第31回日本情報地質学会講演会 講演要旨集 15-16頁 2020年

Comparative Evaluation of Urban Growth Models
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Pavithra JAYASINGHE*, Lakshmi N. KANTAKUMAR**, Venkatesh RAGHAVAN***,Go YONEZAWA***空白*Graduate School for Creative Cities, Osaka City University, 3-3-138 Sugimoto, Sumiyoshi-ku, Osaka
558-5858, Japan. E-mail: pavipj89@gmail.com**Bharati Vidyapeeth Deemed University, Institute of Environment Education and Research, Pune,
India.
*** Graduate School of Engineering, Osaka City University, 3-3-138 Sugimoto, Sumiyoshi-ku,Osaka 558-5858, Japan.

Key words: Urban Simulation, Urban Growth Models, FUTURES, SLEUTH, MOLUSCE

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空白１．Introduction
Models are useful tools for simplifying complex
socioeconomic and biophysical forces that influence therate and spatial pattern of landuse change and for
anticipating future evolutions. The landuse change driven
urban expansion is one of the most influential
transformation that can affect the natural and social
cohesion (Kantakumar et al., 2020). This may bereasoning the use of urban growth models to predict
urban expansion and its forms increasing gradually in thescientific literature. However, several open source urbangrowth models are available and comparative analysis ofthese models is still missing. The attempt of this study isto evaluate the outputs of three urban growth models
namely,FUTURES(FUTureUrban-Regional
Environment Simulation), MOLUSCE (Modules for Land
Use Change Simulations) and SLEUTH to constructquantitative, spatially explicit urban simulation using
Colombo as study area with identical inputs.

２．Data and Methodology
This study uses the urban area maps derived from 30m
spatial resolution Landsat data as input for model
calibration and validation. The data used along with thedata sources are shown in Table 1. FUTURES andMOLUSCE models require a site suitability surface andan estimate of the quantity of future urban growth.
SLEUTH model requires urban extents, roads and a userdefined exclusion layer that denotes the site suitability.
FUTURES model uses past population trends andprojected population to estimate per capita land demandwhen estimating the amount of future urban growth.
Table 1: Input data used in this study
Datayear
Data source
Landsat 5 TM
1997,2005,2008 USGS
Landsat 8 OLI
2019
USGSPopulation
1991,2001,2012 Dept of Census
and Statistics
Road network
2013
JICA
Water bodies
2013
JICADEM
2000
SRTM 30m
Social infrastructure
2004
Survey Dept
(Hospitals, schools)
Growth centers
2010
Survey Dept
Administrative
2010
Survey Deptboundary

FUTURES model is a multilevel modelling framework
consists of three sub models namely, POTENTIAL,
DEMAND and PGA (Meentmayer et al., 2013).
POTENTIAL sub-model quantifies the site suitability
based on hypothesized environmental, infrastructural,and socioeconomic factors. FUTURES model uses logistic
regression to estimate transition potential. DEMAND
sub-model quantifies per capita land demand. PGA is astochastic patch-growing algorithm that determines theshape, size and distribution of urban patches.
SLEUTH is a cellular automata (CA) based urban
growth model (Clarke et al., 1997). The name of the modelis an acronym of inputs used namely, Slope, Land use,
Exclusion, Urban, Transport and Hillshade. SLEUTH
uses four growth rules namely, spontaneous, newspreading center, edge and road-influenced growths.
These four urban growth rules are performed sequentiallyin each growth cycle and are controlled by five-growthcoefficients dispersion, breed, spread, road gravity, andslope resistance coefficients. These growth coefficientsneed to determine by using model calibration withhistorical urban growth. Brute force calibration method
using Monte Carlo simulations with POP metric has beenused to determine these five coefficients in this study.
MOLUSCE is a CA based model developed as a pluginfor QGIS. MOLUSCE uses historical urban maps tocalculate area of change as a first step. One method canbe selected among four available methods; Artificial
Neural Network (ANN), Weight of Evidence, Logistic
Regression or Multi Criteria Evaluation to estimate thetransition potential in second step. We have applied ANN
for estimating the transition potential using distance toroads, growth centers, water bodies, schools, hospitals
and slope as explanatory variable. CA use the area ofchange and transition potentials derived in first andsecond step to simulate the urban growth.
In order to facilitate a fair comparison of simulation
capability of three models under study, the urban area
maps of 1995, 2005, 2014 and 2019 were used forcalibration/training. After calibration, we used 2008
urban area map to initiate the simulation to predict urban
extent of 2019. The simulation maps of three models werevalidated by comparing it remote sensing derived urban
area map of 2019 using a confusion matrix.

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Table 2: Validation matrices for three models

Models

Producer
Accuracy

User
Accuracy

Overall
AccuracySpecificity

Matthews correlation
coefficient (MCC)

Figure ofMeritKappa

MOLUSCESLEUTH
FUTURES

0.27
0.61
0.36

0.44
0.31
0.31

0.98
0.93
0.96

0.98
0.93
0.96

0.31
0.39
0.290.2
0.26
0.2

0.75
0.66
0.69

(a)

(b)

(c)

(d)

Figure 1: (a)Observed urban area map and validation maps of (b)FUTURES, (c) SLEUTH and (d) MOLUSCE models

３．Results and Discussion
The urban growth models are approximation of complex
urban system. Thus, the validation of an urban growthmodel is essentials to determine whether the model iscapable of representing city growth with sufficientaccuracy (Kantakumar et al., 2019). The results ofvalidation are presented in form of hits, misses, false
alarms and correct rejections are presented in Figure 1
and the validation metrics in Table 2.
The results show that the, overall accuracies of all threemodels are over 90% indicates the higher agreement ofsimulated pixels both urban and nonurban at correct
locations. The overall accuracy of MOLUSCE model is thehighest compared to SLEUTH and FUTURES. It is
important to note, the use of overall accuracy cannot beinterpreted as a direct method of model capability, due topersistence of non-urban area is higher in the study area
in comparison to urbanized area (Kantakumar et al.,2019). Therefore, Matthews correlation coefficient (MCC)
was used to avoid unbalanced effect of persistence andchange. The MCC is higher for SLEUTH (0.26) comparedto FUTURES and MOLUSCE. The Producers accuracy of
SLEUTH model is comparatively higher than othermodels which explicit a higher capability of the model tosimulate urban pixels at the correct locations. Comparedto urban area growth 127.37sq.km during 2008-2019,
FUTURES, SLEUTH and MOLUSCE models simulated
148.91, 250.55, 77.10sqkm respectively. Among simulated
quantities, SLEUTH model showed over estimation andMOLUSCE model showed an under estimation while
FUTURE model simulated closely correct quantity ofurban growth. As FUTURES uses sub region wise urbanchange and population growth to determine per capita
land demand which could be the reason for better
estimation of urban growth. The over estimation ofSLEUTH model simulations might be due to the reason of
only excluding water bodies from the development andallowing unrestricted growth at all locations without
considering site suitability. The underestimation of urban

growth by MOLUSCE model might be the reason forhigher accuracy in contrast with other two models.

４．Conclusion
Considering easy implementation with limited datarequirement, MOLUSCE could be identified as a modelwith an acceptable accuracy. FUTURES is a robust, easily
customizable model with flexibility in incorporation ofcomplex policy scenarios. As SLEUTH model isextensively used for urban growth studies, continuous
development of new extensions and usability has widelyexplored. The aim of the study was to use identical inputs
to evaluate the performance of FUTURES, SLEUTH andMOLUSCE models in their simplest status. The present
results reveals that keeping the variations ofimplementation techniques and procedures involved inthese models, it is not fair to conclude which model isperformed better than other based on the current stage ofstudy. Thus, we are interested to carryout the study
further by customizing the models by using the same
method for estimating the transition potential modelling.

Reference

Clarke, K.C., Hoppen, S., & Gaydos, L. (1997). A self-modifying

cellular automaton model of historical urbanization in the San
Francisco Bay Area. Environment and Planning B: Planningand Design, vol.24, no.2, pp.247–261.
Kantakumar, L.N., Kumar, S., & Schneider, K. (2020). Whatdrives urban growth in Pune? A logistic regression and relative
importance analysis perspective. Sustainable Cities and Society,vol.60, 102269.
Kantakumar, L.N., Kumar, S., & Schneider, K. (2019). SUSM: ascenario-based urban growth simulation model using remotesensing data, European Journal of Remote Sensing, vol.52,
no.S2, pp.26-41.
Meentemeyer, R., Tang, W., Dorning, M., Vogler, J., Cunniffe, N.
& Shoemaker, D. (2013). FUTURES: Multilevel Simulations ofEmerging Urban–Rural Landscape Structure Using a
Stochastic Patch-Growing Algorithm. Annals of the Associationof American Geographers, vol.103, no.4, pp.785-807.