Introduction: Geospatial Analysis of Urban Environment

To provide a timely snapshot of current research that utilizes geospatial analysis and modeling in urban environment, the Korea-America Association for Geospatial and Environmental Sciences (KAGES) and the editorship of the International Journal of Geospatial and Environmental Research (IJGER) organized a special issue of IJGER on the theme of geospatial analysis and urban environment. Eight articles out of 13 submitted manuscripts have been published in this issue. This article provides an overview of the articles published in the special issue

Final Results From the Circumarctic Lakes Observation Network (CALON) Project

Since 2012, the physical and biogeochemical properties of ~60 lakes in northern Alaska have been investigated under CALON, a project to document landscape-scale variability of Arctic lakes in permafrost terrain. The network has ten nodes along two latitudinal transects extending inland 200 km from the Arctic Ocean. A meteorological station is deployed at each node and six representative lakes instrumented and continuously monitored, with winter and summer visits for synoptic assessment of lake conditions. Over the 4-year period, winter and summer climatology varied to create a rich range of lake responses over a short period. For example, winter 2012-13 was very cold with a thin snowpack producing thick ice across the region. Subsequent years had relatively warm winters, yet regionally variable snow resulted in differing gradients of ice thickness. Ice-out timing was unusually late in 2014 and unusually early in 2015. Lakes are typically well–mixed and largely isothermal, with minor thermal stratification occurring in deeper lakes during calm, sunny periods in summer. Lake water temperature records and morphometric data were used to estimate the ground thermal condition beneath 28 lakes. Application of a thermal equilibrium steady-state model suggests a talik penetrating the permafrost under many larger lakes, but lake geochemical data do not indicate a significant contribution of subpermafrost groundwater. Biogeochemical data reveal distinct spatial and seasonal variability in chlorophyll biomass, chromophoric dissolved organic carbon (CDOM), and major cations/anions. Generally, waters sampled beneath ice in April had distinctly higher concentrations of inorganic solutes and methane compared with August. Chlorophyll concentrations and CDOM absorption were higher in April, suggesting significant biological/biogeochemical activity under lake ice. Lakes are a positive source of methane in summer, and some also emit nitrous oxide and carbon dioxide. As part of the Indigenous Knowledge component,76 Iñupiat elders, hunters and berry pickers have been interviewed and over 75 hours of videotaped interviews produced. The video library and searchable interview logs are archived with the North Slope community. All field data is archived at ACADIS, and further information is at www.arcticlakes.org

The Distributed HTAP Architecture for Real-Time Analysis and Updating of Point Cloud Data

Updating the most recent set of point cloud data is critical in autonomous driving environments. However, existing systems for point cloud data management often fail to ensure real-time updates or encounter situations in which data cannot be effectively refreshed. To address these challenges, this study proposes a distributed hybrid transactional/analytical processing architecture designed for the efficient management and real-time processing of point cloud data. The proposed architecture leverages both columnar and row-based tables, enabling it to handle the substantial workloads associated with its hybrid architecture. The construction of this architecture as a distributed database cluster ensures real-time online analytical process query performance through query parallelization. A dissimilarity analysis algorithm for point cloud data, built by utilizing the capabilities of the spatial database, updates the point cloud data for the relevant area whenever the online analytical process query results indicate high dissimilarity. This research contributes to ensuring real-time hybrid transactional/analytical processing workload processing in dynamic road environments, helping autonomous vehicles generate safe, optimized routes

Modeling Interregional Commodity Flows with Incorporating Network Autocorrelation in Spatial Interaction Models: An Application of the US Interstate Commodity Flows." Computers, Environment and Urban Systems 36.6 (2012

a b s t r a c t Spatial interaction models are frequently used to predict and explain interregional commodity flows. Studies suggest that the effects of spatial structure significantly influence spatial interaction models, often resulting in model misspecification. Competing destinations and intervening opportunities have been used to mitigate this issue. Some recent studies also show that the effects of spatial structure can be successfully modeled by incorporating network autocorrelation among flow data. The purpose of this paper is to investigate the existence of network autocorrelation among commodity origin-destination flow data and its effect on model estimation in spatial interaction models. This approach is demonstrated using commodity origin-destination flow data for 111 regions of the United States from the 2002 Commodity Flow Survey. The results empirically show how network autocorrelation affects modeling interregional flows and can be successfully captured in spatial autoregressive model specifications

Network Reliability and Resilience of Rapid Transit Systems

The recent increase in demand and transportation security highlights the importance of the public transit system in the United States. This study explores how potential failures on nodal disruptions affect transit system flows and examines the change in the reliability of transit systems with a case study of the Greater Metropolitan Area of Washington, DC. For methodology, we employ network reliability and system flow loss and assess the criticality of stations under a variety of simulated nodal disruptions. We evaluate network resilience by identifying the best and worst geographical impact scenarios on networks