Housing insecurity, an important determinant of mental health, worsened during the COVID-19 pandemic in the US. The federal Emergency Rental Assistance (ERA) program sought to reduce housing insecurity among low-income renters. Using 2021–23 Household Pulse Survey data, we employed a quasi-experimental design to assess the effects of ERA on anxiety and depression symptoms and on mental health care use. We conducted causal mediation analyses to determine whether and how ERA affected these outcomes through indirect effects—by alleviating housing insecurity—or through direct effects, which freed up resources to seek care. ERA significantly reduced anxiety and depression symptoms through both indirect and direct effects. Among renters with anxiety or depression symptoms, it increased psychotherapy use through direct effects. Future rental assistance programs could strengthen these dual impacts by including features to improve both housing and health status—for example, by streamlining applications and expediting benefit delivery to provide resources that recipients can use to address urgent mental health needs even before full housing stability is achieved.

Multiscale geographically weighted regression (MGWR) extends geographically weighted regression (GWR) by allowing process heterogeneity to be modeled at different spatial scales. While MGWR improves parameter estimates compared to GWR, the relationship between spatial scale and correlations within and among covariates—specifically spatial autocorrelation and collinearity—has not been systematically explored. This study investigates these relationships through controlled simulation experiments. Results indicate that spatial autocorrelation and collinearity affect specific model components rather than the entire model. Their impacts are cumulative but remain minimal unless they become very strong. MGWR effectively mitigates local multicollinearity issues by applying varying bandwidths across parameter surfaces. However, high levels of spatial autocorrelation and collinearity can lead to bandwidth underestimation for global processes, potentially producing false local effects. Additionally, strong collinearity may cause bandwidths to be overestimated for some processes, which helps mitigate collinearity but may obscure local effects. These findings suggest that while MGWR offers greater robustness against multicollinearity compared to GWR, bandwidth estimates should be interpreted with caution, as they can be influenced by strong spatial autocorrelation and collinearity. These results have important implications for empirical applications of MGWR.

In the face of a housing affordability crisis, many cities have adopted inclusionary zoning (IZ) policies to increase the supply of affordable housing. Yet, IZ remains a controversial local policy due to its varied and inconclusive effects on housing market outcomes. This study investigates this debate by adopting a quasi-experimental design with a national dataset of IZ policies in the United States. We find that, on average, IZ policies did not affect municipality-wide housing permits or rents. However, the implementation of IZ resulted in an average of 2.1 % increase in home prices. Our results also underscore the connection between IZ policy design and market outcomes: more stringent IZ policies (i.e., those that are mandatory and apply to the entire jurisdiction) led to a higher impact on home prices while mitigating the rent effect. Additionally, IZ’s market effects varied based on market conditions and the time elapsed since policy adoption. We discuss these findings in terms of implications for policy design and planning practice.

This chapter provides an overview of exploratory approaches to spatial dynamics. The focus is on the application and evidence within social sciences. Spatial dynamics is concerned about the changes in both the spatial and temporal dimensions. Two types of exploratory methods to spatial dynamics are differentiated. The first type extends classic longitudinal methods to interrogate the role of locations in shaping the temporal dynamics, while the second type extends exploratory spatial data analysis methods to investigate its temporal changes. Example methods for each type are illustrated, including spatial explicit Markov methods, a family of spatially explicit rank concordance, and space-time LISA. Though these methods are promising in advancing spatial and spatiotemporal thinking in social sciences, a lack of dynamic cross-fertilization between the field of geographical methodology and social sciences is recognized.

Scale is a fundamental geographic concept, and a substantial literature exists discussing the various roles that scale plays in different geographical contexts. Relatively little work exists, though, that provides a means of measuring the geographic scale over which different processes operate. Here we demonstrate how geographically weighted regression (GWR) can be adapted to provide such measures. GWR explores the potential spatial nonstationarity of relationships and provides a measure of the spatial scale at which processes operate through the determination of an optimal bandwidth. Classical GWR assumes that all of the processes being modeled operate at the same spatial scale, however. The work here relaxes this assumption by allowing different processes to operate at different spatial scales. This is achieved by deriving an optimal bandwidth vector in which each element indicates the spatial scale at which a particular process takes place. This new version of GWR is termed multiscale geographically weighted regression (MGWR), which is similar in intent to Bayesian nonseparable spatially varying coefficients (SVC) models, although potentially providing a more flexible and scalable framework in which to examine multiscale processes. Model calibration and bandwidth vector selection in MGWR are conducted using a back-fitting algorithm. We compare the performance of GWR and MGWR by applying both frameworks to two simulated data sets with known properties and to an empirical data set on Irish famine. Results indicate that MGWR not only is superior in replicating parameter surfaces with different levels of spatial heterogeneity but provides valuable information on the scale at which different processes operate.