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In this paper we examine the evolution of urban spatial structure in U.S. metropolitan areas over nearly two decades. Using annual block-level data from the Longitudinal Employment Household Dynamics database, we introduce a technique for identifying regional employment centers that both adheres to urban economic theory and pays homage to classic contributions in local spatial statistics. Centers are defined as local spatial statistical outliers on the network-based job accessibility surface. We proceed by identifying the location and employment makeup of centers for each metropolitan region in the USA from 2002 to 2019 and discuss emergent trends across time and space. Critically, we not only explore empirical patterns, but we discuss the relationship between polycentricity, the evolution of urbanization and localization economies, and regional specialization. We confirm again the pattern of polycentricity in U.S. metros and show that the structure of metropolitan employment is largely stable over time. We also document a continuing trend away from urbanization economies into more specialized subcenters. • We develop a new technique for identifying employment subcenters combining spatial statistics, network analysis, and computational geometry. • the technique generates bespoke polygons that can be used for further analysis or designated as targets for urban development. • We apply our method to 20 years of annual census block -level employment from LEHD and discuss the trend toward increasing polycentric development in the U.S. • We document the increasing prominence of more specialized localization economies. • The methodology we contribute is generalizeable and can be applied anywhere in the world (we provide a Python software package for reproducing the results) [ABSTRACT FROM AUTHOR]

Abstract: Residential segregation is an inherently spatial phenomenon as it measures the separation of different types of people within a region. Whether measured with an explicitly spatial index, or a classic aspatial index, a region’s underlying spatial properties could manifest themselves in the magnitude of measured segregation. In this paper we implement a Monte Carlo simulation approach to investigate the properties of four segregation indices in regions built with specific spatial properties. This approach allows us to control the experiment in ways that empirical data do not. In general we confirm the expected results for the indices under various spatial properties, but some unexpected results emerge. Both the Dissimilarity Index and Neighborhood Sorting Index are sensitive to region size, but their spatial counterparts, the Adjusted Dissimilarity Index and Generalized Neighborhood Sorting Index, are generally immune to this problem. The paper also lends weight to concerns about the downward pressure on measured segregation when multiple neighborhoods are grouped into a single census tract. Finally, we discuss concerns about the way space is incorporated into segregation indices since the expected value of the spatial indices tested is lower than their aspatial counterparts. [Copyright &y& Elsevier]

The best obstacle avoiding path in continuous space, referred to as the Euclidean shortest path, is important for spatial analysis, location modeling and wayfinding tasks. This problem has received much attention in the literature given its practical application, and several solution techniques have been proposed. However, existing approaches are limited in their ability to support real time analysis in big data environments. In this research a multicore computing approach is developed that exploits spatial knowledge through the use of geographic information system functionality to efficiently construct an optimal shortest path. The approach utilizes the notion of a convex hull for iteratively evaluating obstacles and constructing pathways. Further, the approach is capable of incrementally improving bounds, made possible through parallel processing. Wayfinding routes that avoid buildings and other obstacles to travel are derived and discussed. [ABSTRACT FROM AUTHOR]

Abstract: Common to all tests of space–time interaction is the assumption that the population underlying the events of interest exhibits a trajectory of growth that is consistent through time and across space. In practice, however, this assumption is often untenable and, when violated, can introduce population shift bias into the results of these tests. While this problem is widely recognized, more work remains to compare its effect across tests and to determine the extent to which it is a problem for study short periods. This paper quantifies and compares the population shift bias present in the results of the Knox, Mantel, and Jacquez tests of space–time interaction. A simulation study is carried out which quantifies the bias present in each test across a variety of population movement scenarios. Results show a positive relationship between population shift bias and the heterogeneity in population growth across all the tests. They also demonstrate variability in the size of the bias across the three tests for space–time interaction considered. Finally, the results illustrate that population shift bias can be a serious problem for short study periods. Collectively, these findings suggest that an unbiased approach to assessing the significance of space–time interaction test results is needed whenever spatially heterogeneous population change is identified within a study area. [Copyright &y& Elsevier]