Geospatial Simulation Research Articles

Wheat drought-tolerance to enhance food security in Tunisia, birthplace of the Arab Spring

Abstract The beginning of the ‘Arab Spring’ in 2011, a regional revolution which started in the Tunisian city of Sidi Bouzid in late 2010, occurred in part as a result of drought-triggered high wheat prices, which in the past led to ‘bread riots’ across several Middle East and North Africa (MENA) nations. Here we present, for the first time, an analysis of possible amelioration of wheat yield loss and greater stability in bread supply resulting from the incorporation of putative drought-tolerant traits into wheat cultivars grown in Tunisia. To this end, we used a simulation crop modeling approach using SSM-Wheat to evaluate yield loss or gain resulting from three types of water-saving traits that have been recently identified in wheat. These consisted in partial stomatal closure at high soil water content, overall decrease in transpiration rate (TR), and partial stomatal closure under elevated vapor pressure deficit (VPD). To capture large gradients in seasonal precipitation across wheat growing areas over a small country such as Tunisia, a grid pattern of 29 × 29 km was established as a basis for the geospatial simulation. Surprisingly, the simulation reflected opposite strategies in terms of water use (water-saving vs aggressive water use). The highest yield gain (30%) resulting from water-saving modification was found to occur in the food-insecure region of Sidi Bouzid. Traits enabling aggressive water use were found to be generally favorable across Tunisia, with one trait leading to up to 80% and 40% increases in yield and its stability in the food-challenged south of the country. However, major yield penalties were found to occur if water-saving traits were to be deployed in the ‘wrong’ region. Those findings could be used as a blueprint to navigate complex trait × environment interactions and to better inform local breeding and management programs to improve wheat yield and it stability in Tunisia and the MENA region in general.

GeoSim 2018 workshop report the 1st ACM SIGSPATIAL international workshop on geospatial simulation

Geospatial simulation is a powerful tool for many disciplines including urban analytics, social science and meteorology to understand, explain, and predict overly complex natural and human-made systems. For instance, hurricane simulation has been used to predict hurricane paths and to make a decisions such as evacuation plans. Traffic simulation have enabled us to forecast traffic congestion in cities around the world.

Linking Water Infrastructure, Public Health, and Sea Level Rise: Integrated Assessment of Flood Resilience in Coastal Cities

Coastal community water infrastructure is increasingly vulnerable to climate-sensitive coastal hazards. Tides, storm surges, rainfall, and salt intrusion affect infrastructure and human health. In case studies of Charleston, South Carolina, and Morehead City, North Carolina, USA, this project sought to advance risk assessment of urban water and wastewater infrastructure and identify linkages to human health impacts as risk evolves with sea level rise. The methodology integrates community infrastructure, health care, emergency resources, geospatial simulation, and a tabletop exercise with planners, emergency managers, public utilities, and health care providers. Resilience is assessed by community participants using interactive online maps, susceptibility indices, and a resilience matrix. Results highlight differential vulnerability, population susceptibility, and elevation uncertainty. We observe similar trends of increasing magnitude, frequency, and impact of flood events on water infrastructure and public health as sea level rises. Implications for tackling challenges across sectors are highlighted for improving coastal resilience.

A GEOSPATIAL ANALYSIS FRAMEWORK FOR FINE SCALE URBAN INFRASTRUCTURE NETWORKS

Abstract. Understanding the spatial connectivity of urban infrastructure networks that connect assets to buildings is important for the fine-scale spatial analysis and modelling of the resource flows within cities. However, rarely are spatially explicit representations of infrastructure networks available for such analysis. Further, an appropriate database system is the core of development of an infrastructure assets information and management platform, capable of handling the wide range of data for infrastructure system modelling and analysis. In this paper, we develop a geospatial simulation and analysis framework, which is capable of generating fine-scale urban infrastructure networks and storing the network instances in a hybrid database system for further modelling and analysis needs. We demonstrate the use of this platform by simulating the entire-city electricity distribution network for the city of Newcastle upon Tyne. Validation of the resulting network is performed using the network layout diagram from the local power company. The heuristically derived network was found to have a 91 % spatial accuracy.

Design and Implementation of a 4D Web Application for Analytical Visualization of Smart City Applications

Contemporary development of computer hardware and software, WebGIS and geo-web services as well as the availability of semantic 3D city models, facilitate flexible and dynamic implementation of web applications. The aim of this paper is to introduce 4D CANVAS, a web-based application for dynamic visualization of 3D geospatial data for improved decision making in smart city applications. It is based on the Cesium Virtual Globe, an open-source JavaScript library developed with HTML5 and WebGL. At first, different data formats such as JSON, GeoJSON, Cesium Markup Language (CZML) and 3D Tiles are evaluated for their suitability in 4D visualization applications. Then, an interactive Graphical User Interface (GUI) is built observing the principle of cartographic standards to view, manage, understand and explore different simulation outputs at multiple spatial (3D surface of buildings) and temporal (hourly, daily, monthly) resolutions. In this regard, multiple tools such as aggregation, data classification, etc. are developed utilizing JavaScript libraries. As a proof of concept, two energy simulations and their outputs of different spatial and temporal resolutions are demonstrated in five Asian and European cities. Finally, the 4D CANVAS is deployed both in desktop and multi-touch screens. The proposed application allows easy integration of any other geospatial simulation results, thereby helps the users from different sectors to explore them interactively in 4D.

SIMULATING INFRASTRUCTURE OUTAGES: AN OPEN-SOURCE GEOSPATIAL APPROACH

Abstract. Understanding the impact of service outages caused by natural or man-made disasters in utility services is a key part of decision-making in response and recovery efforts. Large-scale outages in the last 15 years, from the 2003 northeast blackout to Hurricane Maria devastating Puerto Rico in 2017, highlighted the importance of tight couplings within and across various utilities. The brittleness of these tight couplings results in long delays in restoring large-scale outages. Such cross-infrastructure effects can make analysis for decision makers and responders far more complex. To facilitate recovery, decision makers need to use specialized Decision Support Systems (DSS) that allow simulation of various alternative enablement options along with their impact on society. In this article, we describe our geo-simulation engine and datasets used for outage modelling. First, we detail our efforts in correcting and completing Electric Power (EP) network for the western US. Next, we explain the architecture and initial implementation of the platform-independent, open-source geospatial simulation engine that we are in the process of developing. Using this engine, we can consider the amount of commodity at the transmission source (power plants) and sinks (substations) and set thresholds at sinks to trigger and simulate outages. For instance, a threshold can be set to trigger an outage at substation level if the available commodity amount drops below 80 % of the demand. Future additions include cross-infrastructure and enablement consequence analysis to provide a complete and transparent DSS to study outages on multiple interrelating infrastructures through scenario-based evaluation criteria.

Tangible topographic modeling for landscape architects

We present Tangible Landscape—a technology for rapidly and intuitively designing landscapes informed by geospatial modeling, analysis, and simulation. It is a tangible interface powered by a geographic information system that gives three-dimensional spatial data an interactive, physical form so that users can naturally sense and shape it. Tangible Landscape couples a physical and a digital model of a landscape through a real-time cycle of physical manipulation, three-dimensional scanning, spatial computation, and projected feedback. Natural three-dimensional sketching and real-time analytical feedback should aid landscape architects in the design of high performance landscapes that account for physical and ecological processes. We conducted a series of studies to assess the effectiveness of tangible modeling for landscape architects. Landscape architecture students, academics, and professionals were given a series of fundamental landscape design tasks—topographic modeling, cut-and-fill analysis, and water flow modeling. We assessed their performance using qualitative and quantitative methods including interviews, raster statistics, morphometric analyses, and geospatial simulation. With tangible modeling, participants built more accurate models that better represented morphological features than they did with either digital or analog hand modeling. When tangibly modeling, they worked in a rapid, iterative process informed by real-time geospatial analytics and simulations. With the aid of real-time simulations, they were able to quickly understand and then manipulate how complex topography controls the flow of water.

Efficient utilization of multi-core processors and many-core co-processors on supercomputer beacon for scalable geocomputation and geo-simulation over big earth data

Digital earth science data originated from sensors aboard satellites and platforms such as airplane, UAV, and mobile systems are increasingly available with high spectral, spatial, vertical, and temporal resolution data. When such big earth science data are processed and analyzed via geocomputation solutions, or utilized in geospatial simulation or modeling, considerable computing power and resources are necessary to complete the tasks. While classic computer clusters equipped by central processing units (CPUs) and the new computing resources of graphics processing units (GPUs) have been deployed in handling big earth data, coprocessors based on the Intel’s Many Integrated Core (MIC) Architecture are emerging and adopted in many high-performance computer clusters. This paper introduces how to efficiently utilize Intel’s Xeon Phi multicore processors and MIC coprocessors for scalable geocomputation and geo-simulation by implementing two algorithms, Maximum Likelihood Classification (MLC) and Cellular Automata (CA), on supercomputer Beacon, a cluster of MICs. Four different programming models are examined, including (1) the native model, (2) the offload model, (3) the symmetric model, and (4) the hybrid-offload model. It can be concluded that while different kinds of parallel programming models can enable big data handling efficiently, the hybrid-offload model can achieve the best performance and scalability. These different programming models can be applied and extended to other types of geocomputation to handle big earth data.

Mapping the temporary and perennial character of whole river networks

AbstractKnowledge of the spatial distribution of temporary and perennial river channels in a whole catchment is important for effective integrated basin management and river biodiversity conservation. However, this information is usually not available or is incomplete. In this study, we present a statistically based methodology to classify river segments from a whole river network (Deva‐Cares catchment, Northern Spain) as temporary or perennial. This method is based on an a priori classification of a subset of river segments as temporary or perennial, using field surveys and aerial images, and then running Random Forest models to predict classification membership for the rest of the river network. The independent variables and the river network were derived following a computer‐based geospatial simulation of riverine landscapes. The model results show high values of overall accuracy, sensitivity, and specificity for the evaluation of the fitted model to the training and testing data set (≥0.9). The most important independent variables were catchment area, area occupied by broadleaf forest, minimum monthly precipitation in August, and average catchment elevation. The final map shows 7525 temporary river segments (1012.5 km) and 3731 perennial river segments (662.5 km). A subsequent validation of the mapping results using River Habitat Survey data and expert knowledge supported the validity of the proposed maps. We conclude that the proposed methodology is a valid method for mapping the limits of flow permanence that could substantially increase our understanding of the spatial links between terrestrial and aquatic interfaces, improving the research, management, and conservation of river biodiversity and functioning.

TANGIBLE LANDSCAPE: COGNITIVELY GRASPING THE FLOW OF WATER

Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load of visualizing and manipulating 3D form. Spatiotemporal processes like the flow of water over a landscape are even more challenging to understand and intentionally direct as they are dependent upon their context and require the simulation of forces like gravity and momentum. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Interacting with computers, however, can also be challenging, often requiring training and highly abstract thinking. Tangible computing – an emerging paradigm of human-computer interaction in which data is physically manifested so that users can feel it and directly manipulate it – aims to offload this added cognitive work onto the body. We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. Conceptually Tangible Landscape couples a malleable physical model with a digital model of a landscape through a continuous cycle of 3D scanning, geospatial modeling, and projection. We ran a flow modeling experiment to test whether tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies. We used hydrological simulations and statistics to quantitatively assess spatial performance. We found that Tangible Landscape enhanced 3D spatial performance and helped users understand water flow.

Performance assessment of geospatial simulation models of land-use change--a landscape metric-based approach.

Performance evaluation is a critical step when developing land-use and cover change (LUCC) models. The present study proposes a spatially explicit model performance evaluation method, adopting a landscape metric-based approach. To quantify GEOMOD model performance, a set of composition- and configuration-based landscape metrics including number of patches, edge density, mean Euclidean nearest neighbor distance, largest patch index, class area, landscape shape index, and splitting index were employed. The model takes advantage of three decision rules including neighborhood effect, persistence of change direction, and urbanization suitability values. According to the results, while class area, largest patch index, and splitting indices demonstrated insignificant differences between spatial pattern of ground truth and simulated layers, there was a considerable inconsistency between simulation results and real dataset in terms of the remaining metrics. Specifically, simulation outputs were simplistic and the model tended to underestimate number of developed patches by producing a more compact landscape. Landscape-metric-based performance evaluation produces more detailed information (compared to conventional indices such as the Kappa index and overall accuracy) on the model's behavior in replicating spatial heterogeneity features of a landscape such as frequency, fragmentation, isolation, and density. Finally, as the main characteristic of the proposed method, landscape metrics employ the maximum potential of observed and simulated layers for a performance evaluation procedure, provide a basis for more robust interpretation of a calibration process, and also deepen modeler insight into the main strengths and pitfalls of a specific land-use change model when simulating a spatiotemporal phenomenon.

Integrating geospatial data and cropping system simulation within a geographic information system to analyze spatial seed cotton yield, water use, and irrigation requirements

The development of sensors that provide geospatial information on crop and soil conditions has been a primary success for precision agriculture. However, further developments are needed to integrate geospatial data into computer algorithms that spatially optimize crop production while considering potential environmental impacts and resource limitations. The objective of this research was to combine several information technologies, including remote sensing, a cropping system model, and a geographic information system (GIS), to synthesize and interpret geospatial data collected during two irrigation scheduling experiments conducted in 2009 and 2011 in a 5-ha cotton field in central Arizona. The Geospatial Simulation (GeoSim) plug-in for Quantum GIS was used to manage geospatial data and conduct site-specific simulations with the CSM-CROPGRO-Cotton model. Simulated annealing optimization was used to adjust five model parameters to simulate site-specific conditions in 320 zones across the field. Using input parameters for GeoSim, a multiple criteria objective function was developed to incorporate measured and simulated leaf area index (LAI), crop canopy height, seed cotton yield, and evapotranspiration (ET) for site-specific optimization of CSM-CROPGRO-Cotton. Parameter identifiability and equifinality issues associated with model inversion were investigated. The optimized model was used for post hoc analysis of irrigation rates that maximized site-specific irrigation water use efficiency. With spatial optimization, the model was able to simulate LAI with root mean squared errors (RMSE) of 15 and 8 % in the 2009 and 2011 experiments, respectively. The RMSEs between measured and simulated canopy height, seed cotton yield, and ET were 5 % or less in both seasons. Some parameters were more identifiable than others during model inversions. Multiple temporal estimates of LAI were effective for constraining the model’s specific leaf area parameter (SLAVR, cm2 g−1), but lack of information on root growth reduce identifiability of a parameter related to that process (SRGF0). Post-hoc simulation analysis of irrigation management options showed that irrigation schedules based on remotely sensed vegetation indices increased irrigation water use efficiency as compared to traditional scheduling methods, particularly in the 2009 growing season. In 2011, the analysis showed that all scheduling methods resulted in excess irrigation application, and higher deep seepage rates were simulated in that season. Taken together, the results demonstrate that well-designed software tools and algorithms for data processing and interpretation can be potentially transformative for integrating multiple geospatial data sets to compute optimum scenarios for precision irrigation management.

The use of temporal dynamics for the automatic calculation of land use impacts in LCA using R programming environment

Purpose Evaluation of soil functionality in Life Cycle Assessment (LCA) has progressively gained importance, although only a small cluster of studies deliver detailed guidelines on how to calculate quantified indicators. In addition, there is a lack of bibliography assessing impacts on the pedosphere due to spatially differentiated land use changes (LUC). In this study, an automated geospatial simulation of LUC based on crop rotation probabilities in Luxembourg was implemented in the programming environment R. Furthermore, this method based on coupling LCA and geographic information system (GIS) was used to calculate changes in soil functionality by implementing both the soil organic carbon (SOC) method and the Land Use Indicator Value Calculation Tool (LANCA®). The developed R script was then applied to a case study dealing with maize production for bioenergy purposes in Luxembourg.

A model-independent open-source geospatial tool for managing point-based environmental model simulations at multiple spatial locations

A novel geospatial tool box named Geospatial Simulation (GeoSim) has been developed, which can be used to manage point-based model simulations at multiple locations using geospatial data within a geographic information system (GIS). The objectives of this paper were to describe GeoSim and demonstrate its use. GeoSim has been developed as a plug-in for Quantum GIS, and both of these software programs are open-source and freely available. An important feature of GeoSim is its model-independent nature, meaning any point-based simulation model that uses ASCII files for input and output can be managed spatially. GeoSim facilitates the transfer of geospatial data from the GIS database to the model input files and from the model output files back to the GIS database. GeoSim presently includes six software tools, each with a graphical user interface. A case study demonstrates the use of GeoSim for processing geospatial data layers at a field site, conducting spatial model simulations, and optimizing model parameters for site-specific conditions. Two cropping system models, AquaCrop and the DSSAT Cropping System Model, were implemented to simulate seed cotton yield in response to irrigation management, nitrogen management, and soil texture variability for a 14 ha study area near Lamesa, Texas. Geoprocessing tools within GeoSim were able to summarize 5592 data points within 405 polygon features in 3.8 s. Simulation tools were able to swap 33,316 and 44,550 parameters values to complete 405 spatial simulations with the AquaCrop and DSSAT models in 112 s and 398 s, respectively. These results demonstrate the utility of GeoSim for summarizing large geospatial data sets and transferring the data to the file formats of multiple models. Simulation duration was increased as compared to stand-alone model simulations without parameter swapping, which may be problematic for applications requiring large numbers of simulations. The flexible design of GeoSim is intended to support spatial modeling exercises for a variety of models and environmental applications.

Enabling Digital Earth simulation models using cloud computing or grid computing – two approaches supporting high-performance GIS simulation frameworks

Geospatial simulation models can help us understand the dynamic aspects of Digital Earth. To implement high-performance simulation models for complex geospatial problems, grid computing and cloud computing are two promising computational frameworks. This research compares the benefits and drawbacks of both in Web-based frameworks by testing a parallel Geographic Information System (GIS) simulation model (Schelling's residential segregation model). The parallel GIS simulation model was tested on XSEDE (a representative grid computing platform) and Amazon EC2 (a representative cloud computing platform). The test results demonstrate that cloud computing platforms can provide almost the same parallel computing capability as high-end grid computing frameworks. However, cloud computing resources are more accessible to individual scientists, easier to request and set up, and have more scalable software architecture for on-demand and dedicated Web services. These advantages may attract more geospatial scientists to utilize cloud computing for the development of Digital Earth simulation models in the future.

Alleviating the Modifiable Areal Unit Problem within Probe‐Based Geospatial Analyses

AbstractWe present a probe‐based interface for the exploration of the results of a geospatial simulation of urban growth. Because our interface allows the user great freedom in how they choose to define regions‐of‐interest to examine and compare, the classic geospatial analytic issue known as the modifiable areal unit problem (MAUP) quickly arises. The user may delineate regions with unseen differences that can affect the fairness of the comparisons made between them. To alleviate this problem, our interface first alerts the user if it detects any potential unfairness between regions when they are selected for comparison. It then presents the dimensions with potential problematic outliers to the user for evaluation. Finally, it provides a number of semi‐automated tools to assist the user in correcting their regions' boundaries to minimize the inequalities they feel could significantly impact their comparisons.

Design of stereoscopic viewing system based on a compact mirror and dual monitor

Stereoscopic technologies continue to accelerate the adoption of 3-D in both professional and consumer applications. Stereo 3-D displays have the potential to benefit geospatial intelligence, complex visualization, pharmaceutical development, petroleum exploration, medicine, entertainment, architecture, simulation, and other difficult tasks. A single-mirror dual monitor stereoscope system that can offer the full resolution of the current LCD display for 3-D viewing is proposed. This new approach utilizes an isosceles trapezoid mirror and two monitors to provide a cost-effective approach for stereoscopic viewing. Mathematical formulas for the design of the compact mirror were derived based on a novel scheme, and an implementation of the system was conducted to verify the practicability of the proposed method. We believe its compactness, low cost, and high image quality can become a very competitive choice among various display techniques currently available.

HPABM: A Hierarchical Parallel Simulation Framework for Spatially‐explicit Agent‐based Models

AbstractA Hierarchical Parallel simulation framework for spatially‐explicit Agent‐Based Models (HPABM) is developed to enable computationally intensive agent‐based models for the investigation of large‐scale geospatial problems. HPABM allows for the utilization of high‐performance and parallel computing resources to address computational challenges in agent‐based models. Within HPABM, an agent‐based model is decomposed into a set of sub‐models that function as computational units for parallel computing. Each sub‐model is comprised of a sub‐set of agents and their spatially‐explicit environments. Sub‐models are aggregated into a group of super‐models that represent computing tasks. HPABM based on the design of super‐ and sub‐models leads to the loose coupling of agent‐based models and underlying parallel computing architectures. The utility of HPABM in enabling the development of parallel agent‐based models was examined in a case study. Results of computational experiments indicate that HPABM is scalable for developing large‐scale agent‐based models and, thus, demonstrates efficient support for enhancing the capability of agent‐based modeling for large‐scale geospatial simulation.

Key challenges in agent-based modelling for geo-spatial simulation

Agent-based modelling (ABM) is becoming the dominant paradigm in social simulation due primarily to a worldview that suggests that complex systems emerge from the bottom-up, are highly decentralised, and are composed of a multitude of heterogeneous objects called agents. These agents act with some purpose and their interaction, usually through time and space, generates emergent order, often at higher levels than those at which such agents operate. ABM however raises as many challenges as it seeks to resolve. It is the purpose of this paper to catalogue these challenges and to illustrate them using three somewhat different agent-based models applied to city systems. The seven challenges we pose involve: the purpose for which the model is built, the extent to which the model is rooted in independent theory, the extent to which the model can be replicated, the ways the model might be verified, calibrated and validated, the way model dynamics are represented in terms of agent interactions, the extent to which the model is operational, and the way the model can be communicated and shared with others. Once catalogued, we then illustrate these challenges with a pedestrian model for emergency evacuation in central London, a hypothetical model of residential segregation model tuned to London data, and an agent-based residential location model, for Greater London. The ambiguities posed by this new style of modelling are drawn out as conclusions, and the relative arbitrariness of such modelling highlighted.

Estimating the Economic Consequences of Terrorist Disruptions in the National Capital Region: An Application of Input-Output Analysis

This paper explores and demonstrates an innovative application of an input-output model for the assessment of the economic consequences of hypothetical disruption events. Such events in a region are first simulated and their affected areas estimated using a GIS-based geo-spatial simulation approach. Then, the consequences of the simulated events on the regional economy are estimated and disaggregated by industry and sub-region. The research is important because it develops a new simulation and input-output model approach to regional and national security analysis that offers promising results. Further, it provides a methodology for assessing the economic consequences of possible disruption events. Knowledge of economic impacts of disruptions is essential for determining and justifying appropriate protection and mitigation policies and measures.