publications

2022

2021

1. Report

   Banking on the Belt and Road: Insights from a new global dataset of 13,427 Chinese development projects

   Malik, Ammar A., Parks, Bradley, Russell, Brooke, Lin, Joyce Jiahui, Walsh, Katherine, Solomon, Kyra, Zhang, Sheng, Elston, Thai-Binh, and Goodman, Seth

   AidData 2021

   Bib HTML

   @article{malik_2021,
     author = {Malik, Ammar A. and Parks, Bradley and Russell, Brooke and Lin, Joyce Jiahui and Walsh, Katherine and Solomon, Kyra and Zhang, Sheng and Elston, Thai-Binh and Goodman, Seth},
     title = {Banking on the Belt and Road: Insights from a new global dataset of 13,427 Chinese development projects},
     year = {2021},
     journal = {AidData}
   }

2. PhD

   Filling in the Gaps: Applications of Deep Learning, Satellite Imagery, and High Performance Computing for the Estimation and Distribution of Geospatial Data

   Goodman, Seth

   The College of William and Mary ProQuest Dissertations Publishing 2021

   Bib HTML

   @article{goodman2021_phd,
     author = {Goodman, Seth},
     title = {Filling in the Gaps: Applications of Deep Learning, Satellite Imagery, and High Performance Computing for the Estimation and Distribution of Geospatial Data},
     year = {2021},
     journal = {The College of William and Mary ProQuest Dissertations Publishing}
   }

2020

1. TGIS

   A convolutional neural network approach to predict non-permissive environments from moderate-resolution imagery

   Goodman, Seth, BenYishay, Ariel, and Runfola, Daniel

   Transactions in GIS 2020

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   Convolutional neural networks (CNNs) trained with satellite imagery have been successfully used to generate measures of development indicators, such as poverty, in developing nations. This article explores a CNN-based approach leveraging Landsat 8 imagery to predict locations of conflict-related deaths. Using Nigeria as a case study, we use the Armed Conflict Location & Event Data (ACLED) dataset to identify locations of conflict events that did or did not result in a death. Imagery for each location is used as an input to train a CNN to distinguish fatal from non-fatal events. Using 2014 imagery, we are able to predict the result of conflict events in the following year (2015) with 80% accuracy. While our approach does not replace the need for causal studies into the drivers of conflict death, it provides a low-cost solution to prediction that requires only publicly available imagery to implement. Findings suggest that the information contained in moderate-resolution imagery can be used to predict the likelihood of a death due to conflict at a given location in Nigeria the following year, and that CNN-based methods of estimating development-related indicators may be effective in applications beyond those explored in the literature.

   @article{goodman_tgis2020,
     doi = {https://doi.org/10.1111/tgis.12661},
     url = {https://doi.org/10.1111/tgis.12661},
     author = {Goodman, Seth and BenYishay, Ariel and Runfola, Daniel},
     journal = {Transactions in GIS},
     title = {A convolutional neural network approach to predict non-permissive environments from moderate-resolution imagery},
     year = {2020}
   }

2. PLoS ONE

   GeoBoundaries: A global database of political administrative boundaries

   Runfola, Daniel, Anderson, Austin, Baier, Heather, Crittenden, Matt, Dowker, Elizabeth, Fuhrig, Sydney,  Goodman, Seth, Grimsley, Grace, Layko, Rachel, Melville, Graham, Mulder, Maddy, Oberman, Rachel, Panganiban, Joshua, Peck, Andrew, Seitz, Leigh, Shea, Sylvia, Slevin, Hannah, Youngerman, Rebecca, and Hobbs, Lauren

   PLoS ONE 2020

   Abs Bib HTML

   We present the geoBoundaries Global Administrative Database (geoBoundaries): an online, open license resource of the geographic boundaries of political administrative divisions (i.e., state, county). Contrasted to other resources geoBoundaries (1) provides detailed information on the legal open license for every boundary in the repository, and (2) focuses on provisioning highly precise boundary data to support accurate, replicable scientific inquiry. Further, all data is released in a structured form, allowing for the integration of geoBoundaries with large-scale computational workflows. Our database has records for every country around the world, with up to 5 levels of administrative hierarchy. The database is accessible at http://www.geoboundaries.org, and a static version is archived on the Harvard Dataverse.

   @article{RunfolaGeoboundaries2020,
     author = {Runfola, Daniel and Anderson, Austin and Baier, Heather and Crittenden, Matt and Dowker, Elizabeth and Fuhrig, Sydney and Goodman, Seth and Grimsley, Grace and Layko, Rachel and Melville, Graham and Mulder, Maddy and Oberman, Rachel and Panganiban, Joshua and Peck, Andrew and Seitz, Leigh and Shea, Sylvia and Slevin, Hannah and Youngerman, Rebecca and Hobbs, Lauren},
     doi = {10.1371/journal.pone.0231866},
     journal = {PLoS ONE},
     title = {{GeoBoundaries: A global database of political administrative boundaries}},
     year = {2020}
   }

3. RS

   Open Earth Observations for Sustainable Urban Development

   Prakash, Mihir, Ramage, Steven, Kavvada, Argyro, and Goodman, Seth

   Remote Sensing 2020

   Abs Bib HTML

   Our cities are the frontier where the battle to achieve the global sustainable development agenda over the next decade would be won or lost. This requires an evidence-based approach to local decision-making and resource allocation, which can only be possible if current gaps in urban data are bridged. Earth observation (EO) offers opportunities to provide timely, spatially disaggregated information that supports this need. Spatially disaggregated information, which is also demanded by cities for forward planning and land management, has not received much attention largely due to three reasons: (i) the cost of generating this data through traditional methods remains high; (ii) the technical capacity in geospatial sciences in many countries is low due to a shortage of skilled professionals who can find and/or process available data; and (iii) the inertia against disturbing routine workflows and adopting new practices that are not imposed through legal requirements at the country level. In support of overcoming the first two challenges, this paper discusses the importance of EO data in the urban context, how it is already being used by some city leaders for decision making, and what other applications it offers in the realm of urban sustainability monitoring. It also illustrates how the EO community, via the Group on Earth Observations (GEO) and its members, is working to make this data more easily accessible and lower barriers of use by policymakers and urban practitioners that are interested in implementing and tracking sustainable development in their jurisdictions. The paper concludes by shining a light on the challenges that remain to be overcome for better adoption of EO data for urban decision making through better communication between the two groups, to enable a more effective alignment of the produced data with the users’ needs.

   @article{rs12101646,
     author = {Prakash, Mihir and Ramage, Steven and Kavvada, Argyro and Goodman, Seth},
     title = {Open Earth Observations for Sustainable Urban Development},
     journal = {Remote Sensing},
     volume = {12},
     year = {2020},
     number = {10},
     article-number = {1646},
     url = {https://www.mdpi.com/2072-4292/12/10/1646},
     issn = {2072-4292},
     doi = {10.3390/rs12101646}
   }

4. Sustainability

   Exploring the Socioeconomic Co-benefits of Global Environment Facility Projects in Uganda Using a Quasi-Experimental Geospatial Interpolation (QGI) Approach

   Runfola, Daniel, Batra, Geeta, Anand, Anupam, Way, Audrey, and Goodman, Seth

   Sustainability 2020

   Abs Bib HTML

   Since 1992, the Global Environment Facility (GEF) has mobilized over 131 billion in funds to enable developing and transitioning countries to meet the objectives of international environmental conventions and agreements. While multiple studies and reports have sought to examine the environmental impact of these funds, relatively little work has examined the potential for socioeconomic co-benefits. Leveraging a novel database on the geographic location of GEF project interventions in Uganda, this paper explores the impact of GEF projects on household assets in Uganda. It employs a new methodological approach, Quasi-experimental Geospatial Interpolation (QGI), which seeks to overcome many of the core biases and limitations of previous implementations of causal matching studies leveraging geospatial information. Findings suggest that Sustainable Forest Management (SFM) GEF projects with initial implementation dates prior to 2009 in Uganda had a positive, statistically significant impact of approximately 184.81 on the change in total household assets between 2009 and 2011. Leveraging QGI, we identify that (1) this effect was statistically significant at distances between 2 and 7 km away from GEF projects, (2) the effect was positive but not statistically significant at distances less than 2 km, and (3) there was insufficient evidence to establish the impact of projects beyond a distance of approximately 7 km.

   @article{su12083225,
     author = {Runfola, Daniel and Batra, Geeta and Anand, Anupam and Way, Audrey and Goodman, Seth},
     title = {Exploring the Socioeconomic Co-benefits of Global Environment Facility Projects in Uganda Using a Quasi-Experimental Geospatial Interpolation (QGI) Approach},
     journal = {Sustainability},
     volume = {12},
     year = {2020},
     number = {8},
     article-number = {3225},
     url = {https://www.mdpi.com/2071-1050/12/8/3225},
     issn = {2071-1050},
     doi = {10.3390/su12083225}
   }

2019

1. DevEng

   Assessing the causal impact of Chinese aid on vegetative land cover in Burundi and Rwanda under conditions of spatial imprecision

   Marty, Robert,  Goodman, Seth, LeFew, Michael, Dolan, Carrie, BenYishay, Ariel, and Runfola, Daniel

   Development Engineering 2019

   Abs Bib HTML

   There has been considerable debate regarding the efficacy of international aid in meeting the dual goals of human development and environmental sustainability. Many donors have sought to engage with this challenge by introducing environmental safeguard and monitoring initiatives; however, evidence on the success of these interventions is limited. Evaluating aid is a particular challenge in the case of donors that do not disclose information on the nature, geographic location, or extents of their interventions. In such cases, new methods that extract and geoparse data on the activities of opaque donors through the manual interpretation of thousands of news and other articles allow us to investigate the impacts of these activities. However, residual spatial uncertainty in these data remains a potential source of bias. In this article, we apply and discuss a Geographic Simulation and Extrapolation (GeoSIMEX) approach to mitigate the spatial imprecision inherent in geoparsed data. In conjunction with GeoSIMEX, we test and contrast multiple approaches to reducing the imprecision of aid, including high-assumption cases in which other covariates (i.e., nighttime lights) are leveraged to allocate aid. In our application, we find that methods which do not account for spatial imprecision find statistically significant relationships between Chinese aid and vegetation change; after accounting for spatial uncertainty, findings are similar for Rwanda and inconclusive for Burundi.

   @article{MARTY2019100038,
     title = {Assessing the causal impact of Chinese aid on vegetative land cover in Burundi and Rwanda under conditions of spatial imprecision},
     journal = {Development Engineering},
     volume = {4},
     pages = {100038},
     year = {2019},
     issn = {2352-7285},
     doi = {https://doi.org/10.1016/j.deveng.2018.11.001},
     url = {https://www.sciencedirect.com/science/article/pii/S2352728517301173},
     author = {Marty, Robert and Goodman, Seth and LeFew, Michael and Dolan, Carrie and BenYishay, Ariel and Runfola, Daniel},
     keywords = {Aid, International development, Spatial imprecision, Geoparsing, Forest cover}
   }

2. C&G

   GeoQuery: Integrating HPC systems and public web-based geospatial data tools

   Goodman, Seth, BenYishay, Ariel, Lv, Zhonghui, and Runfola, Daniel

   Computers & Geosciences 2019

   Abs Bib HTML

   Interdisciplinary use of geospatial data requires the integration of data from a breadth of sources, and frequently involves the harmonization of different methods of sampling, measurement, and technical data types. These integrative efforts are often inhibited by fundamental geocomputational challenges, including a lack of memory efficient or parallel processing approaches to traditional methods such as zonal statistics. GeoQuery (geoquery.org) is a dynamic web application which utilizes a High Performance Computing cluster and novel parallel geospatial data processing methods to overcome these challenges. Through an online interface, GeoQuery users can request geospatial data - which spans categories including geophysical, environmental and social measurements - to be aggregated to user-selected units of analysis (e.g., subnational administrative boundaries). Once a request has been processed, users are provided with permanent links to access their customized data and documentation. Datasets made available through GeoQuery are reviewed, prepared, and provisioned by geospatial data specialists, with processing routines tailored for each dataset. The code used and steps taken while preparing datasets and processing user requests are publicly available, ensuring transparency and replicability of all data and processes. By mediating the complexities of working with geospatial data, GeoQuery reduces the barriers to entry and the related costs of incorporating geospatial data into research across disciplines. This paper presents the technology and methods used by GeoQuery to process and manage geospatial data and user requests.

   @article{GOODMAN2019103,
     title = {GeoQuery: Integrating HPC systems and public web-based geospatial data tools},
     journal = {Computers & Geosciences},
     volume = {122},
     pages = {103-112},
     year = {2019},
     issn = {0098-3004},
     doi = {https://doi.org/10.1016/j.cageo.2018.10.009},
     url = {https://www.sciencedirect.com/science/article/pii/S0098300418305326},
     author = {Goodman, Seth and BenYishay, Ariel and Lv, Zhonghui and Runfola, Daniel},
     keywords = {Geographical information science & systems, Parallel and high-performance computing, Algorithms, Data processing, Spatial statistics, Computational methods}
   }

2018

201