Environmental Component

The Environmental Component of the model investigates the inventory categorization of land areas within Beaufort County, and incorporates the delineation of these areas into representation of land for purposes of development potential. Due to the physical landscape and size of Beaufort County, consisting of many low lying coastal areas and numerous wetlands, it was not in the best interest of the model to not be too restrictive towards development potential. The data sources (table 3) used for this part of the model included the U.S. Fish and Wildlife Services’ National Wetlands Inventories (NWI) data set, as well as the U.S. Census’s TIGER file for rivers.

The indicators were chosen based on the need for such services by year round inhabitants of the area. Distance griding of Beaufort County was conducted with the key indicators as the references points. The rating system was based on two-mile zones extending out from each of the feature points. The highest rating (10) was given for those areas within the first two-mile zone, while the lowest rating (0) was given for those areas outside the eight-mile zone. The following table (6) shows the rating system for land areas with regard to the quality of life indicators.

GIS-Based Modeling Approach

In order to model smart growth properly; a number of factors must be investigated. For the purpose of this model, there are three components that make up the final model: Environmental, Economical and Cultural. While each focuses on the key processes inherent in each area; together they form the balance necessary for modeling the urban ecosystem. In order to determine the resolution to be used during the spatial operations, the data was examined to determine the smallest polygon with population. The cell size of grids created to model the urban environment must be half the size of the smallest polygon. For the purpose of this study, it was determined that the spatial resolution would be 30m x 30m.

Alberti (1999) attempted to represent the urban ecosystem, by combining the conceptual framework of environmental and urban modeling practices. The complex nature of the urban environment warrants an inclusion of many different schools of thought.

Data-Processing Flow Diagram

The analytical portion of this project used both ESRI’s ArcGIS 8.1 and ArcView 3.2 for analysis. Most of the analysis consisted of single functions, which were applied to raster data sets in a predefined order. These single functions described below are highlighted in bold italics for clarity. The economic model is a survey of the existing infrastructure within Beaufort County in relation to the surrounding land area. Please refer to the economic data processing flow diagram (appendix 1) for visual aid. All of the processes in this component are local operations. A location operation is defined as a statistical procedure evaluating grid cells existing in the same geographic location through out multiple raster data sets. This process begins by converting the linear representations (vector) of Beaufort County water lines, sewer lines, and roads into raster formats. The local operation, find distance to, was then conducted on the raster formats, and the resultant grids were reclassified from high (10) to low (1), according to distance from the different themes. The raster calculator, multiply function, was used to combine the economic factors into a weighted grid representation. This map was then multiplied by a factor (.033), for later use in the final model.

The environmental component of the model (appendix 2) also exclusively used local operators. The polygonal representations of Beaufort County’s rivers and land use and land cover classifications (USGS) were both converted into grid formats. The local operation of creating buffers was conducted on the rivers files prior to the grid conversion. Reclassification of the buffered rivers grid was conducted by giving a value of zero to these areas, and a value of one to non-buffered areas. This exclusionary measure represented one of many masks for sensitive environmental areas throughout the county. Wetlands, beaches and water bodies were also excluded in terms of development potential. Other local operations included the reclassification of the remaining land use/land cover categories. The two rasters were then multiplied together in order to properly allocate the exclusionary zones on to the final weighted grid cell map. The final grid cell representation based on environmental factors was then multiplied by the necessary factor (3.3) for use later in the final model.

The Cultural Model (appendix 3) is similar to the economic model in its use of the local operator, Find Distance to. The Cultural component of the model begins with the point locations for five quality of life indicators. The Find Distance to operation was then applied to each of these files, and resulted in raster formats. The reclassification of these grids was then conducted, based once again on the distance to the point locations. The resultant grids were added together to form a weighted cultural representation of Beaufort County. The final cultural map was adjusted using local operation methods for use in the final model.

The three individual models were then incorporated into the final model by simply using the local operation, addition. The resultant grid, Smart Growth Map (figure one), was representative of all the three factors equally. In the real world it is more likely that economic factors might be the most influential in terms of any development options; but for the purpose of this study is was decided that an equal weighting of all the factors would comprise the final model. Figure 1 illustrates the three model components and the resulting final model.

Census block data for the years 1990 and 2000 were utilized to determine the change in population and housing units for the county. The data-processing flow diagram (appendix 4) displays the conversion of two polygonal themes to raster format based on a unique identifier, in this case block #. The resultant grid represents the number of grid cells contained in each of the census blocks. A table join is then conducted between the grids and the previous theme. This allowed for the calculation of population per cell and housing units per cell. Using the local operation, subtract, difference grids for the two variables were calculated.

In addition to conducting local operations on the different raster formats, focal operations were also used to model the data. Focal operations obtain new values as a function of existing values, distances, or directions of neighboring locations. The Focal operation, neighborhood statistics, was conducted on both the resultant difference grids to produce surface mean difference grids based on the surrounding cells.

The focal operation, neighborhood statistics, was also conducted on the Smart Growth Map using a rectangular area of three by three cells to calculate the mean value based on the surrounding cells. The neighborhood statistic operation produces a smoother overall surface, which allows for easier comparisons throughout the study area. Figure 2 illustrates the comparison of the smart growth model and two difference grids (population and housing units) using the neighborhood statistic operation.

Upon completion of the individual components and the final model, additional steps were performed in an effort to better analyze the model. Zonal operations, zonal statistics, were also conducted on the Final Smart Growth dataset. Zonal Operations obtain new values for each location as a function of the existing values from a specific layer of zones. The new value is derived from other values of a layer occurring in the same zone. The zonal operation consisted of computing a zonal statistic based on the census block boundaries. The resultant grid represented mean smart growth ratings for each individual census block. The data were then reclassified into suitable zones based on the mean suitability rating. Difference grids for mean population and mean housing unit difference were combined with the Smart Growth model.

The zonal operation, zonal statistics was conducted using the 2000 census block boundaries and the smart growth-rating map. This resulted in the grouping of census blocks based on their suitability rating. This was followed by the local operation, reclassify into suitable fields. A reclassification of the suitability ratings by census block resulted in an output raster format, which was based on suitability zones for future land uses (10 – highest; 0 – lowest). Figure 3 illustrates the suitability categories by census block.

The last processes conducted, consisted of the local operation reclassification. The difference grids were reclassified based on positive growth only. Those cell values that exhibited negative growth rates were given a value of zero and masked out of the final equation. The zonal operation, zonal statistics (summarize by zone), was then conducted on the positive change grids based on the suitability zones. This resulted in a cell count of positive change by suitability zone. Graphs 1 and 2 illustrate the population and housing unit growth based on the smart growth model’s suitability zones, respectively.

Study Area

The study area for this research is in Beaufort County, South Carolina. Figure 4 displays a map of the study area. Beaufort is a coastal county which includes scores of islands, over ten of which are currently developed to some degree. However, much of the inland acreage of the county remains agricultural, forested or wetlands, with less much less density than the coastal area. Beaufort County’s land area is comprised of 587 square miles and a density of 185.6 persons per square mile (S.C. Statistical Abstract: 2000). That density ranks ninth out of South Carolina’s 46 counties. Beaufort County is one of the fastest growing communities in South Carolina.

Table 7 illustrates the county’s population growth over the last thirty years.

Results

The Smart Growth model involves the combination of multiple factors: environmental, economic, and cultural. These factors were incorporated together to form a map that signifies relative growth potential of land areas throughout the county. The results from this model were gained through graphical analysis and zonal comparisons of the output data. The output from the model was in the form of a smart growth weighted development map of the area and various maps that represented change in population and housing units between 1990 and 2000.

The graphical analysis consisted of an in-depth look several locations throughout the county. Locations with either high population change or high housing unit change were investigated to determine whether or not the development choice was warranted based on the model. Locations with medium and low population and housing unit changes were also looked into to determine how Beaufort County past development patterns measured up against the Smart Growth model.

It is apparent from looking at the smart growth, population and the housing unit maps, that the majority of the increases in population and housing units did not occur in areas of high suitability for development. Figure 2 illustrates these changes. The northern part of the county appears to have gained the most housing units. The smart growth model gave the same area a very low rating with regard to sustainable development. The majority of growth in population and housing units occurred within the low to moderate growth suitability areas. In some cases, there were several instances were significant growth increases occurred within areas designated as no growth potential by the smart growth model. This was either due to the presence of wetlands, beaches or within a ten-meter buffer of a river.

Those areas in the smart growth model that were most suitable for development, by and large, lost population and housing units. This is apparent on the Island of Hilton Head and within the center of Beaufort. These two areas have the existing infrastructure and cultural entities to support new growth, however, it is likely that either land costs are restrictive or the areas are simply built-out.

The focal operation, neighborhood statistic, was conducted on the two change difference maps. Figure 2 illustrates these changes. This resulted in smoother, more continuous surfaces, and allowed for clearer distinction among boundaries. Once again, the majority of land areas that were highly suitable for development had decreasing stocks of population and housing units. While those areas that incurred the highest increases in population and housing, were the least suitable to sustain the development process.

The second type of analysis conducted, consisted of zonal comparisons between the positive growth rates in both population and housing units and the suitability zones. Following the creation of the difference grids for the population and housing units (figure 2), a reclassification of the raster formats were conducted. For the purpose of this study, we were only concerned with those areas that experienced positive population and housing unit growth. The zonal operation, summaries by zone, conducted between the positive growth rasters and the suitability zones, resulted in very interesting results.

In each of the tests, the most suitable zone for development contained the least amount of positive growth cells. For example, the difference in growth cells between suitability zones one (lowest) and ten (highest) and for housing unit increases, was nine times as much. Zone one registered 78,013 counts, where as zone ten registered 8,655. The highest zone of sustainability for growth cells was zone five (87015). Refer to Graph 1 for more details. The summary of population change growth cells for sustainability zone also produced very unbalanced results. Zone ten was just over 50,000 counts, where as zone one was well over 450,000 cell counts. This represents, once again, a 9 times difference between the two zones. Graph 2 illustrates the disparity in sustainability zones. According to the smart growth model, those blocks that are present in zone ten are the most suitable for development purposes. These areas are representative of smart growth planning areas around the country. However, in Beaufort County, development is not taking place in the areas deemed "smart" by this model, as evident by the previously illustrated graphics.

Either there are problems with the sensitivity of the model, or it cannot accurately forecast where the development is taking place. However, this model is not about predicting the future development patterns of the United States. Its main purpose is to establish where the "smart" places for development are and accurately determine where the changes in population have taken place. Therefore, this model determined that the majority of Beaufort County’s growth patterns over the last ten years could not be considered "Smart Growth" based on the criteria used in this research.

Conclusion

Only by understanding the past development patterns, can we hope to better plan for the future. The smart growth model presented here is representative of the issues of concern, with regard to sustainable development and urban sprawl. The urban ecosystem is one of the most complex networks within nature today. This model hopes to provide some of the key factors and linkages (environmental, economical and social) necessary to supply an accurate representation of the real world processes.

There is a long history of economic and environmental process models. Therefore, most smart growth models should provide fairly accurate representations of these areas. However, most have the tendency to fall short in their ability to represent the human systems. It is one thing to model the stream flow and accumulation of a watershed, it is quite another to model the inner workings of an urban ecosystem.

Future Studies

Future Studies that could improve upon this research should focus on the holding capacity of an urban environment and its ability to handle change. The issue of sustainability could lead to an investigation of localized self-sufficient.

Improvements to this model could also begin by establishing firm criteria for smart growth models. A surveying of professionals and scholars familiar with the ideas behind smart growth would lead to a more thorough investigation of the necessary factors to understanding urban sprawl, and better yet which direction smart growth should move in. This model may be improved by not only investigating those areas that increased in size (population and housing units), but also by understanding the dynamics that play apart in the decreasing size of an area.

Lastly, when conducting this research, it was very difficult to determine the appropriate cell size to use in order to determine change in the urban environment. Therefore, further research may be need to determine the proper cell size as the study areas differ. Population data was a key component to this research, so the representation of that data is crucial to the end product.

Economic Model Component
Appendix 1 - Data Processing Flow Diagram

Environmental Model Component
Appendix 2 - Data Processing Flow Diagram

Cultural Model Component
Appendix 3 - Data Processing Flow Diagram

Population and Housing Unit Change Data
Appendix 4 - Data Processing Flow Diagram

Smart Growth Model Combination of Components
Appendix 5 - Data Processing flow Diagram

References

Alberti, M. July (1999). Modeling the urban ecosystem: a conceptual framework. Environment and Planning B- Planning and Design. 26 (4): 605 – 630.

Governor Parris Glendening, Remarks at the National Issues Forum on Forging Metropolitan Solutions to Urban and Regional Problems, Brookings Institution, May 28, 1997.

Grayson, J. E. M. G. Chapman and A. J. Underwood. (1998). The assessment of restoration of habitat in urban wetlands.
Real Estate Research Corporation (RERC). 1974. The Costs of Sprawl: Environmental and Economic Cost of Alternative Residential Development Patterns at the Urban Fringe. Vol.1, Executive Summary; Vol. 2, Detailed Cost Analysis. Washington, D.C.: U.S. Government Printing Office.

Sierra Club. (2001). Sprawl: The Dark Side of the American Dream. (http://www.sierraclub.org/sprawl/report98/report.asp).

Smart Growth Network. 2001. About Smart Growth. (http://smartgrowth.org/information/aboutsg.html)

Tomlin, Dana C. 1990. Geographic Information Systems and Cartographic Modeling.

Urban Land Institute. 2000. The Smart Growth Tool Kit: Community Profiles and Case Studies to Advance Smart Growth Practices.