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Place‐based spatial accessibility quantifies the distribution of access to goods and services across space. The Two‐Step Floating Catchment Area (2SFCA) family of methods have become a default tool for spatial accessibility analysis in part due to their intuitive approach and interpretability. This family of methods relies on calculating catchment areas around supply locations to estimate the area and population that may utilize them. However, these “catchment areas” are generally defined by origin‐destination matrices of travel‐time, giving us point‐to‐point distances and not polygons with actual area. This means that population geographies (census tracts, blocks, etc.) are binarily included or excluded, with no room for partial inclusion. When using nongranular data, which is often the case due to data privacy restrictions, this has the potential to cause significant errors in accessibility measurements. In this article, we propose Areal 2SFCA: a new approach that considers the area of overlap between travel‐time polygons and population geographies. We demonstrate the effectiveness of the Areal 2SFCA method using a case study that compares the Enhanced Two‐Step Floating Catchment Area (E2SFCA) and Areal E2SFCA for the state of Illinois in the USA using multiple population granularities. 

CyberGIS—geographic information science and systems (GIS) based on advanced cyberinfrastructure—is becoming increasingly important to tackling a variety of socio-environmental problems like climate change, disaster management, and water security. While recent advances in high-performance computing (HPC) have the potential to help address these problems, the technical knowledge required to use HPC has posed challenges to many domain experts. In this paper, we present CyberGIS-Compute: a geospatial middleware tool designed to democratize HPC access for solving diverse socio-environmental problems. CyberGIS-Compute does this by providing a simple user interface in Jupyter, streamlining the process of integrating domain-specific models with HPC, and establishing a suite of APIs friendly to domain experts. 

Accomplishing the goals outlined in “Ending the HIV (Human Immunodeficiency Virus) Epidemic: A Plan for America Initiative” will require properly estimating and increasing access to HIV testing, treatment, and prevention services. In this research, a computational spatial method for estimating access was applied to measure distance to services from all points of a city or state while considering the size of the population in need for services as well as both driving and public transportation. Specifically, this study employed the enhanced two-step floating catchment area (E2SFCA) method to measure spatial accessibility to HIV testing, treatment (i.e., Ryan White HIV/AIDS program), and prevention (i.e., Pre-Exposure Prophylaxis [PrEP]) services. The method considered the spatial location of MSM (Men Who have Sex with Men), PLWH (People Living with HIV), and the general adult population 15–64 depending on what HIV services the U.S. Centers for Disease Control (CDC) recommends for each group. The study delineated service- and population-specific accessibility maps, demonstrating the method’s utility by analyzing data corresponding to the city of Chicago and the state of Illinois. Findings indicated health disparities in the south and the northwest of Chicago and particular areas in Illinois, as well as unique health disparities for public transportation compared to driving. The methodology details and computer code are shared for use in research and public policy. 

Abstract Calibration of agent‐based models (ABMs) is a major challenge due to the complex nature of the systems being modeled, the heterogeneous nature of geographical regions, the varying effects of model inputs on the outputs, and computational intensity. Nevertheless, ABMs need to be carefully tuned to achieve the desirable goal of simulating spatiotemporal phenomena of interest, and a well‐calibrated model is expected to achieve an improved understanding of the phenomena. To address some of the above challenges, this article proposes an integrated framework of global sensitivity analysis (GSA) and calibration, called GSA‐CAL. Specifically, variance‐based GSA is applied to identify input parameters with less influence on differences between simulated outputs and observations. By dropping these less influential input parameters in the calibration process, this research reduces the computational intensity of calibration. Since GSA requires many simulation runs, due to ABMs' stochasticity, we leverage the high‐performance computing power provided by the advanced cyberinfrastructure. A spatially explicit ABM of influenza transmission is used as the case study to demonstrate the utility of the framework. Leveraging GSA, we were able to exclude less influential parameters in the model calibration process and demonstrate the importance of revising local settings for an epidemic pattern in an outbreak.