Smart Growth in the Valley
What Does Smart Growth Mean for Small and Rural Communities?
Smart growth has become a guiding principle for city and regional planners. There have been extensive studies and debates on the implication and effectiveness of smart growth policies across the nation. Most existing research on smart growth has been conducted in the context of medium and large size metropolitan areas. Smart growth in small and rural communities, however, has been understudied and the implication of smart growth principles for them is still unclear. This study tries to fill this gap partially by identifying smart growth neighborhoods and evaluating smart growth policies and practices in small and rural communities in the in the San Joaquin Valley, California.
The San Joaquin Valley (Figure 1) has been one of the fastest growing parts in California and has long suffered from some of the nation's worst air pollution. Sprawling pattern dominates urban growth in the valley and leapfrog low-density development in suburbs is encroaching its valuable farmland. Furthermore, the valley is an area facing significant poverty and unemployment issues. Thus, there is an urgent need for the valley to adopt smart growth principles to manage its urban growth.
Figure 1: The San Joaquin Valley and Its Urbanized Areas
Source: compiled by the author based on the data from the Census TIGER data set
Placing our analyses in the context of the San Joaquin Valley, this study seeks to operationalize smart growth at the neighborhood level and display and understand their spatial patterns. By neighborhoods, I refer to census block groups, which have been used and favored over census tracts by previous studies (Miles and Song, 2009; Talen, 2006; Quinn and Pawasarat, 2003) as close approximation of human-scale neighborhoods. Considering that some traditional smart growth components such as mixed housing do not apply to non-residential neighborhoods, this study focuses on residential neighborhoods, which are defined as census block groups in which 25 percent or more land is designated for residential use.
The major purpose of this analysis, however, is not to pick winner neighborhoods or cities in the valley. Instead, I hope to identify areas in the valley that are already following smart growth principles and areas that need improvement. I also hope to illustrate the multi-dimensional nature of smart growth and its diverse patterns in small and rural communities.
Table 1 explains eight indices developed to measure and compare smart growth residential neighborhoods in the San Joaquin Valley. As Table 1 indicates, these eight variables were selected to represent four key aspects of smart growth in residential neighborhoods: residential density, mixed use, mixed housing, and socioeconomic diversity (Detailed formulas used to calculate these indices are available upon request).
Two composite smart growth indices (SGIs) were developed for each residential neighborhood by combining the eight indices discussed above. Before doing that, following Ewing et al. (2002), we converted the eight indices explained in Table 1 to a scale with a mean value of 100 and standard deviation of 25 to make them easier to understand and present. This transformation also allows us to use these factors in quadratic form in models. Based on the standardized scores of the five factors, two composite SGIs were created for each residential neighborhood: a physical SGI (SGI-phy) and a social SGI (SGI-soc). The physical SGI measures the physical environment of a residential neighborhood, which is simply the mean value of the standardized scores of the six physical form factors representing residential density, mixed use, and mixed housing. The social SGI measures socioeconomic diversity of a residential neighborhood, which is the mean value of the two socioeconomic diversity indices.
|Net residential density||total housing units over acres of residential land (unit/acre).||COGs and ACS|
|Land use type diversity||land use diversity of four land use types: residential, comercial, industrial and public facility land.||COGs|
|Housing tenure diversity||mix of owner occupied and renter occupied houses.||ACS|
|Housing structure diversity||mix of four housing structure types by number housing units in structure: 1 unit, 2 units, 3 units, and 4+ units.||ACS|
|Housing size diversity||mix of houses of four sizes by number of bedrooms: 0 or 1 bedroom, 2 bedrooms, 3 bedrooms, and 4+ bedrooms.||ACS|
|Housing value/rent diversity||mix of houses of three value/rent levels: low, medium, and high||ACS|
|Income diversity||mix of households of four income levels: low, medium-low, medium-high, and high.||ACS|
|Racial/ethnic diversity||mix of four racial/ethinic types: non-hispanic white, hispanic, black or african american, and asian.||ACS|
Note: COGs refers to the eight Councils of Government in the Valley.
The land use type and development information were created by Nate Roth at UC Davis. ACS is an abbreviation for American Community Survey (2006-2010).
To identify smart growth neighborhoods, I categorized neighborhoods in the San Joaquin Valley into four quartiles by their physical and Social SGI scores, respectively. Neighborhoods in the first quartile are the top 25% physically/socially smart-growth neighborhoods in the valley. In Figure 1, only residential neighborhoods in the 20 largest urbanized areas are presented. The boundaries of the urbanized areas in the Valley are from the Census TIGER dataset.
Figure 2: Physically and Socially Smart-Growth Neighborhoods in the San Joaquin Valley by Urbanized Area
quartile distribution of neighborhoods by physical indicator in Bakersfield
quartile distribution of neighborhoods by social indicator in Bakersfield
quartile distribution of neighborhoods by physical indicator in Fresno
quartile distribution of neighborhoods by social indicator in Fresno
quartile distribution of neighborhoods by physical indicator in Hanford
quartile distribution of neighborhoods by social indicator in Hanford
quartile distribution of neighborhoods by physcial indicator in Madera
quartile distribution of neighborhoods by social indicator in Madera
Merced & Turlock
quartile distribution of neighborhoods by physical indicator in Merced & Turlock
quartile distribution of neighborhoods by social indicator in Merced & Turlock
quartile distribution of neighborhoods by physical indicator in Modesto
quartile distribution of neighborhoods by social indicator in Modesto
quartile distribution of neighborhoods by physical indicator in Porterville
quartile distribution of neighborhoods by social indicator in Porterville
Stockton, Tracy & Manteca
quartile distribution of neighborhoods by physical indicator in Stockton, Tracy & Manteca
quartile distribution of neighborhoods by social indicator in Stockton, Tracy & Manteca
quartile distribution of neighborhoods by physical indicator in Visalia
quartile distribution of neighborhoods by social indicator in Visalia
Ewing, R., R. Pendall, and D. Chen. 2002. Measuring sprawl and its impact (Volume I). Smart Growth America: http://www.smartgrowthamerica.org/resources/measuring-sprawl-and-its-impact/.
Miles, R. and Y. Song. 2009. "Good" neighborhoods in Portland, Oregon: focus on both social and physical environments. Journal of Urban Affairs 31(4): 491-509.
Quinn, L.M, and J. Pawasarat. 2003. Racial integration in urban America: A block level analysis of African American and white housing patterns. Milwaukee, WI: University of Wisconsin-Milwaukee, Employment and Training Institute.
Talen, E. 2006. Neighborhood-level social diversity. Journal of the American Planning Association 72(4): 431-446.