Analysis Of Urban Growth Research Articles

Fractal analysis of urban growth in complex systems

A complex network consists of number of elements which may interact with each other to form a complex system. The fractal feature is one of the most important characteristics in complex networks. This study examines the fractal analysis of Madaba City Centre as a complex system through the period to 1953-2022. In order to reveal the fractal characteristics of the study area box-counting (Db) and information (Di) dimensions were calculated using Benoit for five satellite images for years 1953, 1981, 1992, 2000, and 2022. Results showed that Madaba road networks have fractal characteristics for the study period, and the calculated fractal dimensions are between 1 and 2. Db values for years 1953, 1981, 1992, 2000, and 2022 are 1.38, 1.51, 1.55, 1.58, and 1.65, while Di values are 1.37, 1.52, 1.57, 1.60, and 1.67 respectively. The difference between Db and Di values for the same year was small, indicating the similarity between the two methods. Increasing the complexity of the system components and dynamic activities are other indicators of its growth, and there is a significant positive relationship between the fractal dimension value and the complexity level of the city in terms of population growth and area expansion; which increase the importance of fractal theory in studying the growth of the complex system like cities.

Spatially Explicit Analysis of Landscape Structures, Urban Growth, and Economic Dynamics in Metropolitan Regions

Assuming that settlement morphologies and landscape structures are the result of economic transformations, the present study illustrates a statistical framework investigating metropolitan growth due to the inherent changes in landscape configurations vis à vis socio-demographic functions. Focusing on the evolution of their spatial drivers over time, metropolitan development was studied by adopting land parcels (or ‘patches’, as they are referred to in the ecological literature) as the elementary analysis unit—with the individual surface area and a specific shape indicator as the dependent variables and background socioeconomic attributes as predictors of landscape change over time. We specifically ran a Multiscale Geographically Weighted Regression (MGWR) testing the spatial dependence of the size and shape of landscape parcels on a vast ensemble of socioeconomic factors in a dense region (metropolitan Athens, Greece) with natural landscapes exposed to increasing human pressure. To investigate the spatial direction and intensity of the settlement expansion and landscape change, local regressions using the parcel area and fractal index (perimeter-to-area ratio) as the dependent variables and the elevation, distance from selected economic nodes, transport infrastructures, and natural amenities as the predictors were run separately for 1990 and 2018, representative of, respectively, a mono-centric configuration and a moderately polycentric organization of economic spaces. In a strictly mono-centric setting (1990), the parcel size showed a linear dependence on the distance from business districts, elevation, and wealth. Changes in the relationship between the parcel size and spatial (economic and non-economic) drivers may suggest a latent process of settlement de-concentration, and a possible shift toward polycentric development (2018), as documented in earlier studies. By integrating socioeconomic and ecological dimensions of landscape analysis and land evaluation, the empirical results of this study outline the increased complexity of dispersed landscape structures within dense metropolitan regions and along urban–rural gradients in Europe.

Sociocultural valuation of public space in a context of rural-urban transition. Case of Morelos Avenue, San Juan Cuautlancingo, Puebla

The municipality of Cuautlancingo used to agricultural production was interfered by the insertion of new urban areas causing land use speculation resulting in the reconfiguration of public spaces such as streets and avenues. The objective is to illustrate how urban expansion, through changes in land use, has interfered with the socio-spatial valuation among the inhabitants of the neighborhood of San Juan and the new housing areas, leading to conflicts in the use, access, and appropriation of public space, exemplified by Morelos Avenue. The methodology used is of a mixed nature, using GIS for the spatial analysis of urban growth. Among the main findings, it is presented how the changes in land use in the last three decades and the appearance of new housing complexes affected the sociocultural valuation of the neighborhood of San Juan Cuautlancingo.

Integrating Spatiotemporal Analysis of Land Transformation and Urban Growth in Peshawar Valley and Its Implications on Temperature in Response to Climate Change

Examining the interconnected dynamics of urbanization and climate change is crucial due to their implications for environmental, social, and public health systems. This study provides a comprehensive analysis of these dynamics in the Peshawar Valley, a rapidly urbanizing region in Khyber Pakhtunkhwa, Pakistan, over a 30-year period (1990–2020). A novel methodological framework integrating remote sensing, GIS techniques, and Google Earth Engine (GEE) was developed to analyze land use/land cover (LULC) changes, particularly the expansion of the built-up environment, along with the land surface temperature (LST) and heat index (HI). This framework intricately links these elements, providing a unique perspective on the environmental transformations occurring in the Peshawar Valley. Unlike previous studies that focused on individual aspects, this research offers a holistic understanding of the complex interplay between urbanization, land use changes, temperature dynamics, and heat index variations. Over three decades, urbanization expanded significantly, with built-up areas increasing from 6.35% to 14.13%. The population surged from 5.3 million to 12.6 million, coupled with significant increases in registered vehicles (from 0.171 million to 1.364 million) and operational industries (from 327 to 1155). These transitions influenced air quality and temperature dynamics, as evidenced by a highest mean LST of 30.30 °C and a maximum HI of 55.48 °C, marking a notable increase from 50.54 °C. These changes show strong positive correlations with built-up areas, population size, registered vehicles, and industrial activity. The findings highlight the urgent need for adaptive strategies, public health interventions, and sustainable practices to mitigate the environmental impacts of urbanization and climate change in the Peshawar Valley. Sustainable urban development strategies and climate change mitigation measures are crucial for ensuring a livable and resilient future for the region. This long-term analysis provides a robust foundation for future projections and policy recommendations.

Evaluating the Spatial-Temporal Dynamics of Urbanization in Prefecture Cities of China Using SNPP-VIIRS Nighttime Light Remote Sensing Data

Ensuring the well-being of urban communities hinges on sustainable urban planning strategies informed by current data, particularly in China since urbanization has been one of the most significant demographic shifts in recent decades. Therefore, our research aimed to evaluate the spatio-temporal dynamics of urbanization and sub urbanization across prefecture and provincial levels in China by utilizing consistent SNPP-VIIRS-like and NPP-VIIRS nighttime data spanning the years 2000 to 2020. The k-means method was applied to derive urban and sub urban features from above datasets. The findings uncovered a significant expansion of urban entities at the prefecture level, escalating from 16,209 km2 to 89,631 km2 over the specified period showing a 5% growth. Among five main urban agglomerations, the Yangtze River Delta stands out with the highest urbanization rate, witnessing a remarkable expansion of urban entities from 2,684 km2 to 41,465 km2. This growth reflects an average growth rate of 72.2% per annum. The analysis revealed that the overall area of suburbs expanded from 59,151 km2 to 120,339 km2 between 2012 and 2020 indicating a proportional growth rate ranging from 0.4% to 1.9%. The peak growth rate of suburbs was recorded between 2012 and 2014, reaching 18%. Guizhou, Hunan, and Hubei provinces have exhibited growth rates of 334%, 258%, and 246% respectively while Beijing, Guangdong, Tianjin, and Shanghai have experienced relatively low growth rates of 50%, 56%, 46%, and 17%. The analysis of urban growth with GDP, population, and electricity consumption revealed an inverse relationship during the specified period. Therefore, the findings of this research can provide immense support to sustainable urban planning initiatives at both the provincial and prefecture-level cities in China. The findings can assist city planning authorities in making informed decisions regarding optimizing resource distribution, all while prioritizing the preservation of ecological footprint within urban environments. Also, the limitations addressed in our study must be taken into account in future research works aimed at deriving reliable urban extraction results using nighttime light remote sensing data.

An analysis of urban sprawl growth and prediction using remote sensing and machine learning techniques

This study examines urban growth to support sustainability by investigating urban sprawl in the Vila Velha Urban Agglomeration (UA). If the spatial area of Vila Velha UA increases by 24.36%, from 22.98 km2 in 1994 to 51.64 km2 in 2024, urban sprawl may affect surrounding areas. Landsat satellite images from 1994, 2004, 2014, and 2024 were analyzed to study the spatiotemporal pattern of urban growth. Shannon's entropy index identified urban sprawl in Vila Velha UA. The QGIS software's MOLUSCE (Modules for Land Use Change Simulations) plug-in, utilizing Cellular Automata (CA) and Artificial Neural Network-Multi Layer Perceptron (ANN-MLP) models, forecasted urban expansion for 2034. The built-up area is projected to increase from 51.64 km2 in 2024 to 62.31 km2 in 2034. Shannon's entropy index indicated a high rate of urban sprawl. Remote sensing and machine learning techniques are crucial for understanding spatial trends, forecasting future expansion, and informing sustainable urban planning. These methods facilitate an accurate assessment of urbanization dynamics, aiding in developing strategies to manage and mitigate sprawl's adverse impacts. This study offers a unique approach to defining urban growth, emphasizing the need for zoning regulations, green space conservation, and infrastructure development to curb urban sprawl and promote sustainable growth in Vila Velha.

Urban growth and vulnerability to climate change of Calderón in the Metropolitan district of Quito, Ecuador

The parish of Calderón, located in the Metropolitan District of Quito, has a spatial dynamic characteristic of the expansion processes, with notable population growth, loss of agricultural land, and settlements in vulnerable areas. This research aimed to identify the vulnerability to climate change by analyzing urban growth in this parish to define measures for its climate change adaptation. A three-stage mixed approach methodology was used: the historical and spatial analysis of urban growth, the relationship between urban growth and vulnerability to climate change, and climate change adaptation measures in the parish of Calderón. The thirteen indicators analyzed show a medium-low risk level in their exposure and sensitivity components. However, the opposite is true for the adaptive capacity component, where vulnerability is high. The relationship between the parish’s urban growth and vulnerability to climate change is not direct. On the one hand, the expansion processes affect the territory and the population, causing environmental and social conflicts. On the other hand, factors immersed in urban expansion's spatial dynamics are revealed.

Remote Sensing-Enabled Urban Growth Simulation Overlaid with AHP-GIS-Based Urban Land Suitability for Potential Development in Mersin Metropolitan Area, Türkiye

This study delves into the integration of analytic hierarchy process (AHP) and geographic information system (GIS) techniques to identify suitable areas for urban development in six districts within the Mersin Metropolitan Area of Turkey. The specific aim is to generate an urban land use suitability map, in order to facilitate informed decision-making for urban development. Drawing on open Landsat satellite imagery and employing the random forest (RF) algorithm, the study spans a fifteen-year period, over which land use/land cover (LULC) changes are measured. Furthermore, a novel approach is introduced by incorporating the urban land use suitability map into an urban growth simulation model developed using a logistic regression (LR) algorithm. This simulation forecasts urban growth up to 2027, enabling planners to evaluate potential development areas against suitability criteria. Findings reveal spatial patterns of land suitability and projected urban growth, aiding decision-makers in selecting optimal areas for development while preserving ecological integrity. Notably, the study emphasizes the importance of considering various factors such as topography, accessibility, soil capability, and geology in urban planning processes. The results showcase significant proportions of the study area as being moderately to highly suitable for urban development, alongside notable shifts in LULC classes over the years. Additionally, the overlay analysis of simulated urban growth and land suitability maps highlights areas with contrasting suitability levels, offering valuable insights for sustainable urban growth strategies. By overlaying the urban land suitability map with a simulated LULC map for 2027, it is revealed that 2247.3 hectares of potential new urbanization areas demonstrate very high suitability for settlement, while 7440.12 hectares exhibit very low suitability. By providing a comprehensive framework for assessing urban land suitability and projecting future growth, this research offers practical implications for policymakers, urban planners, and stakeholders involved in Mersin’s development trajectory, ultimately fostering more sustainable and resilient urban landscapes.

Social mechanisms for the development of irregular human settlements in Chetumal, Quintana Roo, Mexico

The work presents partial results of a research carried out by the Autonomous University of the State of Mexico, oriented to the analysis of urban growth that develops on the periphery of cities through informal and illegal land occupation processes. The analysis aims to address the social practices and mechanisms that the population exercises to satisfy their land and housing needs, taking the city of Chetumal, Quintana Roo, Mexico as a case study. The results show that the State and real estate developers express their interest in developing the city in a formal way, in which they satisfy the needs of the population with high economic resources and leave aside the poorest population, who practice social actions for the development of irregular human settlements.

Accessibility as a critical factor of urban growth in Amazon – Brazil: an application of Plan-Process Theory Lourenço´s Model

Urban growth analysis and territorial plans made by different levels of government in the Amazon brings Santarem as urban center of reference, with strategic location. The evaluation of plans developed in this article is an application of Plan-Process Theory Lourenço´s Model, which deals the implementation of plans between the phases of decision-making and the acts of investment. The methodology starts from the historical analysis of urban development determinants, from the perspective of plans implemented in the place, highlighting the critical factors of results from these planning actions. Among the key findings, lack of planning policies for local needs, absence of problems such as social inequality and poor accessibility. Therefore, system innovation for a modal network does not yet reflect, through the plans, as an opportunity to improve accessibility in the urban areas of the Amazon.

Integrating Passive and Active Remote Sensing Data with Spatial Filters for Urban Growth Analysis in Urmia, Iran

Active remote sensing and related technologies are one of the new tools recently used to monitor complications and urban growth. This research aims to investigate the effect of spatial filters on urban complications. The aim of this paper is to compare Lee, Frost and Average spatial filters with Landsat 8 satellite images and radar images with HH/HV polarization to investigate and identify urban features in the west of Urmia City. The results showed that Filterelli with the kernel 3 x 3 had reduced the spike noise in Alus Palsard satellite radar images in identifying the growth of urban tolls. Also, the results of K-means classification, the Lee filter with kernel size 3 x 3 more accurately identifies the urban features of the west of Urmia City. The kappa coefficient was 0.96%, and the overall accuracy of this filter was 97.36%. Therefore, Lee’s spatial filter has successfully identified the urban features of west Urmia with high accuracy. This system can be implemented in any other field due to its generality and reliability. This system may be a step towards remote sensing automation.

Analysis of past and future urban growth on a regional scale using remote sensing and machine learning

This research investigates Land Use and Land Cover (LULC) changes in the Porto Alegre Metropolitan Region (RMPA). A 30-year historical analysis using Landsat satellite imagery was made and used to develop LULC scenarios for the next 20 years using a Multilayer Perceptrons (MLP) model through an Artificial Neural Network (ANN). These maps analyze the urban area’s expansion over the years and project their potential development in the future. This research considered several critical factors influencing urban growth, including shaded relief, slope, distances from main roadways, railway stations, urban centers, and the state capital, Porto Alegre. These spatial variables were incorporated into the model’s learning processes to generate future urbanization scenarios. The LULC historical maps precision showed excellent performance with a Kappa index greater than 88% for the studied years. The results indicate that the urbanization class witnessed an increase of 236.78 km2 between 1990 and 2020. Additionally, it was observed that the primary concentration of urbanized areas since 1990 has predominantly occurred around Porto Alegre and Canoas. Lastly, the future forecasts for LULC changes in 2030 and 2040 indicate that the urban area of the RMPA is projected to reach 1,137.48 km2 and 1,283.62 km2, respectively. In conclusion, based on the observed urban perimeter in 2020, future projections indicate that urban areas are expected to increase by more than 443.29 km2 by 2040. The combination of remote sensing data and Geographic Information System (GIS) enables the monitoring and modeling the metropolitan area expansion. The findings provide valuable insights for policymakers to develop more informed and conscientious urban plans, as well as enhance management techniques for urban development.

Prediction of land use and land cover changes (LULC) and urban growth analysis in Dhaka Metropolitan Area (DMA) using CA-Markov model and geospatial techniques

Prediction of land use and land cover changes (LULC) and urban growth analysis in Dhaka Metropolitan Area (DMA) using CA-Markov model and geospatial techniques

The Implementation of Classification and Spectral Transformation Methods, Based on Extraction Landsat Imagery for Growth Center Mamminasata Region

Urban growth begins from a center and affects its surrounding area, and this is due to the emergence of additional centers that will serve as a growth pole. Assessment of the dynamics of urban growth, some data, and extraction analysis methods are needed. This research examines various information by extracting of growth center area of Landsat 8 OLI / TIRS 2017 and utilizing spectral resolution on Landsat images in the Mamminasata region. The classification method tested is the OBIA classification and the Maximum Likelihood classification methods. Simultaneously, the Landsat image’s spectral resolution is maximized by spectral channel transformation sensitive to the building object. It is usually called the urban index. This study indicates that the OBIA classification method and NDBI spectral transformation proved to produce maps. It can also show center location growth with the best accuracy, especially in the Mamminasata region. Total accuracy from the accuracy-test matrix is 71.48% for building intensity and 78.57% for growth centers.

Water challenges of Dire Dawa city of Ethiopia: An assessment of urban growth and groundwater supply

Urbanization in Ethiopia is increasing rapidly, leading to environmental changes and necessitating improved urban infrastructure. Dire Dawa, the second most populous city in Ethiopia, has a high rate of urban growth. This study focuses on three main areas: remote sensing analysis of urban growth, urban water supply and water distribution system, and groundwater resources management. Land use and land cover (LULC) analysis over four different time periods showed significant and rapid expansion of the city. The current and future water demand and supply analysis found that the water supply is three times less than the water demand, indicating that existing water sources are not adequate for the future expansion of the city. Moreover, the hydraulic performance of the existing distribution system as analyzed by WaterGems software found that pressure at 22.41% of nodes and velocities at 29.83% of pipes are sufficient to provide water for the study area. Finally, the study investigated the discharge and water level and water quality of each borehole in Dire Dawa City. Overall, this study showed that high urbanization rate together with the population growth has put pressure on the existing water supply source and existing water distribution system calling for feasible water management strategy for the city.

Geographically weighted regression to measure the role of intra-urban drivers for urban growth modelling in Kathmandu, Central Himalayas.

The present study analyzes spatio-temporal urban growth in Kathmandu Urban Agglomeration (KUA) and its impact on ecological infrastructures between 2000 and 2020. A geographically weighted regression (GWR) approach was used to investigate intra-urban drivers of urban growth between 2000-2010 (epoch 1) and 2010-2020 (epoch 2), and to predict built-up growth for the year 2030. The study makes crucial contributions to the urban growth modelling literature by (a) investigating six different neighborhood sizes (3 × 3 to 13 × 13) for computing proportion of built-up cells and (b) eliminating/selecting covariates using correlation check and globallasso regression. The superiority of GWR model over global regression model has been evaluated using the Akaike information criterion score and the stationarity test. The chronological urban growth analysis of KUA highlights rapid growth in mountainous landscape from 54.90 to 166 km2 during 2000-2020 and is projected to increase to 224.22 km2 during 2000-2030. This has significantly altered the forest-agriculture landscape during 2000-2020 (111.2 km2) and is expected to affect a large segment of ecological (blue-green) infrastructure (138 km2) by 2030. The findings of this study may be used for policy formulation, appropriate land use planning, and sustainable urban development.

Modelling and Analysis of Urban Growth of Idah Metropolis Using GIS Integrated Gradient Technique

This thesis aims to better understand changes in the spatial pattern of urban growth and its impact on landscape configuration by conducting a case study in Idah metropolis. The objectives are as follows: 
 
 To model and analyze multi-temporal images (2001, 2010, and 2021) for determining the urban growth in Idah,
 To determine urban development trends using post-classification comparisons,
 Evaluating the degree of urban growth according to urban land cover change over that period 
 
 Satellite imagery was employed to distinguish and identify different land surface categories. Integrated remote sensing and GIS (Geographic Information System) technique was used to analyse both qualitative and quantitative perspectives regarding the objectives. The results indicate that the urban area of Idah experienced changes in the various classes. The vegetated area experienced a great change of 49.98%. That is a decrease from the previous year of 2001 to 2010. Also, another 48.67% decrease from 2010 to 2021 is due to the developments in the study area. The built-up area equally decreased by 1.33 % between 2010 and 2021. There was an increase of 12.04% from the year 2001 to 2010.Beach has a drastic increase from 28.10% observed between 2001 and 2010 to 44.50% between 2010. The geographic footprint demonstrates that the distribution of the built-up area was dispersed and continues to grow more dispersed. The significant contribution of this study could benefit many aspects, such as comparative studies between cities or continuous studies relevant to urban growth.

Estimating the changes in the built-up area and land use development index of Minna, Nigeria

ABSTRACT This study estimates the spatial changes in Minna’s built-up areas. For change detection and urban growth analysis, the topographical map of Minna in 1972, the township map for 1979, and the satellite imageries covering 1986, 1996, 2006, and 2015 were all employed. These maps were georeferenced using ArcGIS 10.3 . The study reveals as follows: 1972 (468.3 hectares); 1979 (893.7 hectares); 1986 (4,579.8 hectares); 1996 (5,205.4.5 hectares); 2006 (8,505.1 hectares); and 2015 (19,777.2 hectares). The land consumption rates were estimated as 0.0084, 0.0117, 0.0356, 0.0215, 0.0204, and 0.0273, respectively.We conclude that strategies birthed by systematic planning conclusions must guide cities.

Monitoring Land Use/Land Cover Change (LULCC) Using Remote Sensing

Land use/land cover change (LULCC) is crucial for social and economic systems. For detecting LULCC, remote sensing technology has become crucial. This paper illustrated the use of remote sensing to monitor LULCC, with particular emphasis on urban planning and the ecological environment which is currently in the news, and the limitations of remote sensing. Remote sensing performs well in urban land cover change, urban growth analysis and urban heat island monitoring, giving these areas a effective way to solve problems. In ecological environment monitoring, it develops some new methods combined with computer science in vegetation monitoring. Meanwhile, more and more researchers focus on the wetland monitoring and ecosystem service evaluation. Remote sensing is anticipated to support the monitoring and study of LULCC in the future. This paper provides an overview of current study and developments in the application of remote sensing to track LULCC.

Urban growth analysis using remote sensing and GIS techniques to support decision-making in Algeria-the case of the city of Setif

Rapid urbanization in Algeria is causing significant problems, such as the emergence of unplanned neighborhoods, and local authorities need to apply modern tools like geomatics to monitor and update spatial planning databases to support sustainable development. More to the point, these tools-geographic information systems (GIS) and remote sensing (RS) are of assistance to update the spatial planning and development database so as to support the decision-making. In fact, for understanding the purpose of the process of urban growth in the region of the interior high plateaus, we chose the city of Setif as it represents an urban and economic pole in the region. Besides, economic activities exacerbate the phenomenon of rapid and unplanned urban growth alongside the environmental impact thereof. In the light of the obtained results, there exists a significant increase in urban lands and a significant decline in forests and agricultural lands, as the urban area has tripled from 10.4% in 1985 to 20.3 in 2003 and to 29.7% in 2021. At the same time, the agricultural area has then shrunk from 76.4% in 1985 to 65.8 in 2003 and to 55.5% in 2021, while the forest areas have decreased from 5.04% in 1985 to 4.4 in 2003 and to 2.3% in 2021.