Quarterly Report on Inequality and Segregation in Southern California

This Report studies the changes in inequality and racial composition and segregation in each of the counties in the Southern California region over a 50 year period.  Many of the graphs display changes over a 50 year period since 1970, and some reach back even further in time.  Some key results of interest include:

  • Orange County has gone from the least racial/ethnic mixing in 1970 to the most by 2018.
  • Los Angeles County has the highest level of income inequality.
  • Ventura County has the lowest level of income inequality.
  • After rising in earlier years, income inequality in Orange County has held relatively steady since 2000.
  • Income segregation is higher in Southern California counties compared to average U.S. large counties.
  • Income segregation rose sharply in the 2000s, though it has fallen a bit since 2010.
  • Incomes are rising fastest in San Diego County since 2000. Whereas the median income in San Diego County was 10% higher than the average large county in the U.S. in 2000, it was 30% higher by 2018.
  • Incomes in Los Angeles County went from equal to the average large county in the U.S. in 2000 to 20% higher by 2018.
  • Median rents and home values are rising faster in Southern California since 2000.
  • In 2018, whereas the median income in Orange County is 50% higher than the average large county in the U.S., median rents are 90% higher, and median home values are 210% higher.

 

The maps here show which neighborhoods have experienced the largest increase in adjusted home values since 1970 for each of the counties.

 

 

 

Read the Report on Inequality and Segregation in Southern California

This Report studies the changes in inequality and racial composition and segregation in each of the counties in the Southern California region over a 50 year period.  Many of the graphs display changes over a 50 year period since 1970, and some reach back even further in time.  Some key results of interest include:

  • Orange County has gone from the least racial/ethnic mixing in 1970 to the most by 2018.
  • Los Angeles County has the highest level of income inequality.
  • Ventura County has the lowest level of income inequality.
  • After rising in earlier years, income inequality in Orange County has held relatively steady since 2000.
  • Income segregation is higher in Southern California counties compared to average U.S. large counties.
  • Income segregation rose sharply in the 2000s, though it has fallen a bit since 2010.
  • Incomes are rising fastest in San Diego County since 2000. Whereas the median income in San Diego County was 10% higher than the average large county in the U.S. in 2000, it was 30% higher by 2018.
  • Incomes in Los Angeles County went from equal to the average large county in the U.S. in 2000 to 20% higher by 2018.
  • Median rents and home values are rising faster in Southern California since 2000.
  • In 2018, whereas the median income in Orange County is 50% higher than the average large county in the U.S., median rents are 90% higher, and median home values are 210% higher.

 

 

Download the full report here.

 

The maps here show which neighborhoods have experienced the largest increase in adjusted home values since 1970 for each of the counties.

 

 

 

 

Appendices with the additional maps for each county separately from the Report are here:

Maps for Los Angeles County.

Maps for Orange County.

Maps for Riverside County.

Maps for San Bernardino County.

Maps for San Diego County.

Maps for Ventura County.

 

Read the Report on Rising Inequality and Neighborhood Mixing in Metropolitan Areas

This Report provides new insights into some of the spatial relationships involved in both neighborhood mixing and regional inequality through an investigation of 381 metropolitan areas in the U.S. in 2010 using advanced measurement strategies and analysis methods.

The study uses a novel neighborhood unit—egohoods—to measure the degree of mixing that occurs within the neighborhoods of these metropolitan areas.  It measures mixing based on income, occupational status, and educational achievement.

We compare the level of mixing on these three dimensions across all metropolitan areas in the U.S. in 2010.

Below is a map showing the level of income mixing in neighborhoods for each of the metropolitan areas in the U.S.

Download the full report here.

 

Quarterly Report on Rising Inequality and Neighborhood Mixing in Metropolitan Areas

This Report provides new insights into some of the spatial relationships involved in both neighborhood mixing and regional inequality through an investigation of 381 metropolitan areas in the U.S. in 2010 using advanced measurement strategies and analysis methods.

The study uses a novel neighborhood unit—egohoods—to measure the degree of mixing that occurs within the neighborhoods of these metropolitan areas.  It measures mixing based on income, occupational status, and educational achievement.

We compare the level of mixing on these three dimensions across all metropolitan areas in the U.S. in 2010.

Below is a map showing the level of income mixing in neighborhoods for each of the metropolitan areas in the U.S.

Read published research on what makes housing accessible to everyday destinations in Southern California?

Publication:

Kane, Kevin, John R. Hipp, and Jae Hong Kim. (2017). “Analyzing accessibility using parcel data: Is there still an access-space trade-off in Long Beach, California? The Professional Geographer 69:3, 486-503.

Abstract:

This article analyzes the impact of changing housing and neighborhood characteristics on the accessibility of neighborhood businesses using Long Beach, California, as a case study. Although advocates of smart growth and New Urbanism encourage land use mixing, aggregate-level analysis can be too coarse to pick up on fine-grained aspects of urban streetscapes. This study uses assessor parcel records and a point-based business establishment data set to analyze city-wide patterns of accessibility from individual dwelling units to thirty-one types of neighborhood businesses, including grocery stores, service shops, drug stores, doctor’s offices, and banks. Regression results compare parcel-level and neighborhood-level drivers of accessibility between 2006 and 2015 to gauge the aggregated effect of recent economic, demographic, and built environment changes on this aspect of urban spatial structure. Larger homes in older, multiunit buildings and higher income neighborhoods show substantial increases in accessibility to most establishment types, suggesting a trend toward both greater accessibility and larger dwelling units—despite the traditional trade-off between access and space. Although gradual increases in home and business density increased overall accessibility over this period, weaker neighborhood-level results indicate that this trend is less pronounced in high-poverty and non-white areas.

Read published research on neighborhood mixing and economic dynamism

Publication:

Hipp, John R., Kevin Kane, and Jae Hong Kim. (2017).  “Recipes for Neighborhood Development: A Machine Learning Approach toward Understanding the Impact of Mixing in Neighborhoods.” Landscape and Urban Planning.

Abstract:

Scholars of New Urbanism have suggested that mixing along various dimensions in neighborhoods (e.g., income, race/ethnicity, land use) may have positive consequences for neighborhoods, particularly for economic dynamism. A challenge for empirically assessing this hypothesis is that the impact of mixing may depend on various socio-demographic characteristics of the neighborhood and takes place in a complex fashion that cannot be appropriately handled by traditional statistical analytical approaches. We utilize a rarely used, innovative estimation technique—kernel regularized least squares—that allows for nonparametric estimation of the relationship between various neighborhood characteristics in 2000 and the change in average household income in the neighborhood from 2000 to 2010. The results demonstrate that the relationships between average income growth and both income mixing and racial/ethnic mixing are contingent upon several neighborhood socio-demographic “ingredients”. For example, racial mixing is positively associated with average income over time when it occurs in neighborhoods with a high percentage of Latinos or immigrants, high population density, or high housing age mixing. Income mixing is associated with worsening average household income in neighborhoods with more poverty, unemployment, immigrants, or population density. It appears that considering the broader characteristics of the neighborhood is important for understanding economic dynamism.