scholarly journals Revealing the Driving Forces of Mid-Cities Urban Growth Patterns Using Spatial Modeling: a Case Study of Los Ángeles, Chile

2007 ◽  
Vol 12 (1) ◽  
Author(s):  
Mauricio I. Aguayo ◽  
Thorsten Wiegand ◽  
Gerardo D. Azócar ◽  
Kerstin Wiegand ◽  
Claudia E. Vega
2016 ◽  
Vol 66 ◽  
pp. 109-118 ◽  
Author(s):  
Yaolin Liu ◽  
Qingsong He ◽  
Ronghui Tan ◽  
Yanfang Liu ◽  
Chaohui Yin

2019 ◽  
Vol 7 (5) ◽  
Author(s):  
Said Masoud Bakhit ◽  
Sbai Abdelkader

Modeling urban growth trends has become one of the critical issues in the last decades. This study aims to evaluate spatio-temporal urban growth trends using spatial modeling. For this purpose, four land-use maps were used to visualize historical urban growth trends in Seremban, Malaysia. Land Change Modeller (LCM) was used to evaluate the spatial trend of Land-use and land-cover (LULC) in Seremban. The results of the study confirm that urban areas in Seremban hugely increased from 1984 to 2010. The main reasons to increase urban areas are that economic and population growth in Malaysia in general and Seremban in particular. This study confirms that the LCM model is one of the effective spatial techniques that should be taken into account in urban planning studies.


Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1094
Author(s):  
Etido Essien ◽  
Cyrus Samimi

Urban growth has transformed many mid-sized cities into metropolitan areas. One of the effects of this growth is a change in urban growth patterns, which are directly linked with household income. Hence, this paper aims to assess the effect of different economic variables that trigger urban built-up patterns, using economic indicators such as city administrative taxes, a socio-economic survey of living standards, household income and satellite data. The regression model was used and adapted, and a case study is presented for the mid-sized city of Uyo in southeastern Nigeria. The result shows sparse built-up growth patterns with numerous adverse effects. Although, there is awareness of the impact of unregulated sparse built-up growth patterns in the literature, little attention has been given to this growth pattern in Africa. The results also show that increases in federal allocation (27%), investment tax (22%), direct tax (52%) and indirect tax (26%) have led to urban expansion into vegetative land and have a causal correlation with different built-up areas. Hence, medium and high-income earners migrate to suburban areas for bigger living space and a lack of basic social amenities affects the land value in suburban areas. They also assist in the provision of social amenities in the neighborhood.


2017 ◽  
Vol 9 (3) ◽  
pp. 226-232 ◽  
Author(s):  
Omar Hamdy ◽  
◽  
Shichen Zhao ◽  
Mohamed A. Salheen ◽  
Y. Y. Eid
Keyword(s):  

2020 ◽  
Vol 12 (20) ◽  
pp. 8480
Author(s):  
Joanne M. Moyer ◽  
Adeeba A. Raheem

As cities continue to grow, their urban form continues to evolve. As a consequence of urban growth, the demand for infrastructure increases to meet the needs of a growing population. Understanding this evolution and its subsequent impingement on resources allows for planners, engineers, and decision-makers to plan for a sustainable community. Patterns and rate of urban expansion have been studied extensively in various cities throughout the United States (U.S.), utilizing remote sensing and geographic information system (GIS). However, minimal research has been conducted to understand urban growth rates and patterns for cities that possess borders, geological attributes, and/or protected areas that confine and direct the cities’ urban growth, such as El Paso, Texas. This study utilizes El Paso, Texas, as a case study to provide a basis for examining growth patterns and their possible impact on the electricity consumption resource, which lies on the U.S./Mexico and New Mexico borders, contains the largest urban park in the nation (Franklin Mountains State Park), and Ft. Bliss military base. This study conducted a change analysis for El Paso County to analyze specific areas of concentrated growth within the past 15-years (2001–2016). The results indicate that county growth has primarily occurred within the city of El Paso, in particular, Districts 5 (east side), 1 (west side), and 4 (northeast), with District 5 experiencing substantial growth. As the districts expanded, fragmentation and shape irregularity of developed areas decreased. Utilizing past growth trends, the counties’ 2031 land-use was predicted employing the Cellular Automata (CA)-Markov method. The counties’ projected growth was evenly distributed within El Paso city and outside city limits. Future growth within the city continues to be directed within the same districts that experienced past growth, Districts 1, 4, and 5. Whereas projected growth occurring outside the city limits, primarily focused within potential city annexation areas in accordance with the cities’ comprehensive plan, Plan El Paso. Panel data analysis was performed to investigate the relationship between urban dynamic growth patterns and electricity consumption. The findings suggest that, as urban areas expanded and fragmentation decreased, electricity consumption increased. Further investigation to include an expansion of urban pattern metrics, an extension of the time period studied, and their influence on electricity consumption is recommended. The results of this study provided a basis for decision-makers and planners with an understanding of El Paso’s concentrated areas of past and projected urban growth patterns and their influence on electricity consumption to mitigate possible fragmentation growth through informed decisions and policies to provide a sustainable environment for the community.


2003 ◽  
Vol 35 (4) ◽  
pp. 679-704 ◽  
Author(s):  
Jianquan Cheng ◽  
Ian Masser

Urban development is a complex dynamic process involving various actors with different patterns of behaviour. Modelling urban development patterns is a prerequisite to understanding the process. This paper presents a preliminary multiscale perspective for such modelling based on spatial hierarchical theory and uses it for the analysis of a rapidly developing city. This framework starts with a conceptual model, which aims at linking planning hierarchy, analysis hierarchy, and data hierarchy. Analysis hierarchy is the focus of this paper. It is divided into three scales: probability of change (macro), density of change (meso), and intensity of change (micro). The multiscale analysis seeks to distinguish spatial determinants on each of the three scales, which are able to provide deeper insights into urban growth patterns shaped by spontaneous and self-organised spatial processes. A methodology is also presented to implement the framework, based on exploratory data analysis and spatial logistic regression. The combination of both is proven to have strong capacity of interpretation. This framework is tested by a case study of Wuhan City, China. The scale-dependent and scale-independent determinants are found significantly on two scales.


2021 ◽  
Vol 13 (5) ◽  
pp. 949
Author(s):  
Salman Qureshi ◽  
Saman Nadizadeh Shorabeh ◽  
Najmeh Neysani Samany ◽  
Foad Minaei ◽  
Mehdi Homaee ◽  
...  

Due to irregular and uncontrolled expansion of cities in developing countries, currently operational landfill sites cannot be used in the long-term, as people will be living in proximity to these sites and be exposed to unhygienic circumstances. Hence, this study aims at proposing an integrated approach for determining suitable locations for landfills while considering their physical expansion. The proposed approach utilizes the fuzzy analytical hierarchy process (FAHP) to weigh the sets of identified landfill location criteria. Furthermore, the weighted linear combination (WLC) approach was applied for the elicitation of the proper primary locations. Finally, the support vector machine (SVM) and cellular automation-based Markov chain method were used to predict urban growth. To demonstrate the applicability of the developed approach, it was applied to a case study, namely the city of Mashhad in Iran, where suitable sites for landfills were identified considering the urban growth in different geographical directions for this city by 2048. The proposed approach could be of use for policymakers, urban planners, and other decision-makers to minimize uncertainty arising from long-term resource allocation.


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