1,121 research outputs found

    A complex network approach to urban growth

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    The economic geography can be viewed as a large and growing network of interacting activities. This fundamental network structure and the large size of such systems makes complex networks an attractive model for its analysis. In this paper we propose the use of complex networks for geographical modeling and demonstrate how such an application can be combined with a cellular model to produce output that is consistent with large scale regularities such as power laws and fractality. Complex networks can provide a stringent framework for growth dynamic modeling where concepts from e.g. spatial interaction models and multiplicative growth models can be combined with the flexible representation of land and behavior found in cellular automata and agent-based models. In addition, there exists a large body of theory for the analysis of complex networks that have direct applications for urban geographic problems. The intended use of such models is twofold: i) to address the problem of how the empirically observed hierarchical structure of settlements can be explained as a stationary property of a stochastic evolutionary process rather than as equilibrium points in a dynamics, and, ii) to improve the prediction quality of applied urban modeling.evolutionary economics, complex networks, urban growth

    Models of Transportation and Land Use Change: A Guide to the Territory

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    Modern urban regions are highly complex entities. Despite the difficulty of modeling every relevant aspect of an urban region, researchers have produced a rich variety models dealing with inter-related processes of urban change. The most popular types of models have been those dealing with the relationship between transportation network growth and changes in land use and the location of economic activity, embodied in the concept of accessibility. This paper reviews some of the more common frameworks for modeling transportation and land use change, illustrating each with some examples of operational models that have been applied to real-world settings.Transport, land use, models, review network growth, induced demand, induced supply

    Configuring the neighbourhood effect in irregular cellular automata based models

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    Cellular automata (CA) models have been widely employed to simulate urban growth and land use change. In order to represent urban space more realistically, new approaches to CA models have explored the use of vector data instead of traditional regular grids. However, the use of irregular CA-based models brings new challenges as well as opportunities. The most strongly affected factor when using an irregular space is neighbourhood. Although neighbourhood definition in an irregular environment has been reported in the literature, the question of how to model the neighbourhood effect remains largely unexplored. In order to shed light on this question, this paper proposed the use of spatial metrics to characterise and measure the neighbourhood effect in irregular CA-based models. These metrics, originally developed for raster environments, namely the enrichment factor and the neighbourhood index, were adapted and applied in the irregular space employed by the model. Using the results of these metrics, distance-decay functions were calculated to reproduce the push-and-pull effect between the simulated land uses. The outcomes of a total of 55 simulations (five sets of different distance functions and eleven different neighbourhood definition distances) were compared with observed changes in the study area during the calibration period. Our results demonstrate that the proposed methodology improves the outcomes of the urban growth simulation model tested and could be applied to other irregular CA-based models

    Linnade laienemine Eestis: seire, analüüs ja modelleerimine

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    Väitekirja elektrooniline versioon ei sisalda publikatsiooneLinnade laienemine, mida iseloomustab vähese tihedusega, ruumiliselt ebaühtlane ja hajutatud areng linna piiridest välja. Kuna linnade laienemine muudab põllumajandus- ja metsamaid ning väikesed muutused linnapiirkondades võivad pikaajaliselt mõjutada elurikkust ja maastikku, on hädavajalik seirata linnade ruumilist laienemist ning modelleerida tulevikku, saamaks ülevaadet suundumustest ja tagajärgedest pikemas perspektiivis. Eestis võeti pärast taasiseseisvumist 1991. aastal vastu maareformi seadus ning algas “maa” üleandmine riigilt eraomandisse. Sellest ajast peale on Eestis toimunud elamupiirkondade detsentraliseerimine, mis on mõjutanud Tallinna ümbruse põllumajandus- ja tööstuspiirkondade muutumist, inimeste elustiili muutusi ning jõukate inimeste elama asumist ühepereelamutesse Tallinna, Tartu ja Pärnu lähiümbruse. Selle aja jooksul on Eesti rahvaarv vähenenud 15,31%. Käesoleva doktoritöö eesmärgiks on "jälgida, analüüsida ja modelleerida Eesti linnade laienemist viimase 30 aasta jooksul ning modelleerida selle tulevikku", kasutades paljusid modelleerimismeetodeid, sealhulgas logistilist regressiooni, mitmekihilisi pertseptronnärvivõrke, rakkautomaate, Markovi ahelate analüüsi, mitme kriteeriumi. hindamist ja analüütilise hierarhia protsesse. Töö põhineb neljal originaalartiklil, milles uuriti linnade laienemist Eestis. Tegu on esimese põhjaliku uuringuga Eesti linnade laienemise modelleerimisel, kasutades erinevaid kaugseireandmeid, mõjutegureid, parameetreid ning modelleerimismeetodeid. Kokkuvõtteks võib öelda, et uusehitiste hajumismustrid laienevad jätkuvalt suuremate linnade ja olemasolevate elamupiirkondade läheduses ning põhimaanteede ümber.Urban expansion is characterized by the low–density, spatially discontinued, and scattered development of urban-related constructions beyond the city boundaries. Since urban expansion changes the agricultural and forest lands, and slight changes in urban areas can affect biodiversity and landscape on a regional scale in the long-term, spatiotemporal monitoring of urban expansion and modeling of the future are essential to provide insights into the long-term trends and consequences. In Estonia, after the regaining independence in 1991, the Land Reform Act was passed, and the transfer of “land” from the state to private ownership began. Since then, Estonia has experienced the decentralization of residential areas affecting the transformation of agricultural and industrial regions around Tallinn, changes in people's lifestyles, and the settling of wealthy people in single-family houses in the suburbs of Tallinn, Tartu, and Pärnu. During this period, Estonia's population has declined dramatically by 15.31%. Therefore, this dissertation aims to "monitor, analyze and model Estonian urban expansion over the last 30 years and simulate its future" using many modeling approaches including logistic regression, multi-layer perceptron neural networks, cellular automata, Markov chain Analysis, multi-criteria evaluation, and analytic hierarchy process. The thesis comprises four original research articles that studied urban expansion in Estonia. So far, this is the first comprehensive study of modeling Estonian urban expansion utilizing various sets of remotely sensed data, driving forces and predictors, and modeling approaches. The scattering patterns of new constructions are expected to continue as the infilling form, proximate to main cities and existing residential areas and taking advantage of main roads in future.https://www.ester.ee/record=b550782

    Simulating the impact of economic and environmental strategies on future urban growth scenarios in Ningbo, China

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    Coastal cities in China are challenged by multiple growth paths and strategies related to demands in the housing market, economic growth and eco-system protection. This paper examines the effects of conflicting strategies between economic growth and environmental protection on future urban scenarios in Ningbo, China, through logistic-regression-based cellular automata (termed LogCA) modeling. The LogCA model is calibrated based on the observed urban patterns in 1990 and 2015, and applied to simulate four future scenarios in 2040, including (a) the Norm-scenario, a baseline scenario that maintains the 1990-2015 growth rate; (b) the GDP-scenario, a GDP-oriented growth scenario emphasizing the development in city centers and along economic corridors; (c) the Slow-scenario, a slow-growth scenario considering the potential downward trend of the housing market in China; and (d) the Eco-scenario, a slow-growth scenario emphasizing natural conservation and ecosystem protections. The CA parameters of the Norm-and Slow-scenarios are the same as the calibrated parameters, while the parameters of proximities to economic corridors and natural scenery sites were increased by a factor of 3 for the GDP-and Eco-scenarios, respectively. The Norm-and GDP-scenarios predicted 1950 km(2) of new growth for the next 25 years, the Slow-scenario predicted 650 km2, and the Eco-scenario predicted less growth than the Slow-scenario. The locations where the newly built-up area will emerge are significantly different under the four scenarios and the Slow-and Eco-scenarios are preferable to achieve long-term sustainability. The scenarios are not only helpful for exploring sustainable urban development options in China, but also serve as a reference for adjusting the urban planning and land policies

    Comparing the structural uncertainty and uncertainty management in four common Land Use Cover Change (LUCC) model software packages

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    Research on the uncertainty of Land Use Cover Change (LUCC) models is still limited. Through this paper, we aim to globally characterize the structural uncertainty of four common software packages (CA_Markov, Dinamica EGO, Land Change Modeler, Metronamica) and analyse the options that they offer for uncertainty management. The models have been compared qualitatively, based on their structures and tools, and quantitatively, through a study case for the city of Cape Town. Results proved how each model conceptualised the modelled system in a different way, which led to different outputs. Statistical or automatic approaches did not provide higher repeatability or validation scores than user-driven approaches. The available options for uncertainty management vary depending on the model. Communication of uncertainties is poor across all models.Spanish GovernmentEuropean Commission INCERTIMAPS PGC2018-100770-B-100Spanish Ministry of Economy and Competitiveness and the European Social Fund [Ayudas para contratos predoctorales para la formacion de doctores 2014]University of Granada [Contratos Puente 2018]Spanish Ministry of Science and Innovation [Ayudas para contratos Juan de la Cierva-for-macion] 2019-FJC2019-040043University of Cape Town (Centre for Transport Studies

    An Integrated Ecological-Social Simulation Model of Farmer Decisions and Cropping System Performance in the Rolling Pampas (Argentina)

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    Changes in agricultural systems are a multi-causal process involving climate change, globalization and technological change. These complex interactions regulate the landscape transformation process by imposing land use and cover change (LUCC) dynamics. In order to better understand and forecast the LUCC process we developed a spatially explicit agent-based model in the form of a Cellular Automata: the AgroDEVS model. The model was designed to project viable LUCC dynamics along with their associated economic and environmental changes. AgroDEVS is structured with behavioral rules and functions representing a) crop yields, b) weather conditions, c) economic profits, d) farmer preferences, e) adoption of technology levels and f) natural resource consumption based on embodied energy accounting. Using data from a typical location of the Pampa region (Argentina) for the period 1988-2015, simulation exercises showed that economic goals were achieved, on average, each 6 out of 10 years, but environmental thresholds were only achieved in 1.9 out of 10 years. In a set of 50-years simulations, LUCC patterns converge quickly towards the most profitable crop sequences, with no noticeable trade-off between economic and environmental conditions.Fil: Pessah, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Ferraro, Diego Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Blanco, Daniela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Castro, Rodrigo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentin

    Space and Price in Adapting Cities : Exploring the Spatial Economic Role of Climate-Sensitive Ecological Risks and Amenities in Finnish Housing Markets

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    As the adaptation of cities to climate change is increasingly overlapping sustainable urban development, the necessity to harmonize climate-proofing with economic objectives becomes ever clearer. Climate-sensitive ecological risks and amenities, and their role in markets and urban planning, are central in this issue. This research explores the reaction of urban housing markets to changes related to green amenities and flood risks; deepens the understanding of complex spatial processes, in housing markets and urban growth, that relate to the implementation of sustainable adaptation strategies; and develops advanced spatial modelling methodology that renders urban economic analysis better suitable to address questions of sustainable and climate-proof urban planning. The results demonstrate that physical or behavioral planning interventions surrounding climate-sensitive ecological risks and amenities generate economic benefits via multiple channels, when attuned with market mechanisms. This is an important building block in synchronizing climate-proofing with economic development objectives, therefore facilitating urban adaptation that is also sustainable. The synchronization requires an evidence-based understanding of the effects linked to particular interventions, at concrete locations and spatiotemporal scales. The overall message is that, while trade-offs are unavoidable, if green cities maintain agglomeration benefits, ensure increased information flows about ecological risks and amenities, while implementing amenities in a spatially parameterized manner, they are able to achieve both climate-proofing and sustainability objectives. The thesis consists of five quantitative analysis articles, while the introductory chapter synthesizes the results in the context of urban planning, spatial economics, and climate change adaptation. The first three articles apply empirical microeconometric methodologies (spatial hedonic and difference-in-differences analysis) to explore the response of housing markets to changes in green infrastructure and to policy instruments related to flood risk information. The fourth and fifth articles apply spatial complexity methods (cellular automata, fractal geometry) to extend the intuitions of microeconometric estimations into dynamic spatial processes in housing prices and urban growth. The five articles use environmental-economic datasets developed by this dissertation research, covering the urban region of Helsinki (Helsinki, Espoo, and Vantaa) and the cities of Pori and Rovaniemi.In future cities, local climate and ecosystems will be an important part of urban planning. This dissertation explores how growing cities can deal with green spaces and flood risks. Climate and environmental changes are not only about threats, but cities can use them as opportunities, provided well-informed policies based on research evidence. The study explores how house prices react to green spaces and to flood risks, and how sustainable development and climate adaptation strategy can be successful. Complicated problems such as these require innovative solutions, and the dissertation uses methods such as fractals, cellular automata, and spatial economic analysis. The study analyzes housing markets and urban dynamics in the Finnish capital region, in Pori, and in Rovaniemi, combining and developing new datasets. The dissertation shows that green spaces and information about climate-related risks are powerful tools for climate-proof sustainable cities, provided that there is a clear understanding of how all their costs and benefits are behaving in time and in different types of neighborhoods

    Flexibility of multi-agent system models for rubber agroforest landscapes and social response to emerging reward mechanisms for ecosystem services in Sumatra, Indonesia

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    Payments for ecosystem services (PES) have been widely recognized as an innovative management approach to address both environment conservation and human welfare while serving as a policy instrument to deal with the ecosystem service (ES) trade-offs resulting from land-use/ cover change (LUCC). However, there is no solid understanding of how PES could affect the synergies and trade-offs among ES. This research focuses on the LUCC and its inherent ES trade-offs in the context of social-ecological systems (SES) that incorporates key feedbacks and processes, and explores the possible impacts of management regimes, i.e., PES schemes (e.g., eco-certification and reduced emissions from deforestation and degradation (REDD)). To address the complexity of this research, a multi-agent simulation (MAS) model (LB-LUDAS - Lubuk Beringin - Land Use DynAmics Simulator) was applied in which process-based decision-making sub-models were incorporated in the decision-making mechanism of agents. The model was developed to explore policy scenarios by quantifying the potential ES trade-offs resulting from the agents’ land-use choices and preferences. It was first implemented for the rubber agroforest landscape in Jambi Province (Sumatra), Indonesia. Species richness, carbon sequestration, opportunity costs, and decision processes such as PES adoption and future land-use preferences sub-models were incorporated to capture as much as possible the real SES of a rubber agroforest landscape. Three scenarios were simulated over a 20-year period, namely the PES scenario, the scenario land-use preference if supported by financial assistance/subsidies (SUB), and the current trend as the baseline scenario. From the simulations, the key findings show that there was a minimal land-cover change under the PES scenario, where an estimated 22% of the species richness in rubber agroforests could be conserved and 97% of the carbon emissions reduced compared to the baseline scenario. For the SUB scenario, an estimated 6% of the species richness could be conserved and 47% of the carbon emissions reduced. With regard to livelihoods, only under the PES scenario was wealth inequality reduced up to 50%. Regarding the return for land investment, the profitability of a land-use type depends considerably on each scenario; however, rubber agroforests would be highly profitable (20%) if a price premium were to be implemented under an eco-certification scheme. The main conclusions of this study are firstly, that PES schemes for rubber agroforests could offer synergies among carbon emission reduction, biodiversity and livelihoods, thus reducing the trade-offs resulting from possible land-use/cover change, and secondly that the LB-LUDAS model as an integrated and MAS model is a useful tool to capture the ES trade-offs as an emergent property of the dynamic social-ecological systems at the same time serving as a negotiation-support system tool to support the design of land-use policies. The use of process-based decision making in the LB-LUDAS model is recommended in order to incorporate intended decisions of agents in various situations. In this way, the triggers, options and temporal and spatial aspects of agents’ reactions are captured in a relatively realistic way.Flexibilität von Multi-Agenten-Modellen für Gummi-Agroforste-Landschaften und die soziale Reaktion auf die neu entstehenden Belohnungsmechanismen für Ökosystemdienstleistungen in Sumatra, Indonesien Bezahlungen für Ökosystemdienstleistungen (PES) sind weit verbreitet und anerkannt als ein Managementansatz sowohl für den Umweltschutz als auch für das menschliche Wohlbefinden. Gleichzeitig dienen sie als Politikinstrument zur Behandlung der Folgen (ES trade-offs) durch Veränderungen in der Landnutzung/-bedeckung (LUCC). Es gibt jedoch kein solides Wissen darüber, wie sich PES auf die Synergien und trade-offs zwischen den ES auswirken könnten. Der Schwerpunkt dieser Studie liegt auf den LUCC und ihren inhärenten ES trade-offs im Kontext von sozial-ökologischen Systemen (SES), die wichtige Feedbacks und Prozesse berücksichtigen. Die Studie untersucht die möglichen Auswirkungen von Managementregimen, d.h., PES-Systeme (z.B. Ökozertifizierung und reduzierte Emissionen von Entwaldung und Degradation (REDD)). Um die Komplexität des Themas zu erfassen, wurde ein Multi-Agentensimulationsmodel (MAS; LB-LUDAS - Lubuk Beringin - Land Use DynAmics Simulator) eingesetzt, in dem prozessbasierte Entscheidungs-Submodelle in den Entscheidungsmechanismus der Agenten berücksichtigt werden. Das Modell wurde entwickelt, um verschiedene Szenarien durch die Quantifizierung der potentiellen ES trade-offs, die durch die Wahl bzw. Vorlieben der Agenten hinsichtlich der Landnutzung entstehen, zu untersuchen. Es wurde zuerst für die Landschaften der Gummi-Agroforste in Jambi Provinz (Sumatra), Indonesien, eingesetzt. Sub-Modelle wie Artenvielfalt, Kohlenstoffspeicherung, Opportunitätskosten und Entscheidungsprozesse wie Anwendung von PES und zukünftige Präferenzen wurden berücksichtigt, um so weit wie möglich die tatsächlichen SES von Gummi-Agroforsten zu erfassen. Drei Szenarien wurden über eine Periode von 20 Jahren simuliert nämlich das PES-Szenario, das Szenario Landnutzungspräferenz, wenn mit finanzieller Unterstützung bzw. Subventionen (SUB), sowie der aktuelle Trend als Grundszenario. Die wichtigsten Ergebnisse der Simulationen zeigen eine minimale Veränderung der Landnutzung im PES-Szenario wobei ca. 22% der Artenvielfalt in den Gummi-Agroforsten erhalten und die Kohlenstoffemissionen um 97% reduziert werden konnten verglichen mit dem Grundszenario. Bei dem SUB-Szenario konnten ca. 6% der Artenvielfalt erhalten und die Kohlenstoffemissionen um 47% reduziert werden. Hinsichtlich der Lebensgrundlagen wurden nur beim PES-Szenario die Wohlstandsungleichheit um bis zu 50% reduziert. Bei den Renditen für Investitionen in Land hängt Wirtschaftlichkeit sehr stark vom Landnutzungstyp ab; Gummi-Agroforste wären jedoch sehr profitabel (20%) bei einem Preisaufschlag in einem Ökozertifizierungsprogramm. Die wichtigsten Schlussfolgerungen dieser Untersuchung sind erstens, dass PES-Programme für Gummi-Agroforste zu Synergien zwischen Reduzierung von Kohlenstoffemissionen und Biodiversität sowie Lebensgrundlagen führen und damit die trade-offs reduzieren, die durch mögliche Veränderungen in der Landnutzung/-bedeckung entstehen können und zweitens, dass das LB-LUDAS-Modell als integriertes sowie als MAS-Modell ein nützliches Instrument darstellt, um die ES trade-offs als eine zu Tage tretende Eigenschaft der dynamischen sozialen-ökologischen Systemen zu erfassen. Gleichzeitig dient das Modell als Instrument zur Unterstützung von Verhandlungen bei der Planung von Landnutzungsmaßnahmen. Der Einsatz prozessbasierter Entscheidungen im LB-LUDAS-Modell um geplante Entscheidungen von Agenten in verschiedenen Situationen zu berücksichtigen, wird empfohlen. Auf diese Art können die Auslöser, die Optionen sowie die zeitlichen und räumlichen Aspekte der Reaktionen der Agenten auf relativ realistische Weise erfasst werden
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