CV

Journal Articles

2024

1. SoftwareX

   CyberGIS-Compute: Middleware for democratizing scalable geocomputation

   Michels, Alexander C., Padmanabhan, Anand, Xiao, Zimo, Kotak, Mit, Baig, Furqan, and Wang, Shaowen

   SoftwareX, 2024

   Abs DOI

   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.

2. IJGIS

   SPASTC: A Spatial Partitioning Algorithm for Scalable Travel-time Computation

   Michels, Alexander, Park, Jinwoo, Kang, Jeon-Young, and Wang, Shaowen

   International Journal of Geographical Information Science, 2024

   Abs DOI

   Travel-time computation with large transportation networks is often computationally intensive for two main reasons: 1) large computer memory is required to handle large networks; and 2) calculating shortest-distance paths over large networks is computing intensive. Therefore, previous research tends to limit their spatial extent to reduce computational intensity or resolve computational intensity with advanced cyberinfrastructure. In this context, this article describes a new Spatial Partitioning Algorithm for Scalable Travel-time Computation (SPASTC) that is designed based on spatial domain decomposition with computer memory limit explicitly considered. SPASTC preserves spatial relationships required for travel-time computation and respects a user-specified memory limit, which allows efficient and large-scale travel-time computation within the given memory limit. We demonstrate SPASTC by computing spatial accessibility to hospital beds across the conterminous United States. Our case study shows that SPASTC achieves significant efficiency and scalability making the travel-time computation tens of times faster.

2023

1. C&C:P&E

   EasyScienceGateway: A new framework for providing reproducible user environments on science gateways

   Michels, Alexander, Padmanabhan, Anand, Li, Zhiyu, and Wang, Shaowen

   Concurrency and Computation: Practice and Experience, 2023

   Abs DOI

   Science gateways have become a core part of the cyberinfrastructure ecosystem by increasing access to computational resources and providing community platforms for sharing and publishing education and research materials. While science gateways represent a promising solution for computational reproducibility, common methods for providing users with their user environments on gateways present challenges which are difficult to overcome. This article presents EasyScienceGateway: a new framework for providing user environments on science gateways to resolve these challenges, provides the technical details on implementing the framework on a science gateway based on Jupyter Notebook, and discusses our experience applying the framework to the CyberGIS-Jupyter and CyberGIS-Jupyter for Water gateways.

2. Appl Geog

   Daily Changes in Spatial Accessibility to ICU Beds and Their Relationship with the Case-Fatality Ratio of COVID-19 in the State of Texas, USA

   Park, Jinwoo, Michels, Alexander, Lyu, Fangzheng, Han, Su Yeon, and Wang, Shaowen

   Applied Geography, 2023

   Abs DOI

   During the COVID-19 pandemic, many patients could not receive timely healthcare services due to limited availability and access to healthcare resources and services. Previous studies found that access to intensive care unit (ICU) beds saves lives, but they overlooked the temporal dynamics in the availability of healthcare resources and COVID-19 cases. To fill this gap, our study investigated daily changes in ICU bed accessibility with an enhanced two-step floating catchment area (E2SFCA) method in the state of Texas. Along with the increased temporal granularity of measurements, we uncovered two phenomena: 1) aggravated spatial inequality of access during the pandemic, and 2) the retrospective relationship between insufficient ICU bed accessibility and the high case-fatality ratio of COVID-19 in rural areas. Our findings suggest that those locations should be supplemented with additional healthcare resources to save lives in future pandemic scenarios.

2022

1. PLOS ONE

   Spatial Accessibility to HIV Testing, Treatment, and Prevention Services in Illinois and Chicago, USA

   Kang, Jeon-Young, Farkhad, Bita Fayaz, Chan, Man-pui Sally, Michels, Alexander, Albarracin, Dolores, and Wang, Shaowen

   PLOS ONE, 2022

   Abs DOI Data

   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.

2. JASSS

   Particle Swarm Optimization for Calibration in Spatially Explicit Agent-Based Modeling

   Michels, Alexander, Kang, Jeon-Young, and Wang, Shaowen

   Journal of Artificial Societies and Social Simulation, 2022

   Abs DOI Code

   A challenge in computational modeling of Agent-Based Models (ABMs) is the amount of time and resources required to tune a set of parameters for reproducing the observed patterns of phenomena being modeled. Well-tuned parameters are necessary for models to reproduce real-world multi-scale space-time patterns, but calibration is often computationally intensive and time consuming. Particle Swarm Optimization (PSO) is a swarm intelligence optimization algorithm that has found wide use for complex optimization including nonconvex and noisy problems. In this study, we propose to use PSO for calibrating parameters in ABMs. We use a spatially explicit ABM of influenza transmission based in Miami, Florida, USA as a case study. Furthermore, we demonstrate that a standard implementation of PSO can be used out-of-the-box to successfully calibrate models and out-performs Monte Carlo in terms of optimization and efficiency.

2021

1. TGIS

   An Integrated Framework of Global Sensitivity Analysis and Calibration for Spatially Explicit Agent-Based Models

   Kang, Jeon-Young, Michels, Alexander, Crooks, Andrew, Aldstadt, Jared, and Wang, Shaowen

   Transactions in GIS, 2021

   Abs DOI

   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.

2020

1. IJHG

   Rapidly Measuring Spatial Accessibility of COVID-19 Healthcare Resources: A Case Study of Illinois, USA

   Kang, Jeon-Young, Michels, Alexander C, Lyu, Fangzheng, Wang, Shaohua, Agbodo, Nelson, Freeman, Vincent L, and Wang, Shaowen

   International Journal of Health Geographics, 2020

   Abs DOI Code

   The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing the coronavirus disease 2019 (COVID-19) pandemic, has infected millions of people and caused hundreds of thousands of deaths. While COVID-19 has overwhelmed healthcare resources (e.g., healthcare personnel, testing resources, hospital beds, and ventilators) in a number of countries, limited research has been conducted to understand spatial accessibility of such resources. This study fills this gap by rapidly measuring the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA. Specifically, the rapid measurement is achieved by resolving computational intensity of an enhanced two-step floating catchment area (E2SFCA) method through a parallel computing strategy based on cyberGIS (cyber geographic information science and systems). The study compared the spatial accessibility measures for COVID-19 patients to those of general population, identifying which geographic areas need additional healthcare resources to improve access. The results also help delineate the areas that may face a COVID-19-induced shortage of healthcare resources caused by COVID-19. The Chicagoland, particularly the southern Chicago, shows an additional need for resources. Our findings are relevant for policymakers and public health practitioners to allocate existing healthcare resources or distribute new resources for maximum access to health services.

Peer-Reviewed Conference Papers

2023

1. I-GUIDE Forum

   An Agent-Based Modeling Approach to Spatial Accessibility

   Michels, Alexander, and Wang, Shaowen

   Forum 2023 - Harnessing the Geospatial Data Revolution for Sustainability Solutions, 2023

   Abs DOI

   Place-based spatial accessibility represents the ability of populations within geographic units to access goods and services, and thus is an important indicator for sustainable development. Existing spatial accessibility models treat population as simply demand, calculating statistics or optimizing average cost for the population within each geographic unit, rather than modeling individual decisions. This paper proposes AgentAccess, a general-purpose Agent-Based Model (ABM) for spatial accessibility analysis. An ABM framework brings us closer to reality by simulating individual and imperfect decision-making. We introduce the model and compare its results against existing spatial accessibility models using a case study of hospital beds in Cook County, IL, USA.

2. I-GUIDE Forum

   Streamlined HPC Environments with CVMFS and CyberGIS-Compute

   Michels, Alexander, Kotak, Mit, Padmanabhan, Anand, and Wang, Shaowen

   Forum 2023 - Harnessing the Geospatial Data Revolution for Sustainability Solutions, 2023

   Abs DOI

   High-Performance Computing (HPC) resources provide the potential for complex, large-scale modeling and analysis, fueling scientific progress over the last few decades, but these advances are not equally distributed across disciplines. Those in computational disciplines are often trained to have the necessary technical skills to utilize HPC (e.g. familiarity with the terminal), but many disciplines face technical hurdles when trying to apply HPC resources to their work. This unequal familiarity with HPC is increasingly a problem as cross-discipline teams work to tackle critical interdisciplinary issues like climate change and sustainability. CyberGIS-Compute is middle-ware designed to democratize to HPC services with the goal of empowering domain scientists, but a key challenge facing model developers on CyberGIS-Compute is creating a containerized software environment for their models. In this paper, we discuss our work to integrate the Cern Virtual Machine File System (CVMFS) into CyberGIS-Compute to provide consistent software environments across science gateways and HPC resources.

3. I-GUIDE Forum

   I-GUIDE Climbers: A Model for Multidisciplinary Academic Labs for Early Career Development

   Haqiqi, Iman, Hu, Wei, Kumaran, Ramya, Li, Pin-Ching, Manning, Nicholas, Michels, Alexander, Nassar, Ayman, Park, Jinwoo, Shi, Jimeng, Tonks, Adam, and Wang, Zhaonan

   Forum 2023 - Harnessing the Geospatial Data Revolution for Sustainability Solutions, 2023

   Abs DOI

   In this paper, we propose a new form of multidisciplinary academic collaboration that goes beyond the traditional modes of knowledge exchange. We argue that most research collaboration today is based on interactions between closely related disciplines, in which researchers share data, methods, and insights within a common framework or problem. However, such collaboration may not foster the development of the communication and management skills essential to a multi-disciplinary research career. Therefore, we suggest establishing a network of researchers from divergent, yet complementary, disciplines who are interested in improving these skills through regular interactions and feedback. The main goal of this network is not to conduct research or address a specific research question, but to create a learning environment where researchers can enhance their interdisciplinary competencies through the diverse perspectives and experiences of their peers. Moreover, a multidisciplinary group of early-career professionals provides a space for collaborations to flourish. In this paper, we also offer practical advice for researchers who wish to join or create a similar network.

4. GIScience

   Impacts of Catchments Derived from Fine-Grained Mobility Data on Spatial Accessibility

   Michels, Alexander, Park, Jinwoo, Li, Bo, Kang, Jeon-Young, and Wang, Shaowen

   12th International Conference on Geographic Information Science (GIScience 2023), 2023

   Abs DOI

   Spatial accessibility is a powerful tool for understanding how access to important services and resources varies across space. While spatial accessibility methods traditionally rely on origin-destination matrices between centroids of administrative zones, recent work has examined creating polygonal catchments - areas within a travel-time threshold - from point-based fine-grained mobility data. In this paper, we investigate the difference between the convex hull and alpha shape algorithms for determining catchment areas and how this affects the results of spatial accessibility analyses. Our analysis shows that the choice of how we define a catchment produces differences in the measured accessibility which correlate with social vulnerability. These findings highlight the importance of evaluating and communicating minor methodological choices in spatial accessibility analyses.

2022

1. PEARC

   CyberGIS-Cloud: A Unified Middleware Framework for Cloud-Based Geospatial Research and Education

   Baig, Furqan, Michels, Alexander, Xiao, Zimo, Han, Su Yeon, Padmanabhan, Anand, Li, Zhiyu, and Wang, Shaowen

   Practice and Experience in Advanced Research Computing, 2022

   Abs DOI

   Interest in cloud-based cyberinfrastructure continues to grow within the geospatial community to tackle contemporary big data challenges. Distributed computing frameworks, deployed over the cloud, provide scalable and low-maintenance solutions to accelerate geospatial research and education. However, for scientists and researchers, the usage of such resources is highly constrained by the steep curve for learning diverse sets of platform-specific tools and APIs. This paper presents CyberGIS-Cloud as a unified middleware to streamline the execution of distributed geospatial workflows over multiple cloud backends with easy-to-use interfaces. CyberGIS-Cloud employs bringing computation-to-data model by abstracting and automating job execution over distributed resources hosted in the cloud environment where the data resides. We present details of CyberGIS-Cloud with support for popular distributed computing frameworks backed by research-oriented JetStream Cloud and commercial Google Cloud Platform.

2020

1. SIGSPATIAL GeoSim

   An Exploration of the Effect of Buyer Preference and Market Composition on the Rent Gradient Using the ALMA Framework

   Michels, Alexander, Kang, Jeon-Young, and Wang, Shaowen

   Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, 2020

   Abs DOI Code

   Urban land markets exhibit complex emergent behaviors that have yet to be fully explained by the microeconomic decision-making which constitutes the market. The Agent-based Land MArket (ALMA) framework has been introduced to simulate a bilateral agent-based land market that produces a rent gradient. In this paper, we extend the ALMA framework by introducing two new parameters, heterogeneity, and stochasticity which allow us to explore how the rent gradient is affected by buyers with diverse preferences and a range of market compositions.

2019

1. SIGSPATIAL GeoSim

   CyberGIS-Jupyter for Spatially Explicit Agent-based Modeling: A Case Study on Influenza Transmission

   Kang, Jeon-Young, Aldstadt, Jared, Michels, Alexander C., Vandewalle, Rebecca, and Wang, Shaowen

   GeoSim ’19: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, 2019

   Abs DOI

   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.

Published Abstracts

2022

1. AGU

   CyberGIS-Jupyter for Water - An Open Geospatial Computing Platform for Collaborative Water Research

   Li, Zhiyu, Michels, Alexander, Padmanabhan, Anand, Nassar, Ayman, Tarboton, David G., and Wang, Shaowen

   AGU Fall Meeting Abstracts, 2022

   Abs DOI

   Recent advances in cyberinfrastructure and data science promise to transform how hydrologic analysis and modeling are conducted. However, the computational capabilities needed for this potential transformation still remain only accessible to a small set of domain experts, hampering the engagement and contribution from the broader water research community. We have developed a domain-specific online computing platform, called CyberGIS-Jupyter for Water (CJW), that aims to integrate advanced cyberinfrastructure and geospatial capabilities for serving the broad water science communities. CJW represents a novel cyber-based geospatial information science and systems (cyberGIS) framework for harnessing distributed high-performance computing resources to enable collaborative and large-scale hydrologic analysis and modeling. CJW provides a stack of integrated geospatial software tools and libraries to facilitate collaborative and reproducible workflows that have been made interoperable with HydroShare, a web-based hydrologic data and model sharing platform, to expand community access. This talk presents the design and implementation of CJW, and demonstrates its capabilities with several success stories from users and a case study on computationally intensive hydrologic modeling based on WRF-Hydro.

2021

1. SIGSPATIAL SpatialAPI

   CyberGIS-Compute for Enabling Computationally Intensive Geospatial Research (Ext. Abs.)

   Padmanabhan, Anand, Xiao, Zimo, Vandewalle, Rebecca, Baig, Furqan, Michels, Alexander, Li, Zhiyu, and Wang, Shaowen

   SpatialAPI’21: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science, 2021

   Abs DOI

   In this tutorial, we will first start with the basics of CyberGISJupyter 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.

2. Gateways

   Towards Reproducible Research on CyberGISX with Lmod and Easybuild (Ext. Abs.)

   Michels, Alexander, Padmanabhan, Anand, Li, Zhiyu, and Wang, Shaowen

   Proceedings of Gateways 2021, 2021

   Abs DOI

   JupyterHub [1] has become a popular choice in many scientific communities, offering an easy-to-use interface for users with little to no frontend development work while promoting reproducible and replicable (R&R) science [2]. In the broad geospatial science community, CyberGISX [3] provides such a gateway environment with many cyberGIS (i.e., geospatial information science and systems based on advanced cyberinfrastructure) and geospatial software packages prebuilt and ready to use. Like other JupyterHub-based solutions, CyberGISX also provides container-based access for its users and must balance a trade-off between providing a static compute environment which enhances R&R and continuously updating the software environment to keep up with advances in scientific software. Solutions such as Binder [4] have attempted to address this trade-off by having required dependencies encoded in the package and building the software environment at the time of use. However, such a solution comes with two major disadvantages: (a) software is built at the time it is needed, increasing startup time and introducing the possibility that some of the dependencies of the environment are no longer available or have changed; and (b) the onus of specifying and managing software installations is passed to notebook developers, many of whom are domain scientists and not comfortable with such responsibilities. To address these challenges and enhance R&R with minimal effort from end-users, we have designed and implemented a solution on CyberGISX that allows software to be kept on an external file server mounted into each user’s environment. Scientific software is installed with Easybuild [5] and managed by Lmod [6] giving a variety of benefits: (1) the compute environment is more standardized and easily reproducible outside of the gateway; (2) multiple versions of software can be made available to users without increasing container size; and (3) the exact copies of software are always available on the gateway instead of being rebuilt for every release, further enhancing R&R. We also employ an Easybuild-installed Anaconda [7] to create and manage conda environments on the file server. The combination of the software stack from Easybuild and Python environment from conda provides end-users with kernels for their Jupyter notebooks which are persistent and unchanged as the gateway’s container updates. This design enhances R&R and adds functionality for advanced users without introducing technical barriers to non-technical end-users. As such, domain scientists using this solution need not build their own software and specify dependencies, which helps prevent the notebooks they have developed from getting broken by the next software release. This talk explores the new architecture and applications of this solution to CyberGISX [3] and CyberGIS-Jupyter for Water (CJW) [8].

3. Gateways

   Enabling Computationally Intensive Geospatial Research on CyberGIS-Jupyter with CyberGIS-Compute (Ext. Abs.)

   Padmanabhan, Anand, Xiao, Zimo, Vandewalle, Rebecca, Michels, Alexander, and Wang, Shaowen

   Proceedings of Gateways 2021, 2021

   Abs DOI

   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. With CyberGIS-Compute we have designed an easy-to-use middleware and associated Python SDK to provide access to CyberGIS capabilities, allowing geospatial applications to easily scale and employ advanced cyberinfrastructure resources. This presentation will first describe the basics of CyberGIS-Jupyter and CyberGIS-Compute, then introduce the Python SDK for CyberGIS-Compute with a simple example. Then, we will take multiple real-world geospatial applications use-cases like spatial accessibility and wildfire evacuation simulation using agent based modeling. Lastly, we will also descrive mechanism to contribute applications to the CyberGIS-Compute framework.