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We propose a new estimator of spatial autocorrelation of areal incidence or prevalence rates in small areas, such as crime and health indicators, for correcting spatially heterogeneous sampling errors in denominator data. The approach is dubbed the heteroscedasticity‐consistent empirical Bayes (HC‐EB) method. As American Community Survey (ACS) data have been released to the public for small census geographies, small‐area estimates now form the demographic landscape of neighborhoods. Meanwhile, there is growing awareness of the diminished statistical validity of global and local Moran’s I when such small‐area estimates are used in denominator data. Using teen birth rates by census tracts in Mecklenburg County, North Carolina, we present comparisons of conventional and new HC‐EB estimates of Global and Local Moran’s I statistics created on ACS data, along with estimates on ground truth values from the 2010 decennial census. Results show that the new adjustment method dramatically enhances the statistical validity of global and local spatial autocorrelation statistics.

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Recent neighborhood studies have focused on longitudinal aspects of neighborhood change and data‐mining methodologies that identify neighborhood trajectory patterns using time‐series multivariate census data. Existing neighborhood trajectory models capture neighborhood change by stacking cross‐sectional neighborhood clustering results across years and analyzing the discrete stepwise switching patterns between the clusters. Taking a different approach, we employ the functional data analysis (FDA) method to analyze longitudinal patterns of neighborhood change from mathematically represented multivariate time‐dependent curves to identify neighborhood trajectory clusters. This FDA‐based neighborhood trajectory model incorporates a multivariate functional principal component analysis and k‐means clustering. We have applied our model to neighborhoods in the Charlotte and Detroit metropolitan areas to identify ongoing racial and socioeconomic segregation patterns and the time dynamics of neighborhood change.

We investigate the effect of domestic armed violence brought about by political instability on the geography of distance frictions in freight mobility and the resulting differential access of regions to global markets. The Colombian transportation system has been found to be impeded by deficiencies in landside transport infrastructure and institutions, and by fragmented political environments. The micro-level analysis of U.S.-bounded export shipping records corroborates that export freight shipping from inland regions is re-routed to avoid exposures to domestic armed violence despite greatly extended landside and maritime shipping distances. We exploit the trajectories of freight shipping from Colombian regions and spatial patterns of violent armed conflicts to see how unstable geopolitical environments are detrimental to freight shipping mobility and market openness. The discrete choice model shows that the shipping flow is greatly curbed by the extended re-routing due to domestic armed violence and that inland regions have restricted access to the global market. The perception of risk and re-routing behavior is found heterogeneous across shipments and conditional to shipment characteristics, such as commodity type, freight value and shipper sizes. The results highlight that political stability must be accommodated for improved freight mobility and export-oriented economic development in the global South.