Eric Shook

An analysis of ozone damage to historical maize and soybean yields in the United States

Significance Although it has long been known that ground-level ozone (O3) damages crops and reduces yield, there has never been an estimate of the total loss attributed to ambient O3 for field-grown maize and soybean in the United States. Knowing the loss caused by this pollutant would be useful for projecting food supply and setting regulatory standards for pollutant emissions. Here we show that ambient O3 has reduced maize and soybean yields in rain-fed fields by ∼10% and 5%, respectively, based on historical observations from the past 31 y. Results suggest that air-quality regulations in the United States have been effective in reducing crop production losses to O3, and indicate that further reductions in ground-level [O3] would be beneficial in the United States and globally. Numerous controlled experiments find that elevated ground-level ozone concentrations ([O3]) damage crops and reduce yield. There have been no estimates of the actual yield losses in the field in the United States from [O3], even though such estimates would be valuable for projections of future food production and for cost–benefit analyses of reducing ground-level [O3]. Regression analysis of historical yield, climate, and [O3] data for the United States were used to determine the loss of production due to O3 for maize (Zea mays) and soybean (Glycine max) from 1980 to 2011, showing that over that period production of rain-fed fields of soybean and maize were reduced by roughly 5% and 10%, respectively, costing approximately

A communication-aware framework for parallel spatially explicit agent-based models

9 billion annually. Maize, thought to be inherently resistant to O3, was at least as sensitive as soybean to O3 damage. Overcoming this yield loss with improved emission controls or more tolerant germplasm could substantially increase world food and feed supply at a time when a global yield jump is urgently needed.

Spatial video geonarratives and health: case studies in post-disaster recovery, crime, mosquito control and tuberculosis in the homeless.

Parallel spatially explicit agent-based models (SE-ABM) exploit high-performance and parallel computing to simulate spatial dynamics of complex geographic systems. The integration of parallel SE-ABM with CyberGIS could facilitate straightforward access to massive computational resources and geographic information systems to support pre- and post-simulation analysis and visualization. However, to benefit from CyberGIS integration, parallel SE-ABM must overcome the challenge of communication management for orchestrating many processor cores in parallel computing environments. This paper examines and addresses this challenge by describing a generic framework for the management of inter-processor communication to enable parallel SE-ABM to scale to high-performance parallel computers. The framework synthesizes four interrelated components: agent grouping, rectilinear domain decomposition, a communication-aware load-balancing strategy, and entity proxies. The results of a series of computational experiments based on a template agent-based model demonstrate that parallel computational efficiency diminishes as inter-processor communication increases, particularly when scaling a fixed-size model to thousands of processor cores. Therefore, effective communication management is crucial. The communication framework is shown to efficiently scale up to 2048 cores, demonstrating its ability to effectively scale to thousands of processor cores to support the simulation of billions of agents. In a simulated scenario, the communication-aware load-balancer reduced both overall simulation time and communication percentage improving overall computational efficiency. By examining and addressing inter-processor communication challenges, this research enables parallel SE-ABM to efficiently use high-performance computing resources, which reduces the barriers for synergistic integration with CyberGIS.

Context and Spatial Nuance Inside a Neighborhood's Drug Hotspot: Implications for the Crime–Health Nexus

BackgroundA call has recently been made by the public health and medical communities to understand the neighborhood context of a patient’s life in order to improve education and treatment. To do this, methods are required that can collect “contextual” characteristics while complementing the spatial analysis of more traditional data. This also needs to happen within a standardized, transferable, easy-to-implement framework.MethodsThe Spatial Video Geonarrative (SVG) is an environmentally-cued narrative where place is used to stimulate discussion about fine-scale geographic characteristics of an area and the context of their occurrence. It is a simple yet powerful approach to enable collection and spatial analysis of expert and resident health-related perceptions and experiences of places. Participants comment about where they live or work while guiding a driver through the area. Four GPS-enabled cameras are attached to the vehicle to capture the places that are observed and discussed by the participant. Audio recording of this narrative is linked to the video via time stamp. A program (G-Code) is then used to geotag each word as a point in a geographic information system (GIS). Querying and density analysis can then be performed on the narrative text to identify spatial patterns within one narrative or across multiple narratives. This approach is illustrated using case studies on post-disaster psychopathology, crime, mosquito control, and TB in homeless populations.ResultsSVG can be used to map individual, group, or contested group context for an environment. The method can also gather data for cohorts where traditional spatial data are absent. In addition, SVG provides a means to spatially capture, map and archive institutional knowledge.ConclusionsSVG GIS output can be used to advance theory by being used as input into qualitative and/or spatial analyses. SVG can also be used to gain near-real time insight therefore supporting applied interventions. Advances over existing geonarrative approaches include the simultaneous collection of video data to visually support any commentary, and the ease-of-application making it a transferable method across different environments and skillsets.

Happy or not: Generating topic-based emotional heatmaps for Culturomics using CyberGIS

New geographic approaches are required to tease apart the underlying sociospatial complexity of neighborhood decline to target appropriate interventions. Typically maps of crime hotspots are used with relatively little attention being paid to geographic context. This article helps further this discourse using a topical study of a neighborhood drug microspace, a phrase we use to include the various stages of production, selling, acquiring, and taking, to show how context matters. We overlay an exploratory data analysis of three cohort spatial video geonarratives (SVGs) to contextualize the traditional crime rate hotspot maps. Using two local area analyses of police, community, and ex-offender SVGs and then comparing these with police call for service data, we identify spaces of commonality and difference across data types. In the Discussion, we change the scale to consider revealed microspaces and the interaction of both “good” and “bad” places. We enrich the previous analysis with a mapped spatial video assessment of the built environment and then return to the narrative to extract additional detail around a crime-associated corner store next to a community center. Our findings suggest that researchers should reevaluate how to enrich typical hotspot approaches with more on-the-ground context.

Parallel cartographic modeling: a methodology for parallelizing spatial data processing

The field of Culturomics exploits “big data” to explore human society at population scale. Culturomics increasingly needs to consider geographic contexts and, thus, this research develops a geospatial visual analytical approach that transforms vast amounts of textual data into emotional heatmaps with fine-grained spatial resolution. Fulltext geocoding and sentiment mining extract locations and latent “tone” from text-based data, which are combined with spatial analysis methods - kernel density estimation and spatial interpolation - to generate heatmaps that capture the interplay of location, topic, and tone toward narrative impacts. To demonstrate the effectiveness of the approach, the complete English edition of Wikipedia is processed using a supercomputer to extract all locations and tone associated with the year of 2003. An emotional heatmap of Wikipedias discussion of “armed conflict” for that year is created using the spatial analysis methods. Unlike previous research, our approach is designed for exploratory spatial analysis of topics in text archives by incorporating multiple attributes including the prominence of each location mentioned in the text, the density of a topic at each location compared to other topics, and the tone of the topics of interest into a single analysis. The generation of such fine-grained emotional heatmaps is computationally intensive particularly when accounting for the multiple attributes at fine scales. Therefore a CyberGIS platform based on national cyberinfrastructure in the United States is used to enable the computationally intensive visual analytics.

The socio-environmental data explorer (SEDE): a social media–enhanced decision support system to explore risk perception to hazard events

ABSTRACT This article establishes a new methodological framework for parallelizing spatial data processing called parallel cartographic modeling, which extends the widely adopted cartographic modeling framework. Parallel cartographic modeling adds a novel component called a Subdomain, which serves as the elemental unit of parallel computation. Four operators are also added to express parallel spatial data processing, namely scheduler, decomposition, executor, and iteration. A parallel cartographic modeling language (PCML) is developed based on the parallel cartographic modeling framework, which is designed for usability, programmability, and scalability. PCML is a domain-specific language implemented in Python for the domain of cyberGIS. A key feature of PCML is that it supports automatic parallelization of cartographic modeling scripts; thus, allowing the analyst to develop models in the familiar cartographic modeling language in a Python syntax. PCML currently supports more than 70 operations and new operations can be easily implemented in as little as three lines of PCML code. Experimental results using the National Science Foundation-supported Resourcing Open Geospatial Education and Research computational resource demonstrate that PCML efficiently scales to 16 cores and can process gigabytes of spatial data in parallel. PCML is shown to support multiple decomposition strategies, decomposition granularities, and iteration strategies that be generically applied to any operation implemented in PCML.

Developing the Modular Information Provider (MIP) to Support Interoperable Grid Information Services

ABSTRACT Social media are increasingly recognized as a useful data source for understanding social response to hazard events in real time and in post-event analysis. This article establishes social media–enhanced decision support systems (SME-DSS) as a synergistic integration of social media and decision support systems (DSSs) to provide structured access to native, near real-time data from a large and diverse population to assess social response to social, environmental, and technological risk and hazard events. We introduce a prototype SME-DSS entitled socio-environmental data explorer (SEDE) to explore the opportunities and challenges of leveraging social media for decision support. We use a winter storm during 25–28 January 2015 that accumulated record amounts of snow along the East Coast of the United States as a case study to evaluate SEDE in helping assess social response to environmental risk and hazard events as well as evaluate social media as a theoretical component within the social amplification of risk framework (SARF) that serves as a theoretical foundation for SME-DSS.

Terra Populus’ architecture for integrated big geospatial services

The Modular Information Provider (MIP) has been developed to systematically aggregate multiple sources of information for grid information services (GIS). MIP undertakes the challenge of mapping information from a large number of sources to information services with minimal human intervention. MIP addresses such mappings using a modular approach to achieve interoperability among grid environments. MIP can be customized in a straightforward way to a specific Grid environment or information schema. Current MIP implementation is based on Globus MDS4 and the XML version of GLUE Schema 1.2. The design of MIP aims to address the shortcomings that exist in current grid information providers (e. g., the generic information provider) as well as to support Web service-based GIS. Our modular approach has been developed to ease maintenance and management of grid information systems by automatically aggregating information sources and providing appropriate pieces of information to GIS based on predefined information schemas. Upon installation, MIP is configured to set up only necessary modules, which minimizes memory usage

Paleoscape model of coastal South Africa during modern human origins: progress in scaling and coupling climate, vegetation, and agent-based models on XSEDE

Big geospatial data is an emerging sub-area of geographic information science, big data, and cyberinfrastructure. Big geospatial data poses two unique challenges to these and other cognate disciplines. First, raster and vector data structures and analyses have developed on largely separate paths for the last twenty years and this creates an impediment to researchers utilizing big data platforms that do not promote the integration for these classes. Second, big spatial data repositories have yet to be integrated with big data computation platforms in ways that allow researchers to spatio-temporally analyze big geospatial datasets. IPUMS-Terra, a National Science Foundation cyberInfrastructure project, begins to address these challenges. IPUMS-Terra is a spatial data infrastructure project that provides a unified framework for accessing, analyzing, and transforming big heterogeneous spatio-temporal data, and is part of the IPUMS (Integrated Public Use Microdata Series) data infrastructure. It supports big geospatial data analysis and provides integrated big geospatial services to its users. As IPUMS-Terras data volume grows, we seek to integrate geospatial platforms that will scale geospatial analyses and address current bottlenecks within our system. However, our work shows that there are still unresolved challenges for big geospatial analysis. The most pertinent is that there is a lack of a unified framework for conducting scalable integrated vector and raster data analysis. We conducted a comparative analysis between PostgreSQL with PostGIS and SciDB and concluded that SciDB is the superior platform for scalable raster zonal analyses.