercial zones and industrial zones. The LH cluster and LL 

cluster were put in locations that designated as industrial and trade zones, despite people settled 

in the area. Cluster and outlier analysis results map and spatial detail planning map for 

Penjaringan RW 008 is presented on Figure 6. The map reveals the impact of industrial zones 

and commercial zones towards slum areas. The Cluster and outlier analysis results can be seen 

on Table 5. 

  

 

 

Figure 6. (a) Cluster and outlier analysis results map, (b) Spatial detail planning map 

 
 

Table 5. Cluster and Outlier Analysis Results Penjaringan RW 008 
 

RT Number Area (Ha) 
Building Density 

Level 
Lmi Index Z-Score P-Value 

Co- 

Type 

Neigh- 

bors 
Spatial Lag 

001 0.495802821 244.0486317 0.035181464 1.977457897 0.026 HH 18 0.035410012 

002 0.452813873 147.9636645 0.00340027 1.85192352 0.03 HH 18 0.030411088 

003 1.487034681 70.61032358 -0.012887621 -1.426214046 0.082 - 19 0.021550046 

004 0.739700924 178.4505004 0.024978276 2.593193752 0.008 HH 18 0.063789024 

005 0.709476909 207.1949038 0.04808212 3.239845847 0.002 HH 19 0.073368285 

006 2.299074609 80.90211568 -0.012279053 -1.774914495 0.028 LH 20 0.024383117 

007 0.271360063 361.1437843 0.140781393 3.33463788 0.002 HH 21 0.068073355 

008 0.539745467 155.6289123 0.012273384 3.848469814 0.002 HH 21 0.067380165 

009 1.217307666 76.39810595 -0.007389487 -0.797000292 0.198 - 21 0.013560685 

010 0.309755994 229.2126752 0.060621196 3.725490858 0.002 HH 20 0.070703378 

011 0.354582012 304.5839785 0.024249285 1.997014978 0.024 HH 21 0.015654237 

012 1.854919545 79.78782714 0.003178869 0.481377685 0.356 - 21 -0.006186812 

013 1.367295966 62.1664966 -0.008427159 -0.380773793 0.382 - 21 0.012475107 

014 1.498585816 31.36290194 0.010542954 0.963948072 0.18 - 21 -0.011002978 

015 4.008211452 10.22900126 0.010665893 1.157341626 0.15 - 21 -0.009257553 

016 3.667775876 30.26357219 0.01191103 1.775755486 0.04 LL 14 -0.012301236 

017 0.713209099 210.3170026 0.043696009 3.241898475 0.002 HH 21 0.063882751 

018 0.437556513 196.5460404 0.0181026 2.454770154 0.008 HH 21 0.032463326 

019 1.109129785 102.7832825 -0.009126339 -1.847478581 0.032 LH 19 0.030140513 

020 0.374971588 306.689903 0.127608409 3.619860606 0.002 HH 21 0.081363146 

021 20.2275822 5.635868828 0.005646748 1.414963492 0.078 - 18 -0.004728168 

022 55.5765592 7.233265352 0.002338318 1.161014202 0.132 - 1 -0.001982265 

023 3.533284563 23.77391306 0.009257242 1.434316635 0.072 - 15 -0.009006569 



 

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3.2.5 Kalibaru RW 007 

In Kalibaru RW 007 area, 89.25% of the area was plotted as an industrial and trade zone 

and 10.75% as green open space or blue open space zone.  The average level of building density 

in the HH cluster was153.05 units/ha, which categorized into medium category. Within this RW, 

there was no area had spatial detail planning for residential zones. The HH cluster was100% in 

the industrial zone.Cluster and outlier analysis results map and spatial detail planning map for 

Kalibaru RW 007is presented on Figure 7. This map reveals the impact of industrial upon slum 

areas. The Cluster and outlier analysis results is shown on Table 6. 

 

 

 

 

 

Figure 7. (a) Cluster and outlier analysis results map, (b) Spatial detail planning map 

 

 

Table 6. Cluster and Outlier Analysis Results Kalibaru RW 007 

RT Number Area (Ha) 
Building Density 

Level 
Lmi Index Z-Score P-Value 

Co- 

Type 

Neigh- 

bors 
Spatial Lag 

001 1.737144174 99.58874027 0.00458499 0.509644235 0.284 - 1 -0.005055485 

002 1.055307687 124.1344127 -0.002695798 -0.291010523 0.404 - 3 0.005994252 

003 0.84579522 169.0716579 -0.005497731 -0.688305876 0.246 - 4 -0.014195057 

004 0.576624363 201.1708272 0.01444728 0.940784057 0.18 - 5 0.014664336 

005 1.211330157 152.7246712 0.002984804 1.931780157 0.022 HH 3 0.036044119 

006 0.549578745 151.0247634 0.003037311 2.086063318 0.006 HH 4 0.059384902 

007 0.585524499 239.1018655 0.014094885 0.76339319 0.254 - 5 0.00833166 

008 0.672974678 130.7627208 -0.00464367 -0.651168343 0.27 - 3 0.014232711 

009 0.46403552 185.3306404 0.01278412 1.010676774 0.152 - 4 0.018523708 

010 1.875343705 75.71945323 0.009775126 1.00673682 0.196 - 1 -0.007232595 

011 2.86229691 106.2084087 -0.01056085 -0.666811523 0.292 - 3 0.013476807 

012 0.559880211 155.3903823 0.005647758 1.991396856 0.03 HH 6 0.042636513 

013 0.934507631 168.0029084 -0.001303836 -0.067590477 0.474 - 2 -0.003548897 

014 0.761461633 215.3752638 0.017805913 0.983593321 0.18 - 3 0.014247248 

015 2.708752569 50.57678637 -0.00776916 -0.32882532 0.296 - 1 0.004269095 

 

 



 

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3.2.6 Kalibaru RW 004 

In Kalibaru RW 04 area, 72.25% of the area was expected to be green open space or blue 

open space zone and 27.75% as industrial and trade zone. The average level of building density 

in the HH cluster was 299.13 units/ha, that turned to be high category. Furthermore, the highest 

indicator was 250 units/ha. Within this RW, there was noarea had spatial detail planningfor 

residential zones. The HH cluster was 100% of the industrial zone. LL cluster was performed for 

fish auction, thusthe building was better organized and less dense compared to residential area, 

although this area was expected as a blue open space zone. Cluster and outlier analysis results 

map and spatial detail planning map for Kalibaru RW 004 is presented on Figure 8. This map 

illustrates the impact of industrial towards slum areas. The Cluster and outlier analysis 

resultscould be observed on Table 7. 

 

 
 

Figure 8. (a) Cluster and outlier analysis results map, (b) Spatial detail planning map 

 

Table 7. Cluster and Outlier Analysis Results of Kalibaru RW 004 

RT Number Area (Ha) 
Building Density 

Level 
Lmi Index Z-Score P-Value 

Co- 

Type 

Neigh- 

bors 
Spatial Lag 

001 0.436869348 235.7684293 0.024902118 2.239643163 0.01 HH 3 0.094305378 

002 0.456607682 310.9890737 0.116878753 2.089503004 0.022 HH 4 0.101034507 

003 0.350335746 353.946183 0.137817159 2.206303646 0.018 HH 6 0.082690636 

004 0.283954308 295.8222424 0.085300183 2.104603301 0.026 HH 6 0.087325124 

005 1.054127606 126.170683 -0.023863091 -1.126039608 0.166 - 3 0.02301808 

006 0.723276474 193.5636027 -0.005356458 -0.867204751 0.194 - 5 0.022615129 

007 0.839642029 152.445918 -0.02602809 -0.640549153 0.284 - 3 0.03590769 

008 0.351977421 230.1283976 -0.000381847 -0.014604488 0.486 - 4 -0.001937138 

009 0.388837959 329.1859679 -0.031896561 -0.417994391 0.388 - 4 -0.023234821 

010 1.170541928 175.9868613 -0.007655461 -0.677320645 0.26 - 4 0.017185376 

011 0.896925791 177.2721909 -0.01288667 -1.556943245 0.068 - 6 0.029954463 

012 2.353795432 124.9046523 0.003343953 0.548240084 0.464 - 1 -0.003179458 

013 0.986840146 179.3603561 0.009617887 1.843403771 0.01 LL 2 -0.023722966 

014 1.339964566 103.7340864 0.006273934 0.562931197 0.466 - 1 -0.004814987 

 

 

 



 

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3.3 Socio-economic Condition Observation 

A brief observation of the socio-economic factors was conducted and decided that 6 RW 

locations were selected based on the Dasawisma Census of Family Welfare Empowerment 2019 

results. The observation was comparing the categories of analysis clusters and outliers which are 

presented on Table 8. Considering the socio-economic conditions observation of six RW’s based 

on analysis results of clustering and outlier, education and income factors did not significantly 

affect the building density in the area. However, for family factors that were active in community 

environmental activities, two regions have HH were categorized with percentages much lower 

than other categories (LL, LH, and HL) namely Kapuk RW 016 and Kalibaru RW 004.  

 

Table 8. Socio-economic Conditions for 6 (Six) Slum Areas 

Location/ 

Cluster - Outlier 

% of Average 

Education is 

% of Average 

families active 

% of Average 

Revenue Category 

elementary school in the community 0-5 million/month 

Kapuk RW 016    

HH 0.243635 0.532892 0.967116 

LH 0.229358 0.908257 0.844037 

LL 0.182019 0.730123 0.938806 

Not Significant 0.225758 0.659241 0.940755 

Penjaringan RW 017    

HH 0.547804 0.976744 0.281654 

HL 0.134615 0.990385 0.298077 

LH 0.476021 0.919210 0.249729 

LL 0.121622 1.000000 0.216216 

Not Significant 0.222666  0.412545  0.959620  

Kapuk RW 012    

HH 0.166345 0.305419 0.982918 

LL - - - 

Not Significant 0.163368 0.391151 0.926129 

Penjaringan RW 008    

HH 0.181128 0.416882 0.938323 

LH 0.150148 0.476811 0.987539 

LL - - - 

Not Significant 0.135797 0.215532 1.000000 

Kalibaru RW 007    

HH 0.300550 0.331892 0.912108 

Not Significant 0.385699 0.234935 0.986591 

Kalibaru RW 004    

HH 0.314215 0.277953 0.966667 

LL 0.426230 0.721311 0.967213 

Not Significant 0.302216 0.428198 0.964598 

Source: Dasawisma Census 2019 Dataset, Jakarta Family Welfare Empowerment Team 

 

The current study discussed the analysis results of the 6 RW’s heavy slum category with 

two RWs located exactly alongside the coastal. The analysis was performed to reveal the fact 



 

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that slums did not cover the entire RW area by analyzing patterns of distribution of building 

densities to smaller areas, that were areas based on RT administrative boundaries.  

The results show that slum locations had direct neighbors to areas were designated as 

industrial zones and warehousing areas as well as office, trade and service zones, obtained a 

higher level of building density compared to slum locations that had neighbors to areas that were 

designated as housing zones according to Jakarta spatial detail planning. These results are in 

accordance with (Roy & Lees, 2020) which revealed attractive economic opportunities would 

likely attract residents of slums. In addition, (Takyi et al., 2020) stated where all slum areas in 

their analysis were located around the Central Business District. The result is also in line with 

(Badmos et al., 2020) that discovered a factor influenced the choice of residence for slum was 

markets adjacency. Zain et al., (2018) in his research indicated that engaging space for the 

development of the trade and services area had an impact on the growth of slums. 

The expansion of an area or space interm of developing trade and services is proportional 

to the growth of slums which could also escalate regional microeconomic growth, where slum 

dwellers operate small-scale businesses (Zain et al., 2018). Slums are growing near strategic 

areas including business centers, trade, markets, or industries (Badmos et al., 2020; Prianto & 

Amalia, 2019; Zain et al., 2018). Slums also contribute to the development of a nearby business 

district, since this sector could provide man power to support the operations of the business 

district (Ray, 2017), and most of the dwellers make living close the slum (Saika & Matsuyuki, 

2017). This condition could be observed clearly, where within slums there is a great human 

potential to support a region's sustainable development policy by involving local communities 

and civil society (Elrayies, 2016). 

The results also show that slum areas are not a concentrated population with low income 

and/or low education. These results are supported by (Roy et al., 2018) and (Uddin, 2018) where 

income levels vary among residents in slums. There are land owners and land tenants upon 

socio-economic system that have been established for a long time in slums, and the arrival of 

poor individuals who stepped in into this circle has no impact towards overall environment 

(Duah & Bugri, 2016; Nakamura, 2016). The impression of poverty attached to slums is a result 

of building density, while slums located in the downtown perform good economic opportunities 

(Bird et al., 2017). 

This study is considered essential to investigate the priority location of slum improvement 

management, hence it was right on target. Studies based on spatial analysis of physical indicators 

of building density would complement previous studies conducted based on population censuses, 

as those studies were able to reduce bias data, where population data were not equivalent to 

location of residence. 



 

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4. Conclusion  

Applying cluster and outlier modeling of building density levels of urban slums in Jakarta 

based on RT boundary level revealed more specific slum locations and identified factors that 

influencing differences of building density levels. Slum locations that had direct neighbors to 

areas designated at Jakarta spatial detail planning as industrial zones and warehousing areas as 

well as office, trade and service zones, possed a higher level of building density compared to 

slum locations that had neighbors to areas that were designated as housing zones. This current 

study showed the level of income and education in each RT area did not significantly providing 

influence upon slum area. Further studies could be applied by analyzing the level of building 

density in all areas of Jakarta combined with other variables to obtain more specific slum 

distribution clusters. This study suggests that DKI Jakarta Provincial Government could 

determine the more appropriate solution based on the characteristics of each slum area in dealing 

with slum improvement in Jakarta. 

 

Conflict of Interests  

The authors declare that there is no conflict of interest with any financial, personal, or other 

relationships with other people or organizations related to the material discussed in the article. 

Acknowledgment 

 

Authors would like to appreciate DKI Jakarta Provincial Government for their data support 

and maps during the study. 

 

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