Smart Growth in the Valley

 

What Does Smart Growth Mean for Small and Rural Communities?

A Case Study on Cities in the San Joaquin Valley, California
 
Hongwei Dong
Geography Department, CSU-Fresno
 
Phase I:
Identifying Smart Growth Neighborhoods in the Valley
 

Introduction

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

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.

Methodology

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.

Table 1

Index Explanation Data Source
Residential density:    
Net residential density total housing units over acres of residential land (unit/acre). COGs and ACS
 Mixed use:    
 Land use type diversity  land use diversity of four land use types: residential, comercial, industrial and public facility land.  COGs
 Mixed housing:    
 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
 Sociodemographic diversity:    
 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).

Results

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

Bakersfield

quartile distribution of neighborhoods by physical indicator in Bakersfield

quartile distribution of neighborhoods by physical indicator in Bakersfield

 

Bakersfield social

quartile distribution of neighborhoods by social indicator in Bakersfield

 

Fresno

Fresno Physical quartile distribution of neighborhoods by physical indicator in Fresno 

Fresno Social

 quartile distribution of neighborhoods by social indicator in Fresno

 

Hanford

Hanford Physical

 quartile distribution of neighborhoods by physical indicator in Hanford

Hanford Social

quartile distribution of neighborhoods by social indicator in Hanford

 

Madera

Madera Physical

quartile distribution of neighborhoods by physcial indicator in Madera

Madera Social

 quartile distribution of neighborhoods by social indicator in Madera

 

Merced & Turlock

Merced & Turlock Physical

 quartile distribution of neighborhoods by physical indicator in Merced & Turlock

 

Merced & Turlock Social

  quartile distribution of neighborhoods by social indicator in Merced & Turlock

 

Modesto

Modesto Physical

 quartile distribution of neighborhoods by physical indicator in Modesto

 

Modesto Social

quartile distribution of neighborhoods by social indicator in Modesto

 

Porterville

Porterville Physical

quartile distribution of neighborhoods by physical indicator in Porterville

 

Porterville Social

quartile distribution of neighborhoods by social indicator in Porterville

 

Stockton, Tracy & Manteca

 Stockon, Tracy & Manteca Physical

 

quartile distribution of neighborhoods by physical indicator in Stockton, Tracy & Manteca                                      

Stockon, Tracy & Manteca Social

 quartile distribution of neighborhoods by social indicator in Stockton, Tracy & Manteca

 

Visalia

Vislia Physical

quartile distribution of neighborhoods by physical indicator in Visalia

 

Visalia Social

quartile distribution of neighborhoods by social indicator in Visalia

 

References

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.