Modeling Land Use in Megacities under Deep Uncertainty: The
Case of Jakarta
‘Januar Gurvinderpal Singh Arora
TU Delft TU Delft
Jaffalaan 5, 2628BX Delft Jaffalaan 5, 2628BX Delft
rizkyjanuar@ student. tudelft.nl G.P.S.Arora@ student.tudelft.nl
Deep Uncertainty, Robust Decision Making, Dynamic Modeling, Socio-Economic
Development, Megacities
As the capital city of Indonesia, Jakarta’s population number keeps increasing; thanks to the city
contributes to a significant portion of economic activities of Indonesia. Accordingly, some trends can
be observed in Jakarta. Firstly, despite the trend of decreasing average house prices in Indonesia, the
average housing price in Jakarta kept increasing and remains the highest in Indonesia. Secondly, the
combination of land scarcity and increasing house price affects the accumulating growth of slum areas
which are built illegally in the non-private lands. Meanwhile, the need to develop more green spaces is
growing due to increasing transportation-induced pollution. Finally, as a city with highest money
circulation rate in Indonesia, it is necessary to consider the land use portion for improving the city
economic growth. Therefore, land use division can lead to policy dilemma with respect to economic
development, housing affordability, and environmental sustainability.
In this research, the focus is set on investigating land use allocation policy for improving urban
settlement indicators in Jakarta. An exploratory system dynamics (SD) model for 40-year time span
(year 2010-2050) is developed to analyze the development of urban land in Jakarta over time; addressing
the availability and affordability of settlements in Jakarta over time while considering plausibility of
land optimization for sustainable city growth with respect to the economy and environment.
The conceptual model for the study is shown in
stum population’ = Figure 1. Two of the feedbacks are reinforcing
a = ane Gs “Sy loops (R1 and R2), which are related to the growth
if 4, \ 2 NS of slum areas and public housing development.
| re mn uubenhowsing Also, negative feedbacks exist, which are
ier associated with multiple land requirements and
+ development
\bn) \e) Le / Se limited land availability.
‘Sag #
Seeley a fr Based on Figure 1, stock-flow model is developed
id 4 2) \ © population / Ba to inform the long-term condition of settlement,
( = \ 7 pet environment, and economic indicators over time;
ocean g#* waning demand given the current development plan and population
3 s growth rate. The result implies that the urban
Figure 1. Causal Loop Diagram settlement indicators may indeed face long-term
risks. This confirms the need for land use policies to consider fulfillment of these objectives, altogether
with the other relevant indicators.
co Er Fea NS
oon y Taking into account the
on uncertainties of
oe parameters defined in the
- model, vulnerability
a5 axl Z » : L ..| analysis is performed
= no mo using Scenario Discovery
—e technique; which
identifies the subspaces of
uncertainties producing
i undesirable outcomes
cs : ? a — is throughout the time span.
For this, the model run is divided into two time periods: 2010 — 2030 and 2010 — 2050, with following
reason. This is to consider the possibilities to tailor the policies for short term period, i.e. until 2030,
while having the strategic policies in place for the entire study period. Also, it is argued this mechanism
can intrinsically reduce the required policy costs while
Tas Vaohiiy Key Uncataitig —Valeabiiy
‘sted ener ee maintaining the fulfillment to the objectives
a rsore ger. so: oo | Optimization. The result is shown in Figure 3. Given
‘ie SumPopulston -->20m0-sSesesum-eanvensen oos-oose | ifferent uncertain variables affecting the KPIs, this
aia. People wth no socono evageinngrtoncate oozs-cce | JUStifies the proposition of treating the policy
me eee fe hronenn ooo) implementation separately for the two time zones.
i Ta ee es Bee ae Table 1. Identified policy parameters based on the result in Figure 3
creen area teens 1035-008 Policy Parameters Levers
Conversion rate: High Rise Floor area ratio
aes iereeaty i eee Slum Revitalization ‘Slum area reconversion rate
Septsumropaiten some rumresnrenen 9-008 ‘Accelerated City Growth Green area reconversion rate
2010. Setlenents Steencan Heeengi genesis: O01: nO6 Inflation Targeting Economic area reconversion rate
2080 UibeeNor Residents! = <tls ns 006 i i :
pea] é ienccnccinw © aiddace’ | | Relocation’ Aiea Development [Ro teanon “e, *elocanian housing:
Comerson ate factor
Ensccreenmrco 002-0084 "
Sake Houag suas Houschold Sm for housing
Figure 3. Scenario Discovery result for two different model time spans purciase)
Aligned with the vulnerability investigation, policy levers specific to land use mechanism are identified
from literature review and interview; altogether with their associated parameter contexts. Table 1
specifies the result. Thereafter, using e-NSGA-II
algorithm, the time-based many- objective optimization
is analyzed. The analysis results in
bE +———._ | Pareto-optimal set of policy outcomes,
SS “== | which is shown in Figure 4. It is
assumed that all policies start post-
ass a0 = ce = | 2020, considering the nature of policy
JE testo ers planning phase.
t
Grow Proper sinncomeen J oe ae = This optimized policy set is then
Figure 4. Summary of the candidate ae set
exposed to the uncertainty analysis,
which shows that the Housing Affordability Rating and Urban Non-Residential Area still have higher
variation in the outcomes relative to the other K
still exists among the land use indicators.
Therefore, vulnerability analysis is once again
performed to identify other plausible measures
tackling these unintended outcomes. The result
in Figure 6 shows in the long term, even after
the different policy programs are combined, the
influx of the population into the city plays a key
role to aid the trade-offs.
All in all, two recommendations can be
provided. Firstly, no single policy can be seen
effective in handling the current situation in
Pls (Figure 5). This implies that trade-off nonetheless
Mv diu
Figure 5. Uncertainty analysis of model with the implemented policy set
Jakarta. Accordingly, the adaptive approach,
in which combination of policy measures is
implemented in phases, is recommended.
Secondly, given the implementation of the
integrated policies, trade-off nonetheless still
exists among the land use _ indicators.
KPI Vulnerability Key Uncertainties Vulnerability
Threshold Range
Housing Affordability Rating >7 average paymenttoincome 032-05
ratio for housing
legal Slum Population 320000 ‘Base Slum Reconversion Rate 0.01-0.0¢5
People with No Settlements 800000 Averegelmmigration rate 0.015 -0.06
Urban idential <5 rate 0029-006,
Green Area 6 Base Economic Area 0.027 -0.06
Conversion rate
Addressing the growing population influx to
Figure 6. Vulnerability analysis result of the policy set
Jakarta without hindering its socio-economic growth may provide sustainable remedy to the issue.
Bibliography
Badan Perencanaan Pembangunan Daerah. (2013). Rencana Pembangunan J angka Menengah Daerah: Daerah
Khusus Ibukota Jakarta. Retrieved from https://www.bappenas.go.id/files/rpjmd_dan_rkpd_provinsi/DKI
Jakarta/RPJMD DKI Jakarta 2013-2017. pdf
Badan Pusat Statistik DKI Jakarta. (2001). Jakarta dalam Angka 2000. DKI Jakarta.
Badan Pusat Statistik DKI Jakarta. (2002). Jakarta dalam Angka 2001. DKI Jakarta.
Badan Pusat Statistik DKI Jakarta. (2003). Jakarta dalam Angka 2002. DKI Jakarta.
Badan Pusat Statistik DKI Jakarta. (2004). Jakarta dalam Angka 2003. DKI Jakarta.
Badan Pusat Statistik DKI Jakarta. (2005). Jakarta dalam Angka 2004. DKI Jakarta.
Badan Pusat Statistik DKI Jakarta. (2006). Jakarta dalam Angka 2005. DKI Jakarta.
Badan Pusat Statistik DKI Jakarta. (2007). Jakarta dalam Angka 2006. DKI Jakarta.
Badan Pusat Statistik DKI Jakarta. (2008). Jakarta dalam Angka 2007. DKI Jakarta.
Badan Pusat Statistik DKI Jakarta. (2009). Jakarta dalam Angka 2008. DKI Jakarta.
Badan Pusat Statistik DKI Jakarta. (2010). Jakarta dalam Angka 2009. DKI Jakarta.
Badan Pusat Statistik DKI Jakarta. (2011). Jakarta dalam Angka 2010. DKI Jakarta.
Bryant, B. P., & Lempert, R. J. (2010). Thinking inside the box: A participatory, computer-assisted approach to
scenario discovery. Technological Forecasting and Social Change, 77(1), 34-49.
https://doi.org/10.1016/j.techfore.2009.08.002
Demographia. (2018). 14th Annual Demographia International Housing Affordability Survey: 2018. Retrieved
from http://www.demographia.com/dhi.pdf
DetikFinance. (2014). Ada 2 Juta Rumah di RI Tak Dihuni, Mayoritas Hunian Mewah. Retrieved February 12,
2018, from https://finance.detik.com/properti/2675180/ada- 2-juta-rumah-di-ri-tak-dihuni-mayoritas-
hunian-mewah
Forrester, J. W. (1958). Industrial dynamics: a major breakthrough for decision makers. Harvard Business
Review, 36(4), 37-66. https://doi.org/10.1225/58404
Forrester, J. W. (1999). Urban dynamics. Pegasus Communications.
Hafiyyan. (2015). HARGA PROPERTI: Kenaikan 20% Per Tahun. Retrieved February 12, 2018, from
http://properti.bisnis.com/read/20150703/107/449887/harga- properti-kenaikan-20-per-tahun-
Hamarat, C., Kwakkel, J. H., & Pruyt, E. (2013). Adaptive Robust Design under deep uncertainty. Technological
Forecasting and Social Change, 80(3), 408-418. https://doi.org/10.1016/j.techfore.2012.10.004
Hoek-smit, M. C. (2005). The Housing Finance Sector in Indonesia. Retrieved from
http://web. worldbank.org/archive/website01055/W EB/IMA GES/EA FINA -6.PDF
Tkhsanudin, A. (2017). Pemprov DKI Targetkan Tambah 20 Hektare Ruang Terbuka Hijau. Retrieved February
12, 2018, from https://news.detik.com/berita/d-3439391/pemprov-dki-targetkan-tambah-20-hektare-ruang-
terbuka-hijau
Jafino, B. A., Soltani, P., & Pruyt, E. (2016). Saving Lives and Time : Tackling Transportation Induced Air
Pollution in Jakarta. 34th International Conference of the System Dynamics Society, (October 2017), 1-22.
Jakarta Open Data. (2017). Data Jumlah Penduduk, Rumah Tangga dan Rata-rata Anggota Rumah Tangga
menurut K abupaten/K ota A dministrasi DKI Jakarta - Jumlah Penduduk, Rumahtangga, dan Rata-rata
Anggota Rumahtangga 2010 - data jakarta.go.id. Retrieved February 12, 2018, from
http://data jakarta.go.id/d N i
c7fla55e-54fc-480b-9ee3-b0c85fb5e8i6
Jefriando, M. (2017). Ini 10 Daerah dengan Ekonomi Terbesar di RI. Retrieved February 11, 2018, from
https://finance.detik.com/berita-ekonomi-bisnis/3496150/ini-10-daerah-dengan-ekonomi-terbesar-di-ri
Kasprzyk, J. R., Nataraj, S., Reed, P. M., & Lempert, R. J. (2013). Many objective robust decision making for
complex environmental systems undergoing change. Environmental Modelling and Software, 42, 55~71.
https://doi.org/10.1016/j.envsoft.2012.12.007
Kompas. (2010). Pemerintah Intervensi Tekan Harga Rumah - Kompas.com. Retrieved February 12, 2018, from
http://ekonomi.kompas.com/read/2010/04/15/20055753/pemerintah. intervensi.tekan. harga.rumah
Kresna, M. (2018). Pilih Rumah DP 1 Persen Jokowi atau DP 0 Rupiah Anies? - Tirto.ID. Retrieved February
12, 2018, from https://tirto.id/pilih-rumah-dp- 1-persen-jokowi-atau-dp-0-rupiah-anies-cEg8
Kwakkel, J. H. (2017). The Exploratory Modeling Workbench: An open source toolkit for exploratory modeling,
scenario discovery, and (multi-objective) robust decision making. Environmental Modelling and Software,
96, 239-250. https://doi.org/10.1016/j.envsoft.2017.06.054
Laksana, B. A. (2017). Masih Ada Warga Relokasi Tak Dapat Rusun, Ahok: Kita Mau Sediakan. Retrieved
February 12, 2018, from https://news.detik.com/berita/d-3477480/masih-ada-warga-relokasi-tak-dapat-
rusun-ahok-kita-mau-sediakan
Lempert, R. J. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy
Analysis. https://doi.org/10.1016/j.techfore.2003.09.006
Lempert, R. J., Groves, D. G., Popper, S. W., & Bankes, S. C. (2006). A General, Analytic Method for
Generating Robust Strategies and Narrative Scenarios. Management Science, 52(4), 514-528.
https://doi.org/10.1287/mnsc.1050.0472
Noviandi, N., Pradono, P., Tasrif, M., & Kusumantoro, I. (2017). Modeling of dynamics complexity of land use
and transport in megapolitan urban fringe (case of Bekasi city). Transportation Research Procedia , 25,
3314-3332. Retrieved from https://ac.els-cdn.com/S$2352146517304908/1-s2.0-S2352146517304908-
main.pdf?_tid=eeafeSbc-0038-4412-9a2a-
clcdb3a4a0cd&acdnat=1522262895_04ce43a77edb475a5871324174a3ccf7
Oliphant, T. E. (2007). SciPy: Open source scientific tools for Python. Computing in Science and Engineering, 9,
10-20. https://doi.org/10.1109/MCSE.2007.58
Pangaribowo, R. L. (2018). Dynamics of land -use change in urban area in West Jakarta. Series: Earth and
Environmental Science, 106. https://doi.org/10.1088/1755-1315/106/1/012040
Pressman, J. L., & Wildavsky, A. B. (1973). mpl ion : how great expectations in hi are dashed
in Oakland. The Oakland Project Series.
Radianti, J., Tasrif, M., & Rostiana, E. (2003). A dynamic model for spatial planning in metropolitan areas.
International Conference of the System Dynamics Society, 1-29. Retrieved from http://www.dinamica-de-
sistemas.com/paper/21_57.pdf
Sancaya, R. (2016). Permukiman Kumuh di Bantaran Ciliwung. Retrieved. February 12, 2018, from
https://news.detik.com/foto-news/d-3374710/per i ti iliwung
Shannon, C. E. (1949). A Matt ical Theory of C ication. The University of Illnois Press. Retrieved
from http://www.mast.queensu.ca/~math474/shannon1948.pdf
Simorangkir, E. (2017). Harga Rumah Jakarta Termahal di RI, Rata-rata Rp 20,75 Juta/M2. Retrieved February
12, 2018, from https://finance.detik.com/properti/d-3459215/harga-rumah-jakarta-termahal-di-ri-rata-rata-
rp-2075-jutam2.
Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World.
https://doi.org/10.1057/palgrave.jors.2601336
Storm, S., & Naastepad, C. W. M. (2015). NAIRU economics and the Eurozone crisis. International Review of
Applied Economics, 29(6), 843-877. https://doi.org/10.1080/02692171.2015.1054367
Suhaida, M. S., Tawil, N. M., Hamzah, N., Che-Ani, A. I., Basri, H., & Yuzainee, M. Y. (2011). Housing
affordability: A conceptual overview for house price index. In Procedia Engineering (Vol. 20, pp. 346-
353). https://doi.org/10.1016/j.proeng.2011.11.176
Tian, L., & Ma, W. (2009). Government intervention in city development of China: A tool of land supply. Land
Use Policy, 26(3), 599-609. https://doi.org/10.1016/] LANDUSEPOL.2008.08.012
Waddell, P. (2002). UrbanSim: Modeling Urban Development for Land Use, Transportation, and Environmental
Planning. Journal of the American Planning Association, 68(3), 297-314.
https://doi.org/10.1080/01944360208976274
Wahyudi, A., Liu, Y., & Corcora, J. (2015). Modelling the spatial decisions of private developers: A case study
of Jakarta Metropolitan Area, Indonesia. 13th International Conference on Computers in Urban Planning
and Urban Management. Retrieved from
https://www.researchgate.net/profile/Y an | Liu40/publication/301749279_ Modelling _ the » spatial_decisions
of_private developers A_case_study_of Jakarta M | Area 725536708ae262
228adbeaa/M odelling- the-spatial-decisions-of-private-develo
Walker, W. E., Haasnoot, M., & Kwakkel, J. H. (2013). Adapt or perish: A review of planning approaches for
adaptation under deep uncertainty. Sustainability (Switzerland), 5(3), 955-979.
https://doi.org/10.3390/su5030955
Walker, W. E., Harremoés, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., Janssen, P., & Krayer von
Krauss, M. P. (2003). Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-
Based Decision Support. Integrated Assessment, 4(1), 5-17. https://doi.org/10.1076/iaij.4.1.5.16466
Wegener, M. (1995). Current and Future Land Use Models. Retrieved from http://www.spiekermann-
wegener.com/pub/pdf/MW _Dallas.pdf
Wiwoho, B. (2017). Program DP Rumah Nol Persen Belum Bisa Direalisasikan 2017. Retrieved February 12,
2018, from https://www.cnnindonesia.com/nasional/20171013195154-20-248280/program-dp-rumah-nol-
persen-belum-bisa-direalisasikan-2017
Wise, R. M., Fazey, I., Stafford Smith, M., Park, S. E., Eakin, H. C., Archer Van Garderen, E.R. M., &
Campbell, B. (2014). Reconceptualising adaptation to climate change as part of pathways of change and
response. Global Environmental Change, 28, 325-336. https://doi.org/10.1016/j.gloenvcha.2013.12.002
