Analysis Of Urban Growth Research Articles

Change Analysis of Land Use and Urban Growth in the Municipalities of Culiacan and Navolato, Sinaloa, Mexico Using Statistical Techniques and GIS

Previous analysis of land use and land cover changes help us to understand the range, importance and effects that this dynamic have in the environment and its relation with the human’s activities. This work consists in analyzing the land use/cover for the Municipalities of Culiacan and Navolato, Mexico, through statistical techniques and Geographic Information Systems. The methodology is allowed to determine the changes, gains, losses and transitions in the different categories in the period studied. The results show significant changes in the denominate categories, agriculture and forest. However, the greatest change is the increase of the urban areas. The knowledge in the studied area and its dynamics are carried out and this work serves as a reference to study, manage and plan for our territory.

Evaluating Urban Landscape Dynamics over Srinagar City and Its Environs

Rapid, unplanned and uncontrolled urbanization causes disorganized growth. The patterns of urban development are changing natural landscapes and their dynamics. This has necessitated understanding of spatial patterns of urbanization and prompted research. Urbanization being a dynamic phenomenon has been facing ambiguities regarding methods to study its dynamism. It has made operational, a wide-variety of spatial metrics methods utilizing multiple dates of remotely sensed data. The present work carries out the quantitative and qualitative analysis of urban growth of Srinagar by using Urban Landscape Analysis Tool (ULAT). Urbanized Area (UA), Urban Footprint (UF) and New Development (ND) maps are generated to quantify the degree of urbanization based on spatial density of built-up area. Comparative analysis of these maps in relation to the built-up spread pattern indicates that mostly the environs of the Srinagar city which includes mostly agricultural, vacant and marshy areas prominently in northern, western and southern areas contribute to the development of the city.

Remote Sensing Image Interpretation for Urban Environment Analysis: Methods, System and Examples

Remote sensing imagery has been widely used in urban growth and environment analysis with many effective and advanced strategies being developed. However, most of these approaches are separated from each other. There is an urgent need to combine different modules into some practical processing chains. Firstly, we present a comprehensive analysis of key processing chains in applying remote sensing images to urban environment analysis from such aspects as Land Use/Land Cover (LULC), urban landscape ecology, Urban Heat Islands (UHIs), vegetation and water monitoring, change detection, urban ecological security assessment and urban environmental mapping. Secondly, an integrated system, namely Urban Environment Analysis System (UEAS), is implemented based on the aforementioned processing chains to analyze urban environment using multi-temporal and multi-source remotely sensed data. Several case studies are demonstrated to confirm the effectiveness of the integrated system and the combined processing chains. The contributions of this paper lie in introducing ensemble learning to urban environment remote sensing, combining remote sensing derived information with thematic models for urban environment assessment, and developing an integrated system for urban environment analysis.

Satellite Image Processing for Land Use and Land Cover Mapping

In this paper, urban growth of Bangalore region is analyzed and discussed by using multi-temporal and multi-spectral Landsat satellite images. Urban growth analysis helps in understanding the change detection of Bangalore region. The change detection is studied over a period of 39 years and the region of interest covers an area of 2182 km. The main cause for urban growth is the increase in population. In India, rapid urbanization is witnessed due to an increase in the population, continuous development has affected the existence of natural resources. Therefore observing and monitoring the natural resources (land use) plays an important role. To analyze changed detection, researcher’s use remote sensing data. Continuous use of remote sensing data helps researchers to analyze the change detection. The main objective of this study is to monitor land cover changes of Bangalore district which covers rural and urban regions using multi-temporal and multi-sensor Landsat multi-spectral scanner (MSS), thematic mapper (TM), Enhanced Thematic mapper plus (ETM+) MSS, TM and ETM+ images captured in the years 1973, 1992, 1999, 2002, 2005, 2008 and 2011. Temporal changes were determined by using maximum likelihood classification method. The classification results contain four land cover classes namely, built-up, vegetation, water and barren land. The results indicate that the region is densely developed which has resulted in decrease of water and vegetation regions. The continuous transformation of barren land to built-up region has affected water and vegetation regions. Generally, from 1973 to 2011 the percentage of urban region has increased from 4.6% to 25.43%, mainly due to urbanization.

Normalization of time series DMSP-OLS nighttime light images for urban growth analysis with Pseudo Invariant Features

Previous studies demonstrated that DMSP-OLS stable nighttime light data are useful data source for delineating urban areas. However, the nighttime light data acquired in different years are not directly comparable, due to the variations in atmospheric condition from year to year and the periodic change in satellite sensor. This makes it difficult to use the time series nighttime light data for urban growth analysis. This paper presents a novel technique for normalizing time series DMSP/OLS nighttime light data and deriving urban detection threshold using Pseudo Invariant Features (PIFs). Our technique consists of three steps: (1) estimate an optimal threshold value for urban area detection for a reference year, when high resolution image data are available for some local areas. (2) Based on the irreversible nature of urbanization process, determine a set of PIFs, which are deemed as urban areas and did not exhibit significant change in nighttime light condition during the study period. (3) Normalize the time series DMSP-OLS data sets based on the PIFs and linear regression, determine optimal threshold values for urban area detection for all years based on the reference year threshold value, and extract urban areas accordingly. This technique was successfully applied to time series DMSP-OLS nighttime light images of the Central Liaoning region in China. Patterns of this urban agglomeration's spatial–temporal evolution from 2000 to 2010 were mapped and analyzed. The reliability and spatial accuracy of this technique were evaluated with multitemporal Landsat TM images. The technique was proved accurate and effective.

Social participation and social capital with equity and efficiency: An approach from central-place theory

A great deal of attention has been paid to the efficiency of the analysis of sustainable urban and regional growth. This includes social participation and social capital, which require a consideration of equity. In this paper, an approach from central-place theory for sustainable urban and regional growth is examined, taking into consideration relevant variables such as ageing, social interaction and accessibility. A lack of consideration of equity could cause several social exclusion problems, and these problems could develop into spatial consumer exclusion. Solutions for these problems would not only improve equity but also efficiency, and the outcome would addresses the necessity of an alternative spatial formation based on a wider-regional rural central-place system according to given economic, social and spatial configurations.

Calibration of an urban cellular automaton model by using statistical techniques and a genetic algorithm. Application to a small urban settlement of NW Spain

Cellular automata (CA) stand out among the most commonly used urban models for the simulation and analysis of urban growth because of their ability to reproduce complex dynamics, similar to those found in real cities, from simple rules. However, CA models still have to overcome some shortcomings related to their flexibility and difficult calibration. This study combines various techniques to calibrate an urban CA that is based on one of the most widely used urban CA models. First, the number of calibration parameters is reduced by using various statistical techniques, and, second, the calibration procedure is automated through a genetic algorithm. The resulting model has been assessed by simulating the urban growth of Ribadeo, a small village of NW Spain, characterized by low, slow urban growth, which makes the identification of urban dynamics and consequently the calibration of the model more difficult. Simulation results have shown that, by automating the calibration procedure, the model can be more easily applied and adapted to urban areas with different characteristics and dynamics. In addition, the simulations obtained with the proposed model show better values of cell-to-cell correspondence between simulated and real maps, and the values for most spatial metrics are closer to real ones.

Landsat analysis of urban growth: How Tokyo became the world's largest megacity during the last 40 years

Combining remote sensing data and socio-economic data to quantitatively analyze urban growth is a topic growing in importance. We used square grid cells to investigate the spatial and temporal dynamics of urban growth in the Tokyo, Japan, metropolitan area by using remote sensing imagery from 1972 to 2011 and census population data from 1970 to 2010. First, we used the subspace classification method to produce land-cover maps by using Landsat images from 1972, 1987, 2001, and 2011. Next, we integrated the land-cover maps with basic grid cell maps (using the standard 1km2 grid cell system of Japan) to represent the proportion of each land-cover category within each 1km2 grid cell area. Finally, we combined the proportional land-cover maps and population census data to investigate the relationship between land-cover changes and population density change based on grid cells. By using grid cells it is straightforward to (i) integrate remote sensing, geographic information system (GIS), and statistical data within the cells; (ii) quantify land-cover changes in terms of the percentage of area affected and rates of change and compare them with population census data; and (iii) analyze the spatial-temporal dynamics of urban growth patterns. Between 1972 and 2011 the rapid expansion of the urban area was accompanied by extensive shrinking of the agricultural area around the new settlements. As a result, the urban growth rate exceeded the population growth rate by more than a factor of 2.6. We used the grid cells to investigate the spatial relationship between the changes of land-cover classes and population density change, and then calculated the correlation coefficients of land-cover categories and population density changes for 3 intervals between 1972 and 2011 (1972–1987, 1987–2001, and 2001–2011). The results showed that the urban/built-up density decreased in the metropolitan inner core as the city center experienced depopulation. Spatial correlation analysis showed a strong positive correlation between urban expansion and population density change (r=0.59), and that urban expansion was strongly negatively correlated with cropland change (r=−0.77). The results also demonstrated that grid cells allow remote sensing and statistics data to be combined, improving the knowledge, understanding, and analysis of urban dynamics.

Urban growth mapping from Landsat data using linear mixture model in Ho Chi Minh City, Vietnam

Rapid urbanization in Ho Chi Minh City (HCMC), Vietnam, is creating societal impacts on the environment attributed to the increasing population. Understanding spatio-temporal dimensions of land-use changes that shape the urbanization is thus critical to the process of urban planning. We explore the urban growth in HCMC through Landsat images for 1990, 2002, and 2010 using the linear mixture model (LMM). The data are processed through four steps: (1) data pre-processing, (2) image classification by LMM using endmembers extracted from the original image using minimum noise fraction, (3) accuracy assessment of the classification results using field verification data, and (4) urban growth analysis to understand the spatial changes of land cover. The results achieved by comparisons between the classification results and ground reference data indicate that the overall accuracy and Kappa coefficient obtained for 1990 were 87.1% and 0.83, respectively, while those for 2002 were 92.5% and 0.89, and those for 2010 were 89.6% and 0.86. The results of urban growth analysis indicate that high albedo class (i.e., built-up areas) expanded from 12.3% in 1990 to 27.2% in 2002 and to 31.1% in 2010. When investigating land-cover conversions to high albedo class from 1990 to 2002, the largest conversion is observed for soil class (9.2%), followed by vegetation class (7.2%), and low albedo class (2.2%). From 2002 to 2010, 4.5% area of soil class was converted to high albedo class, while conversions from vegetation and low albedo classes were 3.5% and 2.5%, respectively.

The spatial analysis and morphological evolution of the ‘Bazaar Streets’ and urban structure of Dhaka city

Four hundred years of developing, shifting and changing marks the city of Dhaka with a strong culture heritage. Traditional hierarchies and organic spatial structures form a rich mix of indigenous morphology, which is obvious in historically famous and organically developed bazaar streets and commercial interfaces. The urban development of Dhaka city is well known for its spatial structure. A ‘space syntax’ technique is used to understand the structure, function, underlying logic, morphological growth and evolution of the urban systems. This article focuses on both the spatial dynamics of the urban structure and on the spatial nature of the bazaar streets (commercial networks) and aims to explicate the notion of the configurational analysis of urban growth. By determining the syntactic pattern and evolution of the bazaar streets, this analysis can be usefully applied to designing for future urban growth.

Land cover and land use analysis of urban growth in Romania

While cities appeared and disappeared during the history, and different disciplines, such as ecology and geography attempted to model the process from a systemic perspective, the growth of modern cities, particularly under the form of urban sprawl, is an important phenomenon due to its environmental and socioeconomic impacts. CORINE data allow for analyzing the growth of cities based on the changes in land cover and use. This study aimed to assess the magnitude of long-term urban growth in Romania, chosen as an example of a transition country, hypothesizing that the phenomenon is visible at the national scale. The results locate urban growth in area where the real estate boom is prominent, but its extent is masked by the small share of urban areas from the total territory. If growth is analyzed in relationship to the urban area, its magnitude becomes visible, supporting the underlying hypothesis.

Spatial–temporal analysis of urban growth and transportation in Jeddah City, Saudi Arabia

During the past decades, the city of Jeddah in Saudi Arabia has witnessed dramatic changes in its urban area, population and transportation. To better understand the relationship between urban growth and transportation, this paper aims to quantify and analyze the spatial–temporal relationship between urban growth and transportation for Jeddah using Remote Sensing (RS) and Geographic Information System (GIS) approaches. In this paper, eight urban growth and transportation indices were developed to analyze the relationship between spatial–temporal urban growth and transportation changes: (1) annual urban spatial expansion index, (2) land use change index, (3) population density index, (4) transportation infrastructure expansion index, (5) road density index, (6) road area density index, (7) urban trips density index, and (8) modal split change index. The results show that in the past four decades (1964–2007), Jeddah has experienced a rapid population growth, a large spatial expansion, rapidly changing land use and expanding transportation infrastructure. As transportation infrastructure expands with population growth, this expansion has not been able to accommodate increases in travel demand. This has led to an increase in urban congestion. The analysis further shows that transportation infrastructure expansion has stimulated Jeddah’s urban spatial expansion and residential area growth. The enormous spatial expansion has also caused significant changes in the daily share of travel modes. The developed indicators in the paper bridge the knowledge gap between urban growth and transportation research, as the results of this study provide a rich understanding of the relationship between urban growth and transportation in rapidly growing cities.

Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area

This paper presents an artificial intelligence approach integrated with geographical information systems (GISs) for modeling urban evolution. Fuzzy logic and neural networks are used to provide a synthetic spatiotemporal methodology for the analysis, prediction and interpretation of urban growth. The proposed urban model takes into account the changes over time in population and building use patterns. A GIS is used for handling the spatial and temporal data, performing contingency analysis and mapping the results. Spatial entities with similar characteristics are grouped together in clusters by the use of a fuzzy c-means algorithm. Each cluster represents a specific level of urban growth and development. A two-layer feed-forward multilayer perceptron artificial neural network is then used to predict urban growth. The model, applied to the prefecture of Attica, Greece, delineates the current and future evolution trends of the Athens metropolitan area, which are illustrated by maps of the urban growth dynamics. The proposed methodology aims to assist planners and decision makers in gaining insight into the transition from rural to urban.

Analysis of urban growth at Cairo, Egypt using remote sensing and GIS

The main objective of the present study was to highlight and analyze the exchange between the land cover components at Cairo with focusing on urban area and agricultural land between 1973 and 2006 using Landsat satellite data with the aid of Digital Elevation Models (DEM). The techniques utilized in this investigation involved a rigorous supervised classification of the Landsat and the DEM images. Results showed that urban area of Cairo was 233.78 km2 in 1973 and increased to 557.87 km2 in 2006. The cut-off from agricultural lands was 136.75 km2, whereas urbanization into the neighboring desert was estimated at 187.32 km2 for the same period. The direction of urban sprawl was mainly controlled by regional topography. Urban sprawl was attributed mainly to accelerated population growth.

An Analysis of Urban Growth and Urbanization in the Sabaragamuwa Province, Sri Lanka

Many countries have identified urbanization as a progressive process and one of the key forces underlying technological innovation, economic development and socio-political development. In this context, this paper attempts to analyze the urban growth and current level of urbanization in the Sabaragamuwa Province which is one of the plantation areas located in the outer periphery in Sri Lanka. In addition, this study examines the hierarchical order of urban centres of the Sabaragamuwa Province. Data for the calculation of urban growth rates and level of urbanization were collected from several institutions and research papers of the university academics.Although the Sabaragamuwa Province as a major plantation area had reported a relatively high average annual growth rate of 5.3 percent of urban population during the inter-censal period of 1963-1971, it has slowed down to 2.0 percent in the intercensal period of 1971-1981. Due to the re-classification of urban areas in 1987 presently the Sabaragamuwa Province has 3 national urban centres. Accordingly, during the inter-censal period of 1981-2001 the average annual growth rate recorded by the Sabaragamuwa Province was -1.6 percent and level of urbanization was 4.2 percent. It is the lowest level of urbanization compared with the other provinces in the country. Accordingly, the slow growth of urban population, low level of urbanization and the very few national urban centres directly and indirectly affect the less dynamic economy of the Sabaragamuwa Province. Key words: Urban centers; Urban growth; Urbanization; Sabaragamuwa Province DOI: http://dx.doi.org/10.4038/suslj.v9i1.3739 Sabaramuwa University Journal, Volume 9 Number 1; December 2010, pp 115-132

A multi-level spatial urban pressure analysis of the Giza pyramid plateau in Egypt

Urban sprawl has deleterious effects on natural and man-made landscapes and, therefore, on the attractiveness of visitor and tourist areas. The spread of urban land use has prompted a significant loss of biodiversity and an increasing vulnerability of fragile natural and man-made systems. A prominent example of this threat for cultural heritage is the urban pressure on the Giza pyramid complex in Cairo, Egypt. This paper analyses urban growth trends within the Greater Cairo Metropolitan Area, especially the city enclave that is encroaching on the pyramid complex. A distinction is made between macro- and micro-level spatial analysis of urban growth, focusing on the micro-level dimension, with direct consequences for the conservation of cultural heritage and architectural monuments. Clearly, if no actions are taken, the unique architectural legacy of the pyramids of Khufu, Khafre and Menkaure might be lost. By combining spatial land-use information with location awareness of the pyramid complex, this paper aims to shed light on the relevance of spatial analysis and urban growth models as tools to analyse the endangerment of valuable cultural heritage. This paper also suggests new directions for architectural heritage management, within the framework of micro-spatial analysis of the historico-cultural legacy in urban regions.

LOCAL ECONOMIC STRUCTURE AND SECTORAL EMPLOYMENT GROWTH IN GERMAN CITIES

ABSTRACTThis study systematically examines the impact of fundamental elements of urban economic structure on sectoral employment growth in German cities (“urban growth”). We test four elements simultaneously – sectoral specialisation, diversification of economic activities, urban size and the impact of local competition. To account for the effect of varying spatial delimitations in the analysis of urban growth, we further differentiate between cities and planning regions as geographical units. Our regression results show a U‐shaped relationship between localisation economies and urban growth and positive effects of local competition on urban growth. With respect to diversification, we find positive effects on urban growth on the city level, but insignificant results on the level of the planning regions. The impact of urban size also differs between free cities and planning regions; in the former, a U‐shaped relationship is found, whereas the effect is inversely U‐shaped for the latter.

Urban growth and change analysis using remote sensing and spatial metrics from 1975 to 2003 for Hanoi, Vietnam

Since the beginning of the ‘Doi Moi’ policy in 1986, Hanoi has witnessed significant changes in its urban areas. Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes were used to identify built-up areas in Hanoi, and spatial metrics were used to characterize urban change patterns from 1975 to 2003. Firstly, a spatial metric called the ‘percentage of like adjacency’ was used to discern urban growth patterns, which were classified into three sub-patterns: expansion growth, infill growth and outlying growth. Secondly, the driving force underlying the urbanization of the city for the 1975–1984, 1984–1992, 1992–2001, 2001–2003 periods was investigated using a spatial metric analysis programme (FRAGSTATS). The expansion of urban areas along major transportation routes in the latter 1980s was identified as the main form of urbanization in Hanoi. This paper shows the potential application of spatial metrics as secondary sources of information for supporting remotely sensed data and their use to characterize urban growth patterns.

Beijing Urban Development Model: Urban Growth Analysis and Simulation

Urban growth analysis and simulation have been recently conducted by cellular automata (CA) models based on self-organizing theory which differs from system dynamics models. This paper describes the Beijing urban development model (BUDEM) which adopts the CA approach to support urban planning and policy evaluation. BUDEM, as a spatio-temporal dynamic model for simulating urban growth in the Beijing metropolitan area, is based on the urban growth theory and integrates logistic regression and MonoLoop to obtain the weights for the transition rule with multi-criteria evaluation configuration. Local sensitivity analysis for all the parameters of BUDEM is also carried out to assess the model's performances. The model is used to identify urban growth mechanisms in the various historical phases since 1986, to retrieve urban growth policies needed to implement the desired (planned) urban form in 2020, and to simulate urban growth scenarios until 2049 based on the urban form and parameter set in 2020. The model has been proved to be capable of analyzing historical urban growth mechanisms and predicting future urban growth for metropolitan areas in China.

Performance evaluation of automated approaches to building detection in multi-source aerial data

Automated approaches to building detection in multi-source aerial data are important in many applications, including map updating, city modeling, urban growth analysis and monitoring of informal settlements. This paper presents a comparative analysis of different methods for automated building detection in aerial images and laser data at different spatial resolutions. Five methods are tested in two study areas using features extracted at both pixel level and object level, but with the strong prerequisite of using the same training set for all methods. The evaluation of the methods is based on error measures obtained by superimposing the results on a manually generated reference map of each area. The results in both study areas show a better performance of the Dempster-Shafer and the AdaBoost methods, although these two methods also yield a number of unclassified pixels. The method of thresholding a normalized DSM performs well in terms of the detection rate and reliability in the less vegetated Mannheim study area, but also yields a high rate of false positive errors. The Bayesian methods perform better in the Memmingen study area where buildings have more or less the same heights.