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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. [ABSTRACT FROM AUTHOR]

We argue that the use of American Community Survey (ACS) data in spatial autocorrelation statistics without considering error margins is critically problematic. Public health and geographical research has been slow to recognize high data uncertainty of ACS estimates, even though ACS data are widely accepted data sources in neighborhood health studies and health policies. Detecting spatial autocorrelation patterns of health indicators on ACS data can be distorted to the point that scholars may have difficulty in perceiving the true pattern. We examine the statistical properties of spatial autocorrelation statistics of areal incidence rates based on ACS data. In a case study of teen birth rates in Mecklenburg County, North Carolina, in 2010, Global and Local Moran's I statistics estimated on 5-year ACS estimates (2006-2010) are compared to ground truth rate estimates on actual counts of births certificate records and decennial-census data (2010). Detected spatial autocorrelation patterns are found to be significantly different between the two data sources so that actual spatial structures are misrepresented. We warn of the possibility of misjudgment of the reality and of policy failure and argue for new spatially explicit methods that mitigate the biasedness of statistical estimations imposed by the uncertainty of ACS data. [ABSTRACT FROM AUTHOR]

This study aims at developing a data‐driven and bottom‐up spatial statistic method for identifying regions of anomalous spatial interactions (clusters of extremely high‐ or low‐value spatial flows), based on which it creates a spatial flow weights matrix. The method, dubbed flowAMOEBA, upgrades a multidirectional optimum ecotope‐based algorithm (AMOEBA) from areal data to spatial flow data through a proper spatial flow neighborhood definition. The method has the potential to dramatically change the way we study spatial interactions. First, it breaks the convention that spatial interaction data are always collected and modeled between spatial entities of the same granularity, as it delineates the OD region of anomalous spatial interactions, regardless of the size, shape, scale, or administrative level. Second, the method creates an empirical spatial flow weights matrix that can handle network autocorrelation embedded in spatial interaction modeling, thus improving related policy‐making or problem‐solving strategies. flowAMOEBA is tested and demonstrated on a synthetic data set as well as a county‐to‐county migration data set. [ABSTRACT FROM AUTHOR]

Abstract: Built environments have played an increasingly important and complex role in contemporary cities in the face of the significant number of discretionary and non‐discretionary event‐based activities that take place indoors, and the great variety of three‐dimensional designs that indoor spaces have grown to assume. However, intelligent tools that can assist designers to achieve high accessibility, particularly for large and complex buildings, are still underdeveloped. This article presents an innovative custom computer‐based design tool, dubbed FloorplanAAU (floor plan accessibility auditing), that couples GIS data modeling, indoor traversable network accessibility analysis, and interactive three‐dimensional visualization for built environments, to interactively evaluate and audit floor plans for high accessibility. The proposed method contributes to the success of applying GIS and transportation geography concepts to establish computational tools toward intelligent design and assessment of built environments. [ABSTRACT FROM AUTHOR]

This article presents a network analytical framework to detect individual-based activity-travel patterns (ATPs) in space and time. Compared to many existing classification methods (e.g., hot-spot detection, sequential alignment method), the network method substantiates the social meanings underlying the interconnectedness and similarities of people's activity trajectories and better integrates spatial interaction (colocation or distance-decay) and temporal connections (concurrence or sequence) of daily lives in the measure of similarity. This approach enables us to detect variant community structures, with individuals in the same community interacting relatively more than individuals belonging to different communities, by decomposing the complex trajectories into different meaningful events (e.g., activities, trips, tours, and subsequences). We also demonstrate the practicality and scientific merit of the network analysis approach in a case study of household travel behavior in Charlotte, North Carolina. Results derived from disaggregated survey data establish the effectiveness and flexibility of the network methods to detect cohesive communities of individuals and ATPs by different narratives of everyday-life events. This study also suggests that the network analysis approach has great potential to classify large datasets of other space-time trajectories and to discover policy-sensitive activity, trip, and tour patterns that help us develop policy and planning alternatives for sustainable communities and mobility. [ABSTRACT FROM AUTHOR]

Context: Forest loss and fragmentation negatively affect biodiversity. However, disturbances in forest canopy resulting from repeated deforestation and reforestation are also likely important drivers of biodiversity, but are overlooked when forest cover change is assessed using a single time interval. Objectives: We investigated two questions at the nexus of plant diversity and forest cover change dynamics: (1) Do multitemporal forest cover change trajectories explain patterns of plant diversity better than a simple measure of overall forest change? (2) Are specific types of forest cover change trajectories associated with significantly higher or lower levels of diversity? Methods: We sampled plant biodiversity in forests spanning the Charlotte, NC, region. We derived forest cover change trajectories occurring within nested spatial extents per sample site using a time series of aerial photos from 1938 to 2009, then classified trajectories by spatio-temporal patterns of change. While accounting for landscape and environmental covariates, we assessed the effects of the trajectory classes as compared to net forest cover change on native plant diversity. Results: Our results indicated that forest stand diversity is best explained by forest change trajectories, while the herb layer is better explained by net forest cover change. Three distinct forest change trajectory classes were found to influence the forest stand and herb layer. Conclusions: The influence of forest dynamics on biodiversity can be overlooked in analyses that use only net forest cover change. Our results illustrate the utility of assessing how specific trajectories of past land cover change influence biodiversity patterns in the present. [ABSTRACT FROM AUTHOR]

This article examines shipments of wine from European producers to 16 European ports. The research question focuses on disaggregated decisions about which port to use controlling for distance, port fixed effects and whether a national border must be crossed to reach the port. Using a conditional logit model we find heterogeneous impacts of transportation costs and national borders on port choice across shippers of various sizes. Based on the estimation results, we calculate the distance equivalents of national borders for shippers of various sizes. We show that border effects are nontrivial and asymmetric, even within the European free-trade zone. [ABSTRACT FROM AUTHOR]

Traffic safety policies are still conventionally ranked on financial and environmental criteria in developing countries, yet the equity concept can be advantageously used as an integral part of the process of traffic safety policy making. Equity in transportation is defined as how appropriately and equally the impacts of transportation are distributed among different types of users. The main contribution of this research is to introduce traffic safety equity in transportation policy making. The authors advocate for a modeling framework that considers traffic safety equity as a focal point. The analytical network process overcomes the drawbacks of data intensive models that are so difficult to implement reliably in developing countries and recognizes the existence of complex interdependencies among traffic safety factors. The approach is demonstrated on the case of Tehran, Iran. In this study, seventeen transportation elements are grouped into four clusters (driver characteristics, roadway characteristics, vehicle characteristics, and traffic control) to evaluate six policies. Public education and information is found to be the most effective policy to increase traffic safety, considering equity. The best equity-conscious policies are independent from physical infrastructures, household income level, and household location. The proposed framework is a sound approach to introduce traffic safety equity in the traffic safety policy making process of developing countries. [ABSTRACT FROM PUBLISHER]

The paper addresses claims implicating higher domestic transportation costs as a significant barrier to Latin America's trade and economic integration - and the region's inadequate transportation infrastructure as the primary cause. The relative “trade space” for U.S.-bound containerized shipments is delineated via reverse spatial interaction modeling. Reconfiguration and reverse calibration of the doubly constrained spatial interaction model is used to derive functional distances between origins and ports of export, as a means of estimating a Trade Impedance Quotient (TIQ) for trade flows at the scale of the continent. Global and local statistics of spatial autocorrelation are then used to analyze the spatial pattern of trade impedance. With this approach, the analysis of containerized flows establishes the existence, extent, location, and spatial distribution of discrepancies in South American landside trade impedance. A large share of trade flows have trade impedance that is disproportionate to distance. Trade impedance at origins is spatially clustered for the total dataset and randomly distributed for commodity segments, and randomly distributed at destinations. Potential focus areas for high trade impedance are identified. [ABSTRACT FROM AUTHOR]