scholarly journals Comparative Evaluation of Open Source Urban Simulation Models Applied to Colombo City and Environs in Sri Lanka

2021 ◽  
pp. 49-60
Author(s):  
P. Jayasinghe ◽  
L.N. Kantakumar ◽  
V. Raghavan ◽  
G. Yonezawa
Keyword(s):  
Open Source ◽  
Urban Growth ◽  
Urban Areas ◽  
Comparative Evaluation ◽  
Growth Models ◽  
Simulation Models ◽  
Good Choice ◽  
Surface Generation ◽  
Suitable Model ◽  
Urban Simulation

Availability of a variety of urban growth models make model selection to be an important factor in urban simulation studies. In this regard, a comparative evaluation of available urban growth models helps to choose a suitable model for the study area. Thus, we selected three open-source simulation models namely FUTURES, SLEUTH and MOLUSCE to compare in their simplest state to provide a guidance for selection of an urban growth model for Colombo. The urban extent maps of 1997, 2005, 2008, 2014 and 2019 derived from Landsat imageries were used in calibration and validation of models. Models were implemented with the minimum required data with default settings. The simulation results indicate that the estimated quantity of urban growth (148.91 km2) during 2008-2019 by FUTURES model is matching closely with observed urban growth (127.37 km2) during 2008-2019. On the other hand, the SLEUTH model showed an overestimation (250.56 km2) and MOLUSCE showed an underestimation (77.11 km2). Further, the spatial accuracy of urban growth simulation of SLEUTH (Figure of Merit = 0.26) is relatively better in comparison to FUTURES (0.20) and MOLUSCE (0.20). Considering the tradeoff between computational overheads and obtained results, FUTURES could be a good choice over SLEUTH and MOLUSCE, when these models implemented in their simplest form with minimum required datasets. As a future work, we propose the incorporation of exclusion factor for potential surface generation to mitigate the overestimation of urban areas in SLUETH.

scholarly journals Improvement of an Urban Growth Model for Railway-Induced Urban Expansion

2020 ◽  
Vol 12 (17) ◽  
pp. 6801
Author(s):  
Alvin Christopher G. Varquez ◽  
Sifan Dong ◽  
Shinya Hanaoka ◽  
Manabu Kanda
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Historical Data ◽  
Growth Models ◽  
Urban Expansion ◽  
Grid Spacing ◽  
Urban Growth Model ◽  
Sleuth Model ◽  
The Right ◽  
Better Than

Increasing population in urban areas drives urban cover expansion and spatial growth. Developing urban growth models enables better understanding and planning of sustainable urban areas. The SLEUTH model is an urban growth simulation model which uses the concept of cellular automata to predict land cover change using six spatial inputs of historical data (slope, land use, exclusion, urban, transportation, and hill-shade). This study investigates the potential of SLEUTH to capture railway-induced urban growth by testing methods that can consider railways as input to the model, namely (1) combining the exclusion layer with a station map; (2) creating a new input layer representing stations in addition to the default six inputs. Districts in Tsukuba, Japan and Gurugram, India which historically showed evidence of urban growth by railway construction are investigated. Results reveal that both proposed methods can capture railway impact on urban growth, while the former algorithm under the right settings may perform better than the latter at finer resolutions. Coarser resolution representation (300-m grid-spacing) eventually reduces the differences in accuracy among the default SLEUTH model and the proposed algorithms.

scholarly journals Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods

2019 ◽  
Vol 11 (20) ◽  
pp. 5579 ◽  
Author(s):  
Ayazli
Keyword(s):  
Urban Growth ◽  
Growth Models ◽  
Simulation Models ◽  
Growth Simulation ◽  
Urban Growth Model ◽  
Growth Properties ◽  
Force Calibration ◽  
Improved Model ◽  
Best Fit

While the rural population is decreasing day by day, the urban population is increasing rapidly. Urban growth, which occurs as a result of this increase, is sprawling toward natural and environmental areas in urban fringes, and constitutes the main source of many environmental, physical, social, and economic problems. In order to overcome these problems, the direction and rate of urban growth should be determined with simulation models. In this context, many urban growth models have been developed since the 1990s; the SLEUTH urban growth model is one of the most popular among them and has been used in many projects around the world. The brute force calibration process in which the best fit values of growth coefficients are determined is the most important stage of simulation models. The coefficient ranges are initially defined as being between 0 and 100 and are then narrowed in this step according to 13 separate regression scores, which are used to specify the characterization of urban growth. Consensus has not yet been reached as to which metrics should be used for calculating the best fit values, but the Lee–Sallee and Optimum SLEUTH Metric (OSM) methods have been mostly used in past studies. However, in rapidly growing study areas, these methods cannot truly explain urban growth properties. The main purpose of this paper is to precisely calibrate urban growth simulation models. Therefore, Exploratory Factor Analysis (EFA) was used to calculate the growth coefficients, as a new statistical approach for calibration, in this study. The district of Sancaktepe, Istanbul, which experienced population growth of 80% between 2008 and 2018, was selected as the study area in order to test the achievement of the EFA method, and two urban growth simulation models were generated for the years 2030 and 2050. According to the results, despite the fact that there is little effect of urban growth in the short term, more than 70% of forests and agricultural lands are at risk of urbanization by 2050.

The Use of Spatial Metrics and Population Data in Mapping the Rural-Urban Transition and Exploring Models of Urban Growth in Hanoi, Vietnam

2021 ◽  
Vol 12 (1) ◽  
pp. 156-168
Author(s):  
Du’o’ng H. Nông ◽  
Jefferson M. Fox ◽  
Sumeet Saksena ◽  
Christopher A. Lepczyk
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Urban Landscape ◽  
Growth Models ◽  
Population Data ◽  
Landscape Fragmentation ◽  
Spatial Metrics ◽  
Transition Zones ◽  
Urban Transition ◽  
Reliable Methods

The process by which cities (or urban areas) expand over time has remained a key focus for geographers, ecologists and other scientists interested in urban phenomena for decades. This study investigated the use of spatial metrics and population data for defining and mapping rural-urban transition zones in Hanoi and exploring urban growth models. The analysis showed that in 2010, about 30% of communes within Hanoi could be defined as rural, 38% as peri-urban and 32% as urban. The peri-urban communes showed a greater level of landscape fragmentation and a higher pace of population growth than rural communes. The urban landscape of Hanoi in 2010 shows characteristics of both transportation corridors and dispersed sites models—the two least eco-friendly models of urbanization. This study provides an effective method for mapping such rural-urban transition and identifies forms of urbanization in places where other socio-economic data sources are limited. This is particularly useful for planners and development agencies that require reliable methods for collecting and analysing data, which can enable them to assess variables along the rural-to-urban continuum.

scholarly journals An Improved Case-Based Reasoning Model for Simulating Urban Growth

2021 ◽  
Vol 13 (11) ◽  
pp. 6146
Author(s):  
Xin Ye ◽  
Wenhui Yu ◽  
Lina Lv ◽  
Shuying Zang ◽  
Hongwei Ni
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Control Method ◽  
Growth Models ◽  
Case Based Reasoning ◽  
Initial State ◽  
Simulation Accuracy ◽  
Complex Dynamic Systems ◽  
Time Control ◽  
Case Based

Developing urban growth models enables a better understanding and planning of sustainable urban areas. Case-based reasoning (CBR), in which historical experience is used to solve problems, can be applied to the simulation of complex dynamic systems. However, when applying CBR to urban growth simulation, problems such as inaccurate case description, a single retrieval method, and the lack of a time control mechanism limit its application accuracy. In order to tackle these barriers, this study proposes a CBR model for simulating urban growth. This model includes three parts: (1) the case expression mode containing the “initial state-geographical feature-result” is proposed to adapt the case expression to the urban growth process; (2) in order to improve the reliability of the results, we propose a strategy to introduce the “retrieval quantity” parameter and retrieve multiple similar cases; and (3) a time factor control method based on demand constraints is proposed to improve the power of time control in the algorithm. Finally, the city of Jixi was used as the study area for simulation, and when the “retrieval quantity” is 10, the simulation accuracy reaches 97.02%, kappa is 85.51, and figure of merit (FoM) is 0.1699. The results showed that the proposed method could accurately analyze urban growth.

scholarly journals Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions

2021 ◽  
Vol 13 (3) ◽  
pp. 512
Author(s):  
Jairo Alejandro Gómez ◽  
ChengHe Guan ◽  
Pratyush Tripathy ◽  
Juan Carlos Duque ◽  
Santiago Passos ◽  
...  
Keyword(s):  
Urban Growth ◽  
Geographical Information Systems ◽  
Growth Models ◽  
A Priori ◽  
Remote Sensing Data ◽  
Public Utility ◽  
Town Planning ◽  
Geographical Information ◽  
New Town ◽  
Spatiotemporal Uncertainty

With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide.

scholarly journals Taking Implementation Seriously in the Evaluation of Urban Growth Management Strategies: “Safeguarding the Future” of the Antwerp City-Region

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 159
Author(s):  
Clemens de Olde ◽  
Stijn Oosterlynck
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Growth Management ◽  
Management Strategies ◽  
Housing Prices ◽  
Policy Formulation ◽  
Land Values ◽  
Implementation Phase ◽  
The Future ◽  
Urban Growth Management

Contemporary evaluations of urban growth management (UGM) strategies often take the shape of quantitative measurements of land values and housing prices. In this paper, we argue that it is of key importance that these evaluations also analyse the policy formulation and implementation phases of growth management strategies. It is in these phases that the institutions and discourses are (trans)formed in which UGM strategies are embedded. This will enable us to better understand the conditions for growth management policies’ success or failure. We illustrate this point empirically with the case of demarcating urban areas in the region of Flanders, Belgium. Using the Policy Arrangement Approach, the institutional dynamics and discursive meanings in this growth instrument’s formulation and implementation phase are unravelled. More specifically, we explain how the Flemish strategic spatial planning vision of restraining sprawl was transformed into one of accommodating growth in the demarcation of the Antwerp Metropolitan Area, epitomised by two different meanings of the phrase “safeguarding the future.” In conclusion, we argue that, in Antwerp, the demarcation never solidified into a stable policy arrangement, rendering it largely ineffective. We end by formulating three recommendations to contribute to future attempts at managing urban growth in Flanders.

scholarly journals A spatial zoning approach to calibrate and validate urban growth models

2016 ◽  
Vol 31 (4) ◽  
pp. 763-782 ◽  
Author(s):  
Ali Kazemzadeh-Zow ◽  
Saeed Zanganeh Shahraki ◽  
Luca Salvati ◽  
Najmeh Neisani Samani
Keyword(s):  
Urban Growth ◽  
Growth Models

Using Land Cover, Population, and Night Light Data for Assessing Local Temperature Differences in Mainz, Germany

2015 ◽  
Vol 54 (3) ◽  
pp. 658-670 ◽  
Author(s):  
Jenny Lindén ◽  
Jan Esper ◽  
Björn Holmer
Keyword(s):  
Twentieth Century ◽  
Light Intensity ◽  
Central Europe ◽  
Urban Growth ◽  
Urban Areas ◽  
Surface Coverage ◽  
Daily Maximum ◽  
Source Areas ◽  
Maximum Temperatures ◽  
The City

AbstractUrban areas are believed to affect temperature readings, thereby biasing the estimation of twentieth-century warming at regional to global scales. The precise effect of changes in the surroundings of meteorological stations, particularly gradual changes due to urban growth, is difficult to determine. In this paper, data from 10 temperature stations within 15 km of the city of Mainz (Germany) over a period of 842 days are examined to assess the connection between temperature and the properties of the station surroundings, considering (i) built/paved area surface coverage, (ii) population, and (iii) night light intensity. These properties were examined in circles with increasing radii from the stations to identify the most influential source areas. Daily maximum temperatures Tmax, as well as daily average temperatures, are shown to be significantly influenced by elevation and were adjusted before the analysis of anthropogenic surroundings, whereas daily minimum temperatures Tmin were not. Significant correlations (p < 0.1) between temperature and all examined properties of station surroundings up to 1000 m are found, but the effects are diminished at larger distance. Other factors, such as slope and topographic position (e.g., hollows), were important, especially to Tmin. Therefore, properties of station surroundings up to 1000 m from the stations are most suitable for the assessment of potential urban influence on Tmax and Tmin in the temperate zone of central Europe.

scholarly journals The Potential Of Simulating Energy Systems: The Multi Energy Systems Simulator Model

2021 ◽  
Author(s):  
Luigi Bottecchia ◽  
Pietro Lubello ◽  
Pietro Zambelli ◽  
Carlo Carcasci ◽  
Lukas Kranzl
Keyword(s):  
Open Source ◽  
Spatial Scales ◽  
Optimal Solution ◽  
Energy Systems ◽  
Energy Transition ◽  
Simulation Models ◽  
Energy System ◽  
System Modelling ◽  
Energy System Model ◽  
Urban Level

Energy system modelling is an essential practice to assist a set of heterogeneous stakeholders in the process of defining an effective and efficient energy transition. From the analysis of a set of open source energy system models, it has emerged that most models employ an approach directed at finding the optimal solution for a given set of constraints. On the contrary, a simulation model is a representation of a system that is used to reproduce and understand its behaviour under given conditions, without seeking an optimal solution. Given the lack of simulation models that are also fully open source, in this paper a new open source energy system model is presented. The developed tool, called Multi Energy Systems Simulator (MESS), is a modular, multi-node model that allows to investigate non optimal solutions by simulating the energy system. The model has been built having in mind urban level analyses. However, each node can represent larger regions allowing wider spatial scales to be be represented as well. MESS is capable of performing analysis on systems composed by multiple energy carriers (e.g. electricity, heat, fuels). In this work, the tool&rsquo;s features will be presented by a comparison between MESS itself and an optimization model, in order to analyze and highlight the differences between the two approaches, the potentialities of a simulation tool and possible areas for further development.

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