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Despite the substantial number of studies on the relationships between crime patterns and built environments, the impacts of street-level built environments on crime patterns have not been definitively determined due to the limitations of obtaining detailed streetscape data and conventional analysis models. To fill these gaps, this study focuses on the non-linear relationships and threshold effects between built environments and local crime patterns at the level of a street segment in the City of Santa Ana, California. Using Google Street View (GSV) and semantic segmentation techniques, we quantify the features of the built environment in GSV images. Then, we examine the non-linear relationships and threshold effects between built environment factors and crime by applying interpretable machine learning (IML) methods. While the machine learning models, especially Deep Neural Network (DNN), outperformed negative binomial regression in predicting future crime events, particularly advantageous was that they allowed us to obtain a deeper understanding of the complex relationship between crime patterns and environmental factors. The results of interpreting the DNN model through IML indicate that most streetscape elements showed non-linear relationships and threshold effects with crime patterns that cannot be easily captured by conventional regression model specifications. The non-linearities and threshold effects revealed in this study can shed light on the factors associated with crime patterns and contribute to policy development for public safety from crime.

While transportation infrastructure and efficiency should inform where to build more housing, little is known about how housing allocation and development processes can be coordinated more systematically with transportation. To date, transportation-housing coordination has often relied on the densification of areas near rail transit stations, putting heavy burdens on these locations and their residents. Much less attention has been paid to how densification can be achieved in a more equitable manner by encompassing other sites. This report directs attention to non-rail locations, specifically low vehicle miles traveled (VMT) areas and bus corridors, and examines the challenges that can arise in promoting densification more broadly. It shows that data uncertainties can make it challenging to identify low VMT locations and that prioritizing only low VMT locations for residential development may have limited effectiveness in expanding housing opportunities in high opportunity areas. The report further explores ways to achieve more inclusive densification of non-rail transit areas and highlights the importance of anti-displacement strategies.

More systematic coordination between transportation and housing development is increasingly recognized as a promising strategy for creating more sustainable communities. In California, the importance of transportation-housing coordination is reflected in recent legislative efforts to address the state’s long-standing housing affordability crisis. One approach is to encourage higher density affordable housing developments near transit or in similarly transportation-efficient areas, such as locations with low vehicle miles traveled (VMT). However, little is known about how transportation access should be considered in guiding housing development, what challenges can arise from coordinating housing development with transportation, and what the state can do to better deal with these challenges and achieve more equitable residential densification.

The presence of employment centers provides the potential for reducing commuting distance. However, employment centers have distinct attributes, which may lead to varied impacts on commuting outcomes. We examine how proximity to employment centers can influence commuting distance with consideration of the heterogeneity of employment centers and workers. Specifically, we consider various attributes of employment centers related to location, persistency, job density, industry diversity, and size and analyze their impacts on the commuting patterns of low- and high-income workers using panel (2002-2019) data. Our analysis of the Los Angeles region shows that increasing proximity to the nearest employment center decreases commuting distance even after controlling for the job attributes located in the neighborhood of workers. The results further suggest that employment centers are not equal in terms of their impact on commute distance and that their impact is different for commuters from different income groups. Our findings contribute to the literature by deciphering the location and attributes of employment centers that may exert a greater impact on commuting patterns.

While there is a long history of racial change in the United States, and how this plays out within neighbourhoods, a key recurring question is whether some neighbourhoods are able to achieve and maintain racial diversity, or whether they simply transition to dominance by a new racial group. We test and find evidence of 1631 neighbourhoods across the United States from 1980 to 2020 that exhibit persistent racial diversity (PRD), and assess where this PRD occurs. Our analysis shows that PRD neighbourhoods (PRDNs) are likely to be present in counties with more economic opportunities–that is, counties with higher socioeconomic status (SES). PRDNs themselves, however, tend to be relatively lower SES neighbourhoods within relatively higher SES counties, suggesting that affordable locations surrounded by more economic opportunities may have served as an environment in which diversity can persist over a long period of time in the United States.

The COVID-19 pandemic has generated renewed interest in how telecommuting can alter the workings of our cities and regions, but there is little guidance on how to align planning practice with the new reality. This report synthesizes the research on telecommuting and its consequences to help planners better understand what effects may occur from the proliferation of telecommuting and what lessons can be drawn from research findings. Emphasis is on the broad relevance of telecommuting to many domains of planning, including housing, land use, community development, and inclusive place-making, while attention is paid to changes in travel demand, vehicle miles traveled (VMT), and greenhouse gas emissions. The research suggests that telecommuting can occur in a variety of ways, and its impacts are largely dependent not only on the type/schedule of telecommuting but on the built environment, transit accessibility, and other amenities/opportunities the location provides. The varying impacts reported in the research can be seen as an encouragement for planners to actively create a better future rather than merely responding to the rise of telecommuting. Given the breadth of telecommuting’s impacts, systematic coordination across various planning domains will be increasingly important. This report also calls for collaboration across cities to guide the ongoing transformation induced by telecommuting not in a way that leads to more residential segregation but in a way that provides more sustainable and inclusive communities.

Researchers at the University of California, Irvine, are looking into what may become the “new normal” in work and work-related travel and the consequences that could have on traffic conditions, efforts to address climate change, and the future of our urban areas, as well as our daily lives. They find, for instance, that current research is largely equivocal about the consequences of telecommuting on where individuals choose to live, their day-to-day travel, and urban/metropolitan development. Equally unclear is how increased telecommuting may impact efforts to create more sustainable and inclusive communities. In light of this uncertainty, they suggest planners and researchers need to pay more attention to the changing nature of urban commuting and how it can play an important role in shaping a more desirable future.

We explore neighborhoods in Southern California from 1980 to 2010 that exhibit persistent racial diversity (PRD) and the consequences of this PRD. Initial exploratory analyses show that the racial composition of the area surrounding the neighborhood in 1980 is associated with which neighborhoods become PRDs. Our primary analyses compare how PRD neighborhoods change over time (1980–2010) based on several socio-demographic measures to a matched group of non-PRD neighborhoods that had similar characteristics in 1980. The key finding is that PRD neighborhoods improved more on per capita income and percent in poverty compared to their matched tracts from 1980 to 2010. We also found that there was not a single route to persistent diversity, but rather a myriad of pathways through which racial/ethnic diversity can persist over a long time period at the neighborhood level.

This article explores ways in which land use policy outcomes vary across contexts focusing, as an example, on urban growth boundaries. Specifically, it analyzes how various contextual factors interact with the policy and generate diverging development outcomes by employing a kernel-based regularized least squares method. Results show that the policy effectiveness is largely dependent on the region’s population size, initial density levels, and organizational conditions. The presence of urban growth boundaries also appears to shape the way other determinants influence development patterns, suggesting that the policy can both directly and indirectly promote a more compact/contiguous pattern of development.

While street view imagery has accumulated over the years, its use to date has been largely limited to cross-sectional studies. This study explores ways to utilize historical Google Street View (GSV) images for the investigation of neighborhood change. Using data for Santa Ana, California, an experiment is conducted to assess to what extent deep learning-based semantic segmentation, processing historical images much more efficiently than visual inspection, enables one to capture changes in the built environment. More specifically, semantic segmentation results are compared for (1) 248 sites with construction or demolition of buildings and (2) two sets of the same number of randomly selected control cases without such activity. It is found that the deep learning-based semantic segmentation can detect nearly 75% of the construction or demolition sites examined, while screening out over 60% of the control cases. The results suggest that it is particularly effective in detecting changes in the built environment with historical GSV images in areas with more buildings, less pavement, and larger-scale construction (or demolition) projects. False-positive outcomes, however, can emerge due to the imperfection of the deep learning model and the misalignment of GSV image points over years, showing some methodological challenges to be addressed in future research.

As universities have become more integrated into their communities and regions, their host cities have become locations of choice for the highly educated facilitating increased innovation and productivity rates in several locales. Recent transitions toward knowledge-driven economies have also intensified growing geographic divides along the lines of education and raised concerns about the possibility of brain drain in a growing number of cities/regions. This study examines the changing geography of highly educated individuals (2000–2017) with a focus on small and medium-sized U.S. metropolitan areas with and without research universities. Results indicate the continuing trend of divergence among regions, but research universities are found to complement (the lack of) talent agglomeration and generate spatial spillovers beyond the county boundaries.

Much of the work concerning economic dynamism has focused on its aggregate-level implications, while there have been rising concerns about business displacement at the community or neighborhood level. In this article, we analyze this important (restructuring) process using detailed establishment-level business information and explore how it manifests itself across space within the Los Angeles—Long Beach—Santa Ana, CA Urbanized Area. We also investigate the association between business churning and neighborhood-level housing vacancy rates to understand the implications of dramatic changes in the business landscape. We find that housing vacancies are more likely to increase in urban neighborhoods with a higher establishment death rate, while the creation of new businesses can mitigate the association to some extent. We also detect substantial variation across decades not only in the spatial distribution of business churning but also in its association with housing vacancy rates, suggesting the evolving nature of business dynamics and their implications.

There is interest in understanding which characteristics of metropolitan areas impact the length of time or distance residents spend commuting. We utilize two measures recently introduced to the urban literature capturing distinct dimensions of employment decentralization –the level of employment deconcentration and employment spatial dispersion in metropolitan areas – to assess how they are related to commuting patterns across metropolitan areas. These two measures of urban/metropolitan spatial structure avoid challenges in identifying “job centers” and allow for a more systematic investigation of how employment decentralization affects commuting patterns. Furthermore, we detect key differences for the implications of these measures for commuting across 329 US metropolitan regions based on their population size. We find that greater employment deconcentration in very small MSAs is associated with longer commute times and distances, whereas greater employment deconcentration in large or very large MSAs is associated with shorter commutes. And whereas spatial dispersion is not related to commute times in very small MSAs, greater spatial dispersion is associated with longer commutes in very large MSAs. This study also shows that the spatial pattern of employment in regions, captured by these new measures, is associated with the proportion of very short and very long duration commutes.

Objectives: Despite theoretical interest in how dimensions of the built environment can help explain the location of crime in micro−geographic units, measuring this is difficult. Methods: This study adopts a strategy that first scrapes images from Google Street View every 20 meters in every street segment in the city of Santa Ana, CA, and then uses machine learning to detect features of the environment. We capture eleven different features across four main dimensions, and demonstrate that their relative presence across street segments considerably increases the explanatory power of models of five different Part 1 crimes. Results: The presence of more persons in the environment is associated with higher levels of crime. The auto−oriented measures—vehicles and pavement—were positively associated with crime rates. For the defensible space measures, the presence of walls has a slowing negative relationship with most crime types, whereas fences did not. And for our two greenspace measures, although terrain was positively associated with crime rates, vegetation exhibited an inverted−U relationship with two crime types. Conclusions: The results demonstrate the efficacy of this approach for measuring the built environment.

California’s legislature has attempted to address the state’s housing affordability crisis in recent years by adopting numerous laws encouraging new development in transit-accessible and/or jobs-rich areas, but the evidence concerning the impacts of these laws on housing development remains largely anecdotal. In particular, policymakers lack adequate information concerning: (1) the types of neighborhoods where developers are more likely to build; and (2) the causes of delays in approvals for proposed projects in jobs-rich and transit-accessible areas. In new research, scholars from UC Irvine and UC Berkeley address this problem by drawing on a unique project-level dataset, the Comprehensive Assessment of Land Use Entitlements (CALES), to analyze development projects including five or more residential units that were approved for development from 2014 through 2017 in six cities: Inglewood, Long Beach, Los Angeles, Pasadena, Redondo Beach, and Santa Monica.

Recent state legislation addresses California’s housing affordability crisis by encouraging new development in transitaccessible and/or jobs-rich areas. But policymakers lack key information about the effects of laws and plans on developers’ decisions about whether and where to build housing, and factors contributing to delays in receiving government development approvals in target areas. Drawing on a unique dataset detailing all residential projects of five units or more that were approved from 2014 through 2017 in selected California jurisdictions, this project analyzes how project attributes and transportation-related factors affected infill housing construction. We find that in cities with extensive transit infrastructure, new projects were generally located in parts of the city with high proximity to transit, but that proximity to rail stops or high frequency bus stops was not associated with extreme delays in project approval compared to all projects in general. The only factors related to extreme delay are the percentage of land within a half mile radius of dedicated single-family housing and whether a multiunit project required a rezoning or general plan amendment, the latter of which is associated with 326% increase in the odds of a project being extremely delayed. Our findings suggest that cities could expedite transit-accessible housing development by ensuring that general plans and zoning accommodate multifamily development near transit.

The US spends two times more than the OECD average in health expenditure but has a much smaller portion of public health spending to total health expenditure than other OECD countries. While it has been suggested that public health and social services spending is crucial to promoting health outcomes, less is known about what drives variations in public health expenditure across regions. This study aims to examine whether political fragmentation in local governance is associated with variations in public health and social services expenditures. Using the US Census of Governments, we constructed a panel dataset of political fragmentation and local government spending patterns (1997-2012) for 792 US counties (population > 60,882, top 25%) and employed Least Squares Dummy Variable (LSDV) and Generalized Estimating Equations (GEE) models. We found that per capita public health spending tended to be smaller in areas where the degree of political fragmentation was higher (Coef:  - 0.034; p

Cities worldwide are facing new challenges and opportunities with the proliferation of Unmanned Aerial Vehicles (UAVs), or drones. However, despite the increasing importance of managing drone activities at the local level, little is known about the extent to which cities adopt regulatory measures to deal with the challenges posed by this emerging technology and the factors that determine the local policy adopted. This study examines the dynamics of local drone policy adoption, focusing on California, which has the largest population of drones currently registered in the United States. A review of 482 California cities' municipal codes shows evolving patterns of policy adoption over the last two decades, with a rapid expansion since 2015. Multivariate survival analysis indicates that policy adoption at the local level is shaped by both municipal capacity and motivation factors, including the actions of neighboring cities. The analysis also finds evidence of disparities associated with socio-demographics, calling for more attention to the variation in local policy responsiveness to technology-driven challenges and ways to support local efforts and their collaboration.

Criminological theories have posited that the built environment impacts where crime occurs; however, measuring the built environment is difficult. Furthermore, it is uncertain whether the built environment differentially impacts crime in high-disadvantage neighborhoods. This study extracts features of the built environment from Google Street View images with a machine learning semantic segmentation strategy to create measures of fences, walls, buildings, and greenspace for over 66,000 street segments in Los Angeles. Results indicate that the presence of more buildings on a segment was associated with higher crime rates and had a particularly strong positive relationship with robbery and motor vehicle theft in low-disadvantage neighborhoods. Notably, fences and walls exhibited different relationships with crime. Walls, which do not allow visibility, were strongly negatively related to crime, particularly for robbery and burglary in high-disadvantage neighborhoods. Fences, which allow visibility, were associated with fewer robberies and larcenies, but more burglaries and aggravated assaults. Fences only exhibited a negative relationship with violent crime when they were located in low-disadvantage neighborhoods. The results highlight the importance of accounting for the built environment and the surrounding level of disadvantage when exploring the micro-location of crime.

Although building more housing near transit has gained increasing popularity as a strategy for addressing housing unaffordability while promoting sustainability, the effectiveness of this strategy has remained unclear, particularly in auto-oriented metropolises where land use planning authority largely rests with local governments. This article provides an analysis of how parcel-level residential land use intensification takes place under the influence of public transit expansion, with explicit attention to the interactions between current and planned land use changes, in a five-county Southern California region. The analysis using a generalized structural equation modeling approach shows that residential properties are more likely to be densified in transit-rich areas. This tendency is detected not only in the existing high-quality transit areas but also in locations where transit services will be available in the future. It is also found that relaxing zoning restrictions increases the probability of parcel-level densification, and the resultant density increase can induce further zoning or plan changes in nearby areas.

The unfolded protein response (UPR) homeostatically matches endoplasmic reticulum (ER) protein-folding capacity to cellular secretory needs. However, under high or chronic ER stress, the UPR triggers apoptosis. This cell fate dichotomy is promoted by differential activation of the ER transmembrane kinase/endoribonuclease (RNase) IRE1α. We previously found that the RNase of IRE1α can be either fully activated or inactivated by ATP-competitive kinase inhibitors. Here we developed kinase inhibitors, partial antagonists of IRE1α RNase (PAIRs), that partially antagonize the IRE1α RNase at full occupancy. Biochemical and structural studies show that PAIRs promote partial RNase antagonism by intermediately displacing the helix αC in the IRE1α kinase domain. In insulin-producing β-cells, PAIRs permit adaptive splicing of Xbp1 mRNA while quelling destructive ER mRNA endonucleolytic decay and apoptosis. By preserving Xbp1 mRNA splicing, PAIRs allow B cells to differentiate into immunoglobulin-producing plasma cells. Thus, an intermediate RNase-inhibitory 'sweet spot', achieved by PAIR-bound IRE1α, captures a desirable conformation for drugging this master UPR sensor/effector.

While Google Street View (GSV) has been increasingly available for large-scale examinations of urban landscapes, little is known about how to use this promising data source more cautiously and effectively. Using data for Santa Ana, California, as an example, this study provides an empirical assessment of the sensitivity of GSV-based streetscape measures and their variation patterns. The results show that the measurement outcomes can vary substantially with changes in GSV acquisition parameter settings, specifically spacing and direction. The sensitivity is found to be particularly high for some measurement targets, including humans, objects, and sidewalks. Some of these elements, such as buildings and sidewalks, also show highly correlated patterns of variation indicating their covariance in the mosaic of urban space.

A well-known challenge is measuring employment concentration across metropolitan areas and analysing the evolving spatial structure. We introduce a new approach that avoids identifying “job centres” and conceptualizes the distribution of employment based on two dimensions: (1) employment deconcentration; and (2) spatial dispersion of high employment locations. We apply this framework to study 329 US metropolitan regions based on 1 sq km. grid cells. We find diverse trajectories of metropolitan restructuring between 2000 and 2010, and substantial variation across regions in employment concentration. The new framework enables researchers to compare metropolitan regions to gain insights into the dynamic nature of metropolitan spatial structure.