Map algebras are used for the manipulation of spatial data and form the basis for many types of spatial analyses and modeling efforts. The most basic form of a map algebra applies the same function (e.g., addition, subtraction) across the study area. To account for local variation of modeling parameters, we present a spatially dynamic map algebra to demonstrate the need for and utility of algebraic functions where the function is determined based on a specific and relative location. The need for such an algebra comes from the growth of complex models where the values of variables or parameters are not fixed across space. Locally derived parameters are not new, as shown by geographically weighted regression. This type of algebra is needed in cases of complex models, such as those found in flow networks, where network dynamics vary across space. This includes hydrologic processes, movement of people and goods, and the transmission of ideas. We test the approach on a case study of nitrogen flow in the Niantic River watershed, Connecticut. Findings show that the results from a spatially dynamic map algebra can differ from a fixed function. Fixed functions can result in model outputs that under- or overestimate by up to 50%.

With social media becoming increasingly location-based, there has been a greater push from researchers across various domains including social science, public health, and disaster management, to tap in the spatial, temporal, and textual data available from these sources to analyze public response during extreme events such as an epidemic outbreak or a natural disaster. Studies based on demographics and other socio-economic factors suggests that social media data could be highly skewed based on the variations of population density with respect to place. To capture the spatio-temporal variations in public response during extreme events we have developed the Socio-Environmental Data Explorer (SEDE). SEDE collects and integrates social media, news and environmental data to support exploration and assessment of public response to extreme events. For this study, using SEDE, we conduct spatio-temporal social media response analysis on four major extreme events in the United States including the “North American storm complex” in December 2015, the “snowstorm Jonas” in January 2016, the “West Virginia floods” in June 2016, and the “Hurricane Matthew” in October 2016. Analysis is conducted on geo-tagged social media data from Twitter and warnings from the storm events database provided by National Centers For Environmental Information (NCEI) for analysis. Results demonstrate that, to support complex social media analyses, spatial and population-based normalization and filtering is necessary. The implications of these results suggests that, while developing software solutions to support analysis of non-conventional data sources such as social media, it is quintessential to identify the inherent biases associated with the data sources, and adapt techniques and enhance capabilities to mitigate the bias. The normalization strategies that we have developed and incorporated to SEDE will be helpful in reducing the population bias associated with social media data and will be useful for researchers and decision makers to enhance their analysis on spatio-temporal social media responses during extreme events.

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.

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.

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.

BACKGROUND: A 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.\n\nMETHODS: The 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.\n\nRESULTS: SVG 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.\n\nCONCLUSIONS: SVG 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.

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 $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.

Objective: Disasters have devastated communities, impacted the economy, and resulted in a significant increase in injuries. As the use of mobile technology increasingly becomes a common aspect of everyday life, it is important to understand how it can be used as a resource. The authors examined the use of American Red Cross mobile apps and aimed to characterize user trends to better understand how mobile apps can help bolster individual and community preparedness, resilience, and response efforts. Design/main outcome measures: Tornado data were obtained from the National Oceanic and Atmospheric Administration and the National Weather Service. Data for the mobile apps were provided by the American Red Cross. All data were reviewed for 2013, 2014, and three specific tornado events. Data were organized in Microsoft Excel spreadsheets and then graphed or mapped using ArcMap 10.2(™). Results: Between 2013 and 2014, 1,068 tornado watches and 3,682 tornado warnings were issued. Additionally, 37,957,560 Tornado app users and 1,289,676 First Aid app users were active from 2013 to 2014. Overall, there was an increase in the use of American Red Cross mobile apps during tornado occurrences. Yet the increase does not show a consistent correlation with the number of watches and warnings issued. Conclusions: Mobile apps can be a resourceful tool. This study shows that mobile app use increases during a disaster. The findings indicate that there is potential to use mobile apps for building resilience as the apps provide information to support individuals and communities in helping before, during, and after disasters.

Epidemic agent-based models (ABMs) simulate individuals in artificial societies that are capable of movement, interaction, and transmitting disease among themselves. ABMs have been used to study the spread of disease at various spatial and temporal scales ranging from small communities to the world, over days, months, and years. The representations of space and time often vary between different epidemic ABMs and can be influenced by factors such as the size of a modeled population, computational requirements, population environments, and disease-related data. The influence that the representations of space and time have on epidemic ABMs is difficult to assess. Here we show that the finest representations of space and time—termed spatial and temporal granularities (STGs)—in a parsimonious ABM affect speed, intensity, and spatial spread of a synthetic disease. Specifically, we found disease spread faster and more intensely as spatial granularity is coarsened, whereas disease spread slower and less intensely as temporal granularity is coarsened in a parsimonious ABM. Our study is the first to use the same epidemic ABM to examine the influence of STGs. Our results demonstrate that STGs influence ABM dynamics including early disease burnout and that an interrelationship exists between the coarsening of STGs and the speed and intensity at which disease spreads. Our parsimonious ABM is extended based on a structured community model and we found STGs also influence ABM dynamics in a more realistic context that includes hierarchical movement. Broadly, our study serves as a basis for further inquiry toward the influence of space–time representations on more realistic models that include multiscale mobility, routine movements (e.g., commuting), and heterogeneous population distributions.

To better understand the origins of modern humans, we are developing a paleoscape model that simulates the climatic conditions and distribution of natural resources available to humans during this critical stage of human evolution. Our geographic focus is the southern Cape region of South Africa, which was rich in natural resources for hunter-gatherer groups including edible plants, shellfish, animals, and raw materials. In this article we report our progress in using the Extreme Science and Engineering Discovery Environment (XSEDE) to realize the paleoscape model, which consists of four models: a climate model, correlative and dynamic vegetation models, and agent-based models. We adopt a workflow-based approach that combines modeling and data analytics to couple these modeling components, which will leverage multiple XSEDE resources to generate and analyze multi-terabyte datasets. We have made significant progress in scaling climate and agent-based models on XSEDE. Our next steps will be to couple these models to the vegetation models to complete the workflow, which will require overcoming multiple theoretical, methodological, and technical challenges.

In just under seven years, Twitter has grown to count nearly three percent of the entire global population among its active users who have sent more than 170 billion 140-character messages. Today the service plays such a significant role in American culture that the Library of Congress has assembled a permanent archive of the site back to its first tweet, updated daily. With its open API, Twitter has become one of the most popular data sources for social research, yet the majority of the literature has focused on it as a text or network graph source, with only limited efforts to date focusing exclusively on the geography of Twitter, assessing the various sources of geographic information on the service and their accuracy. More than three percent of all tweets are found to have native location information available, while a naive geocoder based on a simple major cities gazetteer and relying on the user-provided Location and Profile fields is able to geolocate more than a third of all tweets with high accuracy when measured against the GPS-based baseline. Geographic proximity is found to play a minimal role both in who users communicate with and what they communicate about, providing evidence that social media is shifting the communicative landscape. © 2013, First Monday. © 2013, Kalev H. Leetaru, Shaowen Wang, Guofeng Cao, Anand Padmanabhan, and Eric Shook.

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.

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 Wikipedia's 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.