Geospatial modeling of population growth scenarios for the Humboldt Bay, California region: adapting SLEUTH to a rural environment

Graduation Date

2012

Document Type

Thesis

Program

Other

Program

Thesis (M.S.)--Humboldt State University, Natural Resources, Planning and Interpretation, 2012

Committee Chair Name

Steven J. Steinberg

Committee Chair Affiliation

HSU Faculty or Staff

Keywords

GIS, Humboldt State University -- Theses -- Natural Resources Planning and Interpretation, SLEUTH, Pacific Ocean--Humboldt Bay, Population growth, Geospatial modeling, Humboldt County, Environmental impact, California

Abstract

Humboldt County is a modestly populated region on the northern coast of California. Much of the county is held in State and National parks and unavailable for development. While growth rates remained steady for several decades, the region is not immune to population growth and poorly-planned expansion. Geospatial modeling provides a means to explore various development scenarios examining the likelihood of urban sprawl encroaching upon agricultural lands and other protected areas. Communities surrounding Humboldt Bay are particularly susceptible to rapid growth, representing approximately 60 percent of the population. Understanding impacts of growth around existing population centers is necessary to assess potential benefits of regional smart growth strategies. The SLEUTH urban growth model uses cellular automata, terrain mapping, and land cover modeling to generate potential population growth scenarios. It has been successfully applied to numerous metropolitan areas; however, its application to rural environments has not been fully explored. Using SLEUTH, I generated multi-scenario population growth estimates projecting 100 years into the future. These will inform city and county planning departments in developing smart and sustainable growth strategies and provide resource managers information useful in selecting an optimal expansion plan for the region.

https://scholarworks.calstate.edu/concern/theses/8c97ks955

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