Information System Platform Research Articles

Technological, political and economic challenges of urban solid waste in the state of Pará, Brasil

Due to population growth and urbanization itself, the amount of urban solid waste generated every day that needs to be destined for an environmentally appropriate purpose is increasing, therefore, this article aimed to investigate and analyze the technological, political and economic challenges of urban solid waste in the State of Pará. In methodological terms, a bibliographic review was carried out based on scientific articles that deal with the theme of this work and document analysis on the management of solid waste, its problems and challenges in the state of Pará. The data used are primary and secondary, they were collected in November 2022 on the platform of the national information system on solid waste management (SINIR) and the National Sanitation Information System (SNIS). For the execution of the work, the information considered was about the generation, collection, disposal and treatment of urban solid waste in the state of Pará, in addition to compliance with the National Solid Waste Policy. After that, the data analysis step was done through graphs and tables to represent the information studied. The results showed that the only technology to which urban solid waste in the state was destined until the year 2019 was recycling, and that the main method of collecting this waste was selective collection. Most municipalities do not properly dispose of their waste or even do not provide data on this situation, which can generate management difficulties. In addition, several financial support mechanisms were found for the destination of this waste.

Assessing the sustainability of bioenergy pathways through a land-water-energy nexus approach

Bioenergy can significantly contribute to facilitating the transition to a self-sufficient low-carbon energy sector in Europe. The establishment of sustainable bioenergy systems needs ex-ante analysis that considers specific contexts and potential trade-offs, including competing uses of natural resources such as land, water and energy. The use of marginal and underutilized lands has been emphasized as a valuable strategy to overcome the food vs fuel paradigm and the direct and indirect land use change that can derive from the cultivation of bioenergy crops. Nevertheless, the fragmentation of these lands can significantly affect the energy efficiency of the systems, due to increased energy needs for inputs and transport, with trade-offs on the carbon footprint of the pathway. This study performs an ex-ante assessment of the sustainability of a short bioenergy pathway through the Land-Water-Energy nexus approach, by the iterative use of a web geographic information systems platform, developed on the methodology of the Global Bioenergy Partnership. The simulations show that the production of bioenergy from oilseed crops is expected to reduce greenhouse gas emissions by 73% compared to the reference emission value for fossil fuels in transport. The platform proofs to be effective and user-friendly for assessing and optimizing the intricate interlinkages among the various nexus domains that coexist within the bioenergy system based on the cultivation of underutilized land. Its use can facilitate the nexus operationalization serving as a timesaving decision-support tool for the development of sustainable bioenergy systems that contribute to the transition towards a low-carbon energy sector in Europe.

Investigation on the spatial distribution of landslides in Sichuan Province, southwest China

In this study, a total of 34,893 landslides collected from the Sichuan Province of China are used to reveal their spatial distribution. Correlations between the spatial distribution of landslides and five main factors [i.e. topography, rainfall, lithology, peak ground acceleration (PGA), and human activity] are analyzed based on the Geographical Information System (GIS) platform. Topographic factors exert a significant influence on landslide occurrence, and other factors also sensitive to landslides include lithology (Classes 3 and 4), PGA (≤0.15 g), rainfall (90–140 mm), and road density (≤0.6 km/km2). The geographical detector model is used to detect the relative importance of individual influencing factors and the factor interaction effects on the landslide distribution. The results show that the interacting factors between rainfall and PGA, lithology, river density, and road density are more closely related to the spatial distribution of landslides than every single factor. The results of this study can provide a foundation and insights for hazard reduction schemes in the future.

Effect of inactivated SARS-CoV-2 vaccine on the clinical outcomes of patients infected with the Omicron variant in Guangdong Province

Objective: To evaluate the effect of inactivated SARS-CoV-2 vaccine on the clinical outcomes of patients infected with the Omicron variant. Methods: A total of 1 403 Omicron-infected patients admitted to 20 designated hospitals in Guangdong Province from January 1 to May 31, 2022, were selected as subjects in this study. A case-control study was conducted to collect the demographic data, underlying disease, vaccination status, last exposure date, gene sequencing of infected strains and clinical outcomes from the China Disease Prevention and Control Information System and Guangdong telemedicine platform. Pneumonia (common, severe and critical) and non-pneumonia (asymptomatic and mild) were selected as the case group and control group. The effect of inactivated SARS-CoV-2 vaccine on the clinical outcomes of patients infected with the Omicron variant was analyzed. Results: The median age [M (Q1, Q3)] of the subjects was 36 (27-47) years old, with males accounting for 52.25% (733 cases). The main outcome of the infection was non-pneumonia, accounting for 92.09% (1 292 cases), and the duration [M (Q1, Q3)] of the disease was 18 (14-22) days. There were 134 (9.55%), 39 (2.78%), 403 (28.72%), 437 (31.15%) and 390 (27.80%) cases with no or partial vaccination, within 90 days of primary vaccination, over 90 days of primary vaccination, within 90 days of booster vaccination and over 90 days of booster vaccination, respectively. Multivariate logistic regression analysis showed that after adjusting for gender, age, underlying disease, and location of the report, compared with those with no or partial vaccination, the risk of developing pneumonia was lower in those with over 90 days of primary vaccination, within 90 days of booster vaccination and over 90 days of booster vaccination [OR (95%CI) values were 0.52 (0.28-0.98), 0.39 (0.21-0.73) and 0.40 (0.21-0.77), respectively]. Cox proportional hazard regression model analysis showed that after adjusting for gender, age, underlying disease and location of the report, the duration of the disease was shorter in those who received booster vaccinated for more than 90 days compared with that in those who had no or partial vaccination [HR (95%CI): 1.26 (1.03-1.55)]. Conclusion: The inactivated SARS-CoV-2 vaccine affects the clinical outcomes of patients infected with the Omicron variant.

Effects of reverse geocoding on OpenStreetMap tag quality assessment

AbstractOpenStreetMap (OSM), a widely‐used open‐source geographic information system platform, provides a vast geographic dataset in which users contribute both geometric information (nodes, ways, and relations) and semantic information (tags). This method of voluntary contributions is governed by the collective effort of the users. It is widely acknowledged that the quantity of tag information is substantial, but its quality is often poor. Researchers are therefore trying to assess the quality of the tags and enhance the data through various integration experiments. This article investigates the validity of the tags for geographical objects in metropolitan areas using municipal data and a reverse geocoding technique. The proposed method evaluates the data quality and the matching process carried out by reverse geocoding, using municipal points of interest as a reference. The accuracy of the tag and address information and road network centrality metrics were assessed for the OSM objects that were matched to the locations of interest. The tags were found to match the points of interest with an accuracy of 88%. Furthermore, the tag values were categorized and analyzed based on their similarity. It is concluded that in metropolitan settings where centers of interest are closely located, the accuracy of tags and addresses tends to decrease.

Multi-Scale Analysis of PM2.5 Concentrations in the Yangtze River Economic Belt: Investigating the Combined Impact of Natural and Human Factors

Air pollutants, primarily PM2.5, have inflicted significant harm on public health and sustainable urban development in the Yangtze River Economic Belt (YREB). Previous studies often neglected the coordinated measurement of PM2.5 human and natural factors in this area. Therefore, this paper focuses on the YREB. Using a geographic information system (GIS) platform, along with remote sensing and statistical data spanning from 2000 to 2020, this study employs spatial analysis to uncover the spatial-temporal characteristics of PM2.5 and its spatial agglomeration patterns. Furthermore, this study further employs the spatial panel Durbin model to investigate the natural and anthropogenic factors driving PM2.5 concentrations across multiple scales. The analysis of the results reveals an “M”-shaped change trend in PM2.5 concentrations within the YREB. PM2.5 concentrations exhibit significant spatial agglomeration characteristics, whereby most urban agglomerations are high-pollution areas. Moreover, the changes in PM2.5 concentrations are jointly influenced by several factors, including the secondary industry, urban built-up area, population density, annual precipitation, and NDVI. Furthermore, the dominant factors influencing PM2.5 concentrations in the three major urban agglomerations exhibit both similarities and differences. In addition, for effective governance coordination across regions, policymakers should diligently consider both the shared predominant factors and the varying factors specific to each region in the future. This study expands the research content of watershed PM2.5 collaborative governance, and further provides practical support for other watershed environmental governance and urban sustainable management.

Pavement surface distress evaluation using a terrestrial laser scanner

The accurate measurement of pavement distresses is essential for setting preservation and rehabilitation priorities. The manual measurement of surface distresses is the most commonly used method for evaluating the severity of pavement distresses. However, the manual method has low accuracy and consistency. Thus, in the present study, images of different pavement distresses – potholes, cracking, patching and ravelling of selected sections – were captured using a terrestrial laser scanner (TLS). The surface area of these distresses was measured using the Leica Cyclone software. The obtained results were validated by collecting a similar distress pattern at a different location using the same equipment. The measured surface area was executed in a geographic information system platform to develop an automated database for the prioritisation of selected pavement sections of Mumbai City, India for pavement maintenance. It was observed that the TLS technique could be used effectively for safer and more accurate surface distress measurement.

SIGeR-RV: A Web-Geographic Information System-Based System for Risk Management of Road Networks Exposed to Natural Hazards

The consequences of natural hazards are frequently estimated by the direct cost of recovering damaged infrastructure and the indirect costs to road users, economic activities, and impacts on society. Road networks are crucial in economic systems, logistic chain continuity, accessibility, mobility, and the evacuation of population during and after extreme events. Risk management systems (RMS) are used to estimate the potential consequences of natural events and to assess strategies for risk reduction. These commonly assess hazards, assets exposure, economic losses, and risk mitigation actions, among others. The general framework of RMS can be adapted to different scenarios. Still, local characteristics, such as the types of hazards and physical assets, cannot always be directly adopted from these systems. This study discusses the development of SIGeR-RV, an RMS developed in Chile for road networks exposed to multiple natural hazards. The RMS was implemented in a web-based geographic information system platform able to display hazard maps, quantify risk levels, prioritize mitigation strategies, and estimate direct and indirect costs and social vulnerability. The content and various capacities of the system are detailed, following the steps marked in its framework. This first version of SIGeR-RV considers seismic hazard, volcanic lahars, and hydro-meteorological hazards that affect road platforms, bridges, tunnels, and cut slopes. The system currently serves as a tool for the Ministry of Public Works of Chile and other decision makers to estimate budget requirements for increasing resilience of the road network, identifying vulnerable road segments, and assessing the socioeconomical impacts of risk reduction.

Advancing Agricultural Predictions: A Deep Learning Approach to Estimating Bulb Weight Using Neural Prophet Model

A deep learning methodology was utilized to predict the bulb weights of garlic and onions in the Jeolla Province of Korea. The Korea Rural Economic Institute (KREI) operates the Outlook & Agricultural Statistics Information System (OASIS) platform, which provides actual measurements of garlic and onions. We trained the Neural Prophet (NP) lagged time-series model using this data. The NP model effectively handles lagged variables and their covariates by inserting a hidden layer. Our results indicate that the NP model performed with around 5% mean absolute error in predicting bulb weights, with a gap of 3.3 g and 4.7 g with average weights of 63.7 g and 129.9 g for garlic and onions, respectively. This experimental research was based on only three years of measurement data. Hence, the gap between observed and predicted data can be reduced by accumulating more measurement data in the future.

Impact of Integrated Personnel and Payroll Information System (IPPIS) on Federal Universities’ Administration in Nigeria

This paper discussed the Impact of the Integrated Personnel and Payroll Information System (IPPIS) on the federal universities administration in Nigeria. Secondary data were employed in the paper. The secondary data were collected from print and online publications. The paper concluded that the positive impact of the Integrated Personnel and Payroll Information System (IPPIS) on the federal universities administration includes; a reduction of strikes in federal universities as a result of the management-labour union crisis over unpaid arrears, a reduction of corruption and reduction in administrative wastages. The paper also identified the negative impacts of the implementation of the Integrated Personnel and Payroll Information System (IPPIS) on the federal universities administration including termination of appointment of contract staff, shortage of academic staff, slow in federal university administration and violation of federal universities autonomy. Based on the impacts identified, the paper hereby recommended that federal universities in Nigeria should be removed from the Integrated Personnel and Payroll Information System (IPPIS) platform and be granted full autonomy to operate according to global best practice.

A research on data integration and application technology of urban comprehensive pipe gallery based on three‐dimensional geographic information system platform

AbstractThree‐dimensional Geographic Information System (3D GIS) technology is often used in geology, mining, hydrology and other fields. It is also used in intelligent management of urban comprehensive pipe gallery projects, because the real environment with the sky, ground, and underground can be intuitively simulated in the virtual reality environment by 3D GIS platform. Through the research on data integration and application technology of urban comprehensive pipe gallery, the 3D model of comprehensive pipe gallery project data can be created based on Building Information Modelling and GIS technologies, and a comprehensive underground pipelines network management information system has been developed to realise the integration of various data, including 3D data of comprehensive pipe gallery model and underground pipe network model, 2D or 3D GIS data of the ground and underground etc. On this basis, the functions of intelligent planning and approval management, the safety analysis and disaster prevention planning and decision‐making of urban comprehensive pipe gallery projects can be further realised. The research results have been applied to the planning and construction process management of the comprehensive pipe gallery project in the sub‐centre of Beijing.

Using digital technologies to plan and manage the pipelines network in city

AbstractPromoting the intelligent level of urban pipelines network planning and management is an important way to improve the rational development and utilisation of urban underground space and ensure the safe operation of urban infrastructure systems. The research on using the rapid and visual expression technology of 3D pipelines network model data, which has the functions of techno‐economic indexes calculation and automatic comparison of underground pipeline construction projects, effectively avoids possible errors in manual calculation and comparison and improves the efficiency and accuracy of the digital declaration process for project planning. In the 3D Geographic Information Systems (GIS) platform environment, this research help to develop many analysis and approval functions based on the data of the underground pipelines network model and the integrated pipes gallery Building Information Modelling, such as evaluating the compliance, feasibility and scientific of the planning scheme of the underground pipeline construction project, comparing the completion data with the data approved by the administrative departments of the project, evaluating the consistency of them and judging the compliance of the project completion data. The application results of some actual projects cases show that this research can improve the efficiency of planning declaration and approval analysis.

ASSESSMENT OF CROPLAND TRANSITION TO SOLAR FARMS AND OTHER LAND USE/COVER USING RS, GIS AND ANN-CA

Abstract. The balance between food security and energy security is a national issue of extreme importance. A more stable supply of electricity could be achieved as solar farms expand but at the expense of losing some of the prime agricultural lands which endangers availability of sufficient agricultural produce. This study aims to use ANN-Cellular Automata (CA) via the Geographic Information System (GIS) platform and remote sensing (RS) data to assess the impact of cropland transition to solar farms and other land use/land cover (LULC). Several remotely sensed data were processed including MODIS land cover data (MCD12Q1), VIIRS nighttime lights (VNL v2.1), Advanced Himawari Imager Shortwave Radiation (AHI-SWR) product, and population density (LandScan) as inputs to the Cellular Automata-Artificial Neural Network (CA-ANN) model to simulate LULC changes in Tarlac Province, Philippines via the Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS. For years 2019, 2023 and 2027 with 2015 as the base year, results showed an increasing trend for savannas and grassland with ΔLULC values of +11.4% to +15.1% and +0.2% to 3.5%, respectively. Meanwhile, a decreasing trend is observed for built-up/water, forest, and cropland with ΔLULC values of −3.0% to −6.3%, −8.5% to −21.1%, and −3.9% to −4.2%, respectively. Results also showed a conversion of a 100-ha area of croplands to solar farm from year 2019 to 2023 which translates to an estimated monetary loss from agricultural produce due to solar farm conversion amounting to Php 7,584,720.00 (~USD 138,000) which is equivalent to the total average annual income of about 67 families in Tarlac. Lastly, the simulated 2027 LULC map showed pixels with unrealistic conversions from solar farm (year 2023) to cropland (year 2027). To improve the model, it is recommended to add more spatial data to effectively capture factors that may contribute to the expansion of solar farms in the future. Moreover, high resolution LULC maps (vector maps if available) can be used instead of a course resolution satellite-derived raster data. Nonetheless, this study has demonstrated the use of RS, GIS and machine learning techniques to model cropland conversion to solar farms and other LULC classes. Results from this study can provide scientific data to policy makers, solar industry players and other relevant stakeholders in doing technoeconomic assessment of solar farm development and expansion considering its effect on energy security and food security towards national sustainable development.

State and situation of avian influenza in the Eastern Mediterranean Region.

Avian influenza viruses have had a significant burden of disease on animal and public health in countries of the Eastern Mediterranean Region. In this review, we aimed at describing the state of avian influenza in the region from 2011 to 2021. We gathered information available through the peer-reviewed scientific literature, public gene sequence depositories, OIE World Animal Health Information System platform, World Health Organization FluNet, Joint External Evaluation reports, and governmental, Food and Agriculture Organization of the United Nations, and World Organization for Animal Health websites. We used an interdisciplinary perspective consistent with the One Health approach to perform a qualitative synthesis and making recommendations. Analysis showed that although avian influenza research in the Eastern Mediterranean Region has gained more attention during the last decade, it was limited to only few countries and to basic science research. Data highlighted the weakness in surveillance systems and reporting platforms causing underestimation of the actual burden of disease among humans and animals. Inter-sectoral communication and collaboration for avian influenza prevention, detection, and response remain weak. Influenza surveillance at the human-animal interface and the application of the One Health paradigm are lacking. Countries' animal health and public health sectors rarely publish their surveillance data and findings. This review suggested that surveillance at the human-animal interface, research, and reporting capacities should be enhanced to improve understanding and control of avian influenza in the region. Implementing a rapid and comprehensive One Health approach for zoonotic influenza in the Eastern Mediterranean Region is recommended.

Route selection for road vehicle real-driving emissions test using GIS

The European Commission has developed a Real-Driving Emissions (RDE) test for road vehicles that complies with the urban, rural and motorway criteria to ensure the exhaust emission produced could be accurately evaluated. Therefore, careful measures are taken in selecting the best routes to ensure the testing fulfils the targeted criteria. As there is no common method exist to select the testing route, analysis on the testing route selection was implemented using Geographic Information System (GIS) platform that is capable to deal with large vector data. Thus, the proposed framework facilitates the testing route selection process which is difficult to determine without a proper spatial analysis of network data. Specific route information comprises of road categories, route speed range and route classes were collected from the Open StreetMap (OSM) database. The generated route density and the topological route network structure were used to further analyse the suitability route as the requirement of Real-Driving Emissions test. As a result, five candidates of Real-Driving Emissions test route have been selected in Malaysia as a case study using GIS platform.

Logistics 4.0 in the agri-food supply chain with blockchain: a case study

ABSTRACT Blockchain is an emerging technology with the potential to radically transform logistics in the highly complex environment of the agri-food supply chain. The key challenges of the agri-food sector include overcoming the limitations (time, cost and quality) to developing global and efficient transparency and traceability of food, eliminating expensive intermediaries, and ensuring no disruption between suppliers and retailers. This paper describes a practical approach by not only using smart contracts to integrate various stakeholders but also incorporating a Public Participation Geographic Information System platform to ensure traceability of the operations conducted in an agri-food supply chain. As a factor in improving distribution and minimising intermediaries, third-party logistics participation is included in this research. Through a case study, the entities and interactions between processes within a blockchain system based on a smart contract are identified. The findings reveal an innovative blockchain approach to logistics in the agri-food supply chain.

Evaluating the influence of environmental variables on the length-weight relationship and prediction modelling in flathead grey mullet, Mugil cephalus Linnaeus, 1758.

Fish stocks that are grown under diverse environmental conditions have different biometric relationships and growth patterns. The biometric length-weight relationship (LWR) is an essential fishery assessment tool, as fish growth is continuous and depends on genetic and environmental factors. The present study attempts to understand the LWR of the flathead grey mullet, Mugil cephalus Linnaeus, 1758, from different locations. The study area encompassed its distribution in the wild across freshwater location (one), coastal habitats (eight locations), and estuaries (six locations) in India to determine the relationship between various environmental parameters. Specimens (n = 476) of M. cephalus were collected from commercial catches and the length and weight of individual specimens were recorded. Monthly data from the study locations were extracted for nine environmental variables from the datasets downloaded from the Physical Oceanography Distributed Active Archive Center (PO.DAAC) and the Copernicus Marine Environment Monitoring Service (CMEMS) over 16 years (2002 to 2017) on the Geographical Information System platform. The parameters of the LWR, intercept 'a' and slope or regression coefficient 'b', varied from 0.005321 to 0.22182 and 2.235 to 3.173, respectively. The condition factor ranged from 0.92 to 1.41. The partial least squares (PLS) score scatter plot matrix indicated differences in the environmental variables between the locations. PLS analysis of the regression coefficient and environment parameters revealed that certain environment variables viz., sea surface temperature, salinity, dissolved oxygen, nitrate, and phosphate, played a positive role. However, chlorophyll, pH, silicate, and iron played a negative role in influencing weight growth across various locations. The results revealed that the M. cephalus specimens from three locations, Mandapam, Karwar, and Ratnagiri, possessed significantly higher fitness to their environment than those from the other six locations. The PLS model can be used to predict weight growth under the various environmental conditions of different ecosystems. The three identified locations are useful sites for the mariculture of this species considering their growth performance, the environmental variables, and their interactions. The results of this study will improve the management and conservation of exploited stocks in regions affected by climate change. Our results will also aid in making environment clearance decisions for coastal development projects and will improve the efficiency of mariculture systems.

GIS-based revised universal soil loss equation for estimating annual soil erosion: a case of lower Kulsi basin, India

Assessment and estimation of soil loss is a fundamental aspect of land and water resource conservation and management practices as it provides necessary information in the course of watershed-level development of a region. The soil loss model of Wischmeier and Smith, popularly known as the Revised Universal Soil Loss Equation, was selected to estimate soil loss in the lower Kulsi river basin due to its simplicity, versatility, and flexibility nature method in the Geographic information system platform. Most original governmental datasets, mainly daily gauge rainfall from 2009 to 2018, satellite images for land use land cover, digital elevation model of Shuttle Radar Topographic Mission for topographic factor, and National Bureau of Soil Survey and Land-use Planning, India soil map were utilized to estimate the average annual soil erosion. The estimated average annual soil erosion ranges from 0.0 to 6.45 thousand t ha−1y−1, grouped into low, moderate, high, and very high risk of soil erosion. A basin area of 36.235 km2 (1.85%) basin area was identified as high to very high zones of soil erosion risk and needed immediate conservation measures to reduce the erosion risk.

A REVIEW OF 3D SPATIAL DATA MODELS FOR SURFACE AND SUBSURFACE INTEGRATION OF SPATIAL OBJECTS

Abstract. Three dimensional (3D) spatial data models have been known as a geographic information system platform for representing the dimensionality of spatial objects with respect to the real world. They can be used to mimmick the real-world objects above the ground, on the ground and underground. A 3D spatial data model that can integrate surface and subsurface elements will have a significant impact on engineering, spatial and urban planning, and the built environment. Such an impact includes good infrastructure development, proper planning, result-oriented installation of utilities, location-specific excavation of existing utilities, and cost-effective economy resulting from minimal damage of infrastructures. This article explores and documents the state-of-the-art with respect to 3D spatial data models specifically for the integration of surface and subsurface objects based on a thorough review of the literature in academic articles, technical reports, and web-based materials. To better understand the scope, this paper reviews the different 3D spatial modelling approach for surface spatial objects, subsurface spatial objects, and with keen focus on surface and subsurface spatial objects integration. The Open Geospatial Consortium (OGC) has played an important role in data integration and has a core space in 3D spatial data model evolution. One of the recent data models for such task is CityJSON due to its simplicity in data storage, visualization, manipulation, and data update compared to CityGML. The parameters influencing the choice of models can be attributed to the availability of data structure, applicability, exchange format and relevance. Based on the range of 3D spatial data model reviewed, a unified model is expedient for integration of surface and subsurface spatial objects.

Earthquake hotspot and coldspot: Where, why and how?

• Global earthquake hotspot and coldspot were identified. • Statistical methods of hotspot analysis and optimized hotspot analysis. • Benioff/subduction zones are the most potential hotspot zone. • Optimized hotspot analysis emerges as the most probabilistic method. Global tectonic activities are playing an important role in the occurrences of devastating earthquakes and related long-term changes in the earth's system surface. However, the plate tectonics processes and their interaction with the earth's crust are very much complex, and it is a subject of unending debate. Therefore, tectonism-induced landslide, tsunami, liquefaction, and fire are significant earthquake-related hazards, which have a larger potential and overwhelming impact on life and infrastructural properties throughout the world. In this study, we have emphasized the identification of earthquake hotspot and coldspot zones considering historical earthquake data across the plate boundary of the world. Here, a total of 7773 historical earthquake points were collected as input parameters with three-moment magnitude (M w ) classes (<4.5, 4.5-6.0, and >6.0). Two statistical methods namely hotspot analysis (Getis-Ord GI*) and optimized hotspot analysis were used in the detection of global earthquake hotspot and coldspot zones using the geographic information system (GIS) platform. Hotspot and coldspot zone are identified under 99%, 95%, and 90% confidence levels. Alongside, here we have also discussed the paradigm, evidence of tectonic, and historical earthquakes, and how and why they are formed with the help of the existing theoretical constraints. The result indicates that the Pacific ring of fire, Peru-Chile Trench, and the mid-Atlantic oceanic ridge is fall in the hotspot zones of 99%, 95%, and 90% confidence levels.