DKI JAKARTA: INFORMAL SETTLEMENT...

Cities Alliance Project Output

DKI JAKARTA: INFORMAL SETTLEMENT MAPPING

Indonesia Slum Alleviation Policy and Action Plan (SAPOLA)

P121467

This project output was created with Cities Alliance grant funding.

S A P O L A – Slum Alleviation Policy and Action Plan | i

DKI JAKARTA: INFORMAL

SETTLEMENT MAPPING Final Report

S A P O L A – Slum Alleviation Policy and Action Plan | i

Table of Content Table of Content .......................................................................................................... i

1 INTRODUCTION ............................................................................................ 1 1.1 Background ................................................................................................... 1 1.2 Objective ....................................................................................................... 2 1.3 Method ......................................................................................................... 2 1.4 Structure of Report ....................................................................................... 3

2 SLUM IDENTIFICATION THROUGH OBJECT-BASED IMAGE ANALYSIS............... 4 2.1 Image Analysis Using Satellite Data .............................................................. 4 2.2 Object-oriented Analysis (OOA) for Slum Identification

and Mapping ................................................................................................. 5 2.3 Conceptual Approach on Object Based Classification .................................. 6

2.3.1 Segmentation .................................................................................. 6 2.3.2 Chessboard Segmentation .............................................................. 6 2.3.3 Quadtree-Based Segmentation ....................................................... 7 2.3.4 Contrast Split Segmentation ........................................................... 8 2.3.5 Multiresolution Segmentation ........................................................ 8 2.3.6 Spectral difference Segmentation ................................................10 2.3.7 Scale Parameter ............................................................................10 2.3.8 Composition of Homogeneity Criterion ........................................10 2.3.9 Shape .............................................................................................11 2.3.10 Compactness .................................................................................11 2.3.11 Characteristic of object samples in Quick Bird

data Quick Bird ..............................................................................12 2.4 Methods ......................................................................................................12 2.5 Result and discussion ..................................................................................13

2.5.1 Criteria Classification .....................................................................14 2.5.2 Implemented Tree algorithm ........................................................14

2.6 Ground check ..............................................................................................16 2.7 Result of field survey ...................................................................................17 2.8 Accuracy ......................................................................................................19

Accuracy of slum detection in Jakarta using OBIA .....................................20 2.9 Conclusions .................................................................................................21 2.10 Recommendations ......................................................................................22

3 PROFILE OF SLUM NEIGHBORHOOD (RW’S KUMUH).................................... 23 3.1 Selection of Slum Neighborhood (RW’s Kumuh) ........................................23 3.2 Description of Slum Neighborhood (Selected RW’s

Kumuh) ........................................................................................................26

S A P O L A – Slum Alleviation Policy and Action Plan | ii

3.3 Slum Neighborhood (Selected RW’s Kumuh) Based on Location and Building Density .....................................................................28

3.4 Slum Neighborhood (RW’s Kumuh) Based on Housing Ownership and Proof of Ownership ...........................................................30

3.5 Slum Neighborhood (Selected RW’s Kumuh) Based on Access to Water and Sanitation ..................................................................32

3.6 Slum Neighborhood Based on The Availability of Drainage and Road System .........................................................................35

3.7 Flood Prone Slum Area ...............................................................................37 3.7.1 North Jakarta Slum Areas ..............................................................37 3.7.2 West Jakarta Slum Areas ...............................................................39 3.7.3 Central Jakarta Slum Areas ...........................................................40 3.7.4 East Jakarta Slum Areas ................................................................41 3.7.5 South Jakarta Slum Areas ..............................................................43

4 PROFILE OF HOUSEHOLD IN SELECTED RW’S KUMUH .................................. 45 4.1 Sampling ......................................................................................................45

4.1.1 Coverage Area ...............................................................................45 4.1.2 Sampling Frame .............................................................................45 4.1.3 Number of Sample ........................................................................46 4.1.4 Sampling Plan ................................................................................48

4.2 Analysis by Slum Typology ..........................................................................49 4.2.1 Socio- Economic Characteristics of Household in

Selected RW’s Kumuh ...................................................................49 4.2.2 Housing and Land-Related Characteristics of

Household in RW’s Kumuh ............................................................52 4.2.3 Physical Environment Characteristics of

Household in Selected RW’s Kumuh .............................................60 4.3 Analysis by Selected RW’s Kumuh in Each Kelurahan ................................68

4.3.1 Slum Neighborhood in Selected RW’s Kumuh by Age, Education and Type of Employment .....................................68

4.3.2 Slum Neighborhood in Selected RW’s Kumuh by Length of Stay and Reason to live .................................................69

4.3.3 Slum Neighborhood in Selected RW’s Kumuh by Ownership and Proof of Ownership .............................................71

4.3.4 Slum Neighborhood in Selected RW’s Kumuh by Building Material ...........................................................................72

4.3.5 Slum Neighborhood in Selected RW’s Kumuh by Building Size, Source of Electricity and Source of Water Supply .................................................................................73

4.3.6 Slum Neighborhood in Selected RW’s Kumuh by Sanitation Facility ..........................................................................74

S A P O L A – Slum Alleviation Policy and Action Plan | iii

List Of Table

Table 1-1 Slum Mapping Methodology..................................................................................3 Table 3-1 The Distribution of Sampling Slum Neighborhood ..............................................23 Table 3-2 Slum Neighborhoods by Its Location ...................................................................24 Table 3-3 Description of Slum Neighborhood ......................................................................26 Table 3-4 Slum Neighborhood Based on Location and Building Density .............................28 Table 3-5 Slum Neighborhood Based on Status Ownership and Proof of Ownership .........30 Table 3-6 Slum Neighborhood Based on Access to Water and Sanitation ..........................33 Table 3-7 Slum Neighborhood Based on The Availability of Drainage and Road System ....35 Table 3-8 Number of RW’s in flood prone area based on slum typology in North Jakarta .38 Table 3-9. Number of RW’s in flood prone area based on slum typology in West Jakarta ..40 Table 3-10 Number of RW’s in flood prone area based on slum typology in Central Jakarta

.............................................................................................................................41 Table 3-11 Number of RW’s in flood prone area based on slum typology in East Jakarta ....43 Table 3-12 Number of RW’s in flood prone area based on slum typology in South Jakarta .44 Table 4-1 Population and Sampling .....................................................................................47 Table 4-2 Distribution of Household Based on Typology .....................................................48 Table 4-3 Number of Household by Age Group and Typology ............................................50 Table 4-4 Percentage of Household by Age Group and Highest Educational Attainment ...50 Table 4-5 Number of Household by Type of Employment...................................................51 Table 4-6 Number of Household by Monthly Income .........................................................51 Table 4-7 Number of Household by Monthly Income and Monthly Expenditure ...............52 Table 4-8 Number of Household by Length of Stay and Typology .......................................53 Table 4-9 Percentage of Household by reason to live in slum area .....................................54 Table 4-10 Percentage of Household by Housing Ownership

.............................................................................................................................55 Table 4-11 Percentage of Household by Proof Land Ownership ...........................................56 Table 4-12 Number of Household by Housing Expenditure and Slum Typology ...................58 Table 4-13 Monthly rent price of house at riverbanks slum area ..........................................59 Table 4-14 Monthly rent price of house at railroads slum area ............................................59 Table 4-15 Monthly rent price of house at other slum area ..................................................59 Table 4-16 Percentage of Household by Housing Materials ..................................................60 Table 4-17 Source of clean water at other slum area ............................................................65 Table 4-18 Percentage of Slum Neighborhood in Selected RW’s Kumuh by Age, Education

and Type of Employment .....................................................................................68 Table 4-19 Percentage of Slum Neighborhood in Selected RW’s Kumuh by Length of Stay

and Reason to live ................................................................................................69 Table 4-20 Percentage of Slum Neighborhood in Selected RW’s Kumuh by Ownership and

Proof of Ownership ..............................................................................................71 Table 4-21 Percentage of Slum Neighborhood in Selected RW’s Kumuh by building material

.............................................................................................................................72 Table 4-22 Percentage of Slum Neighborhood in Selected RW’s Kumuh by Building Size,

Source of Electricity and Source of Water Supply................................................73 Table 4-23 Percentage of Slum Neighborhood in Selected RW’s Kumuh by the condition of

sanitation facility. .................................................................................................74

S A P O L A – Slum Alleviation Policy and Action Plan | iv

List of Figures

Figure 2-1 Chessboard Segmentation .................................................................................7 Figure 2-2 Quadtree Based Segmentation .........................................................................7 Figure 2-3 Result of multiresolution segmentation with scale 10, shape 0.1 and

compactness 0.5 ................................................................................................9 Figure 2-4 Multiresolution segmentation work flow diagram (eCognition, 2013) .............9 Figure 2-5 Graph Characteristics Entire Sample Class and Parameters (compactness and

Roundness) ......................................................................................................12 Figure 2-6 Quick Bird data of DKI Jakarta .........................................................................13 Figure 2-7 Manual equalizing radiometric of Quick Bird data ..........................................13 Figure 2-8 OBIA implementation for Slum Mapping: Jakarta Case ..................................14 Figure 2-9 Samples of object based classification for slum detection ..............................15 Figure 2-10 Ground check in Pluit and Pademangan ..........................................................16 Figure 2-11 Ground check in Tegal Arum and Marunda .....................................................17 Figure 2-12 Accuracy and precise .......................................................................................19 Figure 3-1 Location of Slum Neighborhoods ....................................................................25 Figure 3-2. Slum typology and flood prone area in North Jakarta .....................................38 Figure 3-3. Slum typology and flood prone area in West Jakarta ......................................39 Figure 3-4. Slum typology and flood prone area in Central Jakarta ..................................41 Figure 3-5. Slum typology and flood prone area in East Jakarta .......................................42 Figure 3-6. Slum typology and flood prone area in South Jakarta .....................................44 Figure 4-1 Sampling Frame ...............................................................................................46 Figure 4-2 Proof of ownership certificate. Top Left: riverbanks slum area; Top Right:

Coastal slum area; Bottom Left: Railroads slum area; Bottom right: Other slum area .........................................................................................................57

Figure 4-3 Buildings characteristics of riverbanks slum area. Top left: Percentage of roofing material; Top right: Percentage of wall type; Bottom left: Percentage of floor type; Bottom right: Number of houses based on land size ................61

Figure 4-4 Buildings characteristics of coastal slum area. Top left: Percentage of roofing material; Top right: Percentage of wall type; Bottom left: Percentage of floor type; Bottom right: Number of houses based on land size .............................62

Figure 4-5 Buildings characteristics of railroads slum area. Top left: Perentage of roofing material; Top right: Percentage of wall type; Bottom left: Percentage of floor type; Bottom right: Number of houses based on land size .............................63

Figure 4-6 Buildings characteristics of other slum area. Top left: Perentage of roofing material; Top right: Percentage of wall type; Bottom left: Percentage of floor type; Bottom right: Number of houses based on land size .............................64

Figure 4-7 Percentage of septic tanks ownerships in riverbanks slum area .....................65 Figure 4-8 Percentage of septic tanks ownerships in railroads slum area .......................66 Figure 4-9 Percentage of septic tanks ownerships in other slum area .............................67 Figure 4-10 Percentage of drainage type. Top left: riverbanks slum area; Top right:

Coastal slum aea; bottom: railroads slum area ...............................................67

S A P O L A – Slum Alleviation Policy and Action Plan | 1

1 INTRODUCTION

Background 1.1

Along with the increase in the urban population, Indonesia has seen

increased informal settlements, both 'slums settlements' and “squatter

settlements”. BPS MDGs Report 2011 shown that around 12.1% of

Indonesia’s population or 3.9 million households of these live in urban

slums. The latest 2014 data on slum areas as measured by the Ministry of

Public Work’s and Housing and the BPS Jakarta office revealed that the

extent of slum area reached 38,431 Ha, spread-out in 3,286 slum locations

in 2,870 villages/neighborhoods in Indonesia.

As the Government is committed to address slums in its ambitious target of

“Cities without Slums” by 2020, effective and well-targeted programmes are

needed. There is a lack of spatial data on slums across the country. There

are no thematic maps for local governments to identify slums and related

demographic, social and economic information. This creates difficulties for

local governments to prioritize programs and allocate funds while

addressing slums.

The Government of Indonesia is currently designing a national policy on

urban slums. This Slum Alleviation Policy and Action Plan [SAPOLA] exercise

is financed under a Cities Alliance Grant. The national policy is to upgrade

and integrate slums into the urban mainstream, improve urban planning;

rationalize urban land regulations and address the deficit in housing and

infrastructure services. The objective is to transform slums into viable legal

communities with access to services. The SAPOLA recommendations are

being incorporated in the Government of Indonesia Medium Term

Development Plan for 2014-2019 [RPJMN].

SAPOLA subsumes several inter-related activity modules including (i) a

physical mapping of urban slums in Jakarta DKI; (ii) an assessment of the

institutional capacity in local governments to upgrade slums; (iii) a review of

urban land markets as they impact on the poor; and (iv) the formulation of a

National Policy on slums.


The Directorate of Housing and Settlements in Bappenas intends to i) commission a Slum Mapping exercise. The study is intended to identify the location of slums in Jakarta, where they are growing, the estimated population living in slums, and general characteristics of the slum conditions (physical, social, economic) using available secondary data and a primary survey; and ii) develop a “Slum Management Information System” that will be an on-line database for the government to identify where to channel resources.

Jakarta as the capital city of Indonesia has facing problems with the growth of informal slum settlements. Although the number of RW’s slums are decreasing during the period of 2007-2012, but slums settlements are still remain. The existing slums settlement data covered only the administrative neighborhood slums (slum RW’s) without further information on the estimated total area and population living in slums and general characteristics on slum settlements. More detail slum settlements data are needed to describe the characteristics of informal settlements and to identify the appropriate interventions to deal with slum problems.

Objective 1.2

The objective of the slum mapping exercise is to help municipalities develop and maintain a geo-referenced database of slum using satellite imagery, surveys and other data sources. It would record attributes such as land use, land status, population density, disaster risk, transportation networks, access to urban basic services, etc. It will provide a slums profile to help local governments identify appropriate slum policies and estimate investments needed.

Method 1.3

Informal settlement mapping in DKI Jakarta consists of three subsequent

activities which are: i) city-wide slum identification through object-based

image analysis, ii) slum neighborhood profiling, and iii) slum households

survey. Detail description of each activities are as following:


Table 1-1 Slum Mapping Methodology

Stage Activity Data Output

City-wide Slum

Identification

through object-

based image

analysis

Identification of

slum areas in a

city based on

image-based

analysis

Field checking

Quickbird

satellite

imagery 2010

A sample slum

mapping through

object based image

analysis and ground

check in 15

locations

Slum

neighborhood

profiling

Selection of

Surveyed RW’s

Kumuh based on

DKI Jakarta BPS

Data (2013)

Field observations

to slum

neigborhood

(RW’s Kumuh)

Primary data

based on

interview to

RW or RT and

observation

Profile of slum

neighborhood

(selected RW’s

Kumuh)

Slum household

survey

Selection of

household within

RW’s Kumuh as

target sampling

Household survey

Primary data,

household

survey

Profile of slum

households in 50

selected RW’s

Kumuh and 38

Keulurahan

Structure of Report 1.4

This report consist of these following section:

Chapter 1 Introduction

Chapter 2 Slum Identification thorugh object based image analysis

Chapter 3 Profile of Slum Neighborhood (Selected RW’s Kumuh)

Chapter 4 Profile of Slum Household


2 SLUM IDENTIFICATION THROUGH OBJECT-

BASED IMAGE ANALYSIS

Image Analysis Using Satellite Data 2.1

High resolution satellite data is becoming increasingly available and

considered to be a crucial data source for efficiently analyzing the

landscape. The benefits of remotely sensed data can be explained from two

perspectives:

a. They offer quick detection as well as detailed depiction on natural and

manmade features; and

b. They combine both high accuracy and affordability since one data sets

can be used to support many development and program activities by a

cross section of agencies reducing unnecessary redundancy and

duplications.

Image analysis utilizing satellite data can be done in two ways:

a. Utilizing pixel/spectral-based supervised and unsupervised

classifications; where classification is done based on spectral

reflectance of a pixel;

b. Utilizing object based-which utilizes rule and knowledge-based

techniques.

Pixel-based classification can be used to detect urban features. However the

result of past studies applying pixel-based classification led to

misclassifications since urban areas are complex and have a high degree of

heterogeneity. Thomas et al. (2003), who compared three different

methods namely (1) combined supervised/unsupervised spectral

classification, (2) raster-based spatial modelling, and (3) image

segmentation classification using classification tree analysis, found that

derived spectral information resulted from spectral classification gave

relatively low map accuracies while using image segmentation and

classification tree approach increased map accuracy. They have also caused


“mixed pixel‟ problems particularly for classifying urban classes. The

aforementioned classification problems have led to a paradigm shift from

pixel based to object-based methodology.

Object-oriented Analysis (OOA) for Slum Identification and Mapping 2.2

Object Oriented Analysis (OOA) involves partitioning image into meaningful

objects called segments, and assessing their properties by use of spectral

signatures, geographical features and topological properties. These features

are used in recognition and classification process (Durand et al., 2007).

Several studies have demonstrated the usefulness of object-based approach

by comparing pixel based and object oriented classification. For example, a

study to identify urban structures and its dynamics was evaluated using

Quick Bird satellite image in Delhi India. OOA was used to classify different

settlement types in the urban area to detect informal settlements

(Niebergall et al., 2007). The results showed that pixel-based classifications

gave bad results for complex urban environment.

The strengths of OOA are:

1. Spatial relationship can be revealed hence the accuracy of the value of

final classification is increased (which cannot be fulfilled by pixel based

approach).

2. Iterative process in extracting objects of interest in the process of

segmentation and classification can be performed.

3. OOA is also capable of using multiple data types during analysis to help

create meaningful segments.

4. OOA provides meaningful information by allowing integration of

fuzziness in the boundaries of classes.

5. It allows multiple scale: OOA allows for more than one level of analysis.

For identification of features in an image through classification, it

requires objects of different sizes which are linked.


Conceptual Approach on Object Based Classification 2.3

OOA is process of image analysis characterized by transformation of

knowledge which involves feeding rules into the software, on which the

Image segmentation is the first and important step for classification of an

image to group results of pixels with similar values (Nobrega et al., 2008).

Object image analysis consists of two steps: segmentation and classification.

2.3.1 Segmentation

Image segmentation is the first most crucial steps which involve grouping

pixels into meaningful objects of homogeneous spectral properties

(Bhaskaran et al., 2010; Blaschke, 2010). Image segmentation links objects

in a network which offers important context information for classification.

There are two basic segmentation principles; top down segmentation which

cuts the image into smaller pieces and bottom up approach that merges

smaller objects into bigger objects (eCognition, 2010). This depends on the

type of segmentation. Seven types of multiresolution algorithm are

available in eCognition software which includes: chessboard segmentation,

quadtree-based segmentation, contrast split segmentation, multiresolution

segmentation, spectral difference segmentation, multithreshold

segmentation and contrast filter segmentation (eCognition, 2013).

2.3.2 Chessboard Segmentation

Chessboard segmentation is a top down region splitting principle which

splits the pixel domain or an image into square image objects (eCognition,

2010). Object size which is determined by the scale size, defines the square

grid in pixels. Chessboard segmentation is simple and fast. It generates seed

segments which are used for further analysis (eCognition, 2013).


Figure 2-1 Chessboard Segmentation

2.3.3 Quadtree-Based Segmentation

The Quadtree-Based Segmentation algorithm splits the pixel domain or an

image object domain into a quadtree grid formed by square objects. A

quadtree grid consists of squares with sides each having a power of two and

aligned to the image left and top borders. It is applied to all objects in the

domain and each object is cut along these gridlines. The quadtree structure

is built so that each square has a maximum possible size and also fulfills the

homogeneity criteria defined by the mode and scale parameters. The

maximum square object size is 256 x 256, or 65,536 pixels (eCognition,

2013).

Figure 2-2 Quadtree Based Segmentation


2.3.4 Contrast Split Segmentation

The Contrast Split Segmentation algorithm segments an image or image

object into dark and bright regions. It is based on a threshold that

maximizes the contrast between the resulting bright objects (consisting of

pixels with pixel values above the threshold) and dark objects (consisting of

pixels with pixel values below the threshold). The algorithm evaluates the

optimal threshold separately for each image object in the image object

domain. If the pixel level is selected in the image object domain, the

algorithm first executes a chessboard segmentation, then performs the split

on each square. It achieves the optimization by considering different pixel

values as potential thresholds. The test thresholds range from the minimum

threshold to the maximum threshold, with intermediate values chosen

according to the step size and stepping type parameter. If a test threshold

satisfies the minimum dark area and minimum bright area criteria, the

contrast between bright and dark objects is evaluated. The test threshold

causing the largest contrast is chosen as the best threshold and used for

splitting (eCognition, 2013).

2.3.5 Multiresolution Segmentation

The Multiresolution Segmentation algorithm locally minimizes the average

heterogeneity of image objects for a given resolution of image objects. It

can be executed on an existing image object level or the pixel level for

creating new image objects on a new image object level (eCognition, 2013).


Figure 2-3 Result of multiresolution segmentation with scale 10, shape 0.1 and compactness 0.5

The multiresolution segmentation algorithm consecutively merges pixels or

existing image objects. Thus it is a bottom-up segmentation algorithm based

on a pairwise region merging technique. Multiresolution segmentation is an

optimization procedure which, for a given number of image objects,

minimizes the average heterogeneity and maximizes their respective

homogeneity (eCognition, 2013).

Figure 2-4 Multiresolution segmentation work flow diagram (eCognition, 2013)


2.3.6 Spectral difference Segmentation

Spectral difference merges neighbouring image objects according to their

mean image layer intensity values. It cannot be used to create new image

object levels based on the pixel level domain. Instead, it is used to refine

segmentation results by merging layer mean intensities below the value

specified (eCognition, 2013).

2.3.7 Scale Parameter

The Scale Parameter is an abstract term that determines the maximum

allowed heterogeneity for the resulting image objects. For heterogeneous

data, the resulting objects for a given scale parameter will be smaller than in

more homogeneous data. By modifying the value in the Scale Parameter

value you can vary the size of image objects (eCognition, 2013).

2.3.8 Composition of Homogeneity Criterion

The object homogeneity to which the scale parameter refers is defined in

the Composition of Homogeneity criterion field. In this circumstance,

homogeneity is used as a synonym for minimized heterogeneity. Internally,

three criteria are computed: color, smoothness, and compactness. These

three criteria for heterogeneity may be applied in many ways although, in

most cases, the color criterion is the most important for creating meaningful

objects. However, a certain degree of shape homogeneity often improves

the quality of object extraction because the compactness of spatial objects

is associated with the concept of image shape. Therefore, the shape criteria

are especially helpful in avoiding highly fractured image object results in

strongly textured data (for example radar data) (eCognition, 2013).

Homogeneity criterion depends on colour and shape properties. If higher

weight is given to spectral criteria there will be lesser impact of shape in

image object formation and vice versa. Shape criteria are further divided in


smoothness and compactness. Smoothness influences the smoothness of

the object while compactness determines how compact objects will be.

Among these parameters, scale parameter is the most important factor

since it determines the heterogeneity for the target image objects(Chen et

al., 2009).The larger the scale parameter, the more objects are fused and

the larger the objects grow (Benz et al., 2004). This allows for the

representation of image information simultaneously at different scales thus

achieving a hierarchical network of objects. For example the classification of

a single building and a settlement requires would require a different scale to

classify them. Therefore, it is important while analysing various objects in an

image to perform it on several scales in a hierarchical manner (eCognition,

2013).

2.3.9 Shape

The value of the Shape field modifies the relationship between shape and

color criteria; By modifying the Shape criterion,1 you define the color

criteria (color = 1 shape). In effect, by decreasing the value assigned to the

Shape field, you define to which percentage the spectral values of the image

layers will contribute to the entire homogeneity criterion. This is weighted

against the percentage of the shape homogeneity, which is defined in the

Shape field. Changing the weight for the Shape criterion to 1 will result in

objects more optimized for spatial homogeneity. However, the shape

criterion cannot have a value larger than 0.9, due to the fact that without

the spectral information of the image, the resulting objects would not be

related to the spectral information at all. The slider bar adjusts the amount

of Color and Shape to be used for the segmentation. In addition to spectral

information, the object homogeneity is optimized with regard to the object

shape, defined by the Compactness parameter (eCognition, 2013).

2.3.10 Compactness

The compactness criterion is used to optimize image objects with regard to

compactness. This criterion should be used when different image objects


which are rather compact, but are separated from non-compact objects

only by a relatively weak spectral contrast. Use the slider bar to adjust the

degree of compactness to be used for the segmentation (eCognition, 2013).

2.3.11 Characteristic of object samples in Quick Bird data Quick Bird

Figure 2-5 Graph Characteristics Entire Sample Class and Parameters (compactness and Roundness)

Methods 2.4

The image used is the Quick Bird in 2010 with the condition already

orthoimage, but have not done radiometric correction. This image has three

bands of blue, green, red, but not fitted with NIR band and radiometric

metadata.

KARAKTERISTIK SELURUH SAMPEL KELAS & PARAMETER

(Compactness dan Roundness)

0

2

4

6

8

C R

BAND

DN

WADUK

SUNGAI

SWH BERA

SWH GEN

SWH VEG

SWH AIR

HUTAN

BELUKAR

SEMAK/ALANG-ALANG

PERKEBUNAN

PERKEBUNAN (Tebu)

KEBUN CAMPUR

TEGALAN/LADANG

LAHAN TERBUKA

PERMUKIMAN JARANG

PERMUKIMAN PADAT

KAWASAN INDUSTRI


Figure 2-6 Quick Bird data of DKI Jakarta

Location: DKI Jakarta

Software: eCognition 8.7 and QGIS

Result and discussion 2.5

This image was first performed by standard radiometric equalizing the

manual approach. This method is done defining digital number values

manually with linear-inverse method.

Figure 2-7 Manual equalizing radiometric of Quick Bird data


2.5.1 Criteria Classification

Classification use tree algorithm method. It is in 4 level of tree algorithm.

Settlement Location (Kumuh1): < 300 meters from Roads & Rivers + area of

building (LA) < 35 m2

Settlement Location (Kumuh 2): Kumuh 1 + Asymmetry <0.15

Figure 2-8 OBIA implementation for Slum Mapping: Jakarta Case

2.5.2 Implemented Tree algorithm

Level 1: Building and non building Level 2: non rooftile and rooftile, road feature Level 3: tin roof and concrete, road and river Level 4: slum 1 and slum 2

Data

Building Non-building

Non Rooftile

Roof Tile

Tinroof

Concrete


Samples

The number of samples per class is 5-30 pieces per class or 50 houses in the

area

Figure 2-9 Samples of object based classification for slum detection


Ground check 2.6

Ground check area are in Pluit, Penjaringan, Pademangan, Tegal Arum,

Bendungan Hilir, Jatinegara, Bidara Cina, Tebet, Marunda

Figure 2-10 Ground check in Pluit and Pademangan

Pluit

Pademangan


Figure 2-11 Ground check in Tegal Arum and Marunda

Result of field survey 2.7

Next, results directly in the field of evaluation of the results of segmentation

Location Information

1. Kel. Bidara Cina, Kec. Jatinegara (2 gang)

Typology: Edge S. Ciliwung Slum, body building was erected in the river, and has been made permanent. East side of the existing

Tegal Arum

Marunda


Location Information

building public toilets WIKA (the mosque compound) over the tomb. Estimation Accuracy: 80%

2. Kampung Pulo, Jatinegara Village near the bridge, being dismantled. Accuracy: 80%

3. Bukit Duri, Tebet rail station

Houses seedy roadside (for example, a car wash business)

4. Stasiun Manggarai (near river)

Ditch the right way

5. Matraman -

6. Bendungan Hilir near PAM

7. Tanah Abang near Tanah Abang rail stasion

8. Marunda Slum area (80%)

9. Pademangan Timur Down the motorway, riverbanks and railway (impression: many pets sheep left), an accuracy of 70-80%

10. Pademangan Barat Canal of East Pademangan region (80% accurate)

11. Penjaringan Under the slum houses toll up to the port of Sunda Kelapa / village along the river port of Sunda Kelapa

12. Pluit Validated. The edge of the reservoir whistle (estimation accuracy rate: 80%)

13. Tegal Alur Sewer water on the road (border with Tangerang) (Not detected because in the street)

14. Muara Kamal Newly built residential complex, seedy, roadside selling timber businesses. Border with Tangerang (a new area in the image not yet available)

15. Grogol Untidiness Along the river seen from the highway - along the river (level of accuracy: 90%)


Accuracy 2.8

Agreement/accuracy is the probability (%) that the classifier has labeled an

image pixel into the ground truth Class. It is the probability of a reference

pixel being correctly classified. Overall accuracy is the total classification

accuracy. Commission error is represent pixels that belong to another class

but are labeled as belonging to the class. Omission error is represent pixels

that belong to the truth class but fail to be classified into the proper class.

Figure 2-12 Accuracy and precise

A more appropriate way of presenting the individual classification

accuracies.

Commission error = 1 - user's accuracy.

Omission error = 1 - producer's accuracy


Accuracy of slum detection in Jakarta using OBIA

Classification stability and best classification result for all class include its

mean, standard deviation, minimum, and maximum.


The other method of accuracy test in eCognition is error matrix based on

samples. They are Producer, User, Helden, Short, KIA, and Overall accuracy

Overall accuracy is 78,57 % and producer accuracy is 85,71%

Conclusions 2.9

Sattelite imageries can produce good estimate of slums identification. Slums

residential objects can be indetified based upon the following elements:

- Have ironic/asbeto roof and not concrete roof

- Situated close to railway, streets and water bodies

- Grouping and create cluster phenomenon

- Areas of settlement construction are less than 30 m2

Ground truthng is needed to check:

- Roof

- Layout and specification of roads

- Qualitiy of SAL (waste deposit) and SAH (rain drainage)


The results of object-based image analysis is that the resulted overall

accuracy is 78,57 % and producer accuracy is 85,71%

Recommendations 2.10

Recommendation for the next research:

1. Utilisation of high resolution imageries with full band covrag, for

example WorldView-2 using 9 band (coastal blue, blue, red, green,

NIR 1, NIR 2, yellow, red edge, panchromatic is highly recommended.

Minimum recommendation is the use of Ikonos, Quick Bird that have

4 bands only (blue, red, green, NIR).

2. The imageries should bave been ortho-rectified and owing geometric

metadata (RPB or RPC).

3. The imageries should have been radiometrically corrected (standard,

normalization, atmosphere, topography) and have radiometric

metadata.

4. Utilization of 3D model (Digital Surface Model (DSM), Digital Terrain

Model (DTM) that minimum has Digital Elevation Model (DEM) with

vertical accuracy < 1 m wouldbe even better. This data are IFSAR,

TerraSAR X/TanDEM X., DSM Ikonos/QuickBird/WorldView/GeoEye.


3 PROFILE OF SLUM NEIGHBORHOOD (RW’S

KUMUH)

Profile of slum neighborhood (selected RW’s Kumuh) comprises a general

information on physical, socio and economic characteristics of slum areas

which will be used as basis for policy formulation. This profile had been

developed by using data and information collected from field observations

and interviews with key informants in selected RW Kumuh.

Selection of Slum Neighborhood (RW’s Kumuh) 3.1

Field observation was conducted in almost 186 RW’s Kumuh (neighborhood

slums) based on the latest 2013 RW Kumuh Data published by BPS Jakarta

Office. Sampling neighborhood slums (RW’s Kumuh) distributed in five

municipalities, excluding Kepulauan Seribu Regency, and spread-out in 34

sub-districts and 78 villages.

Table 3-1 The Distribution of Sampling Slum Neighborhood

No Municipality Sub-District Village RW’s Kumuh Percentage

1 North Jakarta 6 21 68 37

2 South Jakarta 7 15 24 13

3 Central Jakarta 7 15 37 20

4 West Jakarta 4 8 19 10

5 East Jakarta 10 19 38 20

Total 34 78 186 100

Slum settlements were observed on this survey mostly located close to commercial area (121 RW’s) and nearby riverbank and/or water-pond (103 RWs). There are also slum settlements located on coastal area (17 RW’s), railway (42 RW’s) and toll road (13 RW’s). Detail


Table 3-2 Slum Neighborhoods by Its Location

No Municipality Riverbank

And Water-Pond

Coastal Railway Toll Road Commercial

1 North Jakarta 20 16 12 8 38

2 South Jakarta 22 0 0 0 15

3 Central Jakarta 18 0 18 0 23

4 West Jakarta 9 1 5 0 16

5 East Jakarta 34 0 7 5 29

Total 103 17 42 13 121


Figure 3-1 Location of Slum Neighborhoods


Description of Slum Neighborhood (Selected RW’s Kumuh) 3.2

The following Table III.3 described general information on selected RW’s kumuh based on total area, number of population and population density.

Table 3-3 Description of Slum Neighborhood

Municipality/Sub-District

Village Number of RW’s Kumuh

Area of RW’s

Kumuh (ha)

No of Selected

RW’s Kumuh

Area of Selected

RW’s Kumuh (ha)

No of Population in Selected

RW’s Kumuh (pop)

Population Density in

Selected RW Kumuh

(pop/ha)

WEST JAKARTA 51 244 19 81 63,697 261

Grogol Petamburan Tomang 4 17.85 1 6.54 1,876 105

Palmerah Kota Bambu Utara 8 60.53 1 5.65 4,892 81

Tamansari Keagungan 7 20.88 4 12.29 8,458 405

Tamansari Krukut 5 21.75 3 11.55 11,245 517

Tambora Jembatan Besi 7 30.99 5 24.22 18,755 605

Tambora Jembatan Lima 8 46.20 1 5.40 3,841 83

Tambora Kalibaru 8 29.16 3 9.88 10,541 362

Tambora Krendang 4 16.66 1 5.89 4,089 245

CENTRAL JAKARTA 38 247 17 99 71,574 290

Gambir Petojo Selatan 2 30.76 2 30.76 6,820 222

Johar Baru Galur 7 27.25 2 10.56 7,312 268

Johar Baru Johar Baru 2 13.65 2 13.65 9,704 711

Johar Baru Kampung Rawa 7 23.40 3 9.39 11,001 470

Johar Baru Tanah Tinggi 5 20.32 4 15.63 13,809 680

Kemayoran Utan Panjang 2 6.34 1 2.87 2,205 348

Menteng Menteng 6 34.59 2 10.81 8,818 255

Menteng Pegangsaan 7 90.53 1 5.79 11,905 132

Sawah Besar Karang Anyar 7 23.95 4 13.42 15,022 627

Sawah Besar Kartini 8 40.16 3 11.27 9,915 247

Sawah Besar Mangga Dua Selatan 9 97.05 5 25.04 16,954 175

Senen Kramat 4 26.29 2 9.71 5,218 198

Senen Kwitang 6 24.28 2 6.10 3,710 153

Senen Senen 1 23.63 1 23.63 3,618 153

TanahAbang Kampung Bali 6 35.56 3 15.92 6,299 177

SOUTH JAKARTA 51 244 19 81 63,697 261

Cilandak Cilandak Barat 5 386.50 1 121.29 5,840 15

Cilandak Gandaria Selatan 2 54.82 2 54.82 10,613 194

Kebayoran Baru Gandaria Utara 5 46.68 3 29.17 14,790 317

Kebayoran Baru Kramat Pela 4 25.95 2 11.46 9,790 377

Kebayoran Baru Petogogan 3 33.42 2 20.12 7,822 234

Kebayoran Lama Grogol Utara 3 73.24 1 21.96 5,215 71

Mampang Prapatan Bangka 4 263.64 1 46.68 4,737 18

Mampang Prapatan Kuningan Barat 5 97.72 2 43.05 7,692 79

Mampang Prapatan Mampang Prapatan 3 91.65 2 43.65 5,620 61

Mampang Prapatan Pela Mampang 6 107.97 3 67.49 16,438 152




Area of RW’s

Kumuh (ha)

No of Selected

RW’s Kumuh

Area of Selected

RW’s Kumuh (ha)


RW’s Kumuh (pop)


Selected RW Kumuh

(pop/ha)

Mampang Prapatan Tegal Parang 1 17.12 1 17.12 6,506 380

Pasar Minggu Pejaten Barat 1 74.39 1 74.39 7,455 100

Pasar Minggu Pejaten Timur 2 45.34 1 18.57 7,294 161

Pasar Minggu Ragunan 1 52.24 1 52.24 7,922 152

Tebet Bukit Duri 3 35.27 1 12.55 4,821 137

EAST JAKARTA 51 244 19 81 63,697 261

Cakung Cakung Barat 3 91.21 1 35.53 2,719 30

Cakung Jatinegara 2 152.13 1 57.10 6,652 44

Cakung Rawa Terate 4 213.09 4 213.09 20,033 94

Cipayung Ceger 1 68.54 1 68.54 5,417 79

Ciracas Cibubur 1 35.36 1 35.36 8,434 239

Ciracas Rambutan 1 89.49 1 89.49 5,805 65

Duren Sawit Klender 5 95.63 3 60.72 13,986 146

Jatinegara Cipinang Besar Selatan

3 59.70 3 59.70 13,629 228

Jatinegara Cipinang Besar Utara

9 53.10 4 21.28 17,411 328

Jatinegara Kampung Melayu 6 36.69 6 36.69 27,068 738

Jatinegara Rawa Bunga 6 49.02 1 6.47 2,393 49

KramatJati Cawang 5 73.40 2 40.42 6,630 90

KramatJati Kampung Tengah 2 22.68 2 22.68 16,456 726

Makasar Cipinang Melayu 5 102.00 2 19.58 9,144 90

Makasar Kebon Pala 3 73.74 1 49.49 2,777 38

Matraman Pal Meriem 3 19.44 1 9.95 2,467 127

PasarRebo Pekayon 1 36.71 1 36.71 2,245 61

Pulogadung Kayu Putih 3 149.06 2 128.94 12,400 83

Pulogadung Rawamangun 3 36.30 1 11.65 5,489 151

NORTH JAKARTA 51 244 19 81 63,697 261

Cilincing Cilincing 3 44.76 3 44.76 22019 492

Cilincing Kali Baru 11 250.35 8 203.77 48148 192

Cilincing Marunda 3 319.07 2 270.51 5161 16

Cilincing Semper Barat 4 129.48 1 34.66 1301 10

Cilincing Semper Timur 2 63.76 1 22.77 6252 98

Cilincing Suka Pura 7 497.76 2 197.68 20936 42

Kelapa Gading Kelapa Gading Barat 2 94.35 1 56.63 1219 13

Kelapa Gading Pegangsaan Dua 5 116.98 2 37.80 5458 47

Koja Lagoa 12 113.76 3 30.03 17402 153

Koja Tugu Selatan 3 111.37 2 24.86 15282 137

Koja Tugu Utara 8 92.60 5 50.34 29307 316

Pademangan Ancol 4 249.97 4 249.97 22283 89

Pademangan Pademangan Barat 9 100.48 8 93.18 60617 603

Pademangan Pademangan Timur 2 35.91 1 21.50 7475 208

Penjaringan Penjaringan 12 473.01 11 438.88 82283 174

Tanjung Priok Kebon Bawang 8 85.75 2 21.16 15313 179




Area of RW’s

Kumuh (ha)

No of Selected

RW’s Kumuh

Area of Selected

RW’s Kumuh (ha)


RW’s Kumuh (pop)


Selected RW Kumuh

(pop/ha)

Tanjung Priok Papango 7 132.01 2 49.94 16707 127

Tanjung Priok Sunter Agung 6 146.07 2 39.41 21603 148

Tanjung Priok Sunter Jaya 9 352.00 2 154.24 11272 32

Tanjung Priok Tanjung Priuk 7 56.53 4 30.28 17745 314

Tanjung Priok Warakas 6 54.18 2 18.07 9281 171

Slum Neighborhood (Selected RW’s Kumuh) Based on Location and 3.3Building Density

The following Table III. 4 described selected RW’s Kumuh based on location and building density. The location categorized based on the proximity to city networks system (riverbank, waterpond, coastal, railway and toll road) and commercial activity. Building density categorized into three classifications: high density, medium and low density.

Table 3-4 Slum Neighborhood Based on Location and Building Density

Village

Location Building Density

Riverbank and Water-

Pond Coastal Railway Toll Road Commercial High Medium Low

Tomang 0 0 0 0 1 0 1 0

Kota Bambu Utara 0 0 0 0 1 0 1 0

Keagungan 4 0 3 0 4 3 1 0

Krukut 1 0 0 0 3 2 1 0

Jembatan Besi 2 0 0 0 3 5 0 0

Jembatan Lima 0 0 0 0 1 1 0 0

Kalibaru 2 1 2 0 2 3 0 0

Krendang 0 0 0 0 1 1 0 0

Petojo Selatan 1 0 0 0 0 2 0 0

Galur 0 0 0 0 2 1 1 0

Johar Baru 2 0 1 0 2 2 0 0

Kampung Rawa 2 0 0 0 2 2 1 0

Tanah Tinggi 3 0 0 0 3 3 1 0

Utan Panjang 0 0 0 0 1 1 0 0

Menteng 2 0 2 0 0 1 1 0

Pegangsaan 0 0 1 0 0 1 0 0

Karang Anyar 0 0 2 0 4 4 0 0

Kartini 1 0 3 0 3 2 1 0

Mangga Dua Selatan 4 0 3 0 2 5 0 0

Kramat 1 0 1 0 0 2 0 0

Kwitang 2 0 2 0 0 1 1 0


Village




Senen 0 0 0 0 1 1 0 0

Kampung Bali 0 0 3 0 3 3 0 0

Cilandak Barat 1 0 0 0 1 0 1 0

Gandaria Selatan 2 0 0 0 2 0 2 0

Gandaria Utara 3 0 0 0 1 0 2 1

Kramat Pela 2 0 0 0 1 0 0 2

Petogogan 2 0 0 0 1 0 0 2

Grogol Utara 1 0 0 0 1 0 1 0

Bangka 1 0 0 0 1 1 0 0

Kuningan Barat 2 0 0 0 2 0 0 2

Mampang Prapatan 1 0 0 0 2 0 1 1

Pela Mampang 2 0 0 0 1 1 2 0

Tegal Parang 1 0 0 0 0 0 1 0

Pejaten Barat 1 0 0 0 0 0 0 1

Pejaten Timur 1 0 0 0 1 1 0 0

Ragunan 1 0 0 0 1 1 0 0

Bukit Duri 1 0 0 0 0 0 1 0

Cakung Barat 1 0 0 0 1 0 1 0

Jatinegara 1 0 0 0 1 0 1 0

Rawa Terate 4 0 0 0 3 3 1 0

Ceger 0 0 0 1 0 0 1 0

Cibubur 1 0 0 0 1 1 0 0

Rambutan 1 0 0 0 1 0 1 0

Klender 2 0 0 0 2 0 3 0

Cipinang Besar Selatan 3 0 0 0 2 2 1 0

Cipinang Besar Utara 4 0 0 0 3 3 1 0

Kampung Melayu 6 0 5 3 4 5 1 0

Rawa Bunga 0 0 1 0 1 0 1 0

Cawang 1 0 0 0 2 0 2 0

Kampung Tengah 2 0 0 0 2 1 1 0

Cipinang Melayu 2 0 0 0 2 0 2 0

Kebon Pala 1 0 0 1 1 0 1 0

Pal Meriem 1 0 1 0 1 0 1 0

Pekayon 1 0 0 0 1 0 1 0

Kayu Putih 2 0 0 0 1 1 1 0

Rawamangun 1 0 0 0 0 0 1 0

Cilincing 1 1 0 0 3 3 0 0

Kali Baru 0 7 0 0 3 8 0 0

Marunda 0 1 0 0 2 0 1 1

Semper Barat 0 0 0 1 1 1 0 0

Semper Timur 0 0 0 0 1 1 0 0

Suka Pura 0 0 0 0 2 1 1 0

Kelapa Gading Barat 0 0 0 0 0 0 0 1

Pegangsaan Dua 0 1 1 1 2 2 0 0

Lagoa 2 0 0 0 1 3 0 0


Village




Tugu Selatan 1 0 0 0 0 2 0 0

Tugu Utara 1 1 1 1 5 5 0 0

Ancol 2 1 0 0 2 4 0 0

Pademangan Barat 1 1 5 1 4 6 0 2

Pademangan Timur 1 0 1 1 1 1 0 0

Penjaringan 3 3 1 2 8 9 2 0

Kebon Bawang 2 0 0 0 1 1 1 0

Papango 2 0 0 0 0 1 1 0

Sunter Agung 2 0 0 0 0 2 0 0

Sunter Jaya 1 0 0 0 1 2 0 0

Tanjung Priuk 0 0 3 0 0 4 0 0

Warakas 1 0 0 1 1 2 0 0

Slum Neighborhood (RW’s Kumuh) Based on Housing Ownership and 3.4Proof of Ownership

The following Table III. 5 described selected RW’s Kumuh based on housing ownership and proof of ownership. Housing ownership status categorized based on the right of slum dwellers that allow them to occupy land and housing. There are four categories of ownership, which are: Owned right, usage land, private owned and public owned. Proof of ownership categorized based on the type of document hold by slum households ranging from more “secure tenure” such as certificate, Purchase Notarial to medium to low “secure tenure” such as Girik Letter, Purchase Receipt, Proof Letter from RT/RW/Head of Village.

Table 3-5 Slum Neighborhood Based on Status Ownership and Proof of

Ownership

Village

Status of Ownership Proof of Ownership

Ow

ne

d-

Rig

ht

Usa

ge L

and

Pri

vate

-O

wn

ed

Pu

blic

-O

wn

ed

Ce

rtif

icat

e

(HM

/HG

B)

Pu

rch

ase

N

ota

rial

(AJB

)

Gir

ik’s

Lett

er

Pu

rch

ase

Re

ceip

t

Pro

of

lett

er

fro

m

RT/

RW

/He

a

d o

f V

illag

e

Tomang 1 0 0 0 1 0 0 0 0

Kota Bambu Utara 1 0 0 0 1 0 0 0 0

Keagungan 4 0 0 0 4 0 0 0 0

Krukut 3 0 0 0 3 0 0 0 0

Jembatan Besi 5 0 0 0 5 0 0 0 0

Jembatan Lima 1 0 0 0 1 0 0 0 0

Kalibaru 2 1 0 0 2 0 0 0 1

Krendang 1 0 0 0 1 0 0 0 0


Village


Ow

ne

d-

Rig

ht

Usa

ge L

and

Pri

vate

-O

wn

ed

Pu

blic

-O

wn

ed

Ce

rtif

icat

e

(HM

/HG

B)

Pu

rch

ase

N

ota

rial

(AJB

)

Gir

ik’s

Lett

er

Pu

rch

ase

Re

ceip

t

Pro

of

lett

er

fro

m

RT/

RW

/He

a

d o

f V

illag

e

Petojo Selatan 2 0 0 0 2 0 0 0 0

Galur 2 0 0 0 2 0 0 0 0

Johar Baru 2 0 0 0 2 0 0 0 0

Kampung Rawa 3 0 0 0 3 0 0 0 0

Tanah Tinggi 4 0 0 0 4 0 0 0 0

Utan Panjang 1 0 0 0 1 0 0 0 0

Menteng 2 0 0 0 1 0 1 0 0

Pegangsaan 0 0 0 1 0 1 0 0 0

Karang Anyar 3 0 0 1 3 1 0 0 0

Kartini 1 1 0 1 1 2 0 0 0

Mangga Dua Selatan 5 0 0 0 5 0 0 0 0

Kramat 2 0 0 0 2 0 0 0 0

Kwitang 2 0 0 0 2 0 0 0 0

Senen 1 0 0 0 1 0 0 0 0

Kampung Bali 3 0 0 0 3 0 0 0 0

Cilandak Barat 1 0 0 0 1 0 0 0 0

Gandaria Selatan 2 0 0 0 2 0 0 0 0

Gandaria Utara 3 0 0 0 2 0 1 0 0

Kramat Pela 2 0 0 0 2 0 0 0 0

Petogogan 2 0 0 0 1 1 0 0 0

Grogol Utara 1 0 0 0 1 0 0 0 0

Bangka 1 0 0 0 1 0 0 0 0

Kuningan Barat 2 0 0 0 2 0 0 0 0

Mampang Prapatan 1 1 0 0 1 1 0 0 0

Pela Mampang 3 0 0 0 3 0 0 0 0

Tegal Parang 1 0 0 0 1 0 0 0 0

Pejaten Barat 1 0 0 0 1 0 0 0 0

Pejaten Timur 1 0 0 0 1 0 0 0 0

Ragunan 1 0 0 0 1 0 0 0 0

Bukit Duri 1 0 0 0 1 0 0 0 0

Cakung Barat 1 0 0 0 0 1 0 0 0

Jatinegara 1 0 0 0 1 0 0 0 0

Rawa Terate 4 0 0 0 0 1 3 0 0

Ceger 1 0 0 0 1 0 0 0 0

Cibubur 1 0 0 0 1 0 0 0 0

Rambutan 1 0 0 0 1 0 0 0 0

Klender 1 2 0 0 2 1 0 0 0

Cipinang Besar Selatan 3 0 0 0 2 1 0 0 0

Cipinang Besar Utara 2 2 0 0 1 1 0 0 2

Kampung Melayu 6 0 0 0 6 0 0 0 0

Rawa Bunga 1 0 0 0 1 0 0 0 0

Cawang 2 0 0 0 1 1 0 0 0

Kampung Tengah 2 0 0 0 1 1 0 0 0


Village


Ow

ne

d-

Rig

ht

Usa

ge L

and

Pri

vate

-O

wn

ed

Pu

blic

-O

wn

ed

Ce

rtif

icat

e

(HM

/HG

B)

Pu

rch

ase

N

ota

rial

(AJB

)

Gir

ik’s

Lett

er

Pu

rch

ase

Re

ceip

t

Pro

of

lett

er

fro

m

RT/

RW

/He

a

d o

f V

illag

e

Cipinang Melayu 2 0 0 0 0 2 0 0 0

Kebon Pala 1 0 0 0 0 1 0 0 0

Pal Meriem 0 1 0 0 0 0 0 0 1

Pekayon 1 0 0 0 1 0 0 0 0

Kayu Putih 2 0 0 0 1 1 0 0 0

Rawamangun 0 1 0 0 0 1 0 0 0

Cilincing 3 0 0 0 3 0 0 0 0

Kali Baru 1 4 0 3 2 1 0 0 4

Marunda 1 1 0 0 0 0 1 0 1

Semper Barat 1 0 0 0 1 0 0 0 0

Semper Timur 1 0 0 0 1 0 0 0 0

Suka Pura 1 0 1 0 2 0 0 0 0

Kelapa Gading Barat 0 0 0 0 0 0 0 0 0

Pegangsaan Dua 2 0 0 0 1 0 1 0 0

Lagoa 3 0 0 0 3 0 0 0 0

Tugu Selatan 1 1 0 0 1 0 0 0 0

Tugu Utara 4 0 0 1 4 0 0 0 1

Ancol 3 1 0 0 0 1 1 0 2

Pademangan Barat 7 1 0 0 8 0 0 0 0

Pademangan Timur 1 0 0 0 1 0 0 0 0

Penjaringan 5 5 0 1 4 2 1 3 1

Kebon Bawang 2 0 0 0 2 0 0 0 0

Papango 1 1 0 0 2 0 0 0 0

Sunter Agung 2 0 0 0 2 0 0 0 0

Sunter Jaya 2 0 0 0 1 0 1 0 0

Tanjung Priuk 1 3 0 0 4 0 0 0 0

Warakas 2 0 0 0 2 0 0 0 0

Slum Neighborhood (Selected RW’s Kumuh) Based on Access to 3.5

Water and Sanitation

The following Table III. 6 described selected RW’s Kumuh based on source of water supply, wastewater and solid waste management. Source of water supply consist of bottled water piped water, deep wells, shallow wells and river. Treatment to waste waste categorized into three options: no treatment, septic tank and waste into river. Solid waste management comprises three conditions: collected by operator, burn trash and no collection system.


Table 3-6 Slum Neighborhood Based on Access to Water and Sanitation

Village

Source of Water Supply Waste water Solid Waste

Bo

ttle

d

Wat

er

Pip

ed

Wat

er

De

ep

We

ll

Shal

low

W

ell

Riv

er

No

tr

eat

me

nt

Sep

tic

Tan

k

Wat

se in

to

rive

r

Co

llect

ed

by

op

erat

or

Bu

rn t

rash

No

co

llect

ion

syst

em

Tomang 0 1 0 0 0 0 1 0 1 0 0

Kota Bambu Utara 0 1 0 0 0 0 1 0 1 0 0

Keagungan 0 4 0 0 0 0 4 0 4 0 0

Krukut 0 3 0 0 0 0 3 0 3 0 0

Jembatan Besi 0 5 0 0 0 0 5 0 5 0 0

Jembatan Lima 0 1 0 0 0 0 1 0 1 0 0

Kalibaru 0 3 0 0 0 0 3 0 3 0 0

Krendang 0 1 0 0 0 0 1 0 1 0 0

Petojo Selatan 0 2 0 0 0 0 2 0 2 0 0

Galur 0 2 1 0 0 0 2 0 2 0 0

Johar Baru 0 2 0 1 0 0 2 0 2 0 0

Kampung Rawa 0 3 1 0 0 0 3 0 3 0 0

Tanah Tinggi 0 4 1 0 0 0 4 0 4 0 0

Utan Panjang 0 1 1 0 0 0 1 0 1 0 0

Menteng 0 1 1 0 0 2 0 0 2 0 0

Pegangsaan 0 0 1 0 0 0 1 0 1 0 0

Karang Anyar 0 4 0 0 0 2 2 0 4 0 0

Kartini 0 3 0 0 0 0 3 0 3 0 0

Mangga Dua Selatan 0 4 1 0 0 3 2 0 5 0 0

Kramat 1 1 2 0 0 0 2 0 2 0 0

Kwitang 0 2 1 0 0 2 0 0 2 0 0

Senen 0 0 1 0 0 0 1 0 1 0 0

Kampung Bali 0 3 0 0 0 0 3 0 3 0 0

Cilandak Barat 0 0 1 0 0 0 1 0 1 0 0

Gandaria Selatan 0 1 1 0 0 0 2 0 2 0 0

Gandaria Utara 0 2 2 0 0 0 3 0 3 0 0

Kramat Pela 0 1 2 1 0 0 2 0 1 1 0

Petogogan 0 1 2 0 0 0 2 0 2 0 0

Grogol Utara 0 1 0 0 0 0 1 0 1 0 0

Bangka 0 1 0 0 0 1 0 0 1 0 0

Kuningan Barat 0 1 2 0 0 0 2 0 2 0 0

Mampang Prapatan 0 2 0 0 0 0 2 0 1 0 1

Pela Mampang 0 2 1 0 0 0 3 0 3 0 0

Tegal Parang 0 0 1 0 0 0 1 0 1 0 0

Pejaten Barat 0 0 1 0 0 0 1 0 1 0 0

Pejaten Timur 0 0 1 0 0 0 1 0 1 0 0

Ragunan 0 0 1 0 0 0 1 0 0 1 0

Bukit Duri 0 1 1 0 0 0 1 0 1 0 0

Cakung Barat 0 1 0 0 0 0 1 0 1 0 0

Jatinegara 0 1 0 0 0 0 1 0 1 0 0

Rawa Terate 0 4 0 0 0 0 4 0 4 0 0

Ceger 0 0 1 0 0 0 1 0 1 0 0


Village

Source of Water Supply Waste water Solid Waste

Bo

ttle

d

Wat

er

Pip

ed

Wat

er

De

ep

We

ll

Shal

low

W

ell

Riv

er

No

tr

eat

me

nt

Sep

tic

Tan

k

Wat

se in

to

rive

r

Co

llect

ed

by

op

erat

or

Bu

rn t

rash

No

co

llect

ion

syst

em

Cibubur 0 0 1 0 0 0 1 0 1 0 0

Rambutan 0 1 0 0 0 0 1 0 1 0 0

Klender 0 3 0 0 0 0 3 0 3 0 0

Cipinang Besar Selatan

0 2 1 0 0 1 2 0 3 0 0

Cipinang Besar Utara 0 2 2 0 0 0 4 0 4 0 0

Kampung Melayu 0 4 1 0 1 0 3 3 3 0 3

Rawa Bunga 0 0 1 0 0 0 1 0 1 0 0

Cawang 0 2 0 0 0 0 2 0 1 0 1

Kampung Tengah 0 2 0 0 0 0 2 0 2 0 0

Cipinang Melayu 0 0 2 0 0 0 2 0 2 0 0

Kebon Pala 0 0 1 0 0 0 1 0 1 0 0

Pal Meriem 0 1 0 0 0 0 1 0 1 0 0

Pekayon 0 0 1 0 0 0 1 0 1 0 0

Kayu Putih 0 2 0 0 0 0 2 0 1 1 0

Rawamangun 0 1 0 0 0 0 1 0 1 0 0

Cilincing 0 3 0 0 0 0 3 0 3 0 0

Kali Baru 0 8 0 0 0 0 7 1 6 0 2

Marunda 0 1 1 0 0 0 2 0 2 0 0

Semper Barat 0 1 0 0 0 0 1 0 1 0 0

Semper Timur 0 1 0 0 0 0 1 0 0 0 1

Suka Pura 0 2 0 0 0 0 2 0 2 0 0

Kelapa Gading Barat 0 0 0 0 0 1 0 0 1 0 0

Pegangsaan Dua 0 2 0 0 0 0 2 0 2 0 0

Lagoa 0 3 0 0 0 0 3 0 3 0 0

Tugu Selatan 0 2 0 0 0 0 2 0 2 0 0

Tugu Utara 0 5 0 0 0 1 4 0 5 0 0

Ancol 0 4 0 0 0 0 4 0 4 0 0

Pademangan Barat 0 8 1 0 0 0 8 0 7 1 0

Pademangan Timur 0 1 0 0 0 0 1 0 1 0 0

Penjaringan 0 11 1 0 0 1 10 0 11 0 0

Kebon Bawang 0 2 0 0 0 0 1 1 2 0 0

Papango 1 1 0 0 0 0 2 0 1 0 1

Sunter Agung 0 2 0 0 0 0 2 0 2 0 0

Sunter Jaya 0 2 0 0 0 0 2 0 2 0 0

Tanjung Priuk 0 4 0 0 0 1 3 0 3 1 0

Warakas 0 2 0 0 0 0 2 0 2 0 0


Slum Neighborhood Based on The Availability of Drainage and Road 3.6System

The following Table III. 7 described selected RW’s Kumuh based on the availability of drainage and construction of road. The availability of drainage system categorized into three sources: no drainage, discharge into soil and stone pavement. Road construction categorized into: no road, dirt road, asphalt, paving block and cement pavement. Table 3-7 Slum Neighborhood Based on The Availability of Drainage and

Road System

Village

Flood Prone

Availability of drainage system

Construction of road N

o d

rain

age

Dis

char

ge

into

so

il

Sto

ne

pav

emen

t

No

ro

ad

Dir

t ro

ad

Asp

hal

t

Pav

ing

Blo

ck

Ce

men

t p

avem

ent

Tomang 0 0 0 1 1 0 0 0 0

Kota Bambu Utara 0 0 0 1 0 0 1 0 0

Keagungan 0 0 0 4 0 0 4 0 0

Krukut 0 0 0 3 0 0 3 0 0

Jembatan Besi 0 0 0 5 0 0 5 0 0

Jembatan Lima 0 0 0 1 0 0 1 0 0

Kalibaru 0 0 0 3 0 0 3 0 0

Krendang 0 0 0 1 0 0 1 0 0

Petojo Selatan 0 0 0 2 0 0 2 0 0

Galur 1 0 0 2 0 0 2 0 0

Johar Baru 0 0 0 2 0 0 2 0 0

Kampung Rawa 0 0 0 3 0 0 3 0 0

Tanah Tinggi 0 0 0 4 0 0 4 0 0

Utan Panjang 0 0 1 0 0 0 1 0 0

Menteng 0 0 0 2 0 0 2 0 0

Pegangsaan 0 0 0 1 0 0 1 0 0

Karang Anyar 0 2 0 2 3 0 1 0 0

Kartini 0 0 0 3 0 1 2 0 0

Mangga Dua Selatan 0 3 0 2 3 0 2 0 0

Kramat 0 0 0 2 0 0 2 0 0

Kwitang 0 0 0 2 0 0 2 0 0

Senen 0 0 0 1 0 0 1 0 0

Kampung Bali 0 0 0 3 0 0 3 0 0

Cilandak Barat 0 0 0 1 0 0 1 0 0

Gandaria Selatan 0 0 0 2 0 0 2 0 0

Gandaria Utara 0 0 0 3 1 0 2 0 0

Kramat Pela 0 0 0 2 0 0 2 0 0

Petogogan 2 0 0 2 0 0 2 0 0

Grogol Utara 0 0 0 1 0 0 1 0 0


Village

Flood Prone


Construction of road

No

dra

inag

e

Dis

char

ge

into

so

il

Sto

ne

pav

emen

t

No

ro

ad

Dir

t ro

ad

Asp

hal

t

Pav

ing

Blo

ck

Ce

men

t p

avem

ent

Bangka 0 0 0 1 0 0 1 0 0

Kuningan Barat 0 0 0 2 1 0 1 0 0

Mampang Prapatan 0 0 0 2 0 0 2 0 0

Pela Mampang 0 0 0 3 0 0 3 0 0

Tegal Parang 0 0 0 1 0 0 1 0 0

Pejaten Barat 0 0 0 1 0 0 1 0 0

Pejaten Timur 0 0 0 1 0 0 1 0 0

Ragunan 0 0 0 1 0 0 1 0 0

Bukit Duri 1 0 0 1 0 0 1 0 0

Cakung Barat 0 0 0 1 0 0 1 0 0

Jatinegara 0 0 0 1 0 0 1 0 0

Rawa Terate 2 0 0 4 0 0 4 0 0

Ceger 0 0 0 1 0 0 1 0 0

Cibubur 0 0 0 1 0 0 1 0 0

Rambutan 0 0 0 1 0 0 1 0 0

Klender 0 0 0 3 0 0 3 0 0

Cipinang Besar Selatan 0 0 0 3 0 0 3 0 0


Kampung Melayu 3 0 0 6 0 2 4 0 0

Rawa Bunga 0 0 0 1 0 0 1 0 0

Cawang 1 0 0 2 0 0 2 0 0

Kampung Tengah 0 0 0 2 0 0 2 0 0

Cipinang Melayu 0 0 0 2 0 0 2 0 0

Kebon Pala 1 0 0 1 0 0 1 0 0

Pal Meriem 0 0 0 1 0 0 1 0 0

Pekayon 0 0 0 1 0 0 1 0 0

Kayu Putih 0 0 0 2 0 0 2 0 0

Rawamangun 0 0 0 1 0 0 1 0 0

Cilincing 3 0 1 2 0 0 0 1 2

Kali Baru 0 0 0 8 0 1 5 0 2

Marunda 1 1 0 1 0 1 1 0 0

Semper Barat 1 0 0 1 0 0 0 0 1

Semper Timur 1 1 0 0 1 0 0 0 0

Suka Pura 1 0 0 2 0 1 1 0 0

Kelapa Gading Barat 0 1 0 0 1 0 0 0 0

Pegangsaan Dua 1 0 0 2 0 0 2 0 0

Lagoa 0 0 0 3 0 0 3 0 0

Tugu Selatan 0 0 1 1 0 0 0 0 2

Tugu Utara 0 0 1 4 0 1 3 0 1

Ancol 4 0 0 4 0 0 4 0 0


Pademangan Timur 1 0 0 1 0 0 1 0 0


Village

Flood Prone


Construction of road

No

dra

inag

e

Dis

char

ge

into

so

il

Sto

ne

pav

emen

t

No

ro

ad

Dir

t ro

ad

Asp

hal

t

Pav

ing

Blo

ck

Ce

men

t p

avem

ent

Penjaringan 11 1 0 10 4 0 5 0 2

Kebon Bawang 2 0 0 2 0 0 1 0 1

Papango 0 0 0 2 1 0 0 0 1

Sunter Agung 0 0 0 2 0 0 1 0 1

Sunter Jaya 0 1 1 0 0 1 1 0 0

Tanjung Priuk 4 0 0 4 1 0 3 0 0

Warakas 0 0 0 2 0 0 1 0 1

Flood Prone Slum Area 3.7

3.7.1 North Jakarta Slum Areas

North Jakarta area has long history of flooding that caused by river flooding and tidal flooding. Based on the flood prone map produced by BPBD DKI Jakarta, 68,35% of the slum areas in North Jakarta region are located in the flood prone area and experiencing flood every year.

Kelurahan that has many slum areas are Kalibaru (11 RW slums), Lagoa (12 RW slums), Pejagalan (11 RW Slums), and Penjaringan (12 RW slums). Kelurahan Penjaringan has the most vulnerable slum area to the flood because all the 12 RW slums in that area were located in the flood prone area. The next vulnerable kelurahan is Pejagalan where 9 RW out of 11 RW slums in that area were located in the flood prone area, then followed by Kelurahan Lagoa with 50% out of 12 RW slums located in the flood prone areas. Meanwhile in Kelurahan Kalibaru, the number of RW slums located in flood prone areas only 4 RW’s out of 11 RW slums. In North Jakarta area, there are only two kelurahan’s where the slum areas does not located in the flood prone area; they are Kelurahan Tugu Utara and Kelurahan Warakas.


Figure 3-2. Slum typology and flood prone area in North Jakarta

The typology of slum areas in North Jakarta area does not indicate whether the slum area were vulnerable to the flood or not. It is shown by the table below that all slum typology has more than 40% of its location located in the flood prone areas. Even the railroads slum areas which suppose to be in the higher ground have 100% of its slum location located in the flood prone areas.

Table 3-8 Number of RW's in flood prone area based on slum typology in North Jakarta

Slum Typology Flood Prone RW's Grand

Total No Yes

Riverbanks 42 87 129

Others 1 2 3

Coastal 7 6 13

Railroads 0 13 13

Grand Total 50 108 158


3.7.2 West Jakarta Slum Areas

West Jakarta area has a similar flat topography characteristic with North Jakarta Area. This condition also gives similar vulnerability to flood in West Jakarta. Based on the flood prone map produced by BPBD DKI Jakarta, 80,87% of the slum areas in West Jakarta region are located in the flood prone area and experiencing flood every year.

Kelurahan that has many slum areas is Kelurahan Kapuk (10 RW slums), and all of the slum areas in Kelurahan Kapuk located in flood prone area. This condition gives slum areas in Kelurahan Kapuk as the most vulnerable to flood. Based on the overlay between slum area map and flood prone area from BPBD DKI Jakarta, there is only few Kelurahans in West Jakarta Region where their slum area does not located in the flood prone area. Kelurahan Krukut has only 1 RW out of 5 RW slum that located in flood prone area, followed by Kelurahan Taman Sari (1 RW out of 4 RW slum) and Kelurahan Tangki (1 RW out of 3 RW slum).

Figure 3-3. Slum typology and flood prone area in West Jakarta

The typology of slum areas in West Jakarta area does not indicate whether the slum area were vulnerable to the flood or not. It is shown by the table below that


all slum typology has more than 50% of its location located in the flood prone areas.

Table 3-9. Number of RW's in flood prone area based on slum typology in West Jakarta


Total No Yes


Others 3 15 18

Coastal

2 2

Railroads 14 30 44


3.7.3 Central Jakarta Slum Areas

Central Jakarta area is the center of DKI Jakarta’s urban area where majority of government office located and also bussines office. Based on the flood prone map produced by BPBD DKI Jakarta, only 30,07% of the slum areas in Central Jakarta region are located in the flood prone area and experiencing flood every year.

Kelurahan that has many slum areas are Kelurahan Mangga Dua Selatan (9 RW slums) and Kelurahan Kebon Melati (9 RW slums). Both kelurahan has 4 RW out of 9 RW slums (44,44%) located in the flood prone area. Based on the overlay between slum area map and flood prone area from BPBD DKI Jakarta, the most vulnerable slum area to flood is the one located on Kelurahan Galur where all 7 RW slums (100%) located at flood prone area; and followed by Kelurahan Petamburan with 4 RW out of 5 RW slums located in flood prone area.


Figure 3-4. Slum typology and flood prone area in Central Jakarta

The typology of slum areas in Central Jakarta area does not indicate whether the slum area were vulnerable to the flood or not. It is shown by the table below that all slum typology has less than 50% of its location located in the flood prone areas

Table 3-10 Number of RW's in flood prone area based on slum typology

in Central Jakarta


Total No Yes

Riverbanks 55 18 73

Others 16 10 26

Coastal 22 12 34

Railroads 93 40 133


3.7.4 East Jakarta Slum Areas

East Jakarta area is very vulnerable to flood because of the influence of Ciliwung River and Cisadane River, but not all slum area of East Jakarta region located on


flood prone areas. Based on the flood prone map produced by BPBD DKI Jakarta, only 39,75% of the slum areas in East Jakarta region are located in the flood prone area and experiencing flood every year. This condition was the result of the construction of Jakarta East Flood Canal in 2002 – 2010.

Kelurahan that has many slum areas is Kelurahan Cipinang Besar Utara with 9 RW slums, but all of the RW slums did not located in flood prone areas. The most vulnerable slum area in East Jakarta region is Kelurahan Bidara Cina (7 RW slums) and Kelurahan Kampung Melayu (6 RW slums) where all the slum area in that location located in flood prone area.

Figure 3-5. Slum typology and flood prone area in East Jakarta

Slum areas located along the railroads and other slum areas that is not near to river and railroads in East Jakarta region have low vulnerability to flood. This is because of the topographic location of the slum areas were on the higher ground and far from river. Meanwhile for slum areas located in the riverbanks, the vulnerability becomes high because most of the flood were coming from the overflowing river.



in East Jakarta


Total No Yes


Others 6 1 7

Railroads 8 4 12


3.7.5 South Jakarta Slum Areas

South Jakarta area has a hilly topographic charasteristic. This condition causing South Jakarta have less flood prone area compare to other Jakarta areas. Based on the flood prone map produced by BPBD DKI Jakarta, only 31,62% of the slum areas in South Jakarta region are located in the flood prone area and experiencing flood every year.

Kelurahan that has many slum areas is Kelurahan Pela Mampang with 6 RW slums, but only 2 RW out of 6 RW slums in Kelurahan Pela Mampang which located in flood prone areas. The rest of the kelurahans with slum area only consist of 1 to 3 RW slums that located in flood prone area.


Figure 3-6. Slum typology and flood prone area in South Jakarta

In South Jakarta, floods usually caused by overflowing rivers in the north part of South Jakarta and bad drainage. That is why as seen on the table below, the number of riverbanks slum which located in flood prone area is around 30%. The flooded railroads slum areas is mainly caused by the location that is also near the Ciliwung River in the Kelurahan Manggarai.


in South Jakarta


Total No Yes

Riverbanks 60 27 87

Others 7 0 7

Railroads 0 4 4



4 PROFILE OF HOUSEHOLD IN SELECTED RW’S

KUMUH

This section elaborates the results of a household survey in selected slum areas with the aimed to further develop the database and contribute a more robust understanding of the slum areas. It is consisted these four main steps: i) design methodology and questionnaire for household survey; ii) household survey; iii) survey results inputted into slum information database and iv) an analysis based on the survey.

Sampling 4.1

4.1.1 Coverage Area

A household survey conducted in slum neighborhood following the 2011 data on RW Kumuh in DKI Jakarta. The unit of analysis is at neighborhood level (RW) and then, in each RW’s, a number of household are taken as the respondent. The population of slum neighborhoods based on 2011 RW Kumuh data is about 392 RW’s which covered 413,078 slum households.

4.1.2 Sampling Frame

A stratified cluster sampling is used as sampling frame in this survey which consisting these following stages: a. The sampling frame for selection of neighborhood unit based on slum

deprivation levels such as: heavy, medium, light and very light slums. This

survey conducted in heavy and medium slums.

b. The sampling frame for selection of neighborhood unit based on location

typologies such as: proximity to river and water-pond; proximity to

railway; proximity to coastal area and proximity to other infrastructure

facilities (toll road, waste disposal site, etc). The location typology

analysed using GIS tool by overlying neighborhood boundary and

proximity to location typologies, i.e: the buffer distance for

riverbank/waterpond/railway is about 200 meter and for coastal area is

about 500 meter.


c. The sampling frame for selection of household (slum and non-slum

households).

4.1.3 Number of Sample

The number of sampling households is determined based on the population of RW’s kumuh and then distributed based on slum typologies. The sampling household is set at 1,000 slum dwellers spread-out in 50 slum neighborhoods (RW’s Kumuh). Detail calculation of household sampling is following:

a. Calculating the number of neighborhood unit and household based on

location typologies

Figure 4-1 Sampling Frame

b. Calculating sampling for each location typologies

The number of sampling in each typology calculated based on this formula:

Population 392 RW

413.078 KK

Stratum Water Body

288 RW 325.584 KK

Stratum Railway 58 RW

47.478 KK

Stratum Coastal 22 RW

20.852 KK

Stratum Others 24 RW

19.164 KK

Sample 37 RW’s 788 HH





| ( )|

| ( )| ( )

Z = Based on Z score (confidence level). Trust level that used is 99%,

so the z score is 2,58

p (1-p) = Population variation assumed heterogen so p = 0,5 dan p (1-p) =

0,25.

N = Population number: 413.078 KK.

E = Sampling error: 4,10%.

n = Sample number is 987,58 HH ≈ 1,000 HH for all stratum.

Based on Roscoe (1982:253), sample size for studies using the experimental

group and the control group, the number of each sample between 10-20.

For a household this slum survey, RW classified as slum in 2013 act as an

experimental group, while non-slum RW in 2013, is a control group. By

taking the upper limit of 20 (as the initial default number of households per

RW), then the number of surveyed RW is 1000 divided by 20 HH / RW = 50

RW. Sample cluster number then divided proportionally.

c. Distribution of Sampling for Each Typology

Table 4-1 Population and Sampling

Stratum

RW’s (Neighborhood) Household

Population Percent

age

Number of

Sampling Population Percentage

Number of

Sampling

Water Body

288 73.47 37 325,584 78.82 788

Railway 58 14.80 7 47,478 11.49 115

Coastal 22 5.61 3 20,852 5.05 50

Others 24 6.12 3 19,164 4.64 46

Total 392 100 50 413,078 100 1,000


4.1.4 Sampling Plan

Samples are selected using purposive method by considering the operational convenience in the field. It is also considering the time availability of household in each neighborhood unit. Purposive selection of households is conducted in such a way that the sample size is even or relatively has the same distance as the sample frame. The selected neighborhood unit and location typologies are presented in Table IV.2.

Table 4-2 Distribution of Household Based on Typology

No

Sub-District Village

Number of Household

Distribution of HH’s by Typology

River-bank

Railway Coastal Others

North Jakarta 237

1.

Cilincing

Cilincing 37 37

2. Kali Baru 38 38

3. Semper Timur 15 15

4. Koja Tugu Utara 29 29

5.

Pademangan Ancol 19 19

6. Pademangan Barat 23 23

7. Penjaringan Penjaringan 19 19

8. Tanjung Priok Papanggo 57 57

South Jakarta 72

9. Mampang

Prapatan Kuningan Barat

15 15

10. Tebet

Bukit Duri 38 15 23

11. Menteng Dalam 19 19

Central Jakarta 135

12.

Johar Baru

Johar Baru 13 13

13. Kampung Rawa 10 10

14. Tanah Tinggi 12 12

15. Menteng Menteng 12 12

16. Penjaringan Pejagalan 15 15

17.

Sawah Besar

Karang Anyar 20 20

18. Kartini 14

14

19. Mangga Dua Besar 10 10

20. Senen Senen 12 12

21. Tanah Abang Karet Tengsin 17 17


No

Sub-District Village

Number of Household

Distribution of HH’s by Typology

River-bank

Railway Coastal Others

West Jakarta 308

22.

Cengkareng

Cengkareng Timur 75 75

23. Kapuk 114 114

24. Kedaung Kali Angke 25 25

25. Grogol Petamburan

Jelambar 21

21

26. Kebon Jeruk Kedoya Utara 28 28

27. Palmerah Jati Pulo 7 7

28. Tambora

Duri Selatan 11 11

29. Kali Anyar 27

27

East Jakarta 209

30.

Cakung

Jatinegara 24 24

31. Penggilingan 39 39

32. Rawa Terate 15 15

33. Duren Sawit Klender 15 15

34.

Jatinegara Cipinang Besar Utara 17 17

35. Kampung Melayu 35 35

36. Kramat Jati Kampung Tengah 27 27

37.

Pulo Gadung Pisangan Timur 12 12

38. Pulo Gadung 25 25

Total 771 35 38 117

Analysis by Slum Typology 4.2

4.2.1 Socio- Economic Characteristics of Household in Selected RW’s Kumuh

A. Age Group

The survey was conducted on a sample of 1,000 households in 38 villages, in 23 sub-districts. From 1,000 household samples, only 960 households data were analyzed, while others were not valid. Most of the households were in the age group 18-59 years (854 people) while the least were in 0-17 years age group (6 person).


Table 4-3 Number of Household by Age Group and Typology

Age Group Slum Typology Total

Riverbank Railway Coastal Other

0 - 17 4 1 1 6

18 - 59 686 105 31 32 854

60+ 80 11 6 3 100

Total 770 116 38 36 960

B. Education

As shown in Table IV.4, most (35.2 percent) of the respondent household in the sample area had completed primary school as their highest educational attainment. While 28.7 percent of the respondent household had senior school certificate, 27.5 percent completed the junior high school and 5.5 percent had no primary school certificate. Very few households (2.9 percent) were graduated from Diploma and Undergraduate.

Table 4-4 Percentage of Household by Age Group and Highest Educational Attainment

Highest Educational Attainment Slum Typology

Total Riverbank Railway Coastal Other

Have No Primary School Certificate 6.4 0.9 7.9 0.0 5.5

Primary School (SD/MI) 35.0 27.4 60.5 37.1 35.2

Junior High School (SMP/MTS) 25.4 40.2 15.8 42.9 27.5

Senior High School (SMA/MA) 30.0 28.2 13.2 20.0 28.7

Diploma (DI/DII/DIII) 1.8 0.9 2.6 0.0 1.7

Undergraduate and Post Graduate (S1/ S2)

1.2 2.6 0.0 0.0 1.2

Not Answered 0.3 0.0 0.0 0.0 0.2

Total 100.0 100.0 100.0 100.0 100.0

C. Employment

Almost half of respondents (462 people) are working as house wife which meant the surveyor interviewed more women than men during survey. Women usually spend more time at house for domestic activities than men. People living in slum area usually work in informal economy. These can be seen from the number of people working as small trader (184 people), labor (137 people) and private employee (128 people).


Table 4-5 Number of Household by Type of Employment

Type of Employment Slum Typology

Total Riverbank Coastal Railway Other

Small Trader 147 9 19 9 184

Labor 111 5 17 4 137

Teacher 1 1

2

House-Wife 360 18 64 20 462

Private Employee 119 1 8

128

Undergraduate Student 1

1

Fisherman 2

2

Student 3

3

Unemployment 17 5 5 2 29

Retirement 9 3

12

Total 770 38 117 35 960

D. Income

Most (509 HHs) of the respondent household had monthly income ranged from IDR 1 – 2.5 million. While 260 respondent households had monthly income above IDR 2.5 million and 182 respondent households had monthly income below IDR 1 million.

Table 4-6 Number of Household by Monthly Income

Monthly Income

Slum Typology Total Riverbank Railway Coastal Other

< Rp. 1.000.000 153 12 17 ` 182

Rp. 1.000.000 – 2.500.000 406 70 12 21 509

> Rp. 2.500.000 211 35 9 5 260

Total 770 117 38 26 951

E. Income vs Expenditure

The level of income people who are living in slum area just fulfilled their basic needs without any saving capacity to improve their welfare. Most (710 people) of respondent household had monthly expenditure equal to monthly income. It means they don’t have saving capacity to cover other and incidental needs. Only few (99 people) respondent households had saving capacity, while 150 respondent households had deficit income to cover their monthly expenditure.


Table 4-7 Number of Household by Monthly Income and Monthly

Expenditure

Monthly Income

Monthly Expenditure

< Rp. 1.000.000 Rp. 1.000.000 –

2.500.000 > Rp. 2.500.000

< Rp. 1.000.000 110 73 8

Rp. 1.000.000 – 2.500.000 16 423 69

> Rp. 2.500.000 3 80 177

No saving Deficit Saving

4.2.2 Housing and Land-Related Characteristics of Household in RW’s

Kumuh

A. Length of Stay by Typology

Based on the results of a survey of more than 700 people living in slum

areas with riverbanks characteristics, 59% of the populations are native and

41 % are immigrants. Most of the immigrants live in the slum area have

inhabited the location for 20 years, only about 6% of immigrants lived in the

area less than 5 years. The ethnic composition of the origin of most

immigrants came from Javanese, Betawi, and Sundanese.

In the big cities in Indonesia, the sides along the railroad tracks often

become the location of slums and squatter settlements. Based on the

results of field survey on the characteristics of the slum area on the banks of

the rail, the composition of the population 63% are native and 27% are

immigrants. Residents have occupied most of the territory for 10 - 30 years

Based on survey results that people living in areas with coastal slum

characteristics, from 82% of the population are native and 12% are

immigrant. When compared with the riverbanks areas, coastal areas

classified as less variety in the types of jobs in the informal sector. Most jobs

in this region are related to fisheries or marine activities. Referring to the

results of the field survey, the profile of jobs in this area is a food stall, fish

sellers and fishermen. While in the area with the characteristics of

riverbanks slums are much more diverse types of informal jobs. This


condition may cause the number of migrants in the coastal slum areas is

much smaller than in the riverbanks area.

The uniqueness in this area is the majority of the population who calls a

native actually comes from Central or East Java (Javanese) that is equal to

76%. While a native rsidents in riverbanks slum area is Betawi. This

corresponds to the historical development of the northern coasts of Jakarta,

especially like Kali baru and Kamal Muara area, where fishing activities

initiated by Javanese people from the northern coasts of Java such as

Indramayu and Tegal. Therefore they call themselves as native; eventhough

they still holds the Javanese culture and were born and raised in Coastal

Jakarta. Different phenomena shows in the western part of Jakarta Coast,

where the population dominated with Bugis people from South Sulawesi.

Field survey conducted in the Grogol - West Jakarta and Sawah Besar -

Central Jakarta is expected to represent the condition of slums that are not

located on the riverbanks, coastal, and along side of railroads. Based on the

survey, 60% of respondents are immigrants and the other 40% are native.

Residents in the area averagely have occupied its current location for 10

years. For the composition of the ethnics in this region is Betawi as a native

and the immigrants came from Javanese and Sundanese.

Table 4-8 Number of Household by Length of Stay and Typology

Length of Stay (year)

Slum Typology Total Riverbank Railway Coastal Other

< 10 Years 16 16 14 48

10-20 Years 25 25 43 38

21 - 30 Years 19 19 43 14

> 30 Years 40 40 0 0

Total

B. Reason to live in slum area

Proximity to the workplace becomes the main background of migrants in

choosing a location as a place for living. 94% respondents mentioned the

proximity to the workplace becomes a reason to stay at its current location.


Most of the population in this region works in the informal sector as a

trader/entrepreneur, construction workers, and daily labor. Only a small

part of the work as private employees, it is approximately 4.5% of the 770

respondents to the population

Most of the reasons residents choose to live on the banks of the rail is the

proximity to family and workplace. 98% of the population works in the

informal sector such as construction laborers, traders, and motorcycle taxi

drivers (ojek). Only 2% of the population works in the formal sector, as a

factory worker. This condition is often the case in big cities, where the city

alone cannot provide jobs for those newcomers who lack the skills and low

education. Therefore, they will do anything for jobs in order to survive. Low

income makes them choose to live anywhere as long as they feel safe.

Finally they inhabit a particular area that came to be called slums.

As many as 85% of respondents mentioned the proximity to the workplace

be the main background in choosing their current location as their

residence. As in other slum areas, the majority of the population in this

region works in the informal sector as a trader, a motorcycle taxi driver

(Ojek) and a household assistant (maid). At this location of all randomly

selected respondents, it did not found any residents with jobs in the formal

sector. Field survey results showed that only 6% of respondents who have a

plan to move from their current location in the future, while the other 82%

chose to settle in its current location.

Table 4-9 Percentage of Household by reason to live in slum area

Reason Slum Typology Total


Closer to family/relatives 60.3 60.7 44.7 80.0 60.5

Strategic location (close to workplace)

20.0 15.4 5.3 20.0 18.8

Hospitality of local community 2.7 3.4 10.5 0.0 3.0

Low and affordable price of housing and rental

4.0 1.7 0.0 0.0 3.4

Low and affordable price of daily consumer goods

0.4 0.9 0.0 0.0 0.4

Other 12.6 17.9 39.5 0.0 13.8

Total 100.0 100.0 100.0 100.0 100.0


C. Housing Ownership

Based on field survey results we can see that the majority of the population

in slum areas in riverbanks areas lives in privately or family owned homes,

31% others occupy a rental house and a small portion of population stays

without paying (3.8%). This condition is in line with the previous facts that

half the population in this region is a native, and the migrants also have

stayed for a long time. Based on these facts, it is concludes that the natives

residents are those who live in private or family homes. While immigrants

live in this area by renting the house.

In the coastal slum areas, the percentage of house ownership status is

similar to the riverbanks slum areas conditions, where majority of the

population occupies privately owned houses and family owned houses. Only

a small percentage of 13.2%, which occupies rental houses and 10.5%

stayed with other people and did not pay rent. The phenomena differs in

the railroads slum area and other slum area where most of the residents did

not owned the house but they only rent the house (47.6% in railroads slum

area and 42.9% in other slum area).

The majority of the population in railroads slum areas also living in privately

owned and family owned homes. 32% of the population rent the house and

a small portion of 5% live aboard. This condition is also in line with the facts

in the discussion that 63% of the population in this region is a native and

migrants who have lived the majority of the time period of more than 10

years.

Table 4-10 Percentage of Household by Housing Ownership

Housing ownership Slum Typology


Privately Owned 38.1 42.7 47.4 22.9 38.5

Family Owned 26.6 20.5 26.3 34.3 26.1

Rental 31.1 31.6 13.2 42.9 30.9

Not paying rent 3.8 5.1 10.5 0.0 4.1

Others 0.4 0.0 2.6 0.0 0.4

Total 100.0 100.0 100.0 100.0 100.0


D. Proof of Ownership

Identification of proof of ownerships were collected at the privately and

family owned house. The results of the field survey showed that most of the

house in the riverbanks slum area with the status of private or family

ownership has been accompanied by a valid proof of land ownership as a

land certificate (SHM), deed of sale (AJB) and the right to build (HGB). The

rest of the houses, which is about 26% have a certificate from the local

authorities, such as RT / RW or Kelurahan’s, and other evidence in the form

of girik. 7%, 9%, 43%, 10% are the amount of the residents who occupy a

private home / family home but does not have proof of ownership in

riverbanks slum area, coastal slum area, railroads slum area, and other slum

area consecutively.

Table 4-11 Percentage of Household by Proof Land Ownership

Legal Status Slum Typology


High land tenure security 47.6 51.3 71.1 25.7 48.2

Sertifikat Hak Milik (SHM) 23.6 18.8 18.4 17.1 22.6

Hak Guna Bangunan (HGB) 8.0 5.1 7.9 8.6 7.7

Akte Jual Beli (AJB) 16.0 27.4 44.7 0.0 17.9

Less land tenure security 15.8 3.4 0.0 28.6 14.2

Girik 7.0 1.7 0.0 8.6 6.1

Surat Keterangan

RT/RW/Kelurahan 8.8 1.7 0.0 20.0 8.0

No land tenure security 36.6 45.3 28.9 45.7 37.7

Tidak Memiliki Bukti

Kepemilikan Tanah 32.4 42.7 7.9 42.9 33.1

Lainnya: 4.2 2.6 21.1 2.9 4.6

Total 100.0 100.0 100.0 100.0 100.0


Figure 4-2 Proof of ownership certificate. Top Left: riverbanks slum area; Top Right: Coastal slum area; Bottom Left: Railroads slum area; Bottom

right: Other slum area

Referring to Fig. IV.1 above, it can be seen that the majority of the

populations have proof of land ownership of the house. Residents occupy

the land legally. The legality of occupied land can also be seen from the

participation of the population in paying property tax (PBB). Based on field

survey obtained information that 79% of people who have proof of land

ownership in riverbanks slum areas pay the property tax.

In contrast to the conditions that occurs in the slums along the river and

coastal areas, where the majority of the population that occupies a private

home or family home has had proof of land ownership. While in the

railroads slum areas large numbers of residents does not have proof of land

ownership (illegal dwellers). So it can be indicated that the settlements

along the railroads not only have the character of a slum area but also

squatter area.


E. Housing Expenditure

Housing rent expenditure by slum dweller indicated level of affordability to

access housing as basic needs. Most household (621 people) lived in their

own housing without paying any cost related to housing. Table IV.13 shown

number of household by housing expenditure and slum typology in selected

RW’s Kumuh and continued by further explanation of housing expenditure

in each typology.

Table 4-12 Number of Household by Housing Expenditure and Slum

Typology

Monthly Rental Cost Slum Typology


Self Owned 499 74 28 20 621

No Answered 55 6 5

66

0 - 100.000 2

2

100.000 - 200.000 11 4 3

18

200.000 - 300.000 45 6 1 5 57

300.000 - 400.000 40 8 1 4 53

400.000 - 500.000 47 13

3 63

500.000 - 600.000 30 4

34

600.000 - 700.000 21 1

2 24

700.000 - 800.000 9 1

10

800.000 - 900.000 1

1

900.000 - 1.000.000 10

10

> 1.000.000 1

1 2

Total 771 117 38 35 961

Rented house occupied by settlers in riverbanks slum areas have monthly

rental price range worth Rp.100.000 - Rp.1.000.000. Most of the homes

rented by residents have a price range of less than Rp.700.000/month and

only 9% of the people who rented the house at a cost of more than the

price of proficiency level. In the coastal slum area, the monthly rent price is

ranging from Rp.200.000 – Rp.400.000.


Table 4-13 Monthly rent price of house at riverbanks slum area

Monthly Rent Price (Rp)

Percentage (10%)

<500,000 48

500,000 - 700,000 43

700,000 – 1,000,000 9

Source: Field Survey 2014

Meanwhile in railroads slum areas, rent house occupied by settlers have

monthly rental price range worth Rp.100.000 - Rp.800.000. Most of the

population (65%) occupies the house with rents less than

Rp.500.000/month

Table 4-14 Monthly rent price of house at railroads slum area


Percentage (10%)

<500,000 65

500,000 - 800,000 35


In other slum areas, rented house has a price range at Rp.250.000 -

Rp.1.250.000 per month. Most of the homes are rented by residents have a

price ranging from Rp.250.000 - Rp.500.000 and 33% of respondents who

rented the house at a cost of more than Rp.500.000. Table 6 shows the

details of the percentage of the price of the rental house occupied by

settlers in other slum areas.

Table 4-15 Monthly rent price of house at other slum area


Percentage (10%)

250.000 – 500.000 67

500,001 – 1.250.000 33



4.2.3 Physical Environment Characteristics of Household in Selected RW’s Kumuh

Slums are generally begins with the construction of housing by the informal

sector. In line with the activity of the population resulted in dense

residential neighborhood, irregular, and have facilities that do not meet

environmental standards. Slum conditions in the coastal region can be

originated from natural phenomena that interact with people's activities

resulted in unsanitary conditions. As the incidence of tidal inundation

floods, occurs naturally in coastal areas. However becomes unhealthy when

the rest of the region commonly inundated later used by the population as a

waste disposal site either humans or household waste. Coastal slum area

also can be in the form of houses on stilts above the sea water and does not

have a sewer so liquid and solid wastes directly discharged into the sea.

A. Building Condition and Material

Building and housing condition can be analyzed by type of material used for

roof, floor and wall. Percentage of household by housing materials can be

seen from this following Table.

Table 4-16 Percentage of Household by Housing Materials

Material Slum Typology Total


1. Roof 100.0 100.0 100.0 100.0 100.0

Concrete 4.7 2.6 0.0 0.0 4.1

Roof tile 33.7 29.9 10.5 17.1 31.7

Zinc 7.3 13.7 2.6 2.9 7.7

Asbestos 54.0 53.8 86.8 80.0 56.2

Other 0.4 0.0 0.0 0.0 0.3

2. Wall 100 100 100 100 100

Plastered wall 78.5 79.5 42.1 51.4 76.2

Non plastered wall 11.5 13.7 18.4 28.6 12.7

Wood 8.6 4.3 34.2 20.0 9.5

Bamboo 0.0 0.9 0.0 0.0 0.1

Other 1.4 1.7 5.3 0.0 1.6

3. Floor 100 100 100 100 100

Ceramics 61.9 70.1 45.9 42.9 61.6


Material Slum Typology Total


Tile/floor tiles 23.8 21.4 29.7 40.0 24.3

Cement 12.9 7.7 0.0 5.7 11.5

Wood 1.2 0.9 10.8 11.4 1.9

Ground floor 0.3 0.0 13.5 0.0 0.7

Total 100.0 100.0 100.0 100.0 100.0

Physically, most of the houses in the riverbanks slum area have

characteristics of plastered walls, tiled floors and asbestos as a roofing

material. Fig. IV.2 illustrate the percentage of the house condition in the

riverbanks slum area.

Figure 4-3 Buildings characteristics of riverbanks slum area. Top left: Percentage of roofing material; Top right: Percentage of wall type; Bottom

left: Percentage of floor type; Bottom right: Number of houses based on land size

Based on the illustration in Figure IV.2, the physical building of houses in

riverbanks slum area already quite good but in the aspect of buildings land


size is still relatively low (20-60m2). Conditions often found in the slums is

the density of residential homes that have not been in accordance with the

standards of environmental health.

Physically, most of the houses in the coastal slum area have the

characteristics of a rigid walled and plastered walls, tiled floors and asbestos

roofs and concrete. Figure IV.3 describe the percentage of house

characteristic at coastal slum areas.

Figure 4-4 Buildings characteristics of coastal slum area. Top left:

Percentage of roofing material; Top right: Percentage of wall type;

Bottom left: Percentage of floor type; Bottom right: Number of houses based on land size

Every house in coastal slum areas is inhabited by an average of one to two

families (households). The average number of household members in each

house is about 4-6 people. Most houses have an area of 20-60 square

meters. The extent of the house and the number of people living inside it

shows the number of density in the house.

In the physical condition of most of the buildings houses in railroads slum

areas has characteristics of asbestos, cement stucco wall, and ceramic floor


tiles. Figure 4 describe the percentage of house characteristic at railroads

slum areas.

Figure 4-5 Buildings characteristics of railroads slum area. Top left: Perentage of roofing material; Top right: Percentage of wall type; Bottom

left: Percentage of floor type; Bottom right: Number of houses based on

land size

Physically, the material of most of the houses in the slums in other regions

has the characteristics of plaster walls, tiled floors and asbestos as a roofing

material. Here's an illustration of the physical condition of the houses.


Figure 4-6 Buildings characteristics of other slum area. Top left:

Perentage of roofing material; Top right: Percentage of wall type; Bottom left: Percentage of floor type; Bottom right: Number of houses based on

land size

B. Water Supply

The source of water used by residents largely derived from ground water,

water taps and retail. Only about 0.3% that uses river water for

consumption or sanitation needs. The use of water taps dominating source

of water in the riverbanks slum area, approximately 55% of the population

uses water taps for daily needs, the rest use ground water and retail.

Different from the riverbanks slum area, the resident at coastal slum area

did not use ground water for daily needs, most of the residents uses water

taps and retail.

The main source of water used by railroads slum area residents for

consumption and sanitation activities derived from taps and other part

comes from groundwater and retail water. For people who use the services

in the retail water taps or water supply have to pay Rp.50.000 - Rp.200.000

per month.


The source of clean water used by other slum areas residents mainly

originating from retail water, ground water, and water taps. Water

resources in the study area are dominated by retail water usage, and

approximately 55% of the population uses water taps for daily needs, the

rest use ground water and retail.

Table 4-17 Source of clean water at other slum area

Source of Water Percentage (%) of User

1 (Ground Water) 25.7%

2 (Piped Water) 20.0%

3 (Retail) 54.3%


Meanwhile for the sanitary condition in riverbanks slum area, 76% of the

population has a private toilet and 24% use shared or public latrines for

sanitation. Similarly, most of the septic tank houses have had both personal

and communal septic tanks; the remaining 14% do not use septic tanks. In

coastal slum area sanitary conditions, 82% of the population has a private

toilet, 3% use the toilet together and the rest still use public toilet on the

beach. This condition is in line with only 79 % of the population who have a

septic tank and the remaining 21% direct discharge of domestic sewage into

the sea.

Figure 4-7 Percentage of septic tanks ownerships in riverbanks slum area


Sanitary conditions 78% of the population in railroads slum area has a

private toilet and 22% other shared latrines and public latrines for

sanitation. For possession of a septic tank as much as 61% of houses have

had a private septic tank and 14% of the residents use comunal septic tanks.

There are a number of residents as much as 26% do not have a septic tank

as a waste container.

.

Figure 4-8 Percentage of septic tanks ownerships in railroads slum area

For sanitary conditions in other slum areas, 77% of the population has a

private toilet and 23% other using shared or public latrines for sanitation.

Similarly, the presences of septic tanks, 80% of houses have had both

personal and communal septic tanks, rest as much as 20 % do not use septic

tanks.


Figure 4-9 Percentage of septic tanks ownerships in other slum area

Other matter concerning environmental sanitation is drainage. In riverbanks

slum area, the majority of the population drains household waste through

the sewer, but still there are some people who drain their waste into the

river. Only 6 % of the population has a permanent hole to channel the liquid

waste from households. In the coastal slum areas, residents most of the

household still channel waste into the sea although also some residents also

have poured through the gutter. This condition is very prone to trigger the

diseases caused by an unhealthy environment.

Figure 4-10 Percentage of drainage type. Top left: riverbanks slum area; Top right: Coastal slum aea; bottom: railroads slum area


In railroads slum area, the majority of the population drains household

waste through the sewer, but still there are a number of residents in the

amount of 23% flow the waste into the river. Only 2% of the population has

a permanent hole to channel the liquid waste from households. Meanwhile

in other slum area of all respondents answered the household waste stream

through the sewers and garbage transported by bin man.

An open darinage or sewers and even non-current drainage flow is basically

a source of disease. In this study it does not show a strong relationship

between poor environmental sanitation with the outbreak of certain

diseases. However, based on field survey indicate that poor environmental

conditions affect the health of the population. Some diseases have ever

suffered by residents include diarrhea, dengue fever, lung (TBC) and

asthma.

Analysis by Selected RW’s Kumuh in Each Kelurahan 4.3

4.3.1 Slum Neighborhood in Selected RW’s Kumuh by Age, Education and Type of Employment

This following table shown the characteristics of slum neighborhood in selected RW’s Kumuh in Jakarta. It can be used to make a comparison among “indicators” of slums in each village/kelurahan.

Table 4-18 Percentage of Slum Neighborhood in Selected RW’s Kumuh by Age, Education and Type of Employment

Village Number

of HH

Age Education Type of Employment

0 -

17

18

- 5

9

> 6

0

Pre

an

d P

relim

inar

y Sc

ho

ol

Jun

ior

and

Se

nio

r H

igh

Sch

oo

l

Dip

lom

a/ U

nd

er-

grad

uat

e

Info

rmal

Form

al

Re

tire

d

Un

emp

loym

en

t

Cengkareng Timur 75 0 83 17 5 95 0 76 23 0 1

Kapuk 114 0 62 38 10 90 0 61 35 0 4

Kedaung Kali Angke 25 0 80 20 4 96 0 100 0 0 0

Jelambar 21 0 95 5 0 100 0 95 0 0 5

Kedoya Utara 28 4 79 18 7 93 0 68 21 7 4

Jati Pulo 7 0 71 29 0 100 0 100 0 0 0

Duri Selatan 11 9 73 18 9 82 9 73 9 0 18

Kali Anyar 27 0 59 41 0 96 4 70 15 11 4


Village Number

of HH

Age Education Type of Employment

0 -

17

18

- 5

9

> 6

0

Pre

an

d P

relim

inar

y Sc

ho

ol

Jun

ior

and

Se

nio

r H

igh

Sch

oo

l

Dip

lom

a/ U

nd

er-

grad

uat

e

Info

rmal

Form

al

Re

tire

d

Un

emp

loym

en

t

Johar Baru 13 0 69 31 0 92 8 77 23 0 0

Kampung Rawa 10 0 60 40 0 100 0 50 40 10 0

Tanah Tinggi 12 0 50 50 0 100 0 92 8 0 0

Menteng 12 0 75 25 0 92 8 83 8 0 8

Pejagalan 15 0 47 53 7 53 40 93 7 0 0

Karang Anyar 20 0 80 20 0 95 5 95 5 0 0

Kartini 14 0 57 43 0 100 0 93 0 0 7

Mangga Dua Besar 10 0 60 40 0 100 0 90 0 0 10

Senen 12 0 33 67 33 67 0 92 8 0 0

Karet Tengsin 17 0 82 18 6 88 6 100 0 0 0

Kuningan Barat 15 0 87 13 0 100 0 87 13 0 0

Bukit Duri 38 0 55 45 5 92 3 92 0 3 5

Menteng Dalam 19 0 68 32 0 95 5 100 0 0 0

Jatinegara 24 0 71 29 0 100 0 96 4 0 0

Penggilingan 39 0 85 15 0 100 0 100 0 0 0

Rawa Terate 15 0 87 13 7 87 7 100 0 0 0

Klender 15 0 73 27 13 87 0 100 0 0 0

Cipinang Besar Utara 17 12 53 35 24 71 6 94 0 0 6

Kampung Melayu 35 0 83 17 0 91 9 89 9 0 3

Kampung Tengah 27 4 78 19 4 96 0 96 4 0 0

Pisangan Timur 12 0 100 0 0 92 8 92 0 0 8

Pulo Gadung 25 0 76 24 20 80 0 100 0 0 0

Cilincing 37 0 68 32 5 89 5 76 14 8 3

Kali Baru 38 3 58 39 8 89 3 82 3 0 16

Semper Timur 15 0 73 27 0 93 7 93 0 0 7

Tugu Utara 29 0 83 17 7 93 0 83 10 0 7

Ancol 19 0 89 11 11 84 5 84 11 0 5

Pademangan Barat 23 0 70 26 4 78 13 96 0 0 0

Penjaringan 19 0 47 53 21 79 0 100 0 0 0

Papanggo 57 0 60 40 0 100 0 47 51 2 0

4.3.2 Slum Neighborhood in Selected RW’s Kumuh by Length of Stay and Reason to live

This following table shown percentage of household by length of stay and reason to live.

Table 4-19 Percentage of Slum Neighborhood in Selected RW’s Kumuh by Length of Stay and Reason to live


Village Number of

Surveyed HH

Length of Stay (year) Reason to Stay

< 10 10 - 20 > 20 comfortable Strategic location

Low living coost

Cengkareng Timur 75 81 4 15 81 15 4

Kapuk 114 28 20 52 73 22 5

Kedaung Kali Angke 25 32 12 56 84 16 0

Jelambar 21 57 19 24 81 19 0

Kedoya Utara 28 29 18 54 57 21 21

Jati Pulo 7 14 29 57 100 0 0

Duri Selatan 11 64 18 18 55 0 45

Kali Anyar 27 22 7 70 85 0 15

Johar Baru 13 0 0 100 92 8 0

Kampung Rawa 10 100 0 0 100 0 0

Tanah Tinggi 12 92 0 8 92 8 0

Menteng 12 67 25 8 83 0 17

Pejagalan 15 0 7 93 80 7 13

Karang Anyar 20 15 10 75 70 25 5

Kartini 14 7 7 86 79 21 0

Mangga Dua Besar 10 0 10 90 80 20 0

Senen 12 0 25 75 50 17 33

Karet Tengsin 17 35 12 53 82 18 0

Kuningan Barat 15 33 7 60 27 33 40

Bukit Duri 38 5 13 82 26 11 63

Menteng Dalam 19 16 16 68 42 0 58

Jatinegara 24 21 4 75 50 38 13

Penggilingan 39 13 15 72 44 44 13

Rawa Terate 15 20 27 53 80 0 20

Klender 15 20 40 40 47 40 13

Cipinang Besar Utara 17 12 35 53 82 0 18

Kampung Melayu 35 23 11 66 77 17 6

Kampung Tengah 27 19 56 26 48 19 33

Pisangan Timur 12 50 33 17 33 50 17

Pulo Gadung 25 36 24 40 48 40 12

Cilincing 37 32 16 51 27 57 16

Kali Baru 38 53 8 39 55 5 39

Semper Timur 15 13 33 53 53 27 20

Tugu Utara 29 24 10 66 17 17 66

Ancol 19 74 16 11 21 5 74

Pademangan Barat 23 35 13 52 52 26 22

Penjaringan 19 0 5 95 84 5 11

Papanggo 57 96 0 4 91 9 0


4.3.3 Slum Neighborhood in Selected RW’s Kumuh by Ownership and Proof of Ownership

This following table shown the percentage of household by ownership and proof of ownership.

Table 4-20 Percentage of Slum Neighborhood in Selected RW’s Kumuh by Ownership and Proof of Ownership

Village No of

Surveyed HH

Housing Ownership Proof of Land Ownership

Pri

vate

ly

Ow

ne

d

Fam

ily

Ow

ne

d

Re

nta

l

No

t p

ayin

g

ren

t

Oth

ers

Ow

n R

igh

t C

ert

ific

ate

(SH

M)

Bu

ildin

g

Use

Rig

ht

(HG

B)

Pu

rch

ase

N

ota

rial

(AJB

)

Gir

ik L

ett

er

Lett

er

of

Ap

pro

val

fro

m

RT/

RW

/He

ad o

f V

illag

e

No

lan

d

pro

of

do

cum

en

t

Oth

er

Cengkareng Timur 75 29 35 15 20 1 56 0 7 20 3 12 3

Kapuk 114 29 32 37 3 0 3 22 2 10 32 32 0

Kedaung Kali Angke 25 16 16 68 0 0 24 0 0 0 4 72 0

Jelambar 21 24 14 62 0 0 29 0 0 0 10 62 0

Kedoya Utara 28 21 39 39 0 0 7 4 7 43 0 39 0

Jati Pulo 7 71 0 29 0 0 0 0 0 0 100 0 0

Duri Selatan 11 82 9 0 9 0 45 0 36 0 0 18 0

Kali Anyar 27 44 19 33 4 0 11 0 56 0 0 33 0

Johar Baru 13 46 46 8 0 0 92 0 0 0 0 8 0

Kampung Rawa 10 100 0 0 0 0 0 70 30 0 0 0 0

Tanah Tinggi 12 58 42 0 0 0 0 0 100 0 0 0 0

Menteng 12 25 58 17 0 0 33 17 42 0 0 0 8

Pejagalan 15 73 27 0 0 0 80 0 0 7 7 7 0

Karang Anyar 20 45 20 25 10 0 10 20 15 5 5 40 5

Kartini 14 21 64 14 0 0 0 21 0 21 36 14 7

Mangga Dua Besar 10 60 40 0 0 0 30 10 0 30 10 10 10

Senen 12 0 33 33 33 0 8 0 0 8 0 75 8

Karet Tengsin 17 24 41 35 0 0 12 0 24 24 6 35 0

Kuningan Barat 15 13 20 60 0 7 13 0 0 0 0 67 20

Bukit Duri 38 39 21 34 5 0 8 0 11 5 0 71 5

Menteng Dalam 19 16 21 63 0 0 11 0 16 0 0 63 11

Jatinegara 24 42 8 50 0 0 25 0 17 0 4 54 0

Penggilingan 39 36 23 33 8 0 44 0 15 0 0 41 0

Rawa Terate 15 47 33 20 0 0 60 0 20 0 0 20 0

Klender 15 27 0 67 7 0 13 0 13 0 0 73 0

Cipinang Besar Utara 17 29 24 47 0 0 0 0 53 0 0 47 0

Kampung Melayu 35 34 40 26 0 0 0 0 57 6 9 23 6

Kampung Tengah 27 26 11 59 4 0 26 0 15 0 0 59 0

Pisangan Timur 12 83 0 17 0 0 0 0 75 0 0 17 8

Pulo Gadung 25 44 8 48 0 0 4 0 28 0 20 48 0

Cilincing 37 27 30 43 0 0 46 5 0 3 3 43 0

Kali Baru 38 47 26 13 11 3 18 8 45 0 0 8 21


Village No of

Surveyed HH

Housing Ownership Proof of Land Ownership

Pri

vate

ly

Ow

ne

d

Fam

ily

Ow

ne

d

Re

nta

l

No

t p

ayin

g

ren

t

Oth

ers

Ow

n R

igh

t C

ert

ific

ate

(SH

M)

Bu

ildin

g

Use

Rig

ht

(HG

B)

Pu

rch

ase

N

ota

rial

(AJB

)

Gir

ik L

ett

er

Lett

er

of

Ap

pro

val

fro

m

RT/

RW

/He

ad o

f V

illag

e

No

lan

d

pro

of

do

cum

en

t

Oth

er

Semper Timur 15 33 33 33 0 0 7 7 7 13 13 47 7

Tugu Utara 29 24 28 45 3 0 21 3 0 0 0 62 14

Ancol 19 42 0 58 0 0 5 0 5 0 0 32 58

Pademangan Barat 23 39 48 4 4 4 74 0 13 4 0 4 4

Penjaringan 19 68 32 0 0 0 16 16 5 0 37 16 11

Papanggo 57 79 18 4 0 0 23 37 40 0 0 0 0

4.3.4 Slum Neighborhood in Selected RW’s Kumuh by Building Material

This following table shown the percentage of household by building materal.

Table 4-21 Percentage of Slum Neighborhood in Selected RW’s Kumuh by building material

Village

Roof Material Wall Material Floor Material

Co

ncr

ete

Ge

nte

ng

Tin

Ro

of

Asb

es

Oth

ers

Pla

ste

red

Wal

l

Un

pla

ster

ed

wal

l

Wo

od

Oth

ers

Ce

ram

ic t

ile

Ub

in/t

ege

l

Co

ncr

ete

Wo

od

Eart

h

Cengkareng Timur 1 20 1 77 0 76 15 7 3 69 7 21 1 1

Kapuk 3 18 1 76 2 78 11 10 2 76 11 10 2 1

Kedaung Kali Angke 0 20 0 80 0 64 4 32 0 32 44 24 0 0

Jelambar 0 10 0 90 0 67 5 29 0 29 52 10 10 0

Kedoya Utara 4 36 29 32 0 96 4 0 0 82 14 4 0 0

Jati Pulo 0 14 0 86 0 100 0 0 0 14 86 0 0 0

Duri Selatan 9 36 0 55 0 73 9 18 0 64 18 18 0 0

Kali Anyar 4 7 4 85 0 100 0 0 0 96 0 4 0 0

Johar Baru 15 31 0 54 0 100 0 0 0 92 8 0 0 0

Kampung Rawa 10 90 0 0 0 100 0 0 0 100 0 0 0 0

Tanah Tinggi 0 83 17 0 0 67 33 0 0 8 50 42 0 0

Menteng 0 75 0 25 0 92 8 0 0 58 33 8 0 0

Pejagalan 0 13 0 87 0 100 0 0 0 80 0 20 0 0

Karang Anyar 0 30 5 65 0 60 35 5 0 70 30 0 0 0

Kartini 0 29 7 64 0 29 64 7 0 64 21 0 14 0

Mangga Dua Besar 0 40 20 40 0 30 40 30 0 90 0 10 0 0

Senen 8 17 67 8 0 42 0 58 0 33 0 58 8 0

Karet Tengsin 0 24 6 71 0 47 47 6 0 71 12 0 18 0

Kuningan Barat 0 40 20 40 0 87 0 13 0 27 47 27 0 0

Bukit Duri 3 32 55 11 0 84 5 3 8 50 34 16 0 0

Menteng Dalam 0 26 16 58 0 79 16 0 5 53 37 11 0 0


Village

Roof Material Wall Material Floor Material

Co

ncr

ete

Ge

nte

ng

Tin

Ro

of

Asb

es

Oth

ers

Pla

ste

red

Wal

l

Un

pla

ster

ed

wal

l

Wo

od

Oth

ers

Ce

ram

ic t

ile

Ub

in/t

ege

l

Co

ncr

ete

Wo

od

Eart

h

Jatinegara 0 38 0 63 0 75 21 4 0 83 8 4 4 0

Penggilingan 0 51 0 49 0 95 5 0 0 87 10 3 0 0

Rawa Terate 0 33 7 60 0 87 13 0 0 87 13 0 0 0

Klender 7 93 0 0 0 100 0 0 0 100 0 0 0 0

Cipinang Besar Utara 59 0 0 41 0 82 18 0 0 0 88 12 0 0

Kampung Melayu 37 43 3 17 0 91 6 3 0 0 97 3 0 0

Kampung Tengah 4 37 7 52 0 81 11 4 4 70 15 15 0 0

Pisangan Timur 0 100 0 0 0 92 8 0 0 75 17 8 0 0

Pulo Gadung 4 16 0 80 0 100 0 0 0 96 0 4 0 0

Cilincing 0 24 0 76 0 84 16 0 0 70 3 24 0 3

Kali Baru 0 11 3 87 0 42 18 34 5 45 0 29 11 16

Semper Timur 7 13 20 60 0 73 7 7 13 87 7 7 0 0

Tugu Utara 0 24 28 48 0 59 14 24 3 55 21 24 0 0

Ancol 0 5 0 95 0 21 0 79 0 68 5 16 11 0

Pademangan Barat 0 0 9 87 4 91 0 4 4 96 0 0 0 0

Penjaringan 0 0 11 89 0 21 58 16 5 58 5 37 0 0

Papanggo 0 98 2 0 0 82 18 0 0 9 84 7 0 0

4.3.5 Slum Neighborhood in Selected RW’s Kumuh by Building Size, Source of Electricity and Source of Water Supply

This following table shown the percentage of household by Building Size, Source of Electricity and Source of Water Supply

Table 4-22 Percentage of Slum Neighborhood in Selected RW’s Kumuh by Building Size, Source of Electricity and Source of Water Supply

Village

Building size Source of Electricity Source of Water Supply

< 36 36 - 72 > 72 PLN NON PLN

Ground water

Piped water

Water seller

Other

Cengkareng Timur 35 56 9 96 4 5 1 19 0

Kapuk 89 9 2 100 0 15 2 17 0

Kedaung Kali Angke 80 20 0 100 0 88 200 12 0

Jelambar 76 14 10 100 0 0 19 157 0

Kedoya Utara 79 14 7 100 0 7 11 11 32

Jati Pulo 43 57 0 100 0 29 114 14 0

Duri Selatan 55 18 27 100 0 9 36 18 0

Kali Anyar 81 19 0 100 0 26 63 0 0

Johar Baru 77 15 8 100 0 69 15 77 0

Kampung Rawa 0 30 70 100 0 60 70 0 0

Tanah Tinggi 0 67 33 100 0 50 133 42 0


Village Building size Source of Electricity Source of Water Supply

< 36 36 - 72 > 72 PLN NON PLN

Ground water

Piped water

Water seller

Other

Menteng 25 50 25 100 0 0 167 125 25

Pejagalan 7 67 27 100 0 20 47 40 127

Karang Anyar 70 25 5 100 0 0 40 10 0

Kartini 79 14 7 100 0 121 43 29 0

Mangga Dua Besar 70 10 20 100 0 280 770 90 0

Senen 100 0 0 100 0 8 25 133 0

Karet Tengsin 71 18 12 100 0 6 6 41 47

Kuningan Barat 53 47 0 100 0 0 33 60 0

Bukit Duri 42 39 18 100 0 0 58 8 0

Menteng Dalam 53 16 32 100 0 0 121 16 11

Jatinegara 79 13 8 100 0 4 8 50 0

Penggilingan 49 28 23 100 0 28 5 0 0

Rawa Terate 60 13 27 100 0 0 7 60 0

Klender 80 20 0 100 0 13 47 13 7


Kampung Melayu 63 20 17 100 0 0 63 0 3

Kampung Tengah 67 11 22 100 0 56 119 0 37

Pisangan Timur 67 33 0 100 0 33 92 0 0

Pulo Gadung 92 8 0 100 0 44 112 0 0

Cilincing 51 22 27 100 0 3 38 8 3

Kali Baru 47 39 13 97 3 3 0 29 0

Semper Timur 53 33 13 100 0 47 7 113 0

Tugu Utara 52 31 17 100 0 0 52 0 0

Ancol 89 5 5 100 0 5 47 26 0


Penjaringan 84 16 0 100 0 37 16 0 11

Papanggo 0 51 49 100 0 9 16 26 0

4.3.6 Slum Neighborhood in Selected RW’s Kumuh by Sanitation Facility

This following table shown the percentage of household by the condition sanitation facility.

Table 4-23 Percentage of Slum Neighborhood in Selected RW’s Kumuh by the condition of sanitation facility.

Village

Septic Tank Solid Waste Waste water

Ind

ivid

ual

Co

mm

un

al

No

Se

pti

c

Tan

k

Co

llect

ed

b

y o

per

ato

r

Bu

rnin

g

Dis

po

se t

o

rive

r/w

ate

r

stre

am

Dis

po

se t

o

ho

usi

ng

cou

rtya

rd

Thro

wat

Ho

le

Oth

ers

On

D

rain

age

On

Pe

rman

en

t H

ole

Flo

w it

to

The

Riv

er

Oth

ers

:

Cengkareng Timur 55 19 27 99 0 1 0 0 0 29 3 1 0

Kapuk 78 21 1 99 1 0 0 0 0 4 0 4 0

Kedaung Kali Angke 48 48 4 100 0 0 0 0 0 80 0 68 4

Jelambar 48 29 24 100 0 0 0 0 0 48 5 10 0


Village

Septic Tank Solid Waste Waste water

Ind

ivid

ual

Co

mm

un

al

No

Se

pti

c

Tan

k

Co

llect

ed

b

y o

per

ato

r

Bu

rnin

g

Dis

po

se t

o

rive

r/w

ate

r

stre

am

Dis

po

se t

o

ho

usi

ng

cou

rtya

rd

Thro

wat

Ho

le

Oth

ers

On

D

rain

age

On

Pe

rman

en

t H

ole

Flo

w it

to

The

Riv

er

Oth

ers

:

Kedoya Utara 61 32 7 79 11 0 0 0 11 50 7 11 0

Jati Pulo 0 14 86 100 0 0 0 0 0 71 0 86 0

Duri Selatan 64 0 36 82 0 0 0 0 18 0 0 64 0

Kali Anyar 63 7 30 100 0 0 0 0 0 44 0 0 0

Johar Baru 77 0 23 100 0 0 0 0 0 77 0 0 0

Kampung Rawa 100 0 0 100 0 0 0 0 0 180 10 10 0

Tanah Tinggi 92 8 0 92 0 8 0 0 0 158 0 0 0

Menteng 92 8 0 100 0 0 0 0 0 42 67 17 0

Pejagalan 87 13 0 93 0 0 0 0 7 80 0 0 0

Karang Anyar 80 15 5 100 0 0 0 0 0 60 0 0 0

Kartini 86 0 14 100 0 0 0 0 0 79 0 29 0

Mangga Dua Besar 40 0 60 100 0 0 0 0 0 150 0 0 0

Senen 0 92 8 100 0 0 0 0 0 183 0 0 8

Karet Tengsin 47 24 29 65 0 0 0 0 35 88 0 12 0

Kuningan Barat 53 20 27 100 0 0 0 0 0 260 0 0 0

Bukit Duri 39 16 45 58 0 37 3 0 3 250 8 42 0

Menteng Dalam 47 26 26 100 0 0 0 0 0 89 0 0 0

Jatinegara 71 17 13 96 4 0 0 0 0 108 4 0 0

Penggilingan 69 31 0 97 0 0 0 0 3 5 10 49 0

Rawa Terate 47 53 0 93 0 0 0 0 7 180 0 13 0

Klender 47 53 0 100 0 0 0 0 0 133 0 27 0

Cipinang Besar Utara 76 12 12 100 0 0 0 0 0 112 53 0 0

Kampung Melayu 94 3 3 89 0 3 0 9 0 43 0 0 0

Kampung Tengah 15 33 52 59 11 19 4 0 7 52 0 0 0

Pisangan Timur 83 17 0 75 0 0 0 0 25 167 0 100 50

Pulo Gadung 28 72 0 28 0 0 0 0 72 56 0 4 0

Cilincing 92 5 3 86 8 3 0 0 3 57 0 0 0

Kali Baru 79 0 21 53 0 5 0 3 39 32 0 0 0

Semper Timur 87 7 7 87 7 7 0 0 0 33 0 93 0

Tugu Utara 48 28 24 76 10 3 0 0 10 197 0 0 0

Ancol 16 5 79 74 21 0 0 5 0 289 84 5 16

Pademangan Barat 91 4 0 96 0 0 0 0 0 152 0 0 0

Penjaringan 32 0 68 79 5 11 0 0 5 189 0 5 0

Papanggo 96 4 0 100 0 0 0 0 0 28 0 19 0

DKI JAKARTA: INFORMAL SETTLEMENT...

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