Predicting Urban Growth on the Atlantic Coast Using an Integrative Spatial Modeling Approach Jeffery S. Allen and Kang Shou Lu Clemson University Strom.

Predicting Urban Growth on the Atlantic Coast Using an Integrative Spatial

Modeling Approach

Jeffery S. Allen and Kang Shou LuClemson University

Strom Thurmond Institute

Coastal Community Workshop, March 30, 2006, Ridgeland, SC

Population Change in South Carolina Coastal Counties from 1970 - 2000.

County Population 1970 Population 1990 Population 2000Beaufort 51,136 86,425 120,937Berkeley 56,199 128,776 142,651Charleston 247,650 295,039 309,969Colleton 27,622 34,377 38,264Dorchester 32,276 83,060 96,413Georgetown 33,500 46,302 55,797Horry 69,992 144,053 196,629Jasper 11,885 15,487 20,678

South Carolina 2,590,713 3,486,703 4,012,012

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

5.3%

30.2%

0%5%

10%15%20%25%30%35%

South Carolina: Comparison of Population Growth to Increase in Developed Land 1992-97

Developed Land

Population

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, 1992-1997 (thousand acres) Rank STATE Acres converted to developed land (1,000 acres)1 Texas 1219.5 2 Pennsylvania 1123.23 Georgia 1053.24 Florida 945.35 North Carolina 781.56 California 694.87 Tennessee 611.68 Michigan 550.89 South Carolina 539.710 Ohio 521.2

Source: (London and Hill, 2000) -- USDA, 1997 National ResourceInventory Summary Report

Location of Study Area

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)

Purposes and ObjectivesGain a better understanding of urban growth process; Develop a methodology for urban growth prediction; andProvide better information for:

Land use decision-making toward smart growth Impact assessment studies Public education of environmental awareness

Developing an operational urban growth model Calibrating the model using 1990-2000 data Predicting urban extent by year 2030 for the Beaufort-Colleton-Jasper Region

The objectives of this project are:

Urban Growth Models

Lowry’s Model (1957) and Its Variants Cellular Automata (Deltron) Model (San Francisco Bay Area)

--- Clarke (1996) California Urban Future Model (CUF I and II) --- Landis (1994, 1995, and 1997) Land Transformation Model (LTM) (Michigan’s Saginaw Bay Watershed)

---Pijanowski et al (1997)

1. Components or structures of the land use systems:simple vs. complex2. Relationships between components, agents, factors, and processes:

deterministic vs. indeterministic.3. Changes over space (and time): ordered vs. random vs. chaotic4. Spatial distribution or patterns: regularity vs. irregularity (fractal)

Challenges Faced in Urban Land Use Modeling

Land

Land Use Systems

Uses

Economic SocialCultural

•Natural resources•Activity settings•Aesthetic sanities •Natural functions

•Functions•Structures•Activities•Ownership•Use status

GeologyGeomorphologyHydrologyClimateSoilVegetation

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

---200x200 m2 grids (cells) for calibrating models---30x30 m2 grids (cells) for prediction

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 Sources

Land-use (SCDNR)

Population Density(Census)

Elevation(USGS)

Soil Suitability(NRCS)

Reach Files, Version 3(USEPA)

Subwatersheds(SCDNR)

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

Final Suitability

Census

Soils

Distance to Existing Development

Final Study Area Selection

Final Suitability

Most Suitable234Least Suitable

Results---Predicted Urban GrowthBCJ Region

Results---Predicted Urban GrowthBeaufort County

Beaufort County Growth Simulation

Growth Ratio 3:1

Estimated Habitat Losses for Selected Species (1990-2030) Area (in acres) Habitat Loss

Common Name 1990 2030 Acres %

Green Treefrog 301323.57 243374.81 57948.76 19.23 Red Fox 68338.83 41023.59 27315.24 39.97 Red Cockaded Woodpecker (Endangered) 20882.65 17984.84 2897.81 13.88 Wood Stork (Endangered) 135728.03 128129.46 7598.57 5.60

Note: Urban development through 2030 was predicted based on the current growth ratio.

Simulated Growth

Urban Sprawl Problems

Urban growth is necessary and unavoidable. But uncontrolled growth - urban sprawl results in many problems such as:

Increased cost of living Rising taxes and pressure on infrastructure and urban services Traffic congestion and increased (travel) time Environmental pollution Loss of farm/forest land, habitats and rural (natural) landscape Downtown declines and community segregation

Benefits of Urban Growth

Increased standard of living Generation of wealth Increase in amenities Production of affordable housing Increase in tax base New business opportunities New job opportunities Increased “freedom” with the automobile 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?

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