Geospatial Simulation Research Articles

Exploring Family Ties and Interpersonal Dynamics—A Geospatial Simulation Analyzing Their Influence on Evacuation Efficiency within Urban Communities

Guaranteeing efficient evacuations in urban communities is critical for preserving lives, minimizing disaster impacts, and promoting community resilience. Challenges such as high population density, limited evacuation routes, and communication breakdowns complicate evacuation efforts. Vulnerable populations, urban infrastructure constraints, and the increasing frequency of disasters further contribute to the complexity. Despite these challenges, the importance of timely evacuations lies in safeguarding human safety, enabling rapid disaster response, preserving critical infrastructure, and reducing economic losses. Overcoming these hurdles necessitates comprehensive planning, investment in resilient infrastructure, effective communication strategies, and continuous community engagement to foster preparedness and enhance evacuation efficiency. This research looks into the complexities of evacuation dynamics within urban residential areas, placing a particular focus on the interaction between joint-rental arrangements and family ties and their influence on evacuation strategies during emergency situations. Using agent-based modeling, evacuation simulation scenarios are implemented using the Changhongfang community (Shanghai) while systematically exploring how diverse interpersonal relationships impact the efficiency of evacuation processes. The adopted methodology encompasses a series of group experiments designed to determine the optimal proportions of joint-rental occupants within the community. Furthermore, the research examines the impact of various exit selection strategies on evacuation efficiency. Simulation outcomes shed light on the fundamental role of interpersonal factors in shaping the outcomes of emergency evacuations. Additionally, this study emphasizes the critical importance of strategic exit selections, revealing their potential to significantly enhance overall evacuation efficiency in urban settings.

A Web‐Based Architecture to Operationalize Geospatial Simulation Environments

AbstractLarge scale geospatial simulation projects require multidisciplinary efforts by actors with highly variable skills and domains of expertise. Subject matter experts, modelers, developers, analysts, and decision makers must collaborate closely to model a real‐world system, simulate it, analyze its results and disseminate them. Simulation environments, tailored to business scenarios, can provide the necessary support to facilitate their collaboration throughout the simulation lifecycle. Commercial modeling and simulation software can provide an environment to facilitate simulation studies for users but, they tend to be narrowly scoped. This research focuses on the different categories of users and introduces four business processes that carry those users across the simulation lifecycle. These concepts are translated into an architecture that facilitates the operationalization of geospatial simulation environments using modeling and simulation as a service and Discrete Event Systems Specification.

Geospatial simulation and decision optimization towards identifying the layout suitability and priority for wind-photovoltaic-hydrogen-ammonia project: An empirical study in China

China is vigorously developing clean energy to achieve the low-carbon strategy. Fully utilizing renewable energy to build a clean production and consumption system has become the core path. The wind-photovoltaic-hydrogen-ammonia project could fully absorb green electricity and have significant environmental benefits. However, the prerequisite for developing this project is to solve the layout problem. Firstly, a three-step method for identifying the project's critical factors to determine the analysis input is proposed for the first time. Secondly, the suitability and priority of the layout are measured by constructing the geospatial simulation and decision optimization model. Then, the three northern regions of China are selected for empirical study. Geospatial simulation provides the suitability of the overall layout of the region and identifies seven possibilities. The decision technique that considers multiple factors provides priority among possibilities. Finally, the deep analysis demonstrated the reliability and progressiveness of the model. The scenario analysis provides the sensitivity of this project to wind and solar power and discusses the impact of decision preferences on outcomes. This study presents a theoretical model and analytical framework that decision-makers can use to plan clean energy projects strategically. Furthermore, the empirical findings from this study can serve as valuable guidance for practical implementation.

A comparative assessment and geospatial simulation of three hydrological models in urban basins

Abstract The risk of flooding is a destructive natural hazard, and it is increasing due to heavy rainfall and anthropogenic factors. Hydrologic–hydraulic models serve as valuable tools for flood forecasting and predicting future flow patterns. These models evaluate and simplify processes in ungauged basins. In this study, three hydrologic models (soil conservation service [SCS], Snyder, and Temez) were used to calculate synthetic unit hydrographs for the Humaya and Tamazula River (H-T-R) basin. Additionally, the flows derived from the three models were simulated in Hydrological Engineering Center River Analysis System for various return periods (2, 5, 10, 25, 50, and 100 years). The accuracy of the models SCS, Snyder, and Temez was evaluated using the root-mean-square error (1162.44, 144.76, and 2890.6); Nash–Sutcliffe efficiency (−51.12, 0.19, and −312.28); R 2 (0.97, 0.94, and 0.94), and kappa index (0.8534, 0.9895, and 0.7155), respectively. The data used in this study were obtained from a hydrometric station located on the Culiacan River. The main findings indicate that the Snyder model demonstrated a better predictive capability compared to the Temez and SCS models, albeit with a tendency to overestimate. Simulated flood depths are deeper in the upstream areas, particularly upstream from the Musala Island bifurcation on the Tamazula River, with values of 11.82 m for SCS, 9.76 m for Snyder, and 13.5 m for Temez. The simulation revealed potential overflow zones along the Tamazula River, particularly at the channel bifurcation and near the confluence with the Humaya River, during the 100 year return period simulation.

A cooperative model to lower cost and increase the efficiency of methane leak inspections at oil and gas sites

Methane is a potent greenhouse gas that tends to leak from equipment at oil and gas (O&G) sites. Conventional leak detection and repair methods for fugitive methane emissions are labor-intensive and costly because they involve time-consuming close-range, component-level inspections at each site. This has prompted duty holders to examine new methods and strategies that could be more cost-effective. We examined a cooperative model in which multiple duty holders of upstream O&G sites in a region use shared services to inspect on-site equipment using optical gas imaging camera or Method 21. This approach was hypothesized to be more efficient and cost-effective than independent inspection programs by each duty holder in the region. To test this hypothesis, we developed a geospatial simulation model using empirical data from 11 O&G-producing regions in Canada and the United States. We used the model to compare labor cost, transit time, mileage, vehicle emissions, and driving risk between independent and co-op leak inspection programs. The results indicate that co-op leak inspection programs can generate relative savings in labor costs (1.8%–34.2%), transit time (0.6%–38.6%), mileage (0.2%–43.1%), vehicle emissions (0.01–4.0 tCO2), and driving risk (1.9%–31.9%). The largest relative savings and efficiency gains resulting from co-op leak inspection programs were in regions with a high diversity of duty holders, which was confirmed with simulations of fictitious O&G sites and road networks spanning diverse conditions. We also found reducing leak inspection time by 75% with streamlined methods can additionally reduce labor cost by 8.8%–41.1%, transit time by 5.6%–20.2%, and mileage by 2.60%–34.3% in co-op leak inspection programs. Overall, this study demonstrates that co-op leak inspection programs can be more efficient and cost-effective, particularly in regions with a large diversity of O&G duty holders, and that methods to reduce leak inspection time can create additional savings.

A Retrospective Geospatial Simulation Study of Helicopter Emergency Medical Services’ Potential Time Benefit Over Ground Ambulance Transport in Northern South Africa

ObjectiveOne of the most important benefits of helicopter emergency medical services (HEMS) is a time benefit, either through expedited access to the casualty or a reduction in the transport time to definitive care. However, HEMS utilization does not come without risk to the public and crew or at an insignificant cost. Cost is an essential consideration for health policy decisions, especially in low- to middle-income countries, such as South Africa. The aim of this study was to determine whether there is a time benefit of HEMS dispatch in South Africa compared with simulated driving time. A secondary aim was to determine the distance from the incident site to the hospital at which a time benefit can be guaranteed. MethodsA retrospective study was undertaken by comparing the prehospital times of patients who underwent HEMS transportation with simulated ground emergency medical services (GEMS) transportation times. Handwritten patient records of actual flights were reviewed and analyzed. The actual flight times recorded were used to calculate the helicopter transport time, activation to scene time, scene time, and scene to hospital time. Times were assigned based on a nonsimultaneous dispatch model, as is used in South Africa. For each helicopter mission, Google Maps (Google Inc, Mountain View, CA) was used to simulate the fastest ground route from the same location of the incident to the same receiving hospital corrected for typical traffic trends. The actual HEMS and simulated GEMS times were compared using the paired t-test. Linear regression analysis was performed to determine a minimum driving distance at which HEMS provides a time benefit. ResultsA total of 118 HEMS transports were analyzed, the majority of which were trauma related (n = 115, 97%). HEMS transport resulted in a mean time deficit of −15 minutes (95% confidence interval, −18 to −11; P < .05) compared with simulated GEMS drive times. After regression, HEMS transport provides a time benefit at a driving distance greater than 119 km. ConclusionThe current study demonstrated that there was rarely a time benefit for actual primary emergency responses when HEMS was used compared with simulated driving time of GEMS transport. Using a nonsimultaneous dispatch model, a time benefit only occurs when the driving distance from the incident site to the hospital is greater than 119 km. There is an urgent need to critically evaluate HEMS utilization in the South African context.

MANGROVE SIMULATION: ATTENUATION OF STORM SURGES IN PROTECTING COASTAL AREA AND GEOSPATIAL SIMULATION MODEL OF MANGROVE FOREST IN PALSABANGON MANGROVE SWAMP FOREST RESERVE PAGBILAO, QUEZON

The objective of this research is to designed and developed a system known as Mangrove Simulation: Attenuation of Storm Surges in Protecting Coastal Area and Geospatial Simulation Model of Mangrove Forest in Palsabangon Mangrove Swamp Forest Reserve Pagbilao, Quezon. The system incorporates various functionalities including web-based application and geographical information system for administrator and staff of Palsabangon Mangrove Swamp Forest Reserve. To utilize the Agent-Based model that simulate and predict the growth and spatial distribution of various mangrove species with regards to the environment. To evaluate of the accuracy of the algorithm in mangrove simulation with geographic information system mapping aims to improve they understanding of the ecological dynamics and spatial patterns of mangroves, and provide useful information for the management and conservation of mangrove ecosystems. The system underwent a comprehensive testing process to assess its functionality, suitability, reliability, performance efficiency, operability, security, compatibility, and maintainability. The results revealed that the system achieved an overall mean score of 4.09, indicating a "Very Satisfactory" rating. This signifies that both experts and clients were very satisfied with the system's characteristics. Moreover, it passed on the standard rating level of ISO/IEC 25010. This remark indicates that the system performed effectively and achieved its goals.

Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things

The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. The research problems were whether computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology optimize remote sensing robots. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were leveraged by a Shiny app to obtain the flow diagram comprising evidence-based collected and managed data (the search results and screening procedures). Throughout January and July 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms comprising “Internet of Robotic Things” + “big data management algorithms”, “deep learning-based object detection technologies”, and “geospatial simulation and sensor fusion tools”. As the analyzed research was published between 2017 and 2022, only 379 sources fulfilled the eligibility standards. A total of 105, chiefly empirical, sources have been selected after removing full-text papers that were out of scope, did not have sufficient details, or had limited rigor For screening and quality evaluation so as to attain sound outcomes and correlations, we deployed AMSTAR (Assessing the Methodological Quality of Systematic Reviews), AXIS (Appraisal tool for Cross-Sectional Studies), MMAT (Mixed Methods Appraisal Tool), and ROBIS (to assess bias risk in systematic reviews). Dimensions was leveraged as regards initial bibliometric mapping (data visualization) and VOSviewer was harnessed in terms of layout algorithms.

Digital Twin and Metaverse Technologies, Geospatial Simulation and Sensor Fusion Tools, and Object Perception and Motion Control Algorithms in Immersive Hyper-Connected Virtual Spaces

Digital Twin and Metaverse Technologies, Geospatial Simulation and Sensor Fusion Tools, and Object Perception and Motion Control Algorithms in Immersive Hyper-Connected Virtual Spaces

Aridity Analysis Using a Prospective Geospatial Simulation Model in This Mid-Century for the Northwest Region of Mexico

Aridity is a condition in which there is a moisture deficit in the air and soil that affects large areas of the earth’s surface worldwide. It is a global problem caused mainly by factors related to climatic events and human actions. In the arid regions of Mexico, prolonged periods of drought are very common and water scarcity is the predominant feature. The main objective of this study is to develop a prospective geospatial simulation model for arid zones in the short and medium term (2030 and 2050) for the northwestern region of Mexico. A retrospective analysis of the variables that cause aridity was conducted based on historical data from satellite information obtained from various sources between 1985 and 2020, taking 2020 as the reference year; from this information the rate of change per year was obtained, followed by the simulated rates of change for the years 2030 and 2050. A methodology used to obtain arid zones using multicriteria evaluation techniques, weighted linear combination, and Geographic Information Systems. In order to generate the prospective model for arid zones, the variables were modeled to adjust the rate of change for each of them, with the same methodology subsequently applied to obtain the base year (2020), and aridity suitability maps were obtained for the years 2030 and 2050. The main results indicate that the prospective scenarios point to an increase in arid regions of 0.38% and 0.70%, respectively, which is equivalent to an area of approximately 240,164.63 km2 and 241,760.75 km2, respectively. This will cause a decrease in the subhumid–dry and humid regions of 0.10% and 0.19%, respectively, for the projected years. Statistical and geospatial aridity indicators were also generated at different levels, which helps to better understand the problem of aridity in vulnerable regions.

GeoSim 2022 Workshop Report: The 5th ACM SIGSPATIAL International Workshop on Geospatial Simulation

Geospatial simulation is an effective tool to experience physical and/or cyber space (e.g., metaverse). New experiences gained from geospatial simulations can bring significant benefits including situation awareness, insight into environments, and entertainment. To take advantage of such simulations, it is crucial to advance methodology of modeling and simulation, develop plausible models and apply them to domains, leveraging big data and evolving technologies.

Geospatial simulation and mapping of climate suitability for oil palm (Elaeis guineensis) production in Peninsular Malaysia using GIS/remote sensing techniques and analytic hierarchy process

Geospatial simulation and mapping of climate suitability for oil palm (Elaeis guineensis) production in Peninsular Malaysia using GIS/remote sensing techniques and analytic hierarchy process

Geospatial Simulation System of Mountain Area Black Ice Accidents

As the development of mountain areas has recently increased in Korea, existing roads are being renovated, and new highways are being constructed, which increases driving speeds in mountainous areas. However, the mountainous region in northeastern Korea is more likely to form black ice due to higher humidity, frequent fog, and hillshade, depending on the terrain, which can cause serious traffic pileups. In this study, therefore, we present a method to build a more effective black ice prediction and warning system by linking spatial information to the existing road management system that estimates the road surface temperature based on real-time weather information. The spatial information enabled a prediction to be made of the risk level of black ice formation for each time zone by simulating changes in the shadow area based on precise 3D terrain information. Moreover, this information also presented slope and curvature information of the road to estimate the risk zone. The spatial information was integrated with weather data to predict road surface temperature. The proposed system was tested in two mountainous regions with weather data accumulated from 2017 to 2018. As a result, the proposed system anticipated 71% of traffic accidents caused by black ice during the testing period. The results show that the system can contribute significantly to preventing black-ice-related traffic accidents by providing reasonable predictions.

GeoSim 2021 Workshop Report: The 4th ACM SIGSPATIAL International Workshop on Geospatial Simulation

Geospatial simulation is an effective tool to experience physical and/or cyber space (e.g., metaverse). New experiences gained from geospatial simulations can bring significant benefits including situation awareness, insight into environments, and entertainment. To take advantage of such simulations, it is crucial to advance methodology of modeling and simulation, develop plausible models and apply them to domains, leveraging big data and evolving technologies.

Geospatial simulation of urban neighbourhood densification potentials

Although densification of urban areas is being proposed as a sustainable urbanisation strategy, frameworks for detailed large-scale analysis of densification potentials and their evaluation are lacking. A geospatial simulation framework is presented to assess and evaluate densification potentials at the neighbourhood level of already built-up residential areas. The focus is on post-war neighbourhoods, which are particularly promising for sustainable densification. Neighbourhoods are localised using geospatial analysis and based on literature and architectural designs, potentials are estimated for different neighbourhood archetypes and densification strategies. Potentials are simulated at a national scale using supervised archetype classification. The embeddedness into current mobility infrastructure is used as a proxy for evaluating the sustainability of neighbourhood densification. The developed framework is tested for Switzerland. Depending on the densification strategy, the simulated additional inhabitants for populating post-war urban neighbourhoods range between 4–15 % of the current population. More than half of this potential is located in central areas and is well connected by public transportation. The presented approach is suitable for assessing spatially explicit densification potential and for prioritising densification locations. We show that in countries with a high number of post-war neighbourhoods in well-connected locations, considerable densification opportunities could be realised in already built-up residential areas.

The 3rd ACM SIGSPATIAL International Workshop on Geospatial Simulation

Space has long been acknowledged by researchers as a fundamental constraint which shapes our world. As technological changes have transformed the very concept of distance, the relative location and connectivity of geospatial phenomena have remained stubbornly significant in how systems function. At the same time, however, technology has advanced the science of geospatial simulation to bear on our understanding of how such systems work. While previous generations of scientists and practitioners were unable to gather spatial data or to incorporate it into models at any meaningful scale, new methodologies and data sources are becoming increasingly available to researchers, developers, users, and practitioners. These developments present new research opportunities for geospatial simulation.

Geospatial simulation steering for adaptive management

Spatio-temporal simulations are becoming essential tools for decision makers when forecasting future conditions and evaluating effectiveness of alternative decision scenarios. However, lack of interactive steering capabilities limits the value of advanced stochastic simulations for research and practice. To address this gap we identified conceptual challenges associated with steering stochastic, spatio-temporal simulations and developed solutions that better represent the realities of decision-making by allowing both reactive and proactive, spatially-explicit interventions. We present our approach, in a participatory modeling case study engaging stakeholders in developing strategies to contain the spread of a tree disease in Oregon, USA. Using intuitive interfaces, implemented through web-based and tangible platforms, stakeholders explored management options as the simulation progressed. Spatio-temporal steering allowed them to combine currently used management practices into novel adaptive management strategies, which were previously difficult to test and assess, demonstrating the utility of interactive simulations for decision-making.

Spatial analysis of the impact of climate change factors and adaptation strategies on productivity of wheat in Ethiopia

Wheat production is expected to be challenged by future climate change. However, it is unclear how wheat grown in diverse agroecologies will respond to climate change and adaptation management strategies. A geospatial simulation study was conducted to understand the impacts of climate change and adaptation management strategies on wheat (Triticum aestivum L.) production in Ethiopia. Simulation results showed that the average long-term baseline (1980–2005) wheat yield ranged from 1593 to 3356 kg/ha. This wheat yield range is within the national average (2100–2700 kg/ha) for this decade. In regions with cooler temperatures (<21 °C), mid-century temperatures and elevated CO2, along with increased N fertilizer slightly improved attainable yield levels above 3000 kg/ha. Whereas, in regions with heat and drought conditions wheat yield declined regardless the increase of N or CO2 levels. Wheat yield increased at a diminishing rate with increase in N fertilizer rate. However, N fertilizer did not increase yields under low rainfall conditions. Two to five irrigation per season contributed to yield improvement for low rainfall locations, while yield did not substantially improve for locations receiving adequate seasonal rainfall. Therefore, based on this study, improved N fertilizer application in combination with increased CO2 could improve wheat yield under future climate in most wheat producing regions (with adequate rainfall) of Ethiopia. Our results provide valuable information regarding impacts of climate change factors and adaptation strategies for producers, researchers, extension professionals and policy makers.

Mathematical Integration of Remotely-Sensed Information into a Crop Modelling Process for Mapping Crop Productivity

Remote sensing is a useful technique to determine spatial variations in crop growth while crop modelling can reproduce temporal changes in crop growth. In this study, we formulated a hybrid system of remote sensing and crop modelling based on a random-effect model and the empirical Bayesian approach for parameter estimation. Moreover, the relationship between the reflectance and the leaf area index was incorporated into the statistical model. Plant growth and ground-based canopy reflectance data of paddy rice were measured at three study sites in South Korea. Spatiotemporal vegetation indices were processed using remotely-sensed data from the RapidEye satellite and the Communication Ocean and Meteorological Satellite (COMS). Solar insulation data were obtained from the Meteorological Imager (MI) sensor of the COMS. Reanalysis of air temperature data was collected from the Korea Local Analysis and Prediction System (KLAPS). We report on a statistical hybrid approach of crop modelling and remote sensing and a method to project spatiotemporal crop growth information. Our study results show that the crop growth values predicted using the hybrid scheme were in statistically acceptable agreement with the corresponding measurements. Simulated yields were not significantly different from the measured yields at p = 0.883 in calibration and p = 0.839 in validation, according to two-sample t tests. In a geospatial simulation of yield, no significant difference was found between the simulated and observed mean value at p = 0.392 based on a two-sample t test as well. The fabricated approach allows us to monitor crop growth information and estimate crop-modelling processes using remote sensing data from various platforms and optical sensors with different ground resolutions.

Advancing the application of a model-independent open-source geospatial tool for national-scale spatiotemporal simulations

Growing demands for geospatial application of environmental models have led to tool development for conducting simulations spatially. The model-independent, open-source tool “Geospatial Simulation” (GeoSim) has been developed previously. Based on previous applications at field scale, this study advances GeoSim application for national-scale and multi-year simulations. The widely-applied AquaCrop model was implemented by GeoSim to simulate wheat yield and irrigation requirements on a daily step across China from 2000 to 2009. The spatial inputs required by AquaCrop were minimized and 6915 unique response units were identified among the primary 116,801 polygons. It took around 20 h to perform the 10-year simulations. Post-processing of simulation outputs permitted mapping at the original 5 arc-minute resolution. The novel methods developed in this study demonstrate new opportunities for efficiently managing national-scale and multi-year simulations with high resolution. They render AquaCrop more suitable for studies on the water-food nexus at large scales, which are more policy-relevant.