Process Of Urban Expansion Research Articles

Long-term changes in 3D urban form in four Spanish cities

Three-dimensional urban form has a considerable influence on urban sustainability, being the reason spatial planning regulate it. Yet, we know very little about the development of building density and building height over time. In this study, we characterize the horizontal and vertical patterns of urban development in Barcelona, Madrid, Valencia, and Zaragoza between 1965 and 2015. Our analysis is based on a unique combination of cadastral data and LiDAR point clouds, which we use to characterize building footprint, height, and volume, at decadal intervals. Subsequently, we characterize urban expansion and densification processes using building volume and Urban Form Types. We find that height of new buildings shows a significant downward trend during the 70′s for the four urban areas and a decreasing trend after the 2008 real estate bubble for the cases of Barcelona and Valencia. Over the analyzed period a decrease of 116, 313, 217 and 157 cm in average building height was observed for Barcelona, Madrid, Valencia, and Zaragoza, respectively. Urbanized volume of all cities together has expanded by roughly 350% between 1950 and 2015. Sparse built-up form showed the largest absolute increase, although it contains only a low fraction of new built-up volume. A clear trend towards expansion is observed in city outskirts and the development of new urban clusters in municipalities closer to the main city. At the same time, settlements have followed incremental steps towards densification of the city-cores over time. This study provides a first step towards comprehensive understanding of long-term changes in 3D urban form, which can inform the development of policies that target the third dimension in urban form to steer sustainable urban growth.

The colonial hospitals of Mexico City through the process of urban expansion

The colonial hospitals of Mexico City through the process of urban expansion

Urban development wave: Understanding physical spatial processes of urban expansion from density gradient of new urban land

Investigation and planning of future urbanization pathways necessitates knowledge and tools to better understand the physical spatial processes of urban expansion. In this study, the wave-shaped diffusion regularity of urban expansion in terms of the density of new urban land was detected and simulated with a Gaussian-based model, based on which the physical spatial process of urban expansion, as well as its long-term trends were characterized and understood. The Gaussian-based model provided a partitioning scheme to concentrically divide the urbanized areas of a given city into distinctive zones. Indices and methods were further developed to quantify compactness, rate, and development intensity of urban expansion processes. In particular, the model mathematically articulated how the macroscale urban form were invented from overlap of sequential physical spatial processes of urban expansion. The proposed model attained a fine fit with the density gradient of new urban land in 27 sample cities from 1990 to 2017, with an average R2 value of approximately 0.87. The results revealed rapid urban expansion and path dependency in the compactness of urban expansion during this period. The concentric partitioning results in this study demonstrated tendency consistent with that reported in previous studies. The proposed model provides new insights into urban geography with a deeper understanding of the physical spatial processes associated with land urbanization

Real Estate Developers as Agents in the Simulation of Urban Sprawl

Urban expansion is one of the processes that generates the greatest impact on the distribution of land uses, leading to important territorial changes. Its evolution has become a highly relevant object of analysis and studies on the subject have gradually advanced from a focus on patterns of land use and cover, towards a more detailed analysis of the dynamic interactions between all the actors involved in these processes. Among the agents usually included as an active and decisive part of these dynamics are the real estate developers. This study aims to analyze these agents, identify their behavioral patterns, and categorize and better understand them. For this, a qualitative approach was employed, in which a structured questionnaire elaborated from the point of view of an expert was administrated to the real estate developers in the area of the Henares urban–industrial corridor, between Madrid and Guadalajara, where important processes of urban expansion have taken place. The information obtained from an interview with an expert and the questionnaire revealed important information about the work of real estate developers and will be of great help in the elaboration of an Agent-Based Model to simulate and analyze urban growth. This process is crucial to determine in a more empirical way the different decision rules that are necessary to develop this type of model.

Integrating ANNs and Cellular Automata–Markov Chain to Simulate Urban Expansion with Annual Land Use Data

Accurately simulating and predicting the urban expansion process, especially in expeditious urbanization areas, is an important aspect of managing limited land resources and adjusting flawed land use policies. This research was conducted on the basis of a high-temporal-resolution land use dataset to precisely model urban expansion in a rapidly developing zone by integrating the Artificial Neural Network (ANN), cellular automata (CA), and Markov Chain (MC). An urban suitability index (USI) map was created using ANN and fed to CA–MC to identify possible changed-to-urban cells. Two ANNs, multiple-layer perceptron (MLP) and long short-term memory network (LSTM), were implemented as simulation models for comparison. Due to its ability to capture more temporal information, LSTM outperformed MLP in modeling urban expansion dynamics over a short temporal interval. The simulated results were validated by (fuzzy) kappa simulation and the results revealed that the combination of ANN and CA–MC can precisely model the urban development locations due to its strength in revealing the nonlinear relationship between the expansion process and its drivers. The same model was applied to southern Auckland, and the compared results show that the most simulated variance is caused by the land use policies implemented by different types of governments.

Monitoring and Analysis of Urban Sprawl Based on Road Network Data and High-Resolution Remote Sensing Imagery: A Case Study of China's Provincial Capitals

The primary prerequisite for sustainable urban development is to accurately grasp the development of the city. The dynamic changes in the urban area can reflect the urban expansion process and spatial development model. Carrying out urban expansion monitoring and extracting urban areas is of great importance for grasping the law of urban development and promoting the sustainable development of cities. However, the related research reveals several problems such as insufficient accuracy and low intelligence of urban boundary extraction. In response to these problems, this paper proposes a new method for urban area extraction based on the progressive learning model. By combining prior knowledge and image features, the number of training samples required for machine learning is reduced, and the problem of using high-level semantic information expression in the process of urban areas is avoided by using the classification method, and thus the accuracy of urban area extraction is improved. The method uses urban road network data to divide the city into blocks. It applies the scene classification method to extract the urban areas and uses the pyramid layer-by-layer to carry out the space constraint method to integrate the urban extraction principle into the machine learning process, which can be obtained and kept artificial under a small sample condition. Extracting the effect of the urban area is very close, greatly reducing the workload and providing a new solution for high-precision automatic extraction of urban areas. Through the analysis of urban expansion, the following results were obtained: (1) from 2000 to 2015, China's provincial capital cities maintained a high-speed growth trend with a total area increased by 90%; (2) urban expansion showed significant regional differences. The eastern expansion rate gradually slowed down, the western and northeast regions accelerated their expansion, and the central region expanded steadily; (3) 61% of the provincial capital cities expanded exponentially; (4) the development of China's provincial capital cities was highly correlated with national urban development policies and regional development strategies.

Risk of urban flash floods in the Guadalajara Metropolitan Area, Mexico

The Guadalajara Metropolitan Area (GMA), in western Mexico, has experienced a rapid demographic and urban growth in recent decades. As a result of land use changes, an Urban Heat Island (UHI) has developed and intensified, which reflects not only in air surface temperature increases, but also in higher atmospheric moisture content and more intense precipitation events. Storms have become more frequent since the beginning of 1980s, which makes them one of the major meteorological hazards for the city. Such an increase in strong storms appears to be related to the urbanization process which in turn, has increased the vulnerability of the GMA to intense rains. The urban expansion towards the higher parts of the region has led to more water runoff and flash flood events in the low-level parts of the metropolitan area. An analysis by means of observed meteorological data and a rainfall – runoff methodology shows that the combined effect of increased precipitation and vulnerability results in a higher risk of urban flash floods. The loss of natural vegetation, the main roads that act as water channels that converge into the central part of the city, an insufficient drainage system and the lack of prevention actions have resulted in more frequent urban flash floods. All of these vulnerability factors are associated with an inadequate urban expansion process. The model of flood risk may be used to define adaptation measures to face a changing climate.

Exploring the spatio-temporal characteristics and driving factors of urban expansion in Xi'an during 1930–2014

ABSTRACT Analyzing and understanding the spatial and temporal dynamics and the driving factors of urban expansion over time is crucial for providing solutions to address social, economic and ecological problems caused by urbanisation. Previous studies generally relied on remote sensing data and examined the process of urban expansion in relatively shorter time periods. Taking Xi'an as the case study, this paper uses the proposed urban boundary extraction method by integrating multi-source data to analyze the spatio-temporal characteristics of long-term urban expansion. The research focuses on indicators including expansion intensity, expansion direction, city centre of gravity, compactness, and elasticity, and identify and discusses the driving factors of urban expansion. The results show that the urban expansion of Xi'an has undergone two cyclical changes during 1930-2014. The urban form presents a 'horizontal rectangle-inverted triangle-concave' evolution feature. The city centre of gravity shifts to the northwest. The compactness continues to decline, and the elasticity coefficient rise. It has been identified that economic and demographic factors together with the industrial structure have driven urban expansion, and urban planning policies has influenced the directions of the expansion. The conservation of great ruins in Xi'an has affected the evolution of its urban morphology. Based on the results, we further discuss the future expansion directions of the city, and provide specific suggestions for future urban planning in order to better coordinate urban expansion, and at the same time provide adequate protection to large heritage sites and support urban sustainability. These findings provide not only valuable insights about urban planning of Xi'an, the methodological approach developed in this research is also applicable to other historic cities with similar characteristics in China and beyond.

ANALYSIS OF URBAN EXPANSION CHARACTERISTICS OF YANGTZE RIVER DELTA URBAN AGGLOMERATION BASED ON DMSP/OLS NIGHTTIME LIGHT DATA

Abstract. Urban agglomeration is the strongest driving force of national economic development. The formation and development of urban agglomeration is an important feature of modern urbanization process, and it is of great significance to study its spatial expansion and development process for regional sustainable development. Based on the DMSP/OLS nighttime light remote sensing data from 2000 to 2010, this paper uses threshold method to extract the urban built-up area of the Yangtze River Delta urban agglomeration. The expansion characteristics of urban built-up area in Yangtze River Delta urban agglomeration are analysed from three aspects: expansion area, expansion intensity and expansion type. The results show that the urban agglomeration in The Yangtze River Delta is experienced a rapid urban built-up area expansion process during the decade. The proportion of urban built-up area increased from 9.95% to 15.46%, the average nighttime light intensity increased from 10.18 to 23.43, and the type of urban built-up area expansion was filling type.

Spatial Features of Urban Expansion in Vietnam Based on Long-Term Nighttime Lights Data

As a developing country, Vietnam has experienced rapid economic development since the 21st century. It is therefore becoming increasingly important to understand its spatial pattern of urban expansion. However, the key challenge in this endeavor lies in the lack of national accounting data for the sub-administrative divisions of Vietnam at the national level, especially longitudinal data over a long time series. The nighttime lights (NTL) data can objectively reflect the scope and intensity of human development and construction in urban built-up areas, which can effectively support the empirical analysis of the urban expansion process in Vietnam. This paper uses the intercalibration model to correct and fit the long time series of DMSP/OLS and VIIRS/NPP NTL data. Based on this, the data for the urban built areas of Vietnam from 2000 to 2018 are further extracted. The results are as follows. (1) The main urban expansion in Vietnam is concentrated in the southern Mekong Delta and the northern Red River Delta, represented by Ho Chi Minh City and Hanoi City, respectively. (2) Vietnam’s urban NTL has significant high–high clustering characteristics in the north-south delta regions. The main urban expansion hotspots were concentrated around Ho Chi Minh City before 2012, the northern region represented by Hanoi City was gradually transformed into a critical area that gathering urban expansion hotspots after 2012. (3) The cities with significant influence and high coupling degree of industrialization and globalization on the urbanization of Vietnam are concentrated in Ho Chi Minh City, Hanoi, and some northern delta provinces, showing that the impact of industrialization and globalization on urbanization in Vietnam is still limited to some regions. In addition, the results show that the size of the population and the level of industrialization are the main drivers of urban expansion in Vietnam, while the level of foreign investment shows little significance. The results are helpful for promoting the application of long time series NTL data in urban expansion and for further analyzing the urban pattern changes in Vietnam and the influencing factors behind them.

Spatio-temporal evolution of urban built-up areas and analysis of driving factors —A comparison of typical cities in north and south China

Urban expansion is an important manifestation of urbanisation. Based on the impervious surface aggregation density (ISAD) method, this study extracts the urban built-up areas (UBA) range of 12 major cities in the north and south of China from 1995 to 2015. Comprehensive use of the urban centre of gravity, fractal dimension, compactness, urban spatial morphological expansion mode, urban expansion speed, and other indicators was applied to explore the spatio-temporal evolution characteristics of urban expansion. The statistical method GeoDectector (GD) was used to analyse the impact of economic, transportation, natural environment, and social factors on urban expansion. Results show that the 12 cities were shown to have different expansion characteristics and spatio-temporal evolution characteristics. Most northern cities had single-core expansion, while most southern cities had multi-core expansion. Most of the spatial forms of cities tended to be discretized, and the urban expansion process of southern cities was obviously more rapid than that of northern cities. GeoDectector revealed that urban expansion in both the north and south was mainly related to population and economy. Social factors explain the expansion of southern cities, and while the intensity was stronger than that of the northern cities, the natural factors were opposite, and the traffic factors were not significantly different. This analysis confirms the differences in economic development between the north and south of China and provides a scientific reference for the formulation of national-scale urban development policies. In the context of global urbanisation, the results from this study will provide a Chinese sample for understanding the temporal and spatial characteristics of urban expansion, which can serve as a good reference value for other countries and regions worldwide, especially for urbanisation research in developing countries.

Konflikty przestrzenne w procesach suburbanizacyjnych w świetle planowania przestrzennego. Studium przypadku podlubelskiej gminy Strzyżewice

The process of urban expansion is increasingly encompassing suburban areas, in particular undeveloped agricultural areas. As a result of this process, functional changes and increasingly complex spatial structures can be seen in rural areas. Multiple interactions inevitably arise between the elements of these structures, including functional and spatial conflicts. Spatial planning is a tool for advance identification, and in many cases for elimination of such conflicts. The aim of the article is to assess the impact of the suburbanization process on the spatial structure of a suburban commune as well as the identification of individual types of conflicts. This study concerns spatial planning in the rural Strzyżewice commune. The subject of the analyses are conflicts of interest revealed during the preparation of planning documents. Based on the analysis of documentation prepared during the development of local planning documents it was confirmed that the suburbanization processes of the city of Lublin created specific – real or potential – spatial conflicts in the areas subjected to these processes.

Policy-Induced Suburbanization: Mass-Produced Housing and Location Choices in Tijuana, Mexico

ABSTRACT Mexican cities began an urban expansion process fueled principally by public mortgage supply in the early 2000s. The new urban landscape, comprising mass-produced suburban housing developments for low-income families, deepened socioeconomic differences. For years, developers have claimed land prices are the reason for suburban expansion in Mexico, not policy-enabled construction economies. This study tests the hypothesis that cost reduction strategies through scale economies explain the suburban location and the homogeneous landscape built under the reformed mortgage system. Using data on housing production costs for Tijuana, the results show that building homes using technology developed during policy implementation yields scale economies and reduces building costs. Additionally, statistics on housing developers’ location decisions illustrate how production economies have contributed to a landscape that increased segregation, exclusion, and housing vacancy in Mexican cities.

Analysis of the heterogeneity of urban expansion landscape patterns and driving factors based on a combined Multi-Order Adjacency Index and Geodetector model

Under conditions of rapid urbanization, quantitative measurement of the expansion characteristics of urban landscapes and evaluation of their spatial and temporal differentiation properties is an important scientific problem. The Multi-order Adjacency Index (MAI) is an effective indicator of the quantitative characteristics of urban landscape expansion; however, it cannot directly reflect the spatial heterogeneity of this expansion. Therefore, it is necessary to combine the MAI with a spatial heterogeneity detection method to measure and evaluate the spatiotemporal divergence features of urban landscape expansion. In this study, taking Chenggong District, Kunming City, Yunnan Province, China as the study area, we apply MAI to analyze the changing process of urban landscape pattern expansion, and use the Geodetector to analyze the drivers affecting the spatial divergence of this expansion. We compare the results with the Landscape Extension Index and geographically-weighted regression models. The results show that MAI can reveal urban landscape expansion well at the micro-level, and analysis of the urban landscape expansion process reveals that it is mainly socio-economic factors that lead to the spatial heterogeneity of urban landscape expansion. Combining MAI and Geodetector can solve the problem that existing methods cannot directly reflect the heterogeneity of urban expansion landscape patterns and their causes.

Variation Characteristics of Building Height Types in the Vertical Urban Expansion Process

Variation Characteristics of Building Height Types in the Vertical Urban Expansion Process

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

During the last three decades, Delhi has witnessed extensive and rapid urban expansion in all directions, especially in the East South East zone. The total built-up area has risen dramatically, from 195.3 sq. km to 435.1 sq. km, during 1989–2020, which has led to habitat fragmentation, deforestation, and difficulties in running urban utility services effectively in the new extensions. This research aimed to simulate urban expansion in Delhi based on various driving factors using a logistic regression model. The recent urban expansion of Delhi was mapped using LANDSAT images of 1989, 2000, 2010, and 2020. The urban expansion was analyzed using concentric rings to show the urban expansion intensity in each direction. Nine driving factors were analyzed to detect the influence of each factor on the urban expansion process. The results revealed that the proximity to urban areas, proximity to main roads, and proximity to medical facilities were the most significant factors in Delhi during 1989–2020, where they had the highest regression coefficients: −0.884, −0.475, and −0.377, respectively. In addition, the predicted pattern of urban expansion was chaotic, scattered, and dense on the peripheries. This pattern of urban expansion might lead to further losses of natural resources. The relative operating characteristic method was utilized to assess the accuracy of the simulation, and the resulting value of 0.96 proved the validity of the simulation. The results of this research will aid local authorities in recognizing the patterns of future expansion, thus facilitating the implementation of effective policies to achieve sustainable urban development in Delhi.

Detecting ecological spatial-temporal changes by Remote Sensing Ecological Index with local adaptability

Ecological environmental assessment is an indispensable part of the eco-environment protection system. As researchers have increasingly focused on ecological environment protection, the ecological environment evaluation system has been gradually improved. The enhancement of the ecological environment evaluation system provides more scientific and effective data support for ecological environment monitoring and governance. This article examines the Wuhan Urban Development Zone as an example, selects Landsat 8 (Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS)) images of the study area from 2013 to 2019at two-year intervals, and applies a new type of ecological environment evaluation index named the remote sensing ecological index with local adaptability (RSEILA) to assess the eco-environment. The RSEILA represents an improvement of the remote sensing ecological index (RSEI) proposed in 2013. The RSEILA enhancement is mainly reflected in the correlation and spatial distribution characteristics between geographical elements. The results reveal that 1) the overall urban ecological environment in the Wuhan Urban Development Zone demonstrates a downward trend from 2013 to 2019, and the rate of decline during the period varies. 2) RSEILA decline is mainly found in the far suburbs, and ecological environment degradation mainly occurs due to the change in land-use type caused by the suburbanization process of urban expansion. 3) Because of the implementation of urban greening projects, the phenomenon of ecological environment optimization (green recovery) is observed in the central urban area of Wuhan. 4) Land use exhibits a notable correlation with the ecological environment, and different land-use types exhibit distinct degrees of ecological environment deterioration. The order of deterioration is: bare soil/sand>building>cropland>forests.

Urban Land Expansion Simulation Considering the Diffusional and Aggregated Growth Simultaneously: A Case Study of Luoyang City

Restricted by urban development stages, natural conditions, urban form and structure, diffusional growth occupies a large proportion of area in many cities. Traditional cellular automata (CA) has been widely applied in urban growth studies because it can simulate complex system evolution with simple rules. However, due to the limitation of neighborhood conditions, it is insufficient for simulating urban diffusional growth process. A maximum entropy mode was used to estimate three layers of probability spaces: the probability layer of cell transformation from non-urban status to urban status (PLCT), the probability layer for aggregated growth (PLAP), and the probability layer for diffusional growth (PLOP). At the same time, a maxent category selected CA model (MaxEnt-CSCA) was designed to simulate aggregated and diffusional urban expansion processes simultaneously. Luoyang City, with a large proportion of diffusional urban expansion (65.29% in 2009–2018), was used to test the effectiveness of MaxEnt-CSCA. The results showed that: (1) MaxEnt-CSCA accurately simulated aggregated growth of 47.40% and diffusional growth of 37.13% in Luoyang from 2009 to 2018, and the overall Kappa coefficient was 0.78; (2) The prediction results for 2035 showed that future urban expansion will mainly take place in Luolong District and the counties around the main urban area, and the distribution pattern of Luolong District will change from the relative diffusion state to the aggregation stage. This paper also discusses the applicable areas of MaxEnt-CSCA and illustrates the importance of selecting an appropriate urban expansion model in a region with a large amount of diffusional growth.

Assessment on controlling factors of urbanization possibility in a newly developing city of the Vietnamese Mekong delta using logistic regression analysis

Urban development is dominated by various factors ranging from natural and social factors to accessibility to urban infrastructures. Urbanization in an unfavorable location in terms of the above factors can create difficulties with connecting to the city center, adjacent urban areas, and wastage of land resources. In the context of Can Tho city, a newly developing city, its urban expansion process and factors affecting urbanized possibility were explored by applying multiple logistic regression (MLR) on Landsat imagery and accessible geospatial data sources. The analyses confirmed a significant urban expansion in the entire city between 2003 and 2017, mostly in central districts and along the Hau river. The primary dynamics of urban expansion were explained by an efficient MLR (Area Under Receiver Operating Characteristic – AUROC = 0.803), based on six factors related to accessibility to transportation, developed urban areas, industrial zone, elevation, soil type, and population. A simulation of urbanization probability revealed that most remote areas with low accessibility to urban infrastructures are difficult to urbanize with a probability of less than 40%. In contrast, the high potentially urbanized regions expanded the already built-up areas in riverside districts. Our findings facilitate the understanding of urbanized driven factors in the newly developing delta cities for long-term planning when urbanization remains under control.

Spatial Analysis of Intra-Urban Land Use Dynamics in Sub-Saharan Africa: The Case of Addis Ababa (Ethiopia)

Currently, circa 30% of the population in sub-Saharan Africa resides in cities, and this figure is expected to double in 2040. The recent literature describes the urban expansion processes of African cities in much detail. However, the urbanization wave in Africa also leads to important intra-urban land use dynamics, which have important consequences on the quality of life within existing cities, which has received less attention. This study aims to contribute to these information gaps by (1) analyzing the extent of the urban land use conversion in contrasting urban locations using satellite images for physical criteria-based classifications and (2) assessing the potential consequences of these intra-urban conversions on the quality of life. Intra-urban land use changes were documented based on satellite imagery for the period 2002–2020. Based on some representative attributes, Addis Ababa city was selected for the case study. Urban land use dynamics and population density changes were examined based on the selected case study neighborhoods and randomly identified land parcels in the city, respectively. Urban development strategies and programs that emerged over recent decades had caused intra-urban land use dynamics, which brought significant population density changes. Moreover, these changes have caused an unbalanced distribution of socio-economic amenities across the city.