Urban Growth Dynamics Research Articles

Enabling equitable energy access for Mozambique? Heterogeneous energy infrastructures in Maputo's growing urban periphery

This article concerns the relationship between uneven peri-urban growth and heterogeneous energy infrastructures in Metropolitan Maputo, Mozambique. Much research has documented how the diversity and overlap of different energy configurations define energy access in African cities. However, very little attention has been given to how urban growth dynamics shape heterogeneity and vice versa. A growing concern in African cities is highly uneven growth patterns, the rise of middle classes, and the selective forms of infrastructural connectivity linked to such spatial dynamics. However, urban growth is often approached through distinct settlements such as gated or enclave communities. The growth dynamics of energy access in more typical peri-urban neighbourhoods with mixed socio-economic characteristics remains an underexplored theme. Therefore, the critical question of this article is whether heterogeneous energy infrastructures contribute to uneven peri-urban development patterns, or does heterogeneity offer a more pragmatic solution to energy access in such spaces? This question is explored in three mixed-dynamics peri-urban neighbourhoods in Maputo. We find that infrastructural heterogeneity results from the increasingly commercial electrification procedures that privilege urban density and from private solar-home-system providers who privilege higher paying customers. Both phenomena respond to, and subsequently reinforce, divergent underlying social conditions and the divergent demand for energy services. However, we also find that heterogeneous energy configurations embody the considerable capacities and pragmatism of local communities. While we conclude that infrastructural heterogeneity contributes to uneven urban growth, we also suggest that policy-makers work more closely with local actors to distribute the benefits of energy access more equitably.

Machine learning application to spatio-temporal modeling of urban growth

Understanding the dynamics of urban growth is among the most important tasks in urban planning due to their influence on policy decision-making. Specifically, prediction of urban growth at regional levels is crucial for regional policy makers. Making such predictions is difficult because of the existence of complex topological structures and the high-dimensional nature of data sets related to urban growth. Spatial and temporal auto-correlation and cross-correlations, together with regional social and physical covariates, need to be properly accounted for improving the forecasting power of any statistical or machine learning method. To that end, we develop novel machine learning methodologies to perform predictions of urban growth at regional levels by incorporating lead-lag non-linear relationships among past urban changes in each region and its neighbors. Based on this analysis, machine learning algorithms outperform more classical methods, such as a logistic regression, in terms of classifying low/high urban growth regions, and the random forest algorithm seems to have the best prediction accuracy among the selected machine learning methods. Moreover, the random forest method without any external covariates has still a high prediction accuracy which not only confirms that most of variability of urban growth can be described by past observations of self and neighboring changes, but also makes it possible to perform real forecasting of urban growth without accessing any external covariates. The latter makes this modeling framework useful for local policy makers in allocating budget and directing resources appropriately based on such predictions.

Assessing land use/land cover change diversity and its relation with urban dispersion using Shannon Entropy in the Semarang Metropolitan Region, Indonesia

The Semarang Metropolitan Region (SMR) dynamic is difficult to control and related to the urban sprawl phenomenon. This study aimed to explore the relationship between land use/land cover changes (LULCC) diversity with an urban dispersion pattern of the SMR in 2010-2020 using Shannon Entropy. The SMR experienced significant urban growth in the last ten years. The built-up area spread from the downtown area to the peri-urban area of 14,285 ha (53%). Here, the normalised entropy of LULCC was found to be greater than 0.5 in the peri-urban area, indicating more heterogeneous LULCC dynamics. In addition, with a 12% growth rate per year, the SMR has the largest built-up patch area of 16,088 ha out of 25,793 ha, which means that 37% of the built-up area was a dispersed growth. This indicates that the built-up area spread sporadically with an irregular development pattern. Meanwhile, the built-up area increased in a more compact and orderly pattern in the downtown area. This study showed that LULCC diversity affects the spatial dynamics of urban growth in the SMR. The greater the LULCC diversity value, the greater the urban dispersion pattern, which is becoming increasingly dispersed.

The effects of factor generalization scales on the reproduction of dynamic urban growth

ABSTRACT The production and selection of driving factors are essential to building a strong Cellular Automata (CA) model of dynamic urban growth simulation. A critical issue that should be addressed is how the spatial representation and the generalization scale of driving factors affect the CA modeling and the simulation results. It is challenging to evaluate the effectiveness of the selected driving factors because they have no true values. To explore the impacts of the generalization scales, we produced nine sets of driving factors at nine scales to calibrate the CA models based on the Particle Swarm Optimization (CAPSO) and applied them to simulate urban growth of Suzhou during 2000–2020. Our results show that the driving factors at a smaller scale have much better performance in explaining urban growth simulations as inferred by the Explained Residual Deviance (ERD) of the Generalized Additive Models (GAMs). Specifically, the ERD declined from 51.9% to 45.9% as the factor scale became larger during 2000–2020, but there was a peak value (52.2%) at Scale-2. For all simulations during 2000–2020, the CAPSO models with larger-scale factors have slightly lower overall accuracy and Figure-of-Merit (FOM), which respectively decreased by 3.1% and 4.4% as compared to the CA models with scale-free factors. We concluded that the driving factors at a smaller scale (200 ~ 400 m for point-like facilities and 7 ~ 14 m for line-like facilities) can build more accurate CA models to simulate urban growth patterns, and the optimal scale for factors can be identified using the ERD. This study contributes to the methods of evaluating the effectiveness of driving factor production and reveals the impacts of spatial representation of factors on the CA modeling and simulation considering the factor generalization scales.

Monitoring Metropolitan Growth Dynamics for Achieving Sustainable Urbanization (SDG 11.3) in Kolkata Metropolitan Area, India

The mass accumulation of population in the larger cities of India has led to accelerated and unprecedented peripheral urban expansion over the last few decades. This rapid peripheral growth is characterized by an uncontrolled, low density, fragmented and haphazard patchwork of development popularly known as urban sprawl. The Kolkata Metropolitan Area (KMA) has been one of the fastest-growing metropolitan areas in India and is experiencing rampant suburbanization and peripheral expansion. Hence, understanding urban growth and its dynamics in these rapidly changing environments is critical for city planners and resource managers. Furthermore, understanding urban expansion and urban growth patterns are essential for achieving inclusive and sustainable urbanization as defined by the United Nations in the Sustainable Development Goals (e.g., SDGs, 11.3). The present research attempts to quantify and model the urban growth dynamics of large and diverse metropolitan areas with a distinct methodology considering the case of KMA. In the study, land use and land cover (LULC) maps of KMA were prepared for three different years (i.e., for 1996, 2006, and 2016) through the classification of Landsat imagery using a support vector machine (SVM) classification approach. Then, change detection analysis, landscape metrics, a concentric zone approach, and Shannon’s entropy approach were applied for spatiotemporal assessment and quantification of urban growth in KMA. The achieved classification accuracies were found to be 89.75%, 92.00%, and 92.75%, with corresponding Kappa values of 0.879, 0.904, and 0.912 for 1996, 2006, and 2016, respectively. It is concluded that KMA has been experiencing typical urban sprawl. The peri-urban areas (i.e., KMA-rural) are growing rapidly, and are characterized by leapfrogging and fragmented built-up area development, compared to the central KMA (i.e., KMA-urban), which has become more compact in recent years.

Analysis of the urban growth pattern through spatial metrics; Ankara City

Analysis of the urban growth pattern through spatial metrics; Ankara City

Цифровізація розвитку міст: Urban Atlas на основі відкритих даних для міст України

Ukraine is an associate member of the European Union and in the coming years it is expected that all data and services already used by EU countries will be available to Ukraine. The lack of quality national products for assessing the development and planning of urban growth makes it impossible to assess the impact of cities on the environment and human health. The first steps to create such products for the cities of Ukraine were initiated within the European project "SMart URBan Solutions for air quality, disasters and city growth" (SMURBS), in which specialists from the Space Research Institute of NAS of Ukraine and SSA of Ukraine received the first city atlas for the Kyiv city, which was similar to the European one. However, the resulting product had significantly fewer types of land use than the European one and therefore the question of improving the developed technology arose. The main purpose of the work is to analyze the existing technology of European service Urban Atlas creation and its improvement by developing a unified algorithm for building an urban atlas using all available open geospatial and satellite data for the cities of Ukraine. The development of such technology is based on our own technology for classifying satellite time series with a spatial resolution of 10 meters to build a land cover map, as well as an algorithm for unifying open geospatial data to urban atlases Copernicus. The technology of construction of the city atlas developed in work, based on the intellectual model of classification of a land cover, can be extended to other cities of Ukraine. In the future, the creation of such a product on the basis of data for different years will allow to assess changes in land use and make a forecast for further urban expansion. The proposed information technology for constructing the city atlas will be useful for assessing the dynamics of urban growth and closely related social and economic indicators of their development. Based on it, it is also possible to assess indicators of achieving the goals of sustainable development, such as 11.3.1 "The ratio of land consumption and population growth." The study shows that the city atlas obtained for the Kyiv city has a high level of quality and has comparable land use classes with European products. It indicates that such a product can be used in government decision-making services.

Analysing the impact of COVID-19 on urban transitions and urban-regional dynamics in Australia.

In this paper, we draw on insights from economic theory on urban growth, large shocks and spatial dynamics to assess COVID‐19 flow‐on effects and potential disruptive legacy in urban‐regional dynamics. Urban dynamics in Australia are assessed at national, regional and intra‐urban scales. Long‐term and short‐term urban dynamics are analysed against random growth, locational fundamentals and increasing returns theories of urban growth and adjustment. A focus in Australia and elsewhere is the potential effect of COVID‐19 on where people want to live, enabled in part by technological connectivity that releases some workers from proximity to work constraints when choosing a home. Our results suggest that urbanisation trends and adjustments to shocks differ for capital cities and noncapital cities. At the inter‐regional migration level, Australia’s largest urban system, Sydney, is characterised by a cointegration relationship between outmigration and Sydney property prices relative to other housing markets. At finer spatial scales, COVID‐19 had a negative impact on house prices within Sydney and may, for some micro‐geographies and/or towns and regional centres, lead to significant change. However, typically this effect on houses (not units) began to dissipate in the period June‐November 2020, when also controlling for housing policy pre‐ and post‐COVID‐19.

Urban growth dynamics during the period 1992–2013 in Kyrgyzstan based on DMSP-OLS nightlight satellite data

Urban growth dynamics during the period 1992–2013 in Kyrgyzstan based on DMSP-OLS nightlight satellite data

Recognition and Prediction of Land Dynamics and Its Associated Impacts in Al-Qurayyat City and along Al-Shamal Train Pathway in Saudi Arabia

Saudi Arabia has experienced substantial urban growth over the last few decades, transforming from rural to urban communities due to rapid economic growth. Saudi Arabia is ranked as one of the most urbanized countries, with more than 80% of its population existing in urban centers. Four Landsat imagery datasets acquired in 1989, 2002, 2013, and 2021 were used to estimate the dynamics of land cover and urban growth in Al-Qurayyat City and investigate the relationship between the construction of Al-Shamal train in 2011 and the land dynamics. The results emphasize a strong intercorrelation between the construction of the Al-Shamal train pathway and the land development and the rapid urbanization in Al-Qurayyat City. The results show that the urban and built-up area expanded from 1.96% to 7.25% between 1989 and 2021. Future prediction of land cover dynamics and urban growth in 2030 were estimated using the Markov chain and CA-Markov models. The findings of future prediction show that more than 60% of the total area of Al-Qurayyat City will transform into urban and built-up areas by 2030. The dramatic increase in urban and built-up areas and the subsequent reduction in other land cover types will impact the environmental sustainability of Al-Qurayyat City. The findings in this paper recommend smart growth, which guarantees environmentally friendly development for future land use/land cover planning in Al-Qurayyat City. This study will be beneficial to the urban planner and policymakers for proper sustainable development decisions by exploring the land cover changing pattern and the trends of urban expansion.

Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation

Cellular Automata (CA) models are widely used to study spatial dynamics of urban growth and evolving patterns of land use. One complication across CA approaches is the relatively short period of data available for calibration, providing sparse information on patterns of change and presenting problematic signal-to-noise ratios. To overcome the problem of short-term calibration, this study investigates a novel approach in which the model is calibrated based on the urban morphological patterns that emerge from a simulation starting from urban genesis, i.e., a land cover map completely void of urban land. The application of the model uses the calibrated parameters to simulate urban growth forward in time from a known urban configuration.This approach to calibration is embedded in a new framework for the calibration and validation of a Constrained Cellular Automata (CCA) model of urban growth. The investigated model uses just four parameters to reflect processes of spatial agglomeration and preservation of scarce non-urban land at multiple spatial scales and makes no use of ancillary layers such as zoning, accessibility, and physical suitability. As there are no anchor points that guide urban growth to specific locations, the parameter estimation uses a goodness-of-fit (GOF) measure that compares the built density distribution inspired by the literature on fractal urban form. The model calibration is a novel application of Markov Chain Monte Carlo Approximate Bayesian Computation (MCMC-ABC). This method provides an empirical distribution of parameter values that reflects model uncertainty. The validation uses multiple samples from the estimated parameters to quantify the propagation of model uncertainty to the validation measures.The framework is applied to two UK towns (Oxford and Swindon). The results, including cross-application of parameters, show that the models effectively capture the different urban growth patterns of both towns. For Oxford, the CCA correctly produces the pattern of scattered growth in the periphery, and for Swindon, the pattern of compact, concentric growth. The ability to identify different modes of growth has both a theoretical and practical significance. Existing land use patterns can be an important indicator of future trajectories. Planners can be provided with insight in alternative future trajectories, available decision space, and the cumulative effect of parcel-by-parcel planning decisions.

Urban expansion quantification from remote sensing data for sustainable land-use planning in Mangaluru, India

Urban growth is indispensable in developing countries, particularly in the context of rapid economic development and growing urban population. The idea of managing urban growth is often restricted exclusively to large metropolis; ignoring small and emerging cities from the requirement of having sustainable urban growth strategies. India, experiencing a rapid pace of urban growth is expected to host its future urban population in its small and medium-sized cities. In this context, the dynamics of urban growth in numerous small and medium-sized cities must be studied and urban policies must be reoriented to ensure sustainable urbanisation in future. However, the nonavailability of geospatial data for generating information database and models to support decision making processes on a local scale pose challenges in developing countries. The remote sensing technology offers cost-effective, timely, and efficient technology to collect urban spatial data, to generate urban physical and socioeconomic information using various GIS and statistical models. In this study, the spatial pattern, dynamics and goodness of urban growth in Mangaluru city were studied in the context of its implications for future sustainable development using the techniques of remote sensing, GIS and statistical models. The urban land is delineated from the Landsat series of satellites then, the statistical models were applied to study the trend of urban growth. The built-up land in Mangaluru has increased significantly by more than 400 % during 1972–2018. Further, subtleties of urban growth were studied by extracting land-use expansion modes, UEI Index, UED Index, Pearson's Chi-square and Shannon's entropy. Finally, the goodness of urban growth was measured to identify the sustainability of urban growth. The findings suggest that the urban expansion mode was dominated by leapfrogging and edge expansion at the peri-urban area during initial periods which eventually gave way to infilling and edge-expansion. Also, a rapid expansion in urban land was expected in the southern and south-eastern part of the city. The study confirms the prevalence of the highly unsustainable nature of urban growth in the study area and therefore advocates participatory urban planning in managing and for achieving a sustainable urban growth.

Урбанизация национальных республик Саяно-Алтая сквозь призму теории модернизации

Introduction. In the national republics of the Sayan-Altai Region — Altai, Tuva and Khakassia — urbanization has been essentially delayed and is, hence, accelerated. The first urban settlements appeared here only in the 20th century, and many of them were referred to as urban de jure only. Despite the growth of publications studying various aspects of the urbanization in Siberia, there are almost no research works dealing with the development of this historical, cultural, complicated social process and compiled from materials of this region proper. The paper provides a first attempt to assess the features and patterns of urbanization in the Sayan-Altai Region through the prism of modernization theory. Goals. The article aims to analyze urbanization as the most important aspect of modernization through materials collected across Altai, Tuva and Khakassia in the 20th – early 21st centuries. Materials and Methods. The article primarily investigates official statistical data that make it possible to trace the actual urbanization dynamics (for both populations and territories), as well as the process of transformation experienced by material components of city life. Empirically, the study rests on materials of surveys conducted in the Republic of Khakassia (2018) and in the Republics of Tuva and Altai (2019). The total number of interviewees is 2000. The study employs deterministic sampling methods. Results. The work concludes the urbanization development pattern examined in the region is non-linear, which was determined by the difficult and indirect modernization processes nationwide and those in its certain parts in particular. In the history of the ethnic federal subjects of the Sayan-Altai Region, local features of urbanization are more evident than those in other regions of the country, which is largely due to the influence of the ethnic factor, specifics of indigenous cultures and worldviews. Urbanization as a process is studied on the regional materials in two main perspectives: on the one hand, the paper provides analysis of quantitative indicators of urbanization, such as the dynamics of urban population and growth of urban settlements; on the other hand, an important role in the study of urbanization is assigned to its qualitative indicators, the latter including the transformed structure of urban population and evolution of its living standards.

Assessing the Dynamics of Urban Growth and Land Use Changes in Jeli Using Geospatial Technique

Land use change pattern in Jeli is been given a focus as part of tool for land planning anddevelopment. An increasing of population in Jeli make this study is relevant to aid an understandingon land use changes in this area due to the demanded for development and rapid land utilisation.Land use change pattern can be obtained via geospatial technique by Geographical InformationSystem (GIS) together with satellite imagery analysis. In this study, land use maps produced fromsupervised classification using maximum likelihood algorithm give a high accuracy of 92.05%.From classified land use images, urban expansion index (SI) were then calculated from year 1984to 2012. Pearson’s correlation analysis discovered a strong negative correlation between forest andagricultural areas which reflected that with increased of agriculture area, forest reserve was founddepleted. The SI of Jeli showed a low to rapid growth in 1994 and 1997 to 2012 respectively. Thestudy reflected the progressive development in Jeli promotes sustainable usage of forest area wheneffort of reforestation was carried out forming a co-existence land use category between forest andagriculture.

Reducing spatial autocorrelation in the dynamic simulation of urban growth using eigenvector spatial filtering

In cellular automata (CA) based modeling of dynamic urban growth, non-spatial statistical approaches are usually affected by spatial autocorrelation and fail to describe the spatially non-stationary relationships between urban growth and its drivers. The eigenvector spatial filtering (ESF) is a representative method that can well estimate the regression coefficients in the presence of spatial autocorrelation. In this study, we developed two CA models based on the linear ESF (LESF) and the non-stationary ESF (NESF), of which LESF is featured by spatially stationary coefficients while NESF is featured by spatially varying coefficients. These two models were compared to a conventional logistic regression (LOGR) CA model. The three models (i.e. CALOGR, CALESF and CANESF) were used to simulate rapid urban growth in Suzhou between 2009, 2014 and 2019. The statistics suggest significant reduction in the spatial autocorrelation of the residuals in the CALESF and CANESF land transition maps, as well as an increase in the model reliability. The results show that compared with CALOGR, CALESF and CANESF respectively improved the overall accuracy by 2.0% and 2.2% in 2014, and improved by 0.4% and 0.7% in 2019; they also hit more new-urban-cells, respectively increasing the figure-of-merit (FOM) by 3.8% and 4.3% in 2014, 0.3% and 0.9% in 2019. All three models performed better when integrated spatial heterogeneity into the CA neighborhoods, increasing overall accuracy by over 1% and FOM by over 2%. We concluded that the ESF-based CA models can effectively derive spatially varying land transition rules to capture the urban dynamics, and more accurately predict urban growth featured by spatial autocorrelation.

Worldview, religion, and urban growth: a geopolitical perspective on geography of power and conception of space during Islamization in Java, Indonesia

Discussion of Islamization is not only associated with the spread of religious values but also related to the activity of trade and the opening of the new lands. In Java, all three of these themes have occurred simultaneously and then experienced rapid development since the 15th century. During the 15th and 17th centuries, many Islamic kingdoms rose and fell by turns with variuos ideology and their economic and political motives. As a result, Java experienced a complex of territorialization. By a spatial-historical approach, this article shows how territorialization affects the fashion of the emergence and collapse of the cities in Java. The spatial dynamics of urban growth reflected changes in political space production run by each ruling actor. The city played a significant role as a symbol and an identity of the political power of the dominant regime.

Viewpoint Theorisations of African cities need to be careful of jumping onto the ‘urban band wagon’

The observation that cities are growing rapidly especially in the African continent is well acknowledged around the world. Indeed, ‘an urban-centric discourse’ has emerged to assert that the future is ‘urban’ as more people are moving to live in cities (UN, 2018). While the issue of growth cannot be challenged, one needs to look at the dangers of this discourse in African cities. The dynamics of urban growth in African cities are more complex and different from industrialised Europe from which this discourse is modelled. Hence, the patterns of contemporary urban growth and economic transformation in Africa might well undermine assumed urban-centric theoretical associations. This article was published open access under a CC BY licence https://creativecommons.org/licences/by/4.0/ The publication was supported by the German Academic Exchange Service (DAAD) with funds of the German Federal Ministry for Economic Cooperation (BMZ).

Understanding the macro-micro dynamics of urban densification: A case study of different sized Indian cities

Indian cities face unplanned and rapid urbanization, yet limited studies investigate their urban growth dynamics systematically. Urban land density (ULD) analysis is crucial for developing countries worldwide, especially for populous countries like India. To understand the complexities of urban structure and its expansion, we propose a framework for quantifying urban densification by integrating its macro structure and micro dynamics. Five Indian cities are chosen as study areas based on economic categorization and city size. The macro-structure analysis is performed through a non-linear fitting of concentric density zones to derive an urban land density function (ULDF). The parameters of ULDF define the compactness and dispersion of urban expansion. The micro-urban form analysis is carried out by examining the spatial configuration of ULD. The study reveals that Tier-I city suffered from high compact urban growth. In contrast, Tier-II city with the elevated topography is found to have the highest urban land density at the city center in all studied years compared to the remaining chosen cities. The study also reveals that Tier-II city with plane topography has shown a significant increase in urban land density at boundaries. The rest of the chosen cities of Tier-III have contributed more toward dispersive growth. The spatial configuration of urban densities indicates that Tier-I and Tier-II cities have a close relationship in urban patches and class dynamics. Overall results from macro structure analysis depict that Tier-I and Tier-II cities are interrelated and consistent w.r.t. the ULDF, whereas, Tier-III city and town are explainable inconsistent. This inconsistency can be explained through a micro-urban form analysis. The study reveals the varied characteristics of developing cities, which would help policymakers and urban planners enhance the quality of urbanization and promote sustainable development.

Characterizing Urban Expansion Combining Concentric-Ring and Grid-Based Analysis for Latin American Cities

Spatio-temporal characterization of urban expansion is the first step towards understanding how cities grow in space. We summarize two approaches used in urban expansion measurement, namely, concentric-ring analysis and grid-based analysis. Concentric-ring analysis divides urban areas into a series of rings, which is used to quantify the distance decay of urban elements from city centers. Grid-based analysis partitions a city into regular grids that are used to interpret local dynamics of urban growth. We combined these two approaches to characterize the urban expansion between 2000–2014 for five large Latin American cities (São Paulo, Brazil; Mexico City, Mexico; Buenos Aires, Argentina; Bogotá, Columbia; Santiago, Chile). Results show that the urban land (built-up area) density in concentric rings decreases from city centers to urban fringe, which can be well fitted by an inverse S curve. Parameters of fitting curves reflect disparities of urban extents and urban form among these five cities over time. Grid-based analysis presents the transformation of population from central to suburban areas, where new urban land mostly expands. In the global context, urban expansion in Latin America is far less rapid than countries or regions that are experiencing fast urbanization, such as Asia and Africa. Urban form of Latin American cities is particularly compact because of their rugged topographies with natural limitations.

Disputas hídricas y despojoEl caso de los humedales de Montaña en San Cristóbal de Las Casas, Chiapas, México

The processes of geographical expansion carried out by the capitalist economic system mean that the natural reserves that are part of the biocultural heritage of the Mexican people are continually degraded. The incorporation of land into the process of urban growth as the basis for the business of real estate speculation undoubtedly entails a process of dispossession not only of land and social rights, but also of fundamental eco-systemic services such as the right to a healthy environment, to quality water and to biodiversity. This article describes the conceptual categories that allow us to explain the process of loss of wetlands in the city of San Cristóbal de las Casas, as well as the process that is being experienced specifically, in the dispute of territorialities between the defense of water, life and ecology and the process of subsuming the dynamics of urban growth and capitalist valorization of space. The qualitative research methodology was used and a series of interviews were carried out with socio-environmental actors.