1. 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, March 30, 2006, Ridgeland, SC

3. Population Change in South Carolina Coastal Counties from 1970 - 2000. CountyPopulation 1970Population 1990Population 2000 Beaufort 51,136 86,425 120,937 Berkeley 56,199 128,776 142,651 Charleston 247,650 295,039 309,969 Colleton 27,622 34,377 38,264 Dorchester 32,276 83,060 96,413 Georgetown 33,500 46,302 55,797 Horry 69,992 144,053 196,629 Jasper 11,885 15,487 20,678 South Carolina 2,590,713 3,486,703 4,012,012

4. Population density map for North Carolina, South Carolina, and Georgia # of People Per Square Mile* > 800 400 - 800 200 - 400 100 - 200 0 - 100 * 1999 population estimates by CACI International, Inc. based on 1990 US Census

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

6. Total Acres of Land Conversion by State, 1992-1997 (thousand acres) RankSTATEAcres converted to developed land (1,000 acres) 1Texas1219.5 2Pennsylvania1123.2 3Georgia1053.2 4Florida945.3 5North Carolina781.5 6California694.8 7Tennessee611.6 8Michigan550.8 9South Carolina539.7 10Ohio521.2 Source: (London and Hill, 2000) -- USDA, 1997 National Resource Inventory Summary Report

8. Location of Study Area

12. Urban Growth Trends (Past) 1985-1997 Urban Area Grows by 67% 1985-2000 Population Grows by 20.6% Sprawl Index 3 : 1 (ratio of urban area growth to population growth)

13. 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 1990-2000 data è Predicting urban extent by year 2030 for the Beaufort-Colleton-Jasper Region The objectives of this project are:

14. 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)

15. 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

16. 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 ---200x200 m 2 grids (cells) for calibrating models ---30x30 m 2 grids (cells) for prediction

17. 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

18. Data Sources Land-use (SCDNR) Population Density (Census) Elevation (USGS) Soil Suitability (NRCS) Reach Files, Version 3 (USEPA) Subwatersheds (SCDNR)

19. 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

22. Final Suitability Census Soils Distance to Existing Development

23. Final Study Area Selection

24. Results---Predicted Urban Growth BCJ Region

25. Results---Predicted Urban Growth Beaufort County

26. Beaufort County Growth Simulation Growth Ratio 3:1

30. Simulated Growth

31. 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

32. 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”

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

35. 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!!!