Publications

Lessons (Not) Learned: Chicago Death Inequities during the 1918 Influenza and COVID-19 Pandemics

Published in International Journal of Environmental Research and Public Health, 2023

Abstract: During historical and contemporary crises in the U.S., Blacks and other marginalized groups experience an increased risk for adverse health, social, and economic outcomes. These outcomes are driven by structural factors, such as poverty, racial residential segregation, and racial discrimination. These factors affect communities’ exposure to risk and ability to recover from disasters, such as pandemics. This study examines whether areas where descendants of enslaved Africans and other Blacks lived in Chicago were vulnerable to excess death during the 1918 influenza pandemic and whether these disparities persisted in the same areas during the COVID-19 pandemic. To examine disparities, demographic data and influenza and pneumonia deaths were digitized from historic weekly paper maps from the week ending on 5 October 1918 to the week ending on 16 November 1918. Census tracts were labeled predominantly Black or white if the population threshold for the group in a census tract was 40% or higher for only one group. Historic neighborhood boundaries were used to aggregate census tract data. The 1918 spatial distribution of influenza and pneumonia mortality rates and cases in Chicago was then compared to the spatial distribution of COVID-19 mortality rates and cases using publicly available datasets. The results show that during the 1918 pandemic, mortality rates in white, immigrant and Black neighborhoods near industrial areas were highest. Pneumonia mortality rates in both Black and immigrant white neighborhoods near industrial areas were approximately double the rates of neighborhoods with predominantly US-born whites. Pneumonia mortality in Black and immigrant white neighborhoods, far away from industrial areas, was also higher (40% more) than in US-born white neighborhoods. Around 100 years later, COVID-19 mortality was high in areas with high concentrations of Blacks based on zip code analysis, even though the proportion of the Black population with COVID was similar or lower than other racial and immigrant groups. These findings highlight the continued cost of racial disparities in American society in the form of avoidable high rates of Black death during pandemics.

Recommended citation: Ruby Mendenhall, Jong Cheol Shin, Florence Adibu, Malina Marlyn Yago, Rebecca Vandewalle, Andrew Greenlee, and Diana S. Grigsby-Toussaint (2023). "Lessons (Not) Learned: Chicago Death Inequities during the 1918 Influenza and COVID-19 Pandemics" International Journal of Environmental Research and Public Health, 20 (7), 5248, DOI: 10.3390/ijerph20075248 https://doi.org/10.3390/ijerph20075248

CyberGIS-Compute for enabling computationally intensive geospatial research

Published in SpatialAPI '21: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science, 2020

Abstract: Geospatial research and education have become increasingly dependent on cyberGIS to tackle computation and data challenges. However, the use of advanced cyberinfrastructure resources for geospatial research and education is extremely challenging due to both high learning curve for users and high software development and integration costs for developers, due to limited availability of middleware tools available to make such resources easily accessible. This tutorial describes CyberGIS-Compute as a middleware framework that addresses these challenges and provides access to high-performance resources through simple easy to use interfaces. The CyberGIS-Compute framework provides an easy to use application interface and a Python SDK to provide access to CyberGIS capabilities, allowing geospatial applications to easily scale and employ advanced cyberinfrastructure resources. In this tutorial, we will first start with the basics of CyberGIS-Jupyter and CyberGIS-Compute, then introduce the Python SDK for CyberGIS-Compute with a simple Hello World example. Then, we will take multiple real-world geospatial applications use-cases like spatial accessibility and wildfire evacuation simulation using agent based modeling. We will also provide pointers on how to contribute applications to the CyberGIS-Compute framework.

Recommended citation: Anand Padmanabhan, Ximo Ziao, Rebecca C Vandewalle, Furqan Baig, Alexander Michel, Zhiyu Li, and Shaowen Wang (2021). "CyberGIS-compute for enabling computationally intensive geospatial research" SpatialAPI '21: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science, 3, 1-2, DOI: 10.1145/3486189.3490017 https://dl.acm.org/doi/10.1145/3486189.3490017

Enabling computationally intensive geospatial research on CyberGIS-Jupyter with CyberGIS-Compute

Published in Gateways 2021, 2020

Abstract: Geospatial research and education have become increasingly dependent on cyberGIS, defined as geographic information science and systems based on advanced cyberinfrastructure (CI), [1] to tackle computation and data challenges. However, the use of advanced cyberGIS capabilities has typically been constrained to a small set of research groups who have the technical expertise of using CI resources. Over the past few years CyberGIS-Jupyter [2,3] has been developed to provide access to cyberGIS capabilities through an easy-to-use Jupyter Notebook interface which has made cyberGIS more accessible. For many cyberGIS and geospatial applications accessing CI resources needed for solving complex problems at scale. However, leveraging CI resources for geospatial application is challenging both due to the steep learning curve and lack of appropriate tools. CyberGIS-Compute fills this gap by providing an easy-to-use middleware tool for using and contributing geospatial application codes that leverage CI resources. This substantially lowers the learning curve for both geospatial users and developers to access cyberGIS capabilities at scale. CyberGIS-Compute is backed by Virtual ROGER (Resourcing Open Geospatial Education and Research); a geospatial supercomputer with access to a number of readily available popular geospatial libraries.

Recommended citation: Anand Padmanabhan, Zimo Xiao, Rebecca Vandewalle, Alexander Michels, and Shaowen Wang (2021). "Enabling computationally intensive geospatial research on CyberGIS-Jupyter with CyberGIS-Compute" Gateways 2021, DOI: 10.5281/zenodo.5570056 https://dl.acm.org/doi/10.1145/3486189.3490017

Understanding the multifaceted geospatial software ecosystem: a survey approach

Published in International Journal of Geographical Information Science, 2020

Abstract: Understanding the characteristics of the rapidly evolving geospatial software ecosystem in the United States is critical to enable convergence research and education that are dependent on geospatial data and software. This paper describes a survey approach to better understand geospatial use cases, software and tools, and limitations encountered while using and developing geospatial software. The survey was broadcast through a variety of geospatial-related academic mailing lists and listservs. We report both quantitative responses and qualitative insights. As 42% of respondents indicated that they viewed their work as limited by inadequacies in geospatial software, ample room for improvement exists. In general, respondents expressed concerns about steep learning curves and insufficient time for mastering geospatial software, and often limited access to high-performance computing resources. If adequate efforts were taken to resolve software limitations, respondents believed they would be able to better handle big data, cover broader study areas, integrate more types of data, and pursue new research. Insights gained from this survey play an important role in supporting the conceptualization of a national geospatial software institute in the United States with the aim to drastically advance the geospatial software ecosystem to enable broad and significant research and education advances.

Recommended citation: Vandewalle, Rebecca C., William C. Barley, Anand Padmanabhan, Daniel S. Katz, and Shaowen Wang (2021). "Understanding the multifaceted geospatial software ecosystem: a survey approach" International Journal of Geographical Information Science, 35 (11), 2168-2186, DOI: 10.1080/13658816.2020.1831514 https://www.tandfonline.com/doi/pdf/10.1080/13658816.2020.1831514

A CyberGIS Approach to Spatiotemporally Explicit Uncertainty and Global Sensitivity Analysis for Agent-Based Modeling of Vector-Borne Disease Transmission

Published in Annals of the American Association of Geographers, 2020

Abstract: Although agent-based models (ABMs) provide an effective means for investigating complex interactions between heterogeneous agents and their environment, they might hinder an improved understanding of phenomena being modeled due to inherent challenges associated with uncertainty in model parameters. This study uses uncertainty analysis and global sensitivity analysis (UA-GSA) to examine the effects of such uncertainty on model outputs. The statistics used in UA-GSA, however, are likely to be affected by the modifiable areal unit problem. Therefore, to examine the scale-varying effects of model inputs, UA-GSA needs to be performed at multiple spatiotemporal scales. Unfortunately, performing comprehensive UA-GSA comes with considerable computational cost. In this article, our cyberGIS-enabled spatiotemporally explicit UA-GSA approach helps to not only resolve the computational burden but also measure dynamic associations between model inputs and outputs. A set of computational and modeling experiments shows that input factors have scale-dependent impacts on modeling output variability. In other words, most of the input factors have relatively large impacts in a certain region but might not influence outcomes in other regions. Furthermore, our spatiotemporally explicit UA-GSA approach sheds light on the effects of input factors on modeling outcomes that are particularly spatially and temporally clustered, such as the occurrence of communicable disease transmission.

Recommended citation: Kang, Jeon-Young, Jared Aldstadt, Rebecca Vandewalle, Dandong Yin & Shaowen Wang (2020). "A CyberGIS Approach to Spatiotemporally Explicit Uncertainty and Global Sensitivity Analysis for Agent-Based Modeling of Vector-Borne Disease Transmission." Annals of the American Association of Geographers. 110:6, 1855-1873, DOI: 10.1080/24694452.2020.1723400. https://doi.org/10.1080/24694452.2020.1723400

Integrating CyberGIS-Jupyter and spatial agent-based modelling to evaluate emergency evacuation time

Published in GeoSim '19: Proceedings of the 2nd ACM SIGSPATIAL international workshop on geospatial simulation, 2019

Abstract: As large-scale disasters are increasing in severity and frequency, agent-based modeling enables the simulation of disaster and evacuation processes, while exploring the complex interactions of disasters and human behaviors. In this paper, we employ CyberGIS-Jupyter for spatially explicit agent-based modeling to examine dynamic associations between disaster severity and evacuation processes. We find that as the disaster severity increases, the total time for all vehicles to evacuate increases as more vehicles are become stuck. We find that CyberGIS-Jupyter can simplify cyberinfrastructure access to conduct agent-based modeling of emergency evacuation while enabling intuitive sharing and presentation of model components and results and fosters the reproducibility and replicability of agent-based modeling with data- and computation-intensive geospatial problem solving.

Recommended citation: Vandewalle, Rebecca, Jeon-Young Kang, Dandong Yin, and Shaowen Wang. (2015). "Integrating CyberGIS-Jupyter and spatial agent-based modelling to evaluate emergency evacuation time." GeoSim '19: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, pp 28–31, https://doi.org/10.1145/3356470.3365530. https://dl.acm.org/doi/pdf/10.1145/3356470.3365530

The Influence of Signalling upon Milecastle and Turret Positions along Hadrian’s Wall

Published in University of Edinburgh, 2015

Abstract: A signalling system between Hadrian’s Wall and the Stanegate Frontier has been suggested as a reason for deviations between actual and expected spacing of milecastles along Hadrian’s Wall (Woolliscroft, 2001, 63). Expected positions for milecastles are positions where a milecastle would have been placed had it been built one Roman mile (1479 metres) from the milecastle to its east. Measured positions for turrets are 1/3rd of a Roman mile (493 metres) from the turret or milecastle to the east. This report assesses intervisibility between measured and actual Wall positions and Stanegate forts and towers. Positions for both milecastles and turrets measured from the east and the west are considered. Comparisons are also made between actual milecastle and turret positions and local hills and valleys, Wall corners, and random positions. A fuzzy decay equation was applied to binary intervisibility scores to account for distance effects. No significant differences were found between actual Wall positions and other positions (except for valleys), which suggests that milecastles and turrets were not moved from measured positions in order to better signal to Stanegate forts or towers.

Recommended citation: Vandewalle, Rebecca C. (2015). "The Influence of Signalling upon Milecastle and Turret Positions along Hadrian’s Wall" University of Edinburgh MSc thesis, http://hdl.handle.net/1842/11782 http://hdl.handle.net/1842/11782

CyberGIS-Jupyter for spatially explicit agent-based modeling&colon; a case study on influenza transmission

Published in GeoSim '19: Proceedings of the 2nd ACM SIGSPATIAL international workshop on geospatial simulation, 2015

Abstract: Despite extensive efforts on achieving reproducible agent-based models (ABMs) to improve the capability of this widely adopted methodology, it remains challenging to reproduce and replicate pre-existing ABMs, due to a number of factors such as diverse computing resources and ABMs platforms. In this study, we propose to employ CyberGIS-Jupyter for spatially explicit ABMs. CyberGIS-Jupyter is a cyberGIS framework to achieve data-intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on advanced cyberinfrastructure. Influenza transmission in the city of Miami, Florida, USA was used as a case study. In the model, Influenza is transmitted through the contact networks of individual human agents, which are constructed based on commuting behaviors. CyberGIS-Jupyter can support one not only to conduct collaborative and transparent modeling, but also to perform modeling simulation on advanced cyberinfrastructure resources. It may contribute to boosting the reproducibility and replicability of ABMs.

Recommended citation: Kang, Jeon-Young, Jared Aldstadt, Alexander Michels, Rebecca Vandewalle, and Shaowen Wang. (2019). "CyberGIS-Jupyter for spatially explicit agent-based modeling: a case study on influenza transmission." GeoSim '19: Proceedings of the 2nd ACM SIGSPATIAL international workshop on geospatial simulation, pp. 32–35, https://doi.org/10.1145/3356470.3365531. https://dl.acm.org/doi/pdf/10.1145/3356470.3365531

Rebecca C. Vandewalle (Becky)

Publications