Spatial Pattern Of Urban Growth Research Articles

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.

A Spatial Political-Economic Review on Urban Growth in Java under Economic Liberalization of Dutch Colonialism During the 19th Century

This study understands the rise and fall of cities as a result of colonial worldview and practices. Based on spatial political-economic thinking, this study examines the spatial implications of Dutch colonialism on urban growth in Java during the 19th century. The practice of colonialism at that time took place in a relatively stable regional situation. The analysis of textual data from various literature indicates that the economic liberalization of Dutch colonialism in that time was a continuation of the VOC’s mercantilism but with a stronger intention to control the factors of production. Territorial expansion to fertile areas indeed encouraged the emergence of medium-sized cities in the inland, but on the other hand, degenerated several older cities on the coast. Spatially, the ambitions and practices of colonialism had a limited implication on urban growth. The combination of territorial and capital logic triggered political liquidation which was reflected in the spatial pattern of urban growth. The identity and symbol of colonial power were attached as new elements to the cities. This process revealed a locational decision to channel economic benefits to strategic places for sustaining domination of power for the long-term.

Quantification of the spatial patterns of urban growth in Haldwani city

Quantification of the spatial patterns of urban growth in Haldwani city

Urban Growth Derived from Landsat Time Series Using Harmonic Analysis: A Case Study in South England with High Levels of Cloud Cover

Accurate detection of spatial patterns of urban growth is crucial to the analysis of urban growth processes. A common practice is to use post-classification change analysis, overlaying multiple independently derived land cover layers. This approach is problematic as propagation of classification errors can lead to overestimation of change by an order of magnitude. This paper contributes to the growing literature on change classification using pixel-based time series analysis. In particular, we have developed a method that identifies change in the urban fabric at the pixel level based on breaks in the seasonal and year-on-year trend of the normalised difference vegetation index (NDVI). The method is applied to a case study area in the south of England that is characterised by high levels of cloud cover. The study uses the Landsat data archive over the period 1984–2018. The performance of the method was assessed using 500 ground truth points. These points were randomly selected and manually assessed for change using high-resolution earth observation imagery. The method identifies pixels where a land cover change occurred with a user’s accuracy of change 45.3 ± 4.45% and outperforms a post-classification analysis of an otherwise more advanced land cover product, which achieved a user’s accuracy of 17.8 ± 3.42%. This method performs better where changes exhibit large differences in NDVI dynamics amongst land cover types, such as the transition from agricultural to suburban, and less so where small differences of NDVI are observed, such as changes in land cover within pixels that are densely built up already. The method proved relatively robust for outliers and missing data, for example, in the case of high levels of cloud cover, but does rely on a period of data availability before and after the change event. Future developments to improve the method are to incorporate spectral information other than NDVI and to consider multiple change events per pixel over the analysed period.

Classification of residential buildings into spatial patterns of urban growth: A morpho-structural approach

This paper presents a methodological protocol that combines a structural and a morphological approach to classify residential buildings into spatial patterns of urban growth. Variography analysis is employed to endogenously detect thresholds of building agglomerations, subsequently used as distance parameters for series of morphological closings over the distribution of the building centroids. The different bounding regions ultimately allow classifying new residential buildings into different categories according to their degrees of clustering/scattering and to their locations within/without existing urban structures and within/without recent urban sprawl. The protocol, developed for areas where suburbs tend to proliferate, is tested on a region in southern France using residential buildings in 2002, 2017, and those that appeared between these two dates. It successfully classified the new buildings into the following categories: “clustered infill urban densification”, “scattered infill urban densification”, “low-density edge-expansion”, “compact edge-expansion”, “low-density scattered urban development”, and “leapfrog urban development” thus showing that new residential buildings are not contributing to urban sprawl and development in a similar fashion. Open access is provided to the source code and to the test region data.

Urban growth in a post‐2000 central Chinese urban agglomeration: Case study of the Changzhutan region

AbstractThe rapid development of coastal urban agglomerations in China has gained global attention since the 1980s. Recently, several regions in the Central China have also experienced rapid economic development. The objectives of this paper are to explore whether the spatial pattern of urban growth in Central China's urban agglomerations is the same as those costal ones and whether it is driven by anything other than external forces. Using the Changzhutan region as an example, this study analyses the economic transformation and spatial restructuring of the populations in urban agglomerations in Central China and explores the driving factors of urban growth using regression models. We find that most of the population is concentrated in prefecture‐level city districts and county seats, which is markedly different from the large‐scale regional population agglomerations found in the coastal urban agglomerations. Domestic investment, instead of foreign investment, and human capital, has become important factors driving the population migration. This finding sharply contrasts with the pattern found in coastal urban agglomerations, where regional concentrations of the population and economic activities are primarily driven by labour‐intensive and global economies. This case study may serve as a basis for future urban planning and management in Central China.

Tendencies and Spatial Pattern of Urban Growth in the Catchment Area of Hungarian Middle Cities between 1990-2018

Tendencies and Spatial Pattern of Urban Growth in the Catchment Area of Hungarian Middle Cities between 1990-2018

The role of land use change in developing city spatial models in Jordan: The case of the Irbid master plan (1970–2017)

Irbid city witnessed a rapid population growth starting from 2003 up to now, which affected the expansion of the city form. This led to awareness of an urgent need to evaluate the current urban planning practices which have been implemented and how responsive they are to land use changes. This paper investigates the role of land use change in determining the spatial model of urban growth in Irbid. The study outlines the development of planning stages from 1970 to 2017. It also explores the temporal and spatial characteristics of urban expansion by analyzing Irbid city maps at different time intervals. Quantitative methods are used by employing GIS. The literature presents the impact of developing land uses on the expansion of the city borders, and identifies the spatial patterns of urban growth. The study concludes with a spatial model of the city comprising structural plans and practical recommendations for Irbid urban development for a better understanding of the city expansion pattern. It also predicts the way of city urban development, thereby identifying its influence on land use changes. This model is, therefore, valuable for informing policy or responding to current planning practices in Irbid and other similar cities nearly considered irregular pattern.

Changes in the extent and distribution of urban land cover in the Democratic People's Republic of Korea (North Korea) between 1987 and 2010

AbstractLargely due to data unavailability, the spatial pattern of urban growth in North Korea has been rarely studied. This study explored urban changes in North Korea and provided their plausible causes. The present study used satellite‐based land cover data sets produced by the government of South Korea to examine the extent and distribution of urban land cover in North Korea between the late 1980s (1987–1989) and late 2000s (2008–2010) at the municipal level. The urban expansion intensity index (UEII) was calculated for two 10‐year intervals, and the spatial autocorrelation of UEII values was examined. Major findings from the study are summarized as follows: (a) the capital city Pyongyang's dominance continued without any obvious sign of slowing down, (b) economic development districts do not appear to have much influence on urban land cover changes, and (c) the extent of urban land cover slightly decreased in much of the country between the late 1990s (1997–1999) and late 2000s (2008–2010). The results and discussion in this study suggest internal migration and stagnant economy as probably important causes for the phenomena.

Bidirectional urban flows in rapidly urbanizing metropolitan areas and their macro and micro impacts on urban growth: A case study of the Yangtze River middle reaches megalopolis, China

Bidirectional urban flows of materials, technologies and information are becoming more frequent, and cities are closely connected socially, economically and technologically. Thus, an open, mobile and polycentric urban network has gradually formed. However, most of the recent studies on urban landscape dynamics regarded each city as a single entity and ignored intercity relatedness. In this study, we took as an example the Yangtze River middle reaches megalopolis (YRMRM), which is one of the rapidly urbanizing metropolitan areas in China. We pose two research questions: (1) Do intercity factors impact the expansion of urban land at a macro level? (2) Do intercity factors impact the spatial patterns of urban growth at a micro level? We used geospatial big data to measure urban relatedness and spatial interaction among city groups from a macro perspective. Then, a spatially explicit approach based on a data field model was adopted to quantify the micro urban spatial interaction inside each city. The pattern of spatial interaction and its impacts on urban growth were also analysed. The results showed that the three city sub-groups developed well, with Wuhan, Nanchang and Changsha being the dominant cores. However, the macro intercity relatedness in the YRMRM has not broken the shackles of the geographical boundaries of the provinces. A highly connected polycentric urban network system has not been formed to date. Our empirical results indicated that intercity factors were the main drivers of both macro and micro urban growth dynamics in the YRMRM from 2005 to 2015. This finding suggests that population and information flows had strong effects on the promotion of urbanization in the YRMRM during this period. Thus, we argued that urban bidirectional flows should be a vital consideration in future urban spatial planning and regional development strategies.

Capturing the heterogeneity of urban growth in South Korea using a latent class regression model

AbstractThis study aims to analyze the spatial patterns of urban growth in South Korea between 2000 and 2010. Fourteen suspected causative independent variables were selected and latent class regression (LCR) was used to analyze the relationship between dependent (urban growth) and independent (causative) variables. The goodness‐of‐fit was assessed by comparison to logistic regression (LR) analysis. The LR analysis produced consistent coefficients for each independent variable across the study area. In contrast, an LCR analysis, with a three‐class assumption, resulted in a different magnitude and directional effects of the coefficients for each class. The LCR analysis enabled the identification of both spatially homogeneous and heterogeneous areas. In addition, the LCR analysis performed better than the LR analysis with a lower Akaike information criterion and Bayesian information criterion value, and a higher receiver operating characteristic value. We conclude that LCR analysis should be used to establish causative “driving” factors for efficient urban growth planning and urban spatial policy.

Decoding Patterns of Urban Dynamics in Class-1 City of Khammam, Telangana State, India

Quantitative analysis of urban sprawl enables to understand the status, growth, direction, spatial pattern of urban growth. Spatio-temporal change analysis is essential to monitor and understand LULC dynamics. Present study illustrate use of IRS LISS4 and LANDSAT-5 (MSS) remote sensing data of 1991–2015, for change detection of LULC by quadrant and spatial metrics analysis in Class-1 town of Khammam in Telangana in south central India. Study revealed that extent of built-up area grew over 3.78–22.43 km2 in 15 years by > 500% pushing beyond municipal limits and onto peri urban area. Development of urban sprawl into peri urban area has been around 8 km2 during 2001–2015. Six landscape metrics were used to analyze to understand landscape characteristics in relation to urban growth size, shape, pattern; LecoS tool of QuantumGIS was used to compute Number of Patches, Patch Density, Edge Length, Fractal Dimension Index, Shannon’s Diversity Index and Shannon’s Equitable index. Results indicate that fragmentation of LULC increased due to pressure of anthropogenic activity. Core area of Khammam town is dense with single patch and compact shape, whereas, new patches of built up area of town are fragmented. Agriculture area is dominant patch with higher edge length. SDI value: 1.39 and SEI: 0.66 in 2015, show high diversity and low evenness among classes; dispersed fragmented outgrowth of urban sprawl, mostly towards NE and Eastern directions in a ‘leap-frog pattern’.

Analysis of Urban Growth Pattern in Edwardsville/Glen Carbon, Illinois, Using Remote Sensing, Population Change Data, and Landscape Expansion Index

ABSTRACTRapid urbanization is one of the global issues. This significant social and economic phenomenon has brought about much debate in the past twenty years. Understanding its dynamics and patterns is important to develop appropriate policies and make more informed planning decisions. Many dimensions to the urban land growth have been identified in related literature, including drivers, relationship with other factors, such as population, impacts, and methods of measurement. In this study, urban growth in the Edwardsville/glen carbon, Illinois is analyzed spatiotemporally using remote sensing and population change data from 1990 to 2015. The objectives of this study are to (a) identify the major land use changes in the Edwardsville area; (b) analyze the rate of urban growth and its relationship to population change in the study area; and (c) identify the spatial pattern of urban growth in the study area. Using multi-temporal satellite images to classify and derive changes in land cover classes, the results show that the land cover classes with major changes are the urban/built-up land and agricultural/grassland, with a steady increase in the former and steady decrease in the later. Also, landscape expansion index calculation for each study period shows that outlying growth type is the dominant pattern.

Modeling and Simulation of the Future Impacts of Urban Land Use Change on the Natural Environment by SLEUTH and Cluster Analysis

Changing land use and urban expansion are key drivers of global environmental change, which are inevitable consequences of economic and social development for many cities. Most land use changes have a negative impact on the natural environment, especially due to their effects on surface temperature, runoff and habitat diversity. Due to the limitation of local government funding and expenditure, it is a challenge for developing countries to create strategies for urban sustainability. This study provided a systematic assessment method for simulating and analyzing the future impacts and spatial patterns of urban growth via cellular automata and cluster analysis. We used Tainan as a study area and compared the impact of future urban spatial development during two periods: 1993–2008 and 2008–2030. The results indicate that the impact of this development on the natural environment can be divided into six clusters. With an increased distance from the city center, there were increased changes in surface temperature and a decreased amount of runoff. These results indicate the occurrence of urban expansion, with habitat diversity being greater in areas governed by policies or ordinances.

ASSESSMENT OF URBAN SPRAWL USING SHANNON’S ENTROPY AND FRACTAL ANALYSIS: A CASE STUDY OF ATAKUM, ILKADIM AND CANIK (SAMSUN, TURKEY)

Urban sprawl is one of the most important problems in urban development due to its negative environmental and societal impacts. Therefore, the spatial pattern of urban growth should be accurately analyzed and well understood for effective urban planning. This paper focuses on urban sprawl analysis in the Atakum, Ilkadim and Canik districts of Samsun, Turkey. In this study, urban sprawl was examined over a period of 24 years using Shannon's entropy and fractal analysis based on remote sensing and Geographic Information System (GIS). The built-up areas in 1989, 2000 and 2013 were extracted from Landsat TM/ETM+/OLI images using the maximum likelihood classification method, and urban form changes in the 1989–2013 period were investigated. The Shannon's entropy method was used to determine the degree of urban sprawl, and a fractal analysis method based on box counting was used to characterize the urban sprawl. The results show that Atakum, Ilkadim and Canik experienced important changes and have considerable sprawl and complex characteristics now. The study also revealed that there is no monotonic relationship between Shannon's entropy and fractal dimension.

Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm

Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The Artificial Bee Colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits.

Simulating spatial pattern of urban growth using GIS-based SLEUTH model: a case study of eastern corridor of Tehran metropolitan region, Iran

Tehran metropolitan region (TMR) has experienced rapid urbanization in the last few decades. This accelerating urbanization trend mainly arising from high natural population growth and rural–urban migration along with rapid socioeconomic changes formed unplanned and uncontrolled urban expansion in peri-urban areas and resulted in degrading environmental quality and considerable changes in the urban landscapes of the TMR. Thus, the main objective of this research is to model spatial pattern of urban growth in eastern corridor of TMR using GIS-based SLEUTH model and the prediction of future developments of the region from 2014 to 2060. The SLEUTH is one of the most powerful models for urban growth modeling. This model analyzes the spatial pattern of urban growth based on historical data obtained from satellite images of 1987, 2003, 2011, and 2014. The results indicate that the most important factors affecting the urban growth are slope resistance and road gravity. The slope resistance is the highest coefficients value, which illustrates the limiting influence of the slopes on general trend of urban growth in eastern corridor of the TMR. The road gravity stands in second place where it displaces orientation of linear form of outlying pattern alongside the transportation network; it represents that the main pattern of urban growth in peri-urban areas of the region have a linear nature and edge expansion due to slope resistance and road-influenced growth, while spread, diffusion, and breed coefficients display low probability of new spreading center and spontaneous growth in the study area. In addition, the prediction of urban growth for 2020–2060 revealed that urban expansion which was 41,500 ha in 2014 will increase to 179,400 ha in 2060 with noticeable growth rate of 145.6 %. Comparing study area and other researches indicate that the urban growth happens in high rate in eastern corridor. One of the main reasons of this growth goes back to the formation of the second homes for residents of Tehran metropolitan city.

Spatial metrics to study urban patterns in growing and shrinking cities

This article reviews existing literature on spatial metrics, presenting a portfolio of metrics addressing the spatial patterns of growing and shrinking cities and discussing their potential and limitations. A wide and diverse set of spatial metrics was found. While these metrics address most of the identified spatial patterns of urban growth, spatial metrics used in urban shrinkage studies are much scarcer and not nearly sufficient to provide a comprehensive assessment of its spatial patterns. The article concludes that there is great potential for the development of new spatial metrics or mixed indicators, particularly in shrinkage contexts. The article builds on recent literature focusing on reviewing and developing metrics for particular spatial patterns (notably patterns of urban sprawl), while considering a very broad and multidisciplinary set of metrics. It focuses not only on the outcomes of urban growth but also on those of the increasingly common shrinking phenomenon.

Analysis of spatial patterns of urban growth across South Asia using DMSP-OLS nighttime lights data

Over the last quarter of a century, analyzing the pace of urbanization and urban economic growth in South Asia has become increasingly important. However, a key challenge relates to the absence of spatially disaggregated national accounts data – in particular, the absence of GDP data for sub-national administrative units and individual cities. The absence of such data limits the scope for detailed empirical analysis of spatial patterns of economic growth, particularly across individual urban settlements or cities. This paper aims to test the suitability of DMSP-OLS Nighttime Lights (NTL) data as a proxy for GDP to analyze detailed spatial patterns of urban economic growth across South Asia over the period 1999–2010. It will help to build an understanding of the nature and heterogeneity of spatial patterns of urban economic growth within the region and contribute to the development of a framework for the usage of NTL to investigate such patterns. Geographic Information System (GIS) is employed to identify the cities and urban agglomerations together with their NTL data in South Asia, and spatial statistics are used to analyze the spatial and temporal patterns of NTL growth. This paper adopts descriptive and inferential statistics to determine the quantitative relationship between NTL and population, urban size, and proximity to the coast. This paper reveals that the inter-annually calibrated NTL data is a good proxy for changes in national and sub-national GDP. In South Asia, the urban NTL hot spots are around major cities with populations between 1.3 and 2.6 million in 1999 and 0.5 to 1.3 million in 2010. Cities in the region have also become more clustered and connected forming urban agglomerations. NTL per unit of land in such clusters tends to be higher than in single cities in South Asia. India, Pakistan, and Sri Lanka tend to have higher NTL (economic) growth on average, while Nepal and Bangladesh have lower growth or declining NTL. There exists a very strong positive linear relation between distance to the coast and the total NTL within that distance, which leads to similar NTL growth rates among inland and coastal cities.

Numerical modelling spatial patterns of urban growth in Chandigarh and surrounding region (India) using multi-agent systems

This study explores application of multi agent system (MAS) to simulate spatial patterns of urban growth in Chandigarh and its surrounding region (India). A numerical simulation model is developed with MAS considering the dynamics of urban and rural population as the principal driver of urban growth. The model utilizes static and dynamic environment variables initialized using a logistic regression model. The logistic regression model uses pixel wise change/no-change information derived using Landsat TM data (1989–1999) as dependent variable and proximity, density, elevation and slope as independent variables. The optimum resolution of 90 m for modelling is decided using fractal analysis of series of transition probability surfaces generated using logistic regression from 30 to 240 m spatial resolution at 30 m interval. The model was finally calibrated using sensitivity analysis and behaviours space experiments with multiple simulation runs. A change to built-up area of 32.55 km2 is observed during 1989–1999 and 113.51 km2 in 1999–2009. The modelling shows a total 14.42 % disagreement between predicted map and reference map for the year 2009. The results were validated using ROC statistics and accuracy estimates with satellite data. The model was further used to predict urban growth for the year 2019. Diversity index was used to determine the potential of the model to capture overall spatial patterns of urban growth.