Urban Growth Trajectories Research Articles

The role of random forest and Markov chain models in understanding metropolitan urban growth trajectory

IntroductionThis study delves into the spatiotemporal dynamics of land use and land cover (LULC) in a Metropolitan area over three decades (1991–2021) and extends its scope to forecast future scenarios from 2031 to 2051. The intent is to aid sustainable land management and urban planning by enabling precise predictions of urban growth, leveraging the integration of remote sensing, GIS data, and observations from Landsat satellites 5, 7, and 8.MethodsThe research employed a machine learning-based approach, specifically utilizing the random forest (RF) algorithm, for LULC classification. Advanced modeling techniques, including CA–Markov chains and the Land Change Modeler (LCM), were harnessed to project future LULC alterations, which facilitated the development of transition probability matrices among different LULC classes.ResultsThe investigation uncovered significant shifts in LULC, influenced largely by socio-economic factors. Notably, vegetation cover decreased substantially from 49.21% to 25.81%, while forest cover saw an increase from 31.89% to 40.05%. Urban areas expanded significantly, from 7.55% to 25.59% of the total area, translating into an increase from 76.31 km2 in 1991 to 258.61 km2 in 2021. Forest area also expanded from 322.25 km2 to 409.21 km2. Projections indicate a further decline in vegetation cover and an increase in built-up areas to 371.44 km2 by 2051, with a decrease in forest cover compared to its 2021 levels. The predictive accuracy of the model was confirmed with an overall accuracy exceeding 90% and a kappa coefficient around 0.88.DiscussionThe findings underscore the model’s reliability and provide a significant theoretical framework that integrates socio-economic development with environmental conservation. The results emphasize the need for a balanced approach towards urban growth in the Islamabad metropolitan area, underlining the essential equilibrium between development and conservation for future urban planning and management. This study underscores the importance of using advanced predictive models in guiding sustainable urban development strategies.

Optimized layout of supporting facilities in mountainous industrial parks: a case study of Chongqing Dadi Industrial Park

Industrial parks play a pivotal role in driving urban economic growth in China, particularly in light of the country's extensive mountainous terrain. Effective arrangement of supporting facilities within these parks is crucial for steering industries towards high-quality development. However, the intricate topography of mountainous regions presents challenges in devising optimal layouts for these facilities. This article adopts a user-centric perspective to investigate the impact of mountainous terrain on user fatigue within industrial parks. We employ equivalent coefficient methods to establish a correlation between undulating paths in mountainous terrain and flat paths. This correlation serves as the foundation for a visual analytical tool designed to facilitate the rational placement of supporting facilities in mountainous industrial parks. To validate our approach, we conduct an empirical analysis of the supporting facility layout at Chongqing Dadi Industrial Park. The insights gleaned from our study provide a basis for optimizing the placement of supporting facilities in mountainous industrial parks, enhancing user experiences and aligning with urban growth trajectories. In conclusion, our research offers dual benefits: firstly, it provides a scientific framework for guiding the rational layout of supporting facilities in mountainous industrial parks, addressing the challenges posed by complex terrain. Secondly, it offers practical insights into the comprehensive planning and design of these parks, promoting sustainable urban and economic growth. Our research thus emerges as a valuable resource for the advancement and optimization of mountainous industrial parks in China.

The Anti-sprawl Policies in Tehran and the Creation of Spatial Injustice

The proliferation of anti-sprawl policies across the cities in the Global South and North appears to be a legitimate backlash to an ever-increasing rate of urban growth and expansion in the 21st century. Questions remain, however, around the outcomes of sprawl-controlling plans, the extent to which the Northern perspectives dominate the anti-sprawl rhetoric across the globe, and whether transferring them to the rapidly expanding Southern cities is rational and feasible. It is essential to acknowledge that the incentives and, in turn, consequences of urban sprawl in the Southern cities are substantially different from their Northern counterparts. Such divergences call for a new approach towards ‘provincialisation’ (Sheppard, Leitner and Maringanti, 2013) of urban sprawl discourses. This paper examines the intersection of anti-sprawl debates and spatial injustice and incorporates both empirical and theoretical elements. The empirical element examines the historical development of anti-sprawl strategies in Tehran since the 1960s and scrutinises the consequences of the urban containment policies and plans, particularly in relation to the creation of peripheral spatial traps and through the lens of spatial justice, citizenship and the ‘right to the centre’ (Marcuse, 2009; Harvey, 2010; Soja, 2013). The theoretical element contributes to the debate on the ‘theory from the South’ by underlining the dialectical interplay of centre and periphery within the trajectories of urban growth in the Southern cities and arguing for the need to develop multiple urban epistemologies capable of explaining the complexities of multiple urban conditions in the South. The core argument of this paper is thus twofold. First and through an empirically supported argument, it contends that anti-sprawl policies and strategies in Tehran act as catalysts in the densification/sprawl dialectical transformation of the cities and intensify the creation of unjust geographies in the peripheral buffer zones through displacement and compromising the ‘right to the centre’ of (non)citizens. Second and from a wider perspective, the paper argues against theorisation of urban sprawl as a universally relevant and applicable category of urban transformation and calls for a reformulation of the concept, its drivers and materialisation, and the institutional responses given to it from the Southern perspective.

Venerated skylines under pressure: a view of three cities

ABSTRACT For many decades, permissible building heights have been kept relatively low and largely unchanged in cities, which have been home to renowned historic and architectural assets. Recently, attempts to introduce modifications into long-established height limitations have triggered diverse responses among policy-makers, planners and residents of such cities. Using an extensive body of planning documents, protocols, and interviews, and adopting an urban comparative approach, this paper examines how height restrictions are maintained and challenged in three cities characterized by strict height regulations: Washington DC, Paris, and Jerusalem. We argue that the continuous interplay between three dimensions – the market, policy, and culture can lead to a range of responses: strict obedience to a long-established planning tradition, to a relaxation of height limits, and ultimately to the deregulation of height limits and the reshaping of venerated cityscapes. Our study suggests that a longer-view approach involving a comparative examination yields a nuanced and finer understanding of urban growth trajectories.

Urban expansion simulation towards low-carbon development: A case study of Wuhan, China

Rapid urbanization causes great changes of carbon metabolism. Current research mainly focuses on carbon consequences of urban expansion projections, but rarely explores how carbon management strategies affect future urban growth trajectories. Here, we propose a hierarchy of low-carbon management strategies and incorporate it into an integrated cellular automata model to obtain sustainable urban development plans. In the hierarchy, a top-down strategy regarding carbon emission reduction is used to adjust future urban land demand, while a bottom-up strategy regarding carbon sequestration conservation of land patches is used to constrain land use conversions. We design four expansion scenarios based on different combinations of the two low-carbon strategies for Wuhan in 2025, including business as usual (BAU), the scenario with top-town strategy (T-UES), the scenario with bottom-up strategy (B-UES), and the scenario with both two strategies (TB-UES). Our results demonstrate that the proposed method can generate promising urban expansion plans with less ecological loss, and promote compact and infilling urban development.

An agent-based learning-embedded model (ABM-learning) for urban land use planning: A case study of residential land growth simulation in Shenzhen, China

A forward-looking urban land use plan is crucial to a city’s sustainability, which requires a deep understanding of human-environment interactions between different domains, and modelling them soundly. One of the key challenges of modelling these interactions is to understand and model how human individuals make and develop their location decisions by learning that then shape urban land-use patterns. To investigate this issue, we have constructed an extended experience-weighted attraction learning model to represent the human agents’ learning when they make location decisions. Consequently, we propose and have developed an agent-based learning-embedded model (ABM-learning) for residential land growth simulation that incorporates a learning model, a decision-making model, a land use conversion model and the constraint of urban land use master plan. The proposed model was used for a simulation of the residential land growth in Shenzhen city, China. By validating the model against empirical data, the results showed that the site-specific accuracy of the model has been improved when embedding learning model. The analysis on the simulation accuracies has proved the argument that modelling individual-level learning matters in the agent’s decision model and the agent-based models. We also applied the model to predict residential land growth in Shenzhen from 2015 to 2035, and the result can be a reference for land-use allocation in detailed planning of Shenzhen. The ABM-learning is applicable to studying the past urban growth trajectory, aiding in the formulation of detailed residential land and public service facility planning and assessing the land use planning effectiveness.

The long breadth of cities: revisiting worldwide urbanization patterns, 1950–2030

ABSTRACT A comparative analysis of population dynamics worldwide contributes to profile distinctive demographic and economic trajectories of urban growth, discriminating processes of settlement concentration or dispersion under sequential cycles of urbanization. However, a wide-ranging characterization of urban cycles based on demographic dynamics worldwide is still missing. The present work is aimed at filling such a gap analysing long-term changes (1950–2030) in annual population growth rate of 1691 urban agglomerations with more than 300,000 inhabitants in 74 world countries. Results of this study indicate that metropolitan growth worldwide was associated with largely variable rates of population growth, highly positive before 2000 and progressively reducing over recent decades. Despite important differences at continental (and country) scale, demographic expansion of urban agglomerations showed two contrasting phases with a break point in the 1980s denoting a progressive reduction in spatial heterogeneity of population growth rates and a moderate slowdown in demographic dynamics. Intensity of urban expansion and spatial heterogeneity in population growth rates across metropolitan agglomerations evidences a trade-off between fast and slow demographic dynamics. These findings can be better understood to support theories of sequential city growth, making a suitable contribution to policy making, especially in countries where urban population is expanding more rapidly.

Urban Growth Dynamics and Changing Land-Use Land-Cover of Megacity Kolkata and Its Environs

Spatio-temporal land-use land-cover changes have a long-term impact on urban environments. The present study is based on land-use land-cover changes and urban expansion of megacity Kolkata and its environs over three decades (1991–2018) using multitemporal Landsat data. The study aims to explore and explain the spatio-temporal land-use land-cover change, areal differentiation, spatio-temporal urban growth trajectory and future land-use land-cover prediction with population projection. The spatio-temporal representation found rapid urbanization, i.e. 19% to 57%, exactly three times as in 1991, resulting in significant loss of other than urban/built-up area. Urban trajectory reveals that the expansion mainly occurred in north-east to south-west direction, the zone of both sides of River Hooghly. Areal differentiation map with highest urbanization (3146 ha or UII = 0.64) was identified in the north–north-west part, while least urbanization was identified in the east–north-east direction. On the other hand, this urbanization has grabbed most (i.e. 87%) of the areas within 5-km ring buffer compared to other three ring buffers. Being Kolkata as a traditional city, it has all modern facilities since British rule; as a result, the high population growth and rapid urban expansion were explored in the study. Therefore, urban growth led to radical changes in land-use land-cover, which were witnessed by sharp decreases in sparse vegetation and fallow land. The correlation explained that increasing urbanization has decreased the amount of water body and vegetation. The future prediction graph identified the more horrible picture: the city and its environs will be covered by 67% built-up, while there will be only 3% water body, 14% vegetation and 16% fallow land of the total geographical area with a population (projection) of 28 million in 2051 if it is continued. Such expansion will create a wide range of mismanagement and environmental problems. Hence, the intensive explanation and areal differentiation maps and diagrams prepared using geospatial data will definitely help to understand the urban growth dynamics process and changing form of land-use land-cover and simultaneously decision-making process of the local planners, stakeholders and academicians. Therefore, it also guides to future planning to decrease the adverse effects of urbanization and result in the form of land-use land-cover and makes an eco-friendly megacity as well as sustainable urban development too.

Comparing governance and bargaining of livelihoods in informal settlements in Chennai and eThekwini

Although historical developments have differently shaped urban growth trajectories of Indian and South African cities, informal settlements continue to present urgent concerns for city governments in both countries. This research explores urban governance and political bargaining of households residing in informal settlements of Chennai, India and eThekwini, South Africa across institutional and geographic scales. The article analyses two relations in a comparative framework of Chennai and eThekwini- 1) between city governments and informal settlements; and 2) livelihoods building strategy of households in relations to the informal settlements. City and settlement relations are explored by discussing the dominant planning discourses, as illustrated in policy-related documents produced by the two city governments. The article reveals that political bargaining utilising different methods forms a crucial strategy to build livelihoods. The multi-scalar analysis of households within their situated contexts reveals how their distinct trajectories of development are closely associated with the urban and settlement histories of which they are a part. The different mandates within which the settlements exist are influential in allowing households to build up their livelihoods over one or two generations.

Simulating the urban growth of a predominantly informal Ghanaian city-region with a cellular automata model: Implications for urban planning and policy

This paper explores two main objectives: first, simulates the urban growth of a Ghanaian city-region with a CA model, drawing implications for urban planning and policy; and, second, explores the sensitivity of CA models to predominantly informal and un-regulated urban growth trajectories. CA model, SLEUTH, is calibrated for Accra city-region, and the results are triangulated with, knowledge from key local stakeholders, and findings from existing studies. The study finds that, compared with cases around the world, urban expansion in Accra city-region is highly spontaneous, rapid and unguided; and new developments fast turn into urban growth nuclei. These have reflected in a sharp rise of the city-region's land under built-up from less than 4 percent in 1990 to about 15 percent in 2015, and it is further simulated to reach around 33 percent by 2040, posing serious challenges to urban management, and threats to sustainable development. The Ghanaian planning system faces the option of swiftly addressing emerging unguided developments or wait and be confronted with a more daunting challenge of managing multiple informal growth nuclei. We also find that the applied CA model is sensitive to the highly informal urban growth trajectory of the city-region. Going forward, it is imperative for policy makers to build a more proactive, stronger and functional planning system; treat every single informal development as a potential threat to urban sustainability; and target pressure areas of expansion. The model's sensitivity to the growth trajectory of Accra city-region suggests that CA models could function as decision support tools even in overwhelmingly informal and less regulated contexts like Ghana.

Modeling Urban Growth Trajectories and Spatiotemporal Pattern: A Case Study of Lucknow City, India

Indian cities are facing rapid and unplanned urbanization in the present scenario and expanding at a miraculous amount. The unprecedented speed of expansion is a major challenge for urban planners and policy makers due to unavailability of the current database and the lack of appropriate analysis of unplanned urban expansion. This study dispenses an approach to these challenges by deploying the capabilities of remote sensing data for Lucknow city, India. The spatiotemporal analysis of expansion model integrated with driving factors is required for sustainable development of the city. The study aims to analyze the spatiotemporal urban growth trajectories integrated with driving factors of Lucknow city and its surrounding area in the period of 26 years i.e., from 1990 to 2016. An expansion model is proposed in the study for a precise examination of urban expansion topologies and its quantification. The model is processed using remote sensing satellite data of Landsat sensor of the year 1990, 1999, 2009 and 2016. The study utilizes land-use and land-cover classification maps of study years for analyzing urban trajectory model. The expansion pattern is formulated using patch distribution analysis and quantification based upon spatial built-up densities of urban patches. Further, these patches are classified in four classes of expansion—edge, infill, leapfrog and ribbon. Moreover, six different statistical parameters are formulated for quantification of detailed urban growth analysis. Patch distribution analysis has been performed for monitoring the patch size variation during study years. The villages are demarcated and significant changes are analyzed (where transition took place) and integrated with the driving factors responsible for the changes. The study indicates the dominating urban pattern and the driving factors responsible in the expansion of the built-up urban area of Lucknow city.

A modeling approach to path dependent and non-path dependent urban allocation in a rapidly growing region

The present paper combines the strengths of path dependent and non-path dependent modeling approaches to detect sustainable urban development profiles in a rapidly expanding region in Iran. In doing so, the Cellular Automata-Markov Chain (CA-MC) model is calibrated and validated using an integrative application of Multi Criteria Evaluation (MCE) method and Multi Layer Perceptron Neural Network (MLPNN) algorithm in addition to applying different Kappa-based metrics. The CA-MC model is then employed to project urban growth trajectories in the targeted research location. By combining the results of different urban allocation approaches, highly suitable zones for future development of the area are detected. These lands were not identifiable if each approach was implemented alone. The potential lands for future urbanization are evaluated and ranked in terms of their connectivity and compactness as well as their effect on other land-use categories such as forest and agriculture. The suggested methodology not only improves the calibration of a spatially-explicit model, but also it provides a wider range of options for policy makers. The findings of this study are useful to municipal decision makers in order to protect against further ecological consequences of ill-planned and uncontrolled urbanization.

Evaluating the spatial effectiveness of alternative urban growth scenarios in protecting cropland resources: A case of mixed agricultural-urbanized landscape in central Iran

This study aimed to evaluate the effectiveness of a Land Resource Protection (LRP) strategy in protecting cropland resources in Najaf-Abad Hydrological Unit, central Iran. Towards this aim, the Historical Urban Growth (HUG) and LRP scenario maps were produced using the Multi-Criteria Evaluation (MCE) procedure and transplanted into the SLEUTH urban growth model, an acronym taken from Slope, Land use, Exclusion, Urban, Transportation, and Hill-shaded layers, to predict urban growth up to 2030, 2040, and 2050. Suitability of the land surface for crop production was also mapped using the MCE procedure. Maps derived from the SLEUTH and the cropland suitability analysis were overlaid to evaluate and compare the effect of HUG and LRP scenarios on loss of croplands with different suitability degrees. The results of this study showed that, by 2050, changes in the formation (from dispersive to organic pattern) and direction of urban growth under the LRP scenario will protect nearly 1000 ha of cropland resources from conversion to urban areas and occupy statistically-less suitable croplands compared to HUG. Therefore, the formation and direction of the current urban growth trajectory should be modified towards an environmentally sound strategy as revealed in the LRP scenario.

Analyzing Land Cover Change and Urban Growth Trajectories of the Mega-Urban Region of Dhaka Using Remotely Sensed Data and an Ensemble Classifier

Accurate information on, and human interpretation of, urban land cover using satellite-derived sensor imagery is critical given the intricate nature and niches of socioeconomic, demographic, and environmental factors occurring at multiple temporal and spatial scales. Detailed knowledge of urban land and their changing pattern over time periods associated with ecological risk is, however, required for the best use of critical land and its environmental resources. Interest in this topic has increased recently, driven by a surge in the use of open-source computing software, satellite-derived imagery, and improved classification algorithms. Using the machine learning algorithm Random Forest, combined with multi-date Landsat imagery, we classified eight periods of land cover maps with up-to-date spatial and temporal information of urban land between the period of 1972 and 2015 for the mega-urban region of greater Dhaka in Bangladesh. Random Forest—a non-parametric ensemble classifier—has shown a quantum increase in satellite-derived image classification accuracy due to its outperformance over traditional approaches, e.g., Maximum Likelihood. Employing Random Forest as an image classification approach for this study with independent cross-validation techniques, we obtained high classification accuracy, user and producer accuracy. Our overall classification accuracy ranges were between 85% and 97% with kappa values between 0.81 and 0.94. The area statistics derived from the thematic land cover map show that the built-up area in the 43-year study period expanded quickly, from 35 km2 in 1972 to 378 km2 in 2015, with a net increase rate of approximately 980% and an average annual growth rate of 6%. This growth rate, however, was higher in peripheral areas, with a 2903% increase and an annual expansion rate of 8%, compared to a 460% increase with an annual growth rate of 4% in the core city area (Dhaka City Corporation). This huge urban expansion took place in the north, northwest, and southwest regions of Dhaka, transforming areas that were previously agricultural land, vegetation cover, wetland, and water bodies. The main factors driving the city towards northern corridors include flood-free higher land, the availability of a transportation network, and the agglomeration of manufacturing-based employment centers. The resulting thematic map and spatial information produced from this study therefore serve to facilitate a detailed understanding of urban growth dynamics and land cover change patterns in the mega-urban region of Dhaka, Bangladesh.

Monitoring land use/land cover change, urban growth dynamics and landscape pattern analysis in five fastest urbanized cities in Bangladesh

With little known and explored urban morphology in the fastest growing countries like Bangladesh in South Asia, this study aims at exploring urban spatial signature and explaining spatiotemporal land use and land cover patterns in the five cities (Rajshahi, Rangpur, Sylhet, Khulna, and Barisal) in Bangladesh. Using time series Landsat imagery, socioeconomic data and, other geospatial information with ecological analysis tools, this study quantifies and characterize the spatial-temporal landscape patterns and urban growth trajectory across the five selected sites. The spatial representation of these five sites demonstrates a continuous increase in urban/built-up areas replacing arable agricultural land, waterbodies, vegetation cover and wetlands, which thereby substantially altering the structure and function of the ecosystem surrounding the cities. Built up areas, representing impervious surface as observed from land cover maps in these five cities, are expanding quickly. The total built-up cover within the five cities grew from 2356ha in 1973 to 13,435ha in 2014 with a net increase of approximately 468%, while vegetation cover and crops field within same time period declined at 27.77% and 61.91%, respectively. This dramatic urban/built-up expansion has resulted in an increasingly faster alteration in the landscape composition causing to structural complexity at both class level and landscape level. Such rapid and unplanned urban expansion further has brought an overwhelming challenge to planners and policy makers, and has put a strain on local authorities to properly manage and utilize its limited land-based resources due to lack of time series geospatial information. The resulting thematic map and spatial information from this study is, therefore, to facilitate an understanding of urban growth dynamics and land cover change pattern in the five cities in Bangladesh. The result further can aid planners, stakeholders, and other interested groups to make the best possible choices regarding limited land-based resources to achieve an economically prosperous and environmentally sustainable future.

Water-smart growth planning: linking water and land in the arid urbanizing American West

Linking water and land is essential in planning for the future of the western United States. We propose the concept of ‘water-smart growth’ and explore its implications through incorporating water considerations into the SLEUTH land-use model. The urban growth trajectory in Cache County, Utah, is modeled from 2007 to 2030 under four different scenarios: current trend; smart growth; water-smart growth with moderate implementation; and water-smart growth with full implementation. Comparisons of simulation results illustrate the extent and ways in which water-smart growth would alter current established land-use growth patterns. The approach represents an initial step to better integrate land and water in urban growth modeling and planning. This study's purposes are to provide improved understanding and representation of linkages between water and land in urbanizing environments, offer insights from a set of modeled options, and demonstrate the significance of integrating land and water in planning practices.

Monitoring the Relationship Between the Land Surface Temperature Change and Urban Growth in Beijing, China

This paper attempted to evaluate the effects of urban growth on the land surface temperature (LST) in Beijing, China. Firstly, the multi-temporal land use types were derived based on the images of Landsat 5 TM and Landsat 7 ETM+ from 1996 to 2010, and two types of land use change trajectories (existing urban trajectory and urban growth trajectory) were also established to category the land transformation type under urbanization. Then the algorithm developed by the Landsat project Science Office was adopted to retrieve LST, in which the digital number (DN) of Band6 was converted to the spectral radiance, followed by being converted to the brightness temperature under the assumption of unity emissivity, and corrected for emissivity (e) to obtain the natural surface temperature. Finally, to evaluate the effects of urban growth on the LST change, two indices of deviation degree and LST change contribution were built and calculated for each trajectory. The results demonstrated that the average deviation degree of urban growth trajectories was much greater than that of the existing urban trajectory, which presented a more violent LST change. However, the effects of urban growth on the LST change of the study area were very limited because the smaller areas of the urban growth trajectories would generate a limited LST change contributions. The results could not only provide a new way and methodology to assess the relationship between urban growth and the LST change, but also support the management and planning in the urban area.

A game-theory based agent-cellular model for use in urban growth simulation: A case study of the rapidly urbanizing Wuhan area of central China

Accurate modeling of urban growth is an extremely important component of urban geographic studies and is also vital for future urban planning. The trajectories of urban growth can be monitored and modeled by the use of geographic information system techniques, remote sensing data, and statistical analysis. In this study, we couple game theory with an integrated agent-cellular method to develop a model of the major determinants controlling urban development, which not only accounts for socioeconomic driving forces but also captures human actions. Wuhan, the largest city in central China, is undergoing rapid urbanization and is facing uncontrolled urban expansion. The city proper region of Wuhan is selected as the case study area to simulate urban growth during the period between 2003 and 2023. The results indicate that the social conflicts between the different stakeholders in urban development can be identified by utilizing a game tree. The game-theory based agent-cellular model is shown to be more effective than a pure cellular automata model in urban growth simulation. The results also show that, from 2013 to 2023, the urban area of the Wuhan city proper region is predicted to grow to 442.77km2, which is almost two times the area in 2003. This research is the first study to use empirical data and game theory to analyze the decision-making process in urban development in the Wuhan area.

URBAN LANDSCAPES IN TRANSITION, LESSONS FROM INTEGRATING BIODIVERSITY AND HABITAT MODELLING IN PLANNING

Sustainable urban development has been widely recognized as requiring energy and transport efficient urban growth, while ecological issues are often not well integrated in sustainability assessments and Strategic Environmental Assessment (SEA). In order to achieve such integration, methods and tools based on Geographic Information Systems (GIS) in a Landscape Ecological Assessment (LEA) framework were developed and integrated in planning in four case studies in and around the city of Stockholm, Sweden. This involved the application of recently developed methods for impact prediction and for integration into the planning processes. The aim of the study was to compare the case studies concerning strengths and weaknesses of LEA and its GIS-based components. The methodology enabled identification of important structures in the landscape to support biodiversity, across administrative borders. The LEA facilitated discussions on consequences of alternatives for localisation of built-up areas, infrastructure and other developments as well as management. The GIS-based LEA approach could thus contribute to a sustainable urban development with consideration of the landscape-level biodiversity values integrated with energy-efficient urban growth trajectories.

Global analysis and simulation of land-use change associated with urbanization

A combination of rapid population growth and an accelerating demographic shift from rural to urbanized habitats has resulted in urbanization becoming an increasingly global phenomenon. Two alternate hypotheses describing urban landscape trajectories suggest urbanization is either leading to more homogeneous global patterns or urbanization has dichotomous trajectories of increasing dispersal or coalescence. To better understand the global variation in urban land-cover patterns and trajectories we described the variation in urban landscape structure for 120 cities distributed throughout the world assessed at circa 1990 and 2000. We coupled these data to a low-dimensional neighborhood based model of urban growth using a data-model fusion approach. Trajectories of urban growth were assessed using both the original data and model projections to 2030. The patterns of landscape change were related to both the rate of growth and income. The historical patterns of change showed a trend of increasing landscape complexity and this trend was projected to continue. Urban rate of growth was closely related to the change in several landscape metrics. Income was associated with landscape dynamics and this effect interacted with city size. Large cities were less sensitive to the income effect than small cities. Along with changes to the magnitude of each metric, the overall variation in metrics between years generally exhibited a decrease in variability and this variability was projected to continue decreasing. These findings supported the hypothesis that urban landscapes are becoming more homogeneous and that the dispersal-coalescing dichotomy represent endpoints rather than alternate states of urban growth.