Abstract
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.
Highlights
The world’s population is estimated to grow by approximately one billion people within the decade, reaching 8.5 billion by 2030 [1]
The land-use maps were generated based on the developed category, which combines all sub-categories within the National Land Cover Database (NLCD) developed category vs barren class
This study provides a deeper understanding of urban development trends within the past 15-years (2001–2016) and projected urban growth for the 15-years (2031) of the El Paso region by analyzing concentrated locations of development
Summary
The world’s population is estimated to grow by approximately one billion people within the decade, reaching 8.5 billion by 2030 [1]. According to the United Nations, 3.5 billion people, half of the world’s population, currently reside within cities and is expected to increase to 5 billion by the year 2030 [2]. This results in “60% of the world’s population will live in cities by 2030” [1]. LULC change of cities exhibits various growth patterns and size over time This spatial-temporal relationship has played a critical role in analyzing urbanization trends for cities to manage their urban development. Change detection analysis provides both a visual and quantitative analysis, as to the amount of change that has occurred over a specified length of time allowing to measure, monitor, and evaluate LULC changes that have transpired within a study area [10]
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