Predicting Urban Growth on the Atlantic Coast Using an Integrative Spatial Modeling Approach Jeffery S. Allen and Kang Shou Lu Clemson University Strom Thurmond Institute Coastal Community Workshop, April 20, 2006, Conway, SC

Population density map for North Carolina, South Carolina, and Georgia # of People Per Square Mile* > * 1999 population estimates by CACI International, Inc. based on 1990 US Census

Population in the Coastal Counties of South Carolina & Georgia

Percent Change in Population in the Coastal Counties of South Carolina & Georgia

Source: (London and Hill, 2000) -- USDA, US Census Bureau and Jim Self Center on the Future, Clemson University.

Total Acres of Land Conversion by State, (thousand acres) RankSTATEAcres converted to developed land (1,000 acres) 1Texas Pennsylvania Georgia Florida North Carolina California Tennessee Michigan South Carolina Ohio521.2 Source: (London and Hill, 2000) -- USDA, 1997 National Resource Inventory Summary Report

Purposes and Objectives Gain a better understanding of urban growth process; Develop a methodology for urban growth prediction; and Provide better information for: è Land use decision-making toward smart growth è Impact assessment studies è Public education of environmental awareness è Developing an operational urban growth model è Calibrating the model using data è Predicting urban extent by year 2030 for the Beaufort-Colleton-Jasper Region The objectives of this project are:

Urban Growth Models è Lowry’s Model (1957) and Its Variants è Cellular Automata (Deltron) Model (San Francisco Bay Area) --- Clarke (1996) è California Urban Future Model (CUF I and II) --- Landis (1994, 1995, and 1997) è Land Transformation Model (LTM) (Michigan’s Saginaw Bay Watershed) --- Pijanowski et al (1997)

1.Components or structures of the land use systems:simple vs. complex 2.Relationships between components, agents, factors, and processes: deterministic vs. indeterministic. 3.Changes over space (and time): ordered vs. random vs. chaotic 4.Spatial distribution or patterns: regularity vs. irregularity (fractal) Challenges Faced in Urban Land Use Modeling Land Land Use Systems Uses Economic Social Cultural Natural resources Activity settings Aesthetic sanities Natural functions Functions Structures Activities Ownership Use status Geology Geomorphology Hydrology Climate Soil Vegetation Human Systems Physical Systems Availability Suitability Capacity Sustainability Model vs. Reality

Parcel --smallest legal unit Zone --area demarcated by the major roads Grid or Cell --square-shaped area Murrells Inlet Mount Pleasant Part of Mount Pleasant Analysis Units x200 m 2 grids (cells) for calibrating models ---30x30 m 2 grids (cells) for prediction

Georgetown Data

Horry County Data

Predictor Variables Physical suitability –Land cover, Slope, Soil suitability Service accessibility –Transportation, Waterline, Sewer line, CBD, Industrial parks, Demographic Initial conditions –Existing urban, Vacant infill area, Agriculture land, Forest land Policy constraints –Protected land, Comprehensive planning, Growth boundary, Zoning/Ordinance, Natural reserves, Parks, Floodplain, Cultural sites, Land ownership

Data for Deriving Predictor Grids Baseline Years: 1990 and 2000 for Training and Testing Projection Years;

Examples of Predictor Variables Distance to 2000 Urban Area Distance to 80 Industry Point Distance to Roads Distance to Highway System Distance to Water Lines Distance to Sewage system

US Hwys

Waterfront

Pop. Density 2000

Water Lines

Probabilities (dark is higher)

Horry 2010 r 3:1

Horry 2020 r 3:1

Horry 2030 r 3:1

Predicted Urban Growth in the Myrtle Beach Region, South Carolina, sq. mi.164 sq. mi.213 sq. mi sq. mi.

1992

2001

2010 3:1

2020 3:1

2030 3:1

Simulated Growth

Urban Sprawl Problems Urban growth is necessary and unavoidable. But uncontrolled growth - urban sprawl results in many problems such as: è Increased cost of living è Rising taxes and pressure on infrastructure and urban services è Traffic congestion and increased (travel) time è Environmental pollution è Loss of farm/forest land, habitats and rural (natural) landscape è Downtown declines and community segregation

Benefits of Urban Growth è Increased standard of living è Generation of wealth è Increase in amenities è Production of affordable housing è Increase in tax base è New business opportunities è New job opportunities è Increased “freedom” with the automobile è It is what we desire - “Freedom of Choice”

Urban Growth Trends The pattern follows paths of subsidy. Undervalued infrastructure Discounted resources Reductions for individual risk Unintended consequences of past policies

What do we do now? è Growth is coming whether we want it or not è Determine where we do not want to grow è Increase communication among SPD’s, etc. è Be inclusive in planning è Provide incentives for growth in “growth areas” è Provide “dis-incentives” for areas to protect è Make users pay the freight for new growth è It is always easier said than done!!!