Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods

Urban growth is a major threat to biodiversity conservation at the global scale. Its impacts are expected to be especially detrimental when it sprawls into the landscape and reaches sites of high conservation value due to the species and ecosystems they host, such as protected areas. I analyzed the degree of urbanization (i.e., urban cover and growth rate) from 2006 to 2015 in protected sites in the Natura 2000 network, which, according to the Habitats and Birds Directives, harbor species and habitats of high conservation concern in Europe. I used data on the degree of land imperviousness from COPERNICUS to calculate and compare urban covers and growth rates inside and outside Natura 2000. I also analyzed the relationships of urban cover and growth rates with a set of characteristics of Natura sites. Urban cover inside Natura 2000 was 10 times lower than outside (0.4% vs. 4%) throughout the European Union. However, the rates of urban growth were slightly higher inside than outside Natura 2000 (4.8% vs. 3.9%), which indicates an incipient urban sprawl inside the network. In general, Natura sites affected most by urbanization were those surrounded by densely populated areas (i.e., urban clusters) that had a low number of species or habitats of conservation concern, albeit some member states had high urban cover or growth rate or both in protected sites with a large number of species or habitats of high conservation value. Small Natura sites had more urban cover than large sites, but urban growth rates were highest in large Natura sites. Natura 2000 is protected against urbanization to some extent, but there is room for improvement. Member states must enact stricter legal protection and control law enforcement to halt urban sprawl into protected areas under the greatest pressure from urban sprawl (i.e., close to urban clusters). Such actions are particularly needed in Natura sites with high urban cover and growth rates and areas where urbanization is affecting small Natura sites of high conservation value, which are especially vulnerable and concentrated in the Mediterranean region.

Extending the SLEUTH model to integrate habitat quality into urban growth simulation

Rapid urbanization and climate change jointly shape future flood exposure in coastal cities, particularly in the Global South, where urban growth rates are high and adaptive capacity is often constrained. While scenario-based frameworks such as the Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) provide consistent narratives of socioeconomic and climatic change, their integration into spatially explicit urban growth modelling and flood exposure analysis remains limited.This study applies the SLEUTH urban growth model to project high-resolution (30 m) urban expansion trajectories up to 2050 for nine coastal urban agglomerations across Africa, Asia, and Latin America under SSP1, SSP2, and SSP5. SSP-based urban population projections were translated into scenario-specific SLEUTH parameters, enabling consistent representation of divergent socioeconomic pathways. Model calibration was supported by harmonized historical settlement data from the World Settlement Footprint and enhanced through a kernel density–based zonation and spatial tiling approach to capture heterogeneous urban–rural growth dynamics across large study areas. Projected urban extents were overlaid with RCP-based coastal, fluvial, and pluvial flood hazard maps to assess future flood exposure at the 50th and 83rd percentiles for a 100-year return period.Results reveal pronounced inter-city and inter-scenario variability in both urban expansion and flood exposure. Four cities (Dar es Salaam, Ho Chi Minh City, Khulna, and Surat) are projected to expand by more than 50% by 2050 under SSP1 and SSP5. Flood exposure is driven by the combined effects of climate forcing and urban development patterns: high radiative forcing scenarios amplify hazard extent, while socioeconomic pathways strongly influence where and how cities expand into flood-prone areas [1]. In several cities, newly developed urban areas exhibit disproportionately higher exposure than existing settlements, particularly for fluvial and pluvial flooding. However, exposure patterns are highly city-specific, underscoring the limitations of generalized assumptions.The findings demonstrate that future urban flood exposure cannot be explained by climate change alone, but emerges from the interaction between climate scenarios and socioeconomic pathways shaping urban growth. By combining SSP/RCP frameworks with spatially explicit urban growth modelling across multiple cities, this study provides comparative insights relevant for scenario-based urban planning and flood risk management in rapidly urbanizing coastal regions of the Global South.[1] Bachofer, F., Wang, Z., Huth, J., Eisfelder, C., Reimuth, A., & Kuenzer, C. (2026). Urban growth prediction along Shared Socioeconomic Pathways (SSPs) for future flood exposure risk assessment: a cross-continental analysis of coastal cities. Anthropocene Coasts, 9(1). https://doi.org/10.1007/s44218-025-00109-

Sri Lanka is experiencing speedy urbanization by converting the agriculture land and other natural land cover into built-up land. The urban population of Sri Lanka is expected to reach to 60% by 2030 from 14% in 2010. The rapid growth in urban population and urban areas in Sri Lanka may cause serious socioeconomic disparities, if they are not handled properly. Thus, planners in Sri Lanka are in need of information about past and future urban growth patterns to plan a better and sustainable urban future for Sri Lanka. In this paper, we analyzed the characteristics of past land use and land cover trends in Matara City of Sri Lanka from 1980 to 2010 to assess the historic urban dynamics. The land use change detection analysis based on remote sensing datasets reveal that the conversion of homestead/ garden and paddy into urban land is evident in Matara City. The historic urban trends are projected into the near future by using SLEUTH urban growth model to identify the hot spots of future urbanization and as well as the urban growth patterns in Matara City up to the basic administrative level, i.e., Grama Niladari Divisions (GND). The urban growth simulations for the year 2030 reveal that 29 GNDs out of 66 GNDs in Matara City will be totally converted into urban land. Whereas, 28 GNDs will have urban land cover from 75% to 99% by 2030. The urban growth simulations are further analyzed with respect to the proposed Matara city development plan by the Urban Development Authority (UDA) of Sri Lanka. The results show that the UDA’s city development plan of Matara will soon be outpaced by rapid urbanization. Based on the calibration and validation results, the SLEUTH model proved to be a useful planning tool to understand the near future urbanization of Sri Lankan cities.

Time is a fundamental dimension in urban dynamics, but the effect of various definitions of time on urban growth models has rarely been evaluated. In urban growth models such as cellular automata (CA), time has typically been defined as a sequence of discrete time steps. However, most urban growth processes such as land‐use changes are asynchronous. The aim of this study is to examine the effect of various temporal dynamics scenarios on urban growth simulation, in terms of urban land‐use planning, and to introduce an asynchronous parcel‐based cellular automata (AParCA) model. In this study, eight different scenarios were generated to investigate the impact of temporal dynamics on CA‐based urban growth models, and their outputs were evaluated using various urban planning indicators. The obtained results show that different degrees of temporal dynamics lead to various patterns appearing in urban growth CA models, and the application of asynchronous (event‐driven) CA models achieves better simulation results than synchronous models.

Monitoring and managing the rapid growth of cities, especially in developing countries, highlights the need for appropriate spatio‐temporal models to predict urban growth. The parameters affecting the spatio‐temporal analysis of urban growth play a key role in the prediction results. This study proposes an urban growth simulation model by integrating local subjective–objective weights and decision risk values (ORness) into a CA–Markov model. This involves the use of ordered weighting averaging as one of the multicriteria decision analysis methods to combine the weights and degree of risk for generating a variety of risk‐averse and/or risk‐taking urban growth prediction scenarios. The proposed model has been applied to predict the physical growth of Babol city, located in Mazandaran, Iran. The results indicate that the degrees of ORness = 0.3 and ORness = 0.9 yield better prediction results in the case of using the local and global weighting strategies, respectively. Furthermore, the overall accuracy of local and global weighting strategies at different degrees of risk was 87.6 and 86.8, respectively. This implies that the use of local subjective–objective weights leads to more accurate results than the global weights for simulating urban growth.

Changes in land cover driven by urban sprawl increase the threat of urbanization of forests and agricultural lands. Therefore, monitoring urban sprawl by creating simulation models is frequently carried out to understand sustainable city management. Cellular automata‐based models are mostly preferred to reduce the damage led by urban sprawl, and the SLEUTH model is the most well known. Several methods have been developed for the SLEUTH model calibration step, such as optimum SLEUTH metrics and total exploratory factor analysis (T‐EFA), to improve the model accuracy. This study aims to create a high‐accuracy urban growth simulation model using low‐resolution data, investigate the T‐EFA method's success in the calibration step, and find the urban sprawl effects on land cover change. Istanbul was selected as our study area due to witnessing its tremendous urban sprawl since the 1950s. According to our results, the urban growth that occurred between 2000 and 2018 could be defined more closely to reality using the T‐EFA method, and Istanbul will continue to grow until 2040, with approximately 428.7 km2of agricultural lands, 553.4 km2of forests, and 0.1 km2of wetlands being transformed to urban. In addition, the geologically risky areas under threat of urbanization will increase by 60% between 2018 and 2040.

Interactions between cities play a significant role in the development of metropolitan regions. Although these interactions and their role in the urban growth modelling have already been investigated, there is still room for more studies. In this research, in addition to conventional urban growth factors, spatial interactions between the cities (SIBC) are incorporated into urban growth modelling. This causes directional trends in urban growth (DTUG). Therefore, first the DTUG of each city was measured using a developed indicator based on the history of urban growth that was extracted from satellite images and spatial statistics. The SIBC was then estimated by integrating the DTUG of the cities. Finally, the SIBC and other driving forces, including the physical suitability, accessibility and neighbourhood effects, were integrated using a cellular automata-based model. The accuracy of the model in the Tehran metropolitan region was increased by 6.44% after considering the SIBC. The analysis of the DTUG and SIBC in the Tehran metropolitan region during 1991–2000–2007–2014 revealed specific patterns as the spatial interactions intensified over time and usually peaked in the periphery of the central business districts and intense interactions existed between the metropolises and other major cities. These findings could help urban managers with strategic decision-making in the metropolitan regions and adjust the science and practice relation in this field.

Jinan City, with rapid urban growth and prominent ecological environmental pressure, was selected for the research area. We used SLEUTH model to simulate scenes of urban dynamic growth. On the basis of the actual situation of Jinan urban area, this paper presents three urban growth models to reveal the trend of urban growth under different types of land protection intensity. The results show that scene 1 was based on a hypothesis that the intensity of urban development remained basically unchanged in the scene of future urban growth and changed with the form of simulation. At this time, urban land use growth was fast and the urban expansion was mainly marginal growth; Scene 2 increased the degree of water protection to 80%, and the degree of farmland and woodland protection to 50%, which effectively controlled the urban growth; Scene 3 increased the degree of cultivated land and woodland protection to 80% and 90% respectively, resulting in a significant deterrence of the urban growth trend. The study proved the applicability of the SLEUTH model to urban growth prediction in China, and provided a theoretical basis for urban planning of Jinan.

The speed at which cities are growing and developing today cannot be disregarded. Human activities and natural causes are both contributors to urban growth. The relationship between these factors is complex and the complexity makes it difficult for the human mind alone to understand cities. A model that helps reveal the complexity is needed for urban studies. Main objective of this study is to understand the effects of urban planning strategies on the future of the city by utilizing a Cellular Automata and Artificial Neural Networks based simulation model. Driving factors of urban growth according to development scenarios were used in the simulation process. Six different development scenarios were formulated according to the strategic plan of Izmir. Land use and driving factor data used in simulating scenarios were acquired from EarthExplorer and OpenStreetMap databases, and produced in QGIS. Future Land Use Simulation Model (FLUS) based on Cellular Automata (CA) and Artificial Neural Networks (ANN) was used. The results were assessed both by using FRAGSTATS which helped calculate fractal dimensions and visual analysis. Fractal dimension results of each scenario showed that the simulation model respected the overall urban complexity. A closer look at each scenario indicated the diverse local growth possibilities for different scenarios. The results show that urban simulation models when used as decision support tools promise a more inclusive and explicit planning process.

The SLEUTH urban growth model is a cellular automata model that has been widely used by geographers to examine the rural to urban transition as a physical process and to produce forecasts of future urban growth [1]. Previous SLEUTH applications have generally been limited to individual model applications, with little to no comparison of model results [2]. Building upon research by Silva and Clarke [3], and borrowing from their metaphorical comparison of urban growth characteristics to genetic DNA, this research distills a combination of actual city and model behavior in a controlled environment to provide for comparisons between disparate model applications. This work creates a digital “petri dish” capable of producing normalized model forecasts from previously incomparable results. Results indicate that despite the inherent differences between actual model results, sufficient similarities were observed among the forecasts to warrant the creation of an urban behavioral taxonomy, providing for direct comparison of the results.KeywordsCellular AutomatonCellular AutomatonUrban GrowthCellular Automaton ModelCellular Automaton ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Africa has been a hotspot of rapid urban growth since 2000, yet no review exists that describes the current state of urban growth modeling. This review aims to fill this gap by synthesizing urban growth modeling approaches, input variables and challenges across Africa. Using Web of Science, Google Scholar, Geobase, and Compendex databases, we selected 49 studies that projected urban land use and cover change or modeled probabilities of future urban change associated with a variety of input variables. We found increasing trends in the numbers of studies employing cellular automata, statistical regression, agent-based, video prediction, and integrated approaches. The modeling approaches used and their corresponding increases differed across Africa. The input variables used to project urban growth also varied across subregions. Studies were biased towards populous countries rather than the most urbanized. 78 % of the studies focused on primary cities, while secondary cities were less frequently studied. Comparison of urban growth modeling across multiple regions and for different types of cities is currently difficult due to inconsistent definitions and limited availability of data and computational resources. We recommend that the scientific community develop consistent definitions of African city types and urban land use and land cover classifications, create regional urban land cover products for Africa, and build repositories of models and their input data.

Monitoring and modelling spatio-temporal urban growth of Delhi using Cellular Automata and geoinformatics

Trends in urbanization in India and their relationship economic growth since 1960 are analyzed. The authors use the technique of general equilibrium analysis identify the interactions among cities and rural areas in India and between India and the rest of the world. Specifically they develop a model of the Indian experience since 1960 that estimates the economy as it was and allows for the production of different scenarios. The results indicate that although rapid urban population growth occurred during that decade there has been a decline in urban labor force growth rates since then. Indias relatively slow urban growth can be explained partly by unfavorable economic and demographic conditions but is primarily attributed to structural and institutional arrangements that make city growth less responsive favorable conditions than is true of the rest of the Third World. The authors suggest that the slowdown in the rate of urban growth will continue into the 1990s. (EXCERPT)

Many countries, including China, have implemented the spatial government policy widely known as urban growth boundary (UGB) for managing future urban growth. However, few studies have asked why we need UGB, especially pre-evaluating the utility of UGB for reshaping the future spatial patterns of cities. In this research, we proposed a constrained urban growth simulation model (CUGSM) which coupled Markov chain (MC), random forest (RF), and patch growth based cellular automata (Patch-CA) to simulate urban growth. The regulatory effect of UGB was coupled with CUGSM based on a random probability game method. Guangzhou city, a metropolitan area located in the Peral River Delta of China, was taken as a case study. Historical urban growth from 1995 to 2005 and random forests were used to calibrate the conversion rules of Patch-CA, and the urban patterns simulated and observed in 2015 were used to identify the simulation accuracy. The results showed that the Kappa and figure of merit (FOM) indices of the unconstrained Patch-CA were just 0.7914 and 0.1930, respectively, which indicated that the actual urban growth was reshaped by some force beyond what Patch-CA has learned. We further compared the simulation scenarios in 2035 with and without considering the UGB constraint, and the difference between them is as high as 21.14%, which demonstrates that UGB plays an important role in the spatial reshaping of future urban growth. Specifically, the newly added urban land outside the UGB has decreased from 25.13% to 16.86% after considering the UGB constraint; particularly, the occupation of agricultural space and ecological space has been dramatically reduced. This research has demonstrated that the utility of UGB for reshaping future urban growth is pronounced, and it is necessary for the Chinese government to further strengthen UGB policy to promote sustainable urban growth.