Analysis Of Urban Growth Research Articles

On the Use of Historical Flights for the Urban Growth Analysis of Cities Through Time: The Case Study of Avila (Spain)

Historical aerial images are a unique and relatively unexplored means of deriving spatio-temporal information for scenes and landscapes. Such historical imagery can be combined with photointerpretation and image-based 3D modelling techniques, providing the fourth dimension of time to 3D geometrical representations. This allows urban planners, historians, and other specialists to identify, describe, and analyse changes in scenes and landscapes. Urban growth has an important impact on the sustainable development of cities. An important step for the analysis of urban growth is the identification of different urban sectors. To this end, this paper proposes a methodology for the 4D urban growth analysis of cities through time using a free and open source software developed by the authors. This approach uses the latest advances in photogrammetry, including the so-called incremental Structure from Motion, to evaluate the urbanistic changes of a city by means of confronting two-point clouds from different eras. The objectives of this paper are twofold: (i) first, the processing of historical aerial images using modern photogrammetric techniques; (ii) second, deriving spatio-temporal information for urban cities, offering a method for researchers to identify changes over time. In order to validate this method, the urban growth of the city of Avila between 1956 and 2017 was assessed taking the historical American flight of 1956 and the digital aerial flight of 2017. The results were statistically assessed according to georeferencing quality, confirming that the approach developed can be used to support urban growth analysis through time and providing relevant data in 2D and 3D.

Comparing the spatial and temporal dynamics of urban expansion in Guangzhou and Shenzhen from 1975 to 2015: A case study of pioneer cities in China’s rapid urbanization

The distinct urbanization process of China has attracted worldwide attention because of its impressive speed, massive scale, and policy intervention. However, the interrelationship between urban expansion and government policies is still not well understood. The Pearl River Delta Urban Agglomeration of China is the first national pioneering urbanization area since the implementation of the policy “Reform and Opening-up” in the late 1970s. Here we compared the spatial and temporal patterns of urbanization in two leading cities of the Pearl River Delta (i.e., Guangzhou and Shenzhen, the provincial capital and the first Special Economic Zone of China, respectively) from 1975 to 2015, using Landsat data integrated with urban growth and landscape metrics analysis, and examined possible footprints of major economic and urbanization policies. Our results illustrated that urban land areas in both Guangzhou and Shenzhen have experienced magnificent annual growth rates at 8.1% and 11%, respectively between 1975 and 2015. On average, Shenzhen witnessed substantially higher urban growth rate than Guangzhou during the past four decades, particularly in the initial period (1978–1990) when the Reform and Opening-up policy was launched and Shenzhen was designated as the first Special Economic Zone in China in the late 1970s. However, the speed of urban expansion in Shenzhen became considerably lower than Guangzhou from 2005 to 2015, subject to physical conditions and a series of urban land use policies. Both cities showed a generally similar dynamics of urban growth forms with leapfrogging as the predominant type of urban growth at first and then edge-expansion while the contribution of infilling in Shenzhen was higher than that in Guangzhou, especially since 2005. The urbanization processes characterized by landscape and urban growth metrics revealed that a diffusion-coalescence-diffusion-coalescence process was identified for Guangzhou, while Shenzhen was generally consistent with the diffusion-coalescence urban growth hypothesis.

RETRACTED ARTICLE: Investigating urban land dynamic change and its spatial determinants in Harbin city, China

ABSTRACT Research and analysis of urban growth and its driving factors are crucial to the long-term sustainable development of cities. Based on the multi-temporal Landsat remote sensing data, this paper extracts urban information by interpreting and supervising classification, and makes a dynamic study on the urban expansion of Harbin in different temporal intervals. By analyzing the spatial determinants of urban growth, we deeply understand the process of urban growth, thus providing important help for urban planning and policy formulation. In this paper, four landscape metrics (total area, aggregation index, landscape shape index, and total edge) were selected to characterize the urban landscape characteristics from two spatial scales (2 and 5 km grid sizes), then the spatial regression model was used to explore the relationship between the urban landscape and its spatial determinants. These changes exhibit significant spatial variations and spatial autocorrelation at two spatial scales. Topography and proximity factors have important effects on urban landscape change. These research results may help us to better understand the process and driving factors of urban development, so as to help the underdeveloped cities in northeast China to formulate scientific and reasonable development plans and policies.

Application of Geospatial Techniques and Logistic Regression Model for Urban Growth Analysis in Limbe, Cameroon

Little is known about the nature of ecosystem loss, rampant changes in land use and land cover (LULC) and urban growth taking place in Limbe. The aim of this study is to analyze urban growth in Limbe, Cameroon from 1986-2019 using geospatial techniques and Logistic Regression Model (LRM). Landsat Thematic Mapper (1986), Enhanced Thematic Mapper+ (2002) and Operational Land Imagery/Thermal Infrared Sensor (2019) were utilized in this study. The images were classified into land cover classes using supervised image classification algorithm in ENVI software. The classification output was subjected to LRM application to evaluate urban growth. Image difference of urban growth between 1986 and 2019 was calculated as dependent variable and the independent variables were produced by calculating the Euclidean distance and Buffer of built-up, waterbody, road and farmland as driving factor for urban growth. Future urban growth was determined for 2035 using the Land Change Modeler in IDRISI Selva. Classification overall accuracy for the three date were not less than 99%. LRM results show a good fit with relative operation characteristic of 0.8344 and Pseudo R2 of 0.21. Analysis of LULC shows that built-up increased from 3.5% (1986) to 17.6% (2019). An urban land expansion rate of about 23% was observed for 2035. Transition probability matrix revealed high probability (0.6345) of build-up to remaining build-up by 2035, while the probability for it changing to waterbody, bare land, farm land and vegetation are 0.1099, 0.0459, 0.1939 and 0.1221, respectively. This study successfully demonstrates the application of geo-spatial techniques and LRM for land use/land cover change detection and in understanding the urban growth dynamics. It also identifies the potential areas of future urban growth, which can help land use policy planners for making optimum decisions of land use planning and investment.

Integrating Data-Driven and Participatory Modeling to Simulate Future Urban Growth Scenarios: Findings from Monastir, Tunisia

Current rapid urbanization trends in developing countries present considerable challenges to local governments, potentially hindering efforts towards sustainable urban development. To effectively anticipate the challenges posed by urbanization, participatory modeling techniques can help to stimulate future-oriented decision-making by exploring alternative development scenarios. With the example of the coastal city of Monastir, we present the results of an integrated urban growth analysis that combines the SLEUTH (slope, land use, exclusion, urban extent, transportation, and hill shade) cellular automata model with qualitative inputs from relevant local stakeholders to simulate urban growth until 2030. While historical time-series of Landsat data fed a business-as-usual prediction, the quantification of narrative storylines derived from participatory scenario workshops enabled the creation of four additional urban growth scenarios. Results show that the growth of the city will occur at different rates under all scenarios. Both the “business-as-usual” (BaU) prediction and the four scenarios revealed that urban expansion is expected to further encroach on agricultural land by 2030. The various scenarios suggest that Monastir will expand between 127–149 hectares. The information provided here goes beyond simply projecting past trends, giving decision-makers the necessary support for both understanding possible future urban expansion pathways and proactively managing the future growth of the city.

Estimating urban growth in peri-urban areas and its interrelationships with built-up density using earth observation datasets

Understanding the complex nature of urban dynamics, especially in the fast-growing cities of developing countries, has become crucial to the urban planners and researchers. It is also relevant from the viewpoint of smart city projects as the foundation of successful smart city lies in proper planning and urban growth analysis. In this context, the present study attempts to assess the urban expansion and land-change dynamics in and around Delhi. Multi-temporal Landsat data of 1977, 2003 and 2014 were used for analyzing the spatio-temporal pattern of built-up density, urban expansion, spatial change and their interrelationships. The annual urban expansion index of the study area reveals that it was comparatively high in Delhi National Capital Territory (NCT) during 1977–2003, but the expansion was much higher in peri-urban centers in the later period. However, the growth was not happened homogeneously across the peri-urban zones; instead, it occurred around few urban centers of peri-urban area. The annual urban expansion of Delhi NCT (2.52) was significantly less than peri-urban centers like Gurgaon (6.19) in the period, 2003–2014. Since the areas with high built-up density have little or no space for new settlement, the expansion of built-up area took place in the less dense areas. The correlation between urban expansion index and annual rate of change in built-up area shows that there is a good agreement and significant positive relationship (r ≥ 0.62) present between them. A negative correlation (r ≥ 0.92) between built-up density and urban expansion index indicates that areas with high built-up density have less potentiality to expand and vice versa.

Land use and land cover changes impacts in the harbour city of Thoothukudi

Population growth directly disturbs the environment for development, suburbanisation, and social changes. Urban growth leads to shrinking agriculture and forest land and has substantial impact on the land and coastal ecosystem. Regular studies on urban sprawl growth modelling and analysis are needed to provide better urban growth and ensure sustainable urban development. The city of Thoothukudi, located in the states of Tamil Nadu, India, has faced rapid population growth and urban extension over the past three decades. Geospatial tools and satellite imagery were employed to investigate previous changes and growth in the urban landscape. Random forest classification was used to study sustainability and urban growth in Thoothukudi city's administrative border and the suburbs within a 15 km buffer zone. The result shows that Thoothukudi experienced rapid urban growth, increasing by 68.42% between 1997 and 2017. The outcome of this study will assist government officials and urban planners to provide a better strategy to reduce the environmental and social consequences, facilitate the development of the city in a sustainable way and protect the ecosystem services in the near future.

Analysis of urban growth in coastal areas supported by 2D/2.5D GIS data. A comparative study of Boa Viagem Beach (Brazil) and Rocha Beach (Portugal)

The coastal areas, as a place of interaction between terrestrial and marine systems, are environmentally sensitive and, in recent decades, have undergone constant transformation primarily due to human intervention. Subject to great real estate pressure, the natural landscape (the birthplace of complex ecosystems) has been substantially developed, serving the purposes of different socio-economic interests. This modification of the natural landscape is reflected in the expansion (in area) and densification (in volume) of built-up areas, accentuating the trend towards the vertical growth of cities. The objective of this paper is to analyse the vertical growth of coastal built-up areas of Boa Viagem Beach (Brazil) and Rocha Beach (Portugal) during the period 1940–2013. The analysis considers three different dimensions of vertical growth: urban-physical, socio-demographic and political-urban. For this purpose, we used quantitative methods and techniques, supported by 2D/2.5D GIS data. This methodology overcomes the challenge of obtaining quantitative three-dimensional information across a broad time period and allows for the comparison of the development pace in two different countries. Rhythm and volume are correlated with events of national and local impact. The comparison between the two countries reveals substantial differences in the design and implementation of the planning system and land management instruments but, on the other hand, shows interesting time parallels in the phenomenon of vertical growth. Results indicate that development is strongly influenced by the social, political and economic oscillations of the globalized world. We conclude that the main factors driving urban growth in both study areas are internal and external factors, which are strongly associated with political and economic stability. This correlation turns clear that political and economic stability play an important role and have a direct influence on urban development. This work contributes, through a 2D/2.5D GIS approach, to an integrated understanding of the vertical growth processes in intensively built-up coastal areas. The essayed methodology demonstrates that the methods and techniques are flexible enough to be replicated for the analysis of other coastal cities around the world.

Spatial analysis of urban growth based on city center and central business district (a case study of Antananarivo, the capital city of Madagascar)

The use of historical and present spatial data in urban growth analysis has been considered as a fundamental prerequisite in urban geographical studies, future planning and implementation of city development policies. In the current study, a comparative analysis of urban growth based on city center and central business district (CBD) of Antananarivo, the capital city of Madagascar was carried out. Four temporal satellite images of 5 years interval were used to determine the pattern, the process and general conditions of expansion of the urban extents in different directions. Pearson’s chi-square test and Shannon’s entropy measure were used to evaluate the degree-of-freedom and degree of sprawl towards the analysis of urban growth. To combine the previously mentioned statistics, the degree of goodness was used. The comparative analysis shows that there are contradictive results in the derived statistical measures from both considered urban growth centers indicating different conditions of urban growth. Deeper analysis accompanied by the theory of Von Thünen on urban land use support that the CBD is the most appropriate center of growth for the application of the previously mentioned statistical measures for the case of Antananarivo city.

The contours of a new urban world? Megacity population growth and density since 1975

The problems posed by rapid and large-scale urbanisation are complex, and are recognised in the UN’s New Urban Agenda; a declaration of intent that aims to meet such challenges head-on facilitated by the systematic tracking and analysis of global urban growth and change. In this context, the release in 2016 of new small-area Global Human Settlement Layer (GHSL) data was said to represent a unique opportunity to facilitate comparative global analyses of urban change dynamics and, perhaps somewhat idealistically, move forward progressive planning agendas. We therefore focus on population growth and density in 30 major urban agglomerations using the GHSL in order to shed light on the scale and extent of global urbanisation over the past four decades and to interrogate the potential role of the GHSL in tracking urban change.

Spatio-temporal analysis of urban growth and its impact on floods in Ajman City, UAE.

The negative consequences of urbanisation have been recently recognised despite the social and economic benefits it provides to the community. Effects of urbanisation include increases in surface runoff, frequency and magnitude of floods and urban water harvesting capacity. Accordingly, this study utilised multi-spectral and multi-resolution satellite images combined with field data to conduct a quantitative assessment of the impact of urbanisation on urban flooding for the period of 1975-2015 in Ajman City, United Arab Emirates (UAE). Results showed that urban areas in the city have increased by approximately 12-fold over the period 1975-2015, whilst the population increased by approximately 16-fold. Owing to a substantial increase in urbanisation (as impervious areas expanded), minimum precipitation to generate runoff in built areas dropped from approximately 16.37 mm in 1975 to approximately 13.3 mm in 2015, which caused a substantial increase in the surface runoff. To visualise the flooding potential, urban flooding maps were generated using a well-established decision analysis technique called Analytical Hierarchy Process. The latter adopted three thematic factors, namely excess rain, elevation and slope. Flooding potential was then found to have increased substantially, specifically in the downtown area. Finally, this study is expected to contribute highly to flood protection and sustainable urban storm water management in Ajman City.

Spatiotemporal dynamics of urban growth in Latin American cities: An analysis using nighttime light imagery

The impact of urban form on economic performance and quality of life has been widely recognized. Studies regarding urban form have focused on developed countries; only a small number of cities in developing countries have been studied. This paper utilizes nighttime light imagery and information regarding street networks, automatically retrieved from OpenStreetMap, to calculate a series of spatial metrics that capture different aspects of the urban form of 919 Latin American and Caribbean cities. We study the relationship between the urban form metrics and several factors that can correlate with urban form (topography, size, colony, and economic performance) and perform a spatiotemporal analysis of urban growth from 1996 to 2010. Among the results, we highlight the tendency of a group of cities to grow on steeper slopes and several worrying aspects, specifically urban growth in protected areas and a trend to sprawl-growing in certain Latin American and Caribbean cities.

Urban growth (1956-2012) and soil sealing in the metropolitan area of Valencia (Eastern Spain)

The aim of this study is to understand the urban growth dynamics from the mid-1950s to 2012 in the Metropolitan Area of Valencia, eastern Spain, and its impact on soils. The study area is a very interesting example of the many changes in land use and land cover in the landscape of Mediterranean alluvial plains. The analysis of urban growth was based on photo interpretation of aerial photographs and GIS based methodology. At a detailed scale (1:10,000), results show that there has been a highly dynamic process produced by the extent of land developed as urban area. In 1956 only 3,441 hectares (9.3% of the overall study area) were occupied by urban use. In 2012 the total sealed surface was 10,523 hectares, around 30% of the studied area. The increase in built-up areas for the whole period was 206%, representing an average annual rate of 126 ha/yr. In the Metropolitan Area of Valencia much of the land converted to urban use was once highly productive agricultural soils. Around 5,763 ha of soil types with very high and high land capability, mainly Calcaric Fluvisols, were sealed throughout the study period.

Urban growth analysis and simulations using cellular automata and geo-informatics: comparison between Almaty and Astana in Kazakhstan

In this research, the SLEUTH urban growth model is calibrated and validated for the first time to post Soviet Union cities. The aim of the study is to monitor, assess, simulate and compare the spatiotemporal urban growth dynamics and spatial patterns of the two largest cities Almaty and Astana using free remote sensing data. The urban expansion metrics and SLEUTH model are used to assess the urban growth dynamics. Though the capital has been moved to Astana from Almaty in 1998, Almaty is still developing faster than Astana. The urban growth simulation results from SLEUTH show Astana will surpass the urban growth of Almaty to emerge as the largest city in Kazakhstan by 2030. Astana may experience more leapfrog and ribbon developments. In Almaty, the urban growth may likely to take place in north and north-west parts.

Analysis and Prediction of Urban Growth Using Neural-Network-Coupled Agent-Based Cellular Automata Model for Chennai Metropolitan Area, Tamil Nadu, India

Chennai is one of the most densely populated cities in India facing challenges in shifting the city to metropolitan or mega city in the last two decades with continuing agglomeration. To model the growth of Chennai city, we have used cellular automata-based urban growth models based on the historical datasets. In the present study, urban growth of Chennai Metropolitan Area (CMA) was predicted for the year 2017 based on 2010 and 2013 dataset and Chennai city master plan using neural-network-coupled agent-based cellular automata (NNACA) model. Eight different agents of urbanization including transportation, hotspots, and industries were used in the prediction modeling. On validating the 2017 predicted outputs, NNACA model with hotspots proved to be better (hits: 498.52 km2) than that of without hotspots (hits: 488.31 km2). Out of the total eight agents of urbanization, the most influencing agent of urbanization of 2017 was identified to be the neighborhood of ‘Existing built-up of 2013’ using ‘sensitivity analysis’. Further, the urban sprawl of CMA for 2010, 2013 and 2017 was measured through Shannon’s entropy. The study area was divided into five directional and distance-based zones with the State Secretariat as the center. Entropy values suggest the need for more careful planning for further development in the southern region of CMA which has undergone congested urban growth while urbanization is dispersed in the northern part of the study region which can be thought for future urban developments.

Analysis of urban growth from 1960 to 2015 using historical DISP and Landsat time series data in Shanghai

Urbanization is one of the most important dimensions of contemporary global change. Urban growth affects life-supporting environment of human beings, as well as natural ecological system and biological composition. Thus, long time series urban land cover datasets with high spatial and temporal resolution are useful for understanding the dynamics of urban growth processes, providing great insights into urban planning and sustainable development in the future. For the first time, this study analyzes 55-year spatiotemporal patterns of urban changes in Shanghai, China, by integrating the historical Declassified Intelligence Satellite Photography (DISP) and Landsat time series data at 5-year intervals from 1960 to 2015. Here we applied different methods to detect urban land from DISP and Landsat images, respectively, and their accuracies were quantitatively analyzed and evaluated. The spatiotemporal patterns of urban growth were explored. The derived products showed that urban land cover increased at a rapid and accelerating pace with growth concentrated at the expanding fringes of existing urban clusters. Across the study region, the total urban area increased from 205.10 to 2259.97 km2, at an average annual rate of 37.4 km2/year, and the percentage of urban area increased from 3.33 to 31.96%, expanded by more than 10 times between 1960 and 2015. In addition, urban area increased over time in all directions; the relative proportions of the urban area in downtown and non-downtown changed dramatically over the study period, indicating that urban sprawl of the metropolis experienced major transitions under the effect of population increase, economic development, and policies.

Application of Ca-Markov Model for the Analysis of Urban Growth in Kenyir Basin

The Modelling and Simulation of land cover change are fundamental to the evaluation of successive environmental effects. Remote Sensing and Geographical Information System (GIS) give major procedures which can be valuable in the analysis at the town locale as well as the city levels. The study project the future land use change of Kenyir Basin in Hulu Terengganu. RS data from Thematic Mapper sensor of 2000, 2009 and Operational Land Imager of 2016 were used for the analysis. The study used supervised classification technique for classification, and Cellular Automata Markovian (CA-Markov) Model analysis was used for future projection of 2030. The result classified water body, vegetation and urban area. Modelling analysis reveals a continuous pattern of urban growth in the Kenyir Basin from 5.04 km2, 79.51 km2, 117.03 km2 and 220 km2 in the years 2001, 2009, 2016 and the projected the year 2030 with 0.6934 K-standard value of stimulation. This study gives a chance to characterise and apply better methodologies for environmental management of land use to make improved coordination between urban growths, and conservation of natural environment.

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.

Integrating parametric and non-parametric multivariate analysis of urban growth and commuting patterns in a European metropolitan area

Mono-centric regions have experienced a progressive transformation towards polycentric structures. Transition to discontinuous and dispersed spatial configurations was particularly evident in formerly compact and dense European cities, as a result of increasingly complex commuting patterns. Under the hypothesis that central locations have a balanced rate of resident population to internal workers and attract workers from neighbouring places, a multivariate analysis of commuting patterns may reveal latent transformations of compact, mono-centric cities towards polycentrism and settlement scattering. A more comprehensive understanding of the intimate nature and implications of urban transitions taking place in the last decades in Athens (Greece), a representative metropolitan region in southern Europe, was carried out using census data analyzed through a framework integrating descriptive statistics and mapping, multivariate exploratory data analysis, non-parametric inference and regression models. The analytical framework was proposed with the aim to assess commuting patterns vis a vis urban hierarchy in a context of (more or less) de-centralized growth. Empirical results for the study area indicate the persistence of a bi-centric spatial structure gravitating on the central cities of Athens and Piraeus, with early formation of employment sub-centres and local-scale alterations of the mono-centric scheme as a result of discontinuous urban expansion. Exurban development was associated with the formation of self-contained sub-centres with an increasing proportion of workers in total resident population.

The Impacts of Socio-Economic Indicator on Urban Growth in Selangor, Malaysia

All cities in the world including Malaysia are experiencing rapid urbanization which has given significant impact on the variability of socio-economy. The present study is conducted to investigate the expansion of urban growth in the state of Selangor by selecting appropriate socio-economic indicators used in urban growth studies. The study partly aims to describe a set of socio-indicators that most directly contribute to urban growth analysis. The objectives of the research are (i) to test the availability of dataset of each socio-economic indicators, (ii) examine the changes of each indicator in a decade, and (iii) propose new criteria for the selection of socio-economic indicators. Findings are derived from the secondary data from a local government agency and available digital datasets. The result advocates the use of criteria to validate the indicators to be used in future studies.