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Asia–Pacific Economic Statistics Week 2019
Integrating economic statistics in monitoring the Agenda 2030
17-21 June 2019 | Bangkok, Thailand
Asia-Pacific Economic Statistics Week
Seminar Component
Bangkok, 17-21 June 2019
Name of author
Amanda Pratama Putra, Ignatius Aditya Setyadi, Siim Esko, Titi Kanti Lestari
Organization
BPS – Statistics Indonesia
Positium LBS
Contact address
BPS - Statistics Indonesia
Jl. Dr. Sutomo No. 6-8 Jakarta 10710 Indonesia
Contact phone
+62878 6396 9334 (Putra),
Email
amanda.putra@bps.go.id, adityasetyadi@bps.go.id, siim.esko@positium.com, titi@bps.go.id
Title of Paper
Measuring Commuting Statistics in Indonesia Using Mobile Positioning Data
Abstract
Commuting patterns refers to the movement of workers from their homes (origin) to
work areas located in different cities. Commuter movements generally take place in
metropolitan cities as centers of economic activity. Accurate and up-to-date data about
commuting patterns needs to be obtained continuously as a basis for determining policies
related to rapid urbanization and sustainable urban transportation.
The Population Census and surveys are the traditional methods to used to commuter
statistics. These methods could provide an amount of information concerning commuting flow
and the characteristics of workers grouped by their origin and destination. Indeed, BPS-
Statistics Indonesia has conducted commuter surveys in several major cities in Indonesia
since 2014. However, implementation of the survey could not be carried out simultaneously in
all areas due to the limited costs. As a result, the commuter statistics could not be captured
comprehensively. In addition, commuter statistics data from the survey cannot be updated
regularly in a short period of time because it is expensive; and requires a long time to collect
the data from the field.
1
In order to address the weaknesses of traditional methods, this paper utilized the
availability of mobile positioning data as a complementary technique. From our experiment,
the results show that commuting patterns in Bandung City measured using mobile positioning
data provide the same commuting pattern as the survey results. Therefore, it seems mobile
positioning data could be used as an alternative approach to measuring commuting statistics
in all major cities in Indonesia simultaneously.
2
I. Contents
I. Contents 2
II. Introduction 3
III. Data and Methodology 4
Mobile positioning data 4
Home and work identification 5
Commuter identification 6
Household commuter survey data 7
Calibration with population projection 7
IV. Result 9
Result Comparison: MPD vs Traditional Survey 9
V. Conclusion 12
VI. References 13
3
II. Introduction
For citizens who live in major cities in Indonesia, the main places of activity, such as
work or school that are administratively different from places of residence, are a common
phenomenon today. More facilities offered in the city center the high price of land and building
(including its tax) are the main attraction for residents living in the surrounding (suburbs) area.
In addition, the high cost of housing in the city center makes residents prefer living in satellite
areas that are close to the city center. They travel back and forth from home almost every day
to work. This routine then forms a travel pattern, namely commuting patterns.
Commuting patterns refers to the daily movement of people especially workers from
their homes (origin) to work area. The existence of commuters has a positive impact on the
city center, which can reduce the density of the population living in the cities and the growing
development of satellite areas as a place for commuters. However, the existence of
commuters is a big challenge for big cities to improve public facilities, especially in the
transportation sector that directly affect citizens daily lives. This relate to the SDGs Indicator
11 (Make Cities and Human Settlements Inclusive, Safe, Resilient and Sustainable). Target
11.1, by 2030, ensure access for all to adequate, safe and affordable housing and basic
services and upgrade slums. Target 11.2: By 2030, provide access to safe, affordable,
accessible and sustainable transport systems for all, improving road safety, notably by
expanding public transport, with special attention to the needs of those in vulnerable situations,
women, children, persons with disabilities and older persons.
Commuter movements generally take place in metropolitan cities as the center of
economic activity. Since 2014, BPS – Statistics Indonesia has conducted the commuter survey
in some metropolitan areas in Indonesia to produce statistics and monitoring systems that are
able to provide input for the formulation of population mobility policies and the development of
origin and commuter destinations. The conventional methods like survey could provide the
amount of commuting flow information and the characteristics of workers grouped by their
origin and destination. However, one of the survey-based drawbacks is it takes a long time
from the design of a survey to the release of its final result. Besides, the expensive survey
costs make the implementation of commuter surveys in Indonesia unable to be carried out
simultaneously in all metropolitan areas.
The accurate and up-to-date data about commuting patterns is very challenging to be
obtained continuously as a basis for determining policies related to rapid urbanization and
sustainable urban transportation. Consequently, different approaches and data sources are
needed to measure the commuting statistics more intensively and comprehensively.
Traditional census and administrative data are solid sources for long-term processes. For the
4
short term and everyday mobility, more flexible methods such as crowdsourcing, satellite-
based methods, or modern sensing technologies are needed.
To reduce the time lags and cost in producing the commuting statistics, there are a lot
of attempts related to experimental work to use other data sources. Novak et al. [1] in 2013
explore the potential use of mobile phone location data to map the commuting patterns in
Estonia. They found that mobile phone location data represent a significant alternative source
in cases where traditional data are missing or are not appropriate or the method of collection
is not cost effective. It implies that the commuting behaviors from big data (mobile positioning
data) can allow the public sector to produce more frequent and more detail statistics.
As part of innovation and exploiting the new sources of data, we are currently working
with alternative data sources for commuting statistics using mobile positioning data (MPD).
MPD is the pinpoint location of mobile network subscribers which extracted from the location
of exposed base transceiver station (BTS). The data is obtained from Telkomsel, one of the
largest mobile network operator (MNO) in Indonesia. Since 2017 BPS has begun to
collaborate with Telkomsel to explore MPD as an alternative and complementary data source
for some official statistics production. The data obtained is based on contract for a year. While
for the methodology for commuting we got help from Positium LBS. So, the methodology that
we use is similar as in Novak et. al paper. This paper aims to describe the result of the
experience using MPD to detect the commuting pattern, that will be used as the basis for
measuring commuting statistics. Finally, we evaluate the result with official commuting
statistics based on the survey result.
The paper is organized as follows. Section 3, describes the data and methodology.
Section 4, provides the results. The last section is the conclusion.
III. Data and Methodology
Mobile positioning data
Mobile positioning data in this paper refers to the large-scale location data of
subscribers of MNOs that are processed and stored in operator systems (Altin et al. 2015).
The location of the mobile devices can be obtained in real-time or historically. The data is
highly sensitive but also very valuable. It could be used anonymously and aggregated in the
high-level aggregation to ensure customer privacy. MNOs store huge amounts of data for their
business purposes including “location events” (e.g. network infrastructure quality monitoring
(signaling quality), operational activity data, billing data, data for marketing and CRM,
handover, Location Area updates, active positioning requests, etc.) (Tiru, 2014). In Indonesia,
call detail records (CDR) and signaling data are used since Telkomsel is collecting, storing,
and commercializing it. The data period used for measuring the commuting pattern are during
5
2018. Both CDR and signaling data have time and location information (a detailed latitude and
longitude coordinate for cell towers) for every detected event. Telkomsel processes the CDR
and signaling data into staypoint based on Mobility Genome Framework. Staypoint is data
points that have been grouped to the constraint of average distance and the total duration of
stay. according to Dang, et. al. (2017), “a stay point is constructed if the average distance from
its centroid to all other points is less than ∆d and total duration spent is more than ∆t”.
Owing to the privacy policy, there are several restrictions in the cooperation agreement
with Telkomsel including data access rights. Telkomsel applies a sandbox mechanism
approach in the process of developing algorithms and accessing data to protect data
confidentiality. The data provided is staypoint (processed by Telkomsel) due to access to raw
data is restricted. Then the algorithm is set to work on staypoint data. Because of the limitation,
we were unable to estimate in the low administrative level (Desa/Kelurahan or even
Kecamatan), so we decided to use the Kabupaten level which this level also adequate to
compare with official statistics.
Home and work identification
Figure 1. Usual Environment (Saluveer, E)
Implementing home and work to mobile positioning data is quite a tricky part.
Therefore, BPS-Statistics Indonesia asked about 63 employees in all over Indonesia if they
like to be volunteers. BPS uses the MPD of volunteers to develop an algorithm of usual
6
environment identification, especially home and work and then validate the result for each
individual with their actual home and work location at the municipal level. At first, because the
home and work location for each volunteer is estimated based on one past month data only,
the results become inaccurate (only 58,92% accurate). Then, home and work location are
estimated based on past 6 months data which showed more accurate results (up to 90%
accurate) compared to home and work locations that were estimated based on 1-month data.
The long-term home and work location decided for the past 6 months and for every
month select highest ranking home and work (based on duration). Then sum durations of
home and work candidates by grouping them by Kabupaten (municipality) and Kabupaten with
the highest duration will be the actual location for home/work.
Commuter identification
In this paper, commuters are defined as subscribers who live and work in different
municipal and make a trip from home to work and return home within 24 hours routinely.
Currently, it is determined that for the observation month, a subscriber will be classified as a
commuter when he has at least 4 days during that month he has made a trip that follows the
aforementioned rule. These 4 days also have to be registered on 4 different weeks, meaning
that if a subscriber travels between home and work municipal on daily basis for the first two
weeks and then the next two weeks are on a vacation, they will not be counted as a commuter.
Commuters with very distant locations (no direct transportation connections) are
removed (outliers). For example, if we have just couple of subscribers commuting from Jakarta
to distant kabupaten X, then those commuters will be removed. Commuters with non-standard
work patterns, such as night workers might not be identified by this approach. Additionally,
commuters who are on holiday, ill or otherwise absent from work in some specific month may
not be identified because we process the algorithm monthly. Figure 2 describe the Commuters
algorithm proposed by BPS and implemented by Telkomsel.
Figure 2. Commuters algorithm
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Household commuter survey data
The latest commuter survey conducted by BPS-Statistics Indonesia was held in 2017,
with domain estimation covering two different areas, the (1) Bandung Raya and (2)
Gerbangkertosusila.
Figure 3. Area of Bandung Raya and Gerbangkertosusila
This survey aims to understand the behavior of commuters in 5 cities or regencies in
Bandung Raya (Kabupaten Bandung, Kabupaten Sumedang, Kabupaten Bandung Barat,
Kota Bandung, and Kota Cimahi) and 7 cities or regencies in Gerbangkertosusila (Kabupaten
Gresik, Kabupaten Bangkalan, Kabupaten Mojokerto, Kota Mojokerto, Kota Surabaya,
Kabupaten Sidoarjo, and Kabupaten Lamongan). The commuter survey is a household survey
covering 10.600 households from 1.060 enumeration areas. the time reference used in this
survey is June 2017. The survey provide the estimation of flows in the areas and also provide
the demographic characteristics of the commuters as well.
Calibration with population projection
BPS has the duty to produce the latest population data and information, and its
estimates in the future as a basis for determining development policies. Population projection
is a scientific calculation based on the assumptions of the components of the population
growth rate, namely birth, death, and displacement (migration). These three components will
determine the number and age structure of the population in the future. The population
includes all residents in the geographical area of the Republic of Indonesia who has stayed
8
for six months or longer and those who intended to stay even though their length of stay was
less than six months.
To validate our usual environment algorithm, especially home identification in
aggregated level, we compare the number of subscribers in a municipal area with the
population projection for all 514 regions. MPD’s home was only obtained from one MNO (only
Telkomsel), then we have to calibrate those home with the population projection data in each
municipality, so that can reasonably be compared with the statistics result from the survey.
The result is shown in Table 1. For example, the calibration ratio for Kota Bandung is 1.44,
Kota Surabaya is 1.22, and Kabupaten Sumedang with 1.04.
Table 1. Calibration rate between Home from MPD and Population Projection
No
BPS Population Projection
BPS Population Projection
Home for Distinct
Telkomsel Number
Ratio Population Projection
and Home
(1)
(2)
(3)
(4)
(5)
1
Bandung
3,697,346
1,510,948
2.44
2
Sumedang
1,148,766
657,616
1.74
3
Bandung Barat
1,677,985
518,348
3.24
4
Kota Bandung
2,501,823
1,736,766
1.44
5
Kota Cimahi
605,575
372,089
1.62
6
Gresik
1,294,343
652,830
1.98
7
Bangkalan
976,223
329,405
2.96
8
Mojokerto
1,105,643
323,267
3.42
9
Kota Mojokerto
127,948
57,635
2.22
10
Kota Surabaya
2,881,942
2,350,916
1.22
11
Sidoarjo
2,205,717
1,213,393
1.82
12
Lamongan
1,188,773
437,846
2.72
9
IV. Result
Result Comparison: MPD vs Traditional Survey
For this study, we only use the number of commuters who have a home location and
work location within the area that we just mentioned. So, the number of commuters used in
this study (100%) does not take the number of commuters who have work locations outside
the area mentioned. Furthermore, in table 1 and table 2, the number of commuters is
estimated according to the location of the home and work respectively. In the table, two
estimation numbers are obtained from both, MPD and traditional surveys. the numbers in
brackets and thickened are estimated numbers from MPD. The commuting patterns in
Bandung Raya visually presented in the figure 4.
Figure 4. The Commuting Pattern in Bandung Raya
Table 2 is the origin destination matrix (ODM) for commuters in Bandung Raya to
shows the result comparison of MPD and traditional survey. For instance, in the total estimated
number of commuters in Bandung Raya, the traditional survey estimate 36.05% of people
commute from Kabupaten Bandung to Kota Bandung, while MPD estimate for 43.22%. The
result also shows that Kota Bandung is the most favorite destination for commuters in every
Bandung Raya origin, except commuters from Kabupaten Sumedang that only 2.88%
commute to Kota Bandung.
10
Table 2. Origin Destination Matrix (ODM) of Commuters in Bandung Raya
Home
(Origin)
Work (Destination)
Bandung
Bandung
Barat
Kota
Bandung
Kota
Cimahi
Sumedang
Bandung
-
0.89%
(1.21%)
36.05%
(43.22%)
3.31%
(1.99%)
5.72%
(3.41%)
Bandung
Barat
1.12%
(1.53%)
-
10.00%
(15.92%)
8.98%
(6.20%)
0.00%
(0.23%)
Kota
Bandung
6.53%
(6.55%)
1.78%
(1.74%)
-
4.48%
(2.56%)
0.56%
(0.87%)
Kota
Cimahi
0.89%
(0.80%)
1.88%
(1.66%)
10.61%
(8.31%)
-
0.12%
(0.06%)
Sumedang
4.11%
(1.83%)
0.00%
(0.09%)
2.88%
(1.78%)
0.09%
(0.04%)
-
Table 3 is the ODM of commuters in Gerbangkertosusila. Following the result, both
MPD and traditional survey estimate Kota Surabaya as the top destination for commuters. On
the other hand, Kota Mojokerto that also labeled as a city like Kota Surabaya is only favorite
for the commuters from Kabupaten Mojokerto.
Table 3. Origin Destination Matrix (ODM) of Commuters in Gerbangkertosusila
Home
(Origin)
Work (Destination)
Gresik
Bangkalan
Mojokerto
Kota
Mojokerto
Kota
Surabaya
Sidoarjo
Lamongan
Gresik
-
0.00%
(0.20%)
0.83%
(0.31)%
0.00%
(0.02%)
10.89%
(13.28%)
2.17%
(1.35%)
2.27%
(2.05%)
Bangkalan
0.00%
(0.30%)
-
0.00%
(0.00%)
0.00%
(0.00%)
1.23%
(2.58%)
0.00%
(0.08%)
0.00%
(0.00%)
Mojokerto
1.62%
(1.12%)
0.10%
(0.02%)
-
8.65%
(3.34%)
2.34%
(3.01%)
4.99%
(3.25%)
0.29%
(0.54%)
Kota
Mojokerto
0.05%
(0.07%)
0.00%
(0.00%)
2.09%
(1.04%)
-
0.42%
(0.40%)
0.76%
(0.35%)
0.00%
(0.01%)
Kota
Surabaya
5.07%
(4.41%)
0.32%
(0.52%)
0.37%
(0.39%)
0.00%
(0.08%)
-
9.86%
(8.79%)
0.00%
(0.21%)
Sidoarjo
5.25%
(2.43%)
0.00%
(0.12%)
3.89%
(1.44%)
0.65%
(0.27%)
29.00%
(42.27%)
-
0.00%
(0.11%)
11
Lamongan
4.77%
(3.48%)
0.00%
(0.01%)
0.00%
(0.25%)
0.00%
(0.02%)
2.03%
(1.73%)
0.09%
(0.15%)
-
Figure 5 Commuting Pattern from Traditional Survey
The cities with more facilities offered like Kota Bandung and Kota Surabaya are the
main places for commuters to work and make a living. It makes sense because the two cities
are the center of provincial government and they are located right in the middle of their area.
Figure 5 shows the commuting pattern from the traditional survey and figure 6 shows the
commuting pattern from MPD. In the diagrams, each flow visualizes a specific flow between
origin to destination. It describes clearly that these two different methods relatively produce
the same flow.
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Figure 6 Commuting Pattern from MPD
Mathematically, we provide the calculation of Pearson Correlation Coefficient (PCC)
indicates that there is a high positive association between the result from official statistics and
MPD. The PCC for the estimation in Bandung Raya is 0.98 (strong correlation) and for
Gerbangkertosusila is 0.96 (strong correlation).
Figure 7 Linear correlation
V. Conclusion
The use of mobile positioning data changes the process of design, build, data
collection, data processing, and dissemination. Data collection will be more efficient in terms
of cost and time. Near real-time dissemination can be achieved by using mobile positioning
data. Big data is a part of the data revolution and needs to develop as a validated source for
statistics.
The results showed that mobile positioning data has the potential to increase the level
of disaggregation, timeliness, and usefulness for commuting analysis. The method for
calculating commuters is using mobile positioning data and calibrating it with population
projection data and also comparing with Commuter Household Survey for benchmarking. The
commuter household survey can only be conducted every two years and only covered some
municipalities. So, the data frequency is quite low and the data quality of household survey is
depending on respondent’s answer, while Mobile Positioning Data can provide commuter data
from all the municipalities (514 Kabupaten in Indonesia), more frequent data and also it
depends on methodology that can be improved over time and data recalculated as the
13
methodology improving. According to the limitation of our current methodology for MPD that
only get data source from single mobile networks and not be able to detect commuters with
non-standard work patterns, we believe that our approach is still under-coverage even after
we calibrate the result with the population projection.
VI. References
[1]. Ahas, Rein. Siiri Silm, Erki Saluveer, and Olle Jaarv, (2009). Modelling Home and Work
Locations of Population Using Passive Mobile Positioning Data,
https://www.researchgate.net/publication/227086074.
[2] Ahas, Rein. Siiri Silm, Olle Jaarv, Erki Saluveer, and Margus Tiru (2013). Using Mobile
Positioning Data to Model Locations Meaningful to User of Mobile Phones, Journal of
Urban Technology, 17:1, 3-27.
[3] Altin, Laura. Tiru, Margus. Saluveer, Erki. Puura, Anniki. (2015). Using Passive
Mobile Positioning Data in Tourism and Population Statistics, the New Techniques and
Technologies for Statistics 2015 proceedings. Brussel: Eurostat.
[4] Anh Dang, The & Deepak, Jayakumaran & Wang, Jingxuan & Luo, Shixin & Jin, Yunye
& Ng, Yibin & Lim, Aloysius & Li, Ying. (2017). Mobility Genome™- A Framework for
Mobility Intelligence from Large-Scale Spatio-Temporal Data. 449-458.
10.1109/DSAA.2017.20.
[5] Novak, Jakub. Ahas, Rein. Aasa, Anto. and Silm, Siiri. (2013). Application of mobile
phone location data in mapping of commuting patterns and functional regionalization:
a pilot study of Estonia. Journal of Maps, 9:1, 10:15, DOI:
10.1080/17445647.2012.762331.
[6] Raun, Janika and Rein Ahas. (2016). Defining usual environment with mobile tracking data
, the 14th Global Forum on Tourism Statistics, Venice, Italy.
[7] Tiru, Margus. (2014). Overview of the Sources and Challenges of Mobile Positioning Data
for Statistics, presented in the International Conference on Big Data for Official
Statistics. Beijing: UN Trade Statistics, Positium, and University of Tartu.