Information For Better Decision Research Articles

Unveiling the dynamics of crisis events: Sentiment and emotion analysis via multi-task learning with attention mechanism and subject-based intent prediction

In the age of rapid internet expansion, social media platforms like Twitter have become crucial for sharing information, expressing emotions, and revealing intentions during crisis situations. They offer crisis responders a means to assess public sentiment, attitudes, intentions, and emotional shifts by monitoring crisis-related tweets. To enhance sentiment and emotion classification, we adopt a transformer-based multi-task learning (MTL) approach with attention mechanism, enabling simultaneous handling of both tasks, and capitalizing on task interdependencies. Incorporating attention mechanism allows the model to concentrate on important words that strongly convey sentiment and emotion. We compare three baseline models, and our findings show that BERTweet outperforms the standard BERT model and exhibits similar performance to RoBERTa in crisis tweets. Furthermore, we employ natural language processing techniques to extract key subject entities (e.g., police, victims) and leverage the publicly available commonsense knowledge model, COMET-ATOMIC 2020, to identify their intentions in given crisis scenarios. Evaluation of COMET-ATOMIC 2020 on subject-based intent prediction in crisis tweets reveals that BART was superior to GPT2-XL model, providing crisis responders with vital information for better decision making. Notably, the integration of sentiment and emotion classification, identification of attention words and subject-based intent prediction represents a novel methodology, not previously applied in the context of crisis scenarios.

Tracking of Multiple Static and Dynamic Targets for 4D Automotive Millimeter-Wave Radar Point Cloud in Urban Environments

This paper presents a target tracking algorithm based on 4D millimeter-wave radar point cloud information for autonomous driving applications, which addresses the limitations of traditional 2 + 1D radar systems by using higher resolution target point cloud information that enables more accurate motion state estimation and target contour information. The proposed algorithm includes several steps, starting with the estimation of the ego vehicle’s velocity information using the radial velocity information of the millimeter-wave radar point cloud. Different clustering suggestions are then obtained using a density-based clustering method, and correlation regions of the targets are obtained based on these clustering suggestions. The binary Bayesian filtering method is then used to determine whether the targets are dynamic or static targets based on their distribution characteristics. For dynamic targets, Kalman filtering is used to estimate and update the state of the target using trajectory and velocity information, while for static targets, the rolling ball method is used to estimate and update the shape contour boundary of the target. Unassociated measurements are estimated for the contour and initialized for the trajectory, and unassociated trajectory targets are selectively retained and deleted. The effectiveness of the proposed method is verified using real data. Overall, the proposed target tracking algorithm based on 4D millimeter-wave radar point cloud information has the potential to improve the accuracy and reliability of target tracking in autonomous driving applications, providing more comprehensive motion state and target contour information for better decision making.

Tobacco Agridaksh: An Online Expert System

New advances in information technology led to the development of Expert Systems and their application in various sectors including farming. In India, agricultural production has been transformed into a multifaceted business enterprise. Indian agriculture to remain competitive needs the accumulation and integration of scientific knowledge, and information from many diverse sources. Indian farmer often relies on agricultural specialists, advisors and agricultural research and development institutes for agricultural information for better decision making at the actual farm situation. Unfortunately, timely specialist assistance is not available when the farmer needs. Thus the situation demand for a ‘virtual expert’ who can give personalized expert advice to a large community of farmers, specific to their needs and aspirations considering various knowledge sources. The role of expert systems in tobacco sector and its applications in effective production and protection technologies have been discussed in this article. It is almost impossible for any human expert to consider every piece of available information before arriving at optimal decisions. To overcome this problem and provide precise information to the farmers, “expert systems” have been developed with a primary goal to make expertise available to clients and decision makers who need answers swiftly. The main aim is to deliver the required information and disseminate the up-to-date scientific knowledge in a readily accessible and easily understood form to the farmers. It is one of the most efficient extension tools to take the technology from scientists to the growers directly without any distortion of content which normally creeps in because of multiple agencies involved in conventional technology transfer systems. With this aim, ICAR-Central Tobacco Research Institute has developed a web-based expert system on tobacco using “Agridaksh”- an online tool developed by ICAR-Indian Agricultural Statistics Research Institute. This, online expert systems has the tremendous capacity to transfer location specific technologies and advice to the farmers with a greater precision

Use of Proxy Indicators to Interpret the Epi Curve of COVID-19 in Sri Lanka

Sri Lanka has taken measures to avoid the introduction and spread of COVID-19 well before the country confirmed its first COVID 19 case on 27th January 2020, on top of its detailed epidemiological surveillance and infectious disease control system. Traditional indicators such as the daily case numbers have been used as an indicator to keep track of the outbreak progression. However, as the pandemic continued, the need to have proxy indicators was felt, both to better understand its behavior as well as future trends. The objective of this study was to explore the use of proxy indicators, namely the observed test positivity rate (TPR) and the mobility trends against the epidemic curve to interpret the behavior of the COVID-19 pandemic; their usefulness, and limitations. The information for the study was gathered using the COVID-19 daily status reports published and made publicly available by the Sri Lankan health authorities for the duration of 01/04/2021 to 30/06/2021. Trends of change of mobility during the period are gathered using regularly updated Google Community Mobility Reports. It is seen that the ‘trend’ of cases as depicted by weekly Observed TPR carries better information for decision making and evaluation of the methods to contain COVID 19 than using traditional proxy indicators such as the daily case numbers. The reported numbers in cases can be less than the actual numbers due to non-detection of cases in the community, under-reporting, delays in reporting, and PCR backlog. Trends of mobility changes and the trends of observed TPR together can be used to monitor and evaluate the effectiveness of the use of lockdown measures to contain the disease. It is recommended to adopt the use of such proxy indicators both in measuring the disease severity and the evaluation of the use of travel restrictions to contain the disease cost-effectively in a resource-limited setting.

Proposal of a Big data System for an Intelligent Management of Water Resources

Today, advanced technologies like Big Data, IoT, and Cloud Computing can provide new opportunities and applications in all sectors. In the water sector, water scarcity has become a common concern of different institutions and actors worldwide. In this context, several approaches and systems have been proposed and developed, using these technologies, allowing intelligent water resources management. Internet of Things can be used for assisting the Water Industry to collect data, manage and monitor the water infrastructures using smart devices. Big Data is a strategic technology for analyzing and interpreting collected data into valuable and helpful information for better decision making. This paper presents Big Data and Internet of Things technologies. It addresses theirs uses in some use cases such as municipal water losses, water pollution in agriculture, water Leak detection, etc., to provide new systems and innovative solutions for intelligent water resources management. Based on this study, we propose a Big Data and IoT architecture for intelligent water resources management.

Benefits and Challenges of International Financial Reporting Standard (IFRS) Transition in Ethiopia (Case Study on Some Selected Organizations in Ethiopia)

The purpose of this study is to assess the transition of International Financial Reporting Standards (IFRS) in Ethiopia: the benefits, prospects and challenges of its adoption and implementation in the case of 13 significant PIEs. The study employs both quantitative and qualitative methods (mixed) research approach. Both primary and secondary data sources were used for the study. The respondents are selected by using judgmental sampling technique. Then, the collected data from those organizations is analyzed by descriptive and econometric analysis by using SPSS software. The results revealed that the adoption of the IFRSs has improves the efficiency and effectiveness of financial reporting, provides better information for decision making, enhanced the accuracy; reliability and comparability of accounting information, simplify the process of Merger/Acquisition , enables greater effectiveness of the internal audit, brings better corporate governance, reduce cost of capital , better access to capital market , harmonize the reporting standard , better risk management, promotes cross border investments and provide timely financial report. However, there is serious educational, technical and institutional challenges that the country need to overcome in order to benefit fully from the adoption of IFRS. Index Terms - International Financial Reporting Standards, Benefits, prospect, Challenge, implementing. DOI: 10.7176/RHSS/10-11-03 Publication date: June 30th 2020

Price Forecasting and Span Commercialization Opportunities for Mexican Agricultural Products

Decision-making based on data analysis leads to knowing market trends and anticipating risks and opportunities. These allow farmers to improve their production plan as well as their chances to get an economic success. The aim of this work was to develop a methodology for price forecasting of fruits and vegetables using Queretaro state, MX as a case study. The daily prices of several fruits and vegetables were extracted, from January 2009 to February 2019, from the National System of Market Information. Then, these prices were used to compute the weekly average price of each product and their span commercialization in Q4 and over the median of historical data. Moreover, product characterization was performed to propose a methodology for future price forecasting of multiple agricultural products within the same mathematical model and it resulted in the identification of 18 products that fit the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model. Finally, future price estimation and validation was performed to explain the product price fluctuations between weeks and it was found that the relative error for most of products modeled was less than 10%, e.g., Hass avocado (7.01%) and Saladette tomato (8.09%). The results suggest the feasibility for the implementation of systems to provide information for better decisions by Mexican farmers.

Exploring the feasibility of blockchain technology as an infrastructure for improving built asset sustainability

PurposeThe purpose of this paper is to show the feasibility of blockchain technology to perform as an infrastructure for improving built asset sustainability by providing all the necessary information for better decision making at all the stages of its life cycle.Design/methodology/approachBlockchain technology can be used as a tool to build a reliable and secure decentralized information system to capture and disseminate all the data required for different sustainability assessment models. A model is designed and tested through a synthetic scenario to substantiate the research objective with empirical work.FindingsIt is shown that blockchain can revolutionize the current state of knowledge for long-term sustainability thinking and provide necessary information in different stages of the life cycle of a built asset. With the proposed decentralized, transparent and comprehensive database using blockchain, the life cycle assessment methods can become much more inclusive and reliable. The new holistic analysis of the built asset sustainability enables better decision making in design, build, operation and demolition of each asset.Originality/valueThis paper proposes and tests a model for using blockchain as an infrastructure to support built asset sustainability. Practitioners from different backgrounds at different stages of a built asset life cycle can use such a network to make better decisions and better assess the sustainability of their built assets.

Big Data Analytics and the Management Accountant

A business exists to earn more revenue with less costs and thus making it a profitable venture. Therefore it will strive cost containment with investment to develop its competitive position. To arrive at this balance is not easy and it requires deep understanding of the drivers of cost, risk and value in the business. All three factors are highly dynamic and management requires constant and current information for better decision making and performance management. Usage of Big Data Analytics have become imminent to improve operational performance and sustaining or improving competitive position. Research findings indicate that organizations are struggling to get valuable insights from their data. Businesses need to adopt strategic thinking and be innovative to ensure that they have the needed capabilities to exploit advances in big data analytics for not only success but also for survival in the technology and digital age. Management Accountants have an important role to play in ensuring business success in today’s data-driven era. Therefore they need to develop their skills continually so that they remain relevant to their employer’s needs. They also required to stay abreast of developments and upskill themselves in the discipline of analytics and data science so that they continue to contribute their value to the business and to the profession they serve. This paper explains the meaning of business analytics, its value to business and the evolution of roles of management accountants in the era of globalization and digital age and the need for management accountants to acquire the skills of business analytics and data science to retain their relevance to the corporate world.

Aplicación y Evolución de la Armonización Contable gubernamental

La implementación de la Armonización Contable nos proporcionara información útil para una mejor toma de decisiones, fomentar la inversión gracias a la confianza, mejoramiento de la administración pública, satisfacción de la ciudadanía, mecanismos de control, combate y abatimiento a la corrupción. Los objetivos de la armonización contable son: Facilitar la toma de decisiones; Homogeneidad de la información financiera; Establecimiento de normas para el manejo eficiente del gasto público; Registro de operaciones de manera automática, armónica, especifica de las operaciones contables. En crea el En 1986 se crea el Public Sector Comitte (PSC), hoy conocido como el Consejo de Normas Internacionales de la Contabilidad Pública (IPSASB), a partir de 1997 el IPSASB desarrollo un programa elaborando 21 NICPS dirigido a las entidades publicas las cuales deben seguir el criterio del devengo basandose en las NIC; en 2002 se finalizo el proceso de la la aprobación de las 21 NICSP y un glosario de terminos. (Buch Gómez & Cabaleiro Casal, 2006:3) En 2008 se pública la Ley General de Contabilidad Gubernamental, esta Ley propone la creación de un sistema de información contable y financiera del sector público homogéneo en los tres niveles de gobierno.normativo común que sea aplicable en cada uno de los niveles de gobierno. El objetivo de esta Ley es establecer un marco normativo común que sea aplicable en cada uno de los niveles de gobierno. El 7 de mayo de 2008 se publica en el Diario Oficial de la Federación un Decreto que reformo varios artículos de la Constitución, con tres objetivos principales: 1. Incrementar la calidad del Gasto Publico, 2. Prevenir erogaciones plurianuales para proyectos de inversión en infraestructura, 3. Lograr el fortalecimiento y rendición de cuentas. En 2009 se crea el Consejo Nacional de Armonización Contable (CONAC) que tiene como objetivo dar seguimiento a los criterios los cuales regirán la contabilidad gubernamental, esto nos permitirá medir la eficacia, economía y eficiencia del gasto y el ingreso de los Entes Públicos. La fiscalización de los recursos públicos está regulada por la Constitución Política de los Estados Unidos Mexicanos, Ley Orgánica del Poder Legislativo Federal y Ley de Fiscalización Superior Federal. Para llevar a cabo el proceso de fiscalización estos organismos de fiscalización deberán aplicar normas y procedimientos de auditoria, deberán realizar un programa de trabajo, así como la aplicación de diferentes tipos de auditorías.

Real-Time Traffic Event Detection From Social Media

Smart communities are composed of groups, organizations, and individuals who share information and make use of that shared information for better decision making. Shared information can come from many sources, particularly, but not exclusively, from sensors and social media. Social media has become an important source of near-instantaneous user-generated information that can be shared and analyzed to support better decision making. One domain where social media data can add value is transportation and traffic management. This article looks at the exploitation of Twitter data in the traffic reporting domain. A key challenge is how to identify relevant information from a huge amount of user-generated data and then analyze the relevant data for automatic geocoded incident detection. The article proposes an instant traffic alert and warning system based on a novel latent Dirichlet allocation (LDA) approach (“tweet-LDA”). The system is evaluated and shown to perform better than related approaches.

슬라이딩 윈도우 기반의 스트림 하이 유틸리티 패턴 마이닝 기법 성능분석

최근 무선 센서 네트워크, 사물 인터넷, 소셜 네트워크 서비스와 같은 다양한 응용 분야에서 대용량 스트림 데이터가 실시간으로 생성되고 있으며, 효율적인 기법을 통해 처리 및 분석하여 유용한 정보를 찾아내고, 이를 의사 결정을 위해 사용할 수 있도록 하는 것은 중요한 이슈 중에 하나이다. 스트림 데이터는 끊임없이 빠른 속도로 생성되므로 최소한의 접근을 통해 처리해야 하며, 신속한 저전력 처리를 필요로 하는 자원이 제한된 환경에서 분석될 수 있도록 적합한 기법이 요구된다. 이러한 문제를 해결하기 위해, 슬라이딩 윈도우 개념이 제안되어 연구되고 있다. 한편, 대용량 데이터로부터 의미 있는 정보를 찾아내기 위한 데이터 마이닝 기법 중에 하나인 패턴 마이닝은 중요 정보를 패턴 형태로 추출한다. 전통적인 빈발 패턴 마이닝은 이진 데이터베이스를 대상으로 하고 모든 아이템을 동일한 중요도로 고려함으로써 데이터 마이닝 분야에서 중요한 역할을 수행해 왔지만, 실제 데이터 특성을 반영하지 못하는 단점을 지닌다. 하이 유틸리티 패턴 마이닝은 비 이진 데이터베이스로부터 상대적인 아이템 중요도를 반영하여 더욱 의미 있는 정보를 찾아내기 위해 제안되었다. 정적 데이터를 대상으로 하는 하이 유틸리티 패턴 마이닝 기법은 그러나 스트림 데이터 처리에 적합하지 못하다. 제한된 환경에서 스트림 데이터의 특성을 반영하고 효율적으로 처리하여 중요한 정보를 찾아내기 위해 슬라이딩 윈도우 기반의 접근법이 제안되었다. 본 논문은 슬라이딩 윈도우 기반 하이 유틸리티 패턴 마이닝 기법들의 성능을 평가하고 분석하여 해당 기법들의 특성 및 발전 방향을 고찰한다. Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

Big Data and Natural Language Processing came together for better information extraction: Text Analytics

Lot of text content is available in a variety of sources such as blogs, emails, social networking sites, forums etc. that can be analyzed to get some useful information. This type of analysis is called text analytics. Text Analysis is related to Natural Language Processing and now Big Data is a voluminous source of unstructured information for Text Analytics. Big Data analysis is moving towards Text Analytics to get more and more from huge collection of data and use that information for better decisions. Text Analytics techniques include summarization, classification, keyword extraction, information visualization, question answering system, deep learning, clustering, link analysis etc. Varieties of fields such as business, finance, opinion analysis, medical analysis, legal analysis etc. are taking benefits from text analytics. This paper discusses various text analytics techniques in detail and concludes with future research scope of text analytics.

Improving documentation of clinical care within a clinical information network: an essential initial step in efforts to understand and improve care in Kenyan hospitals

In many low income countries health information systems are poorly equipped to provide detailed information on hospital care and outcomes. Information is thus rarely used to support practice improvement. We...

PERANCANGAN APLIKASI SISTEM INFORMASI PENYEWAAN KASET (CD) PADA RENTAL KASET AKIN TANJUNG PENDAM

ABSTRAK Tujuan penelitian ini adalah menghasilkan sistem informasi penyewaan kaset secara komputerisasi dimana sistem penyewaan kaset sebelumnya masih menggunakan sistem manual, sehingga lebih efisien dan optimal guna mendukung kemajuan dan perkembangan rental tersebut. Dengan memanfaatkan sistem informasi penyewaan kaset yang sudah terkomputerisasi ini dapat memberikan kemudahan dalam pelayanan dan penyajian informasi, meningkatkan kinerja serta pengolahan data yang lebih baik, tepat, cepat dan akurat. Peningkatan kinerja kerja tidak terlepas dari sarana dan prasarana, salah satunya adalah dukungan teknologi dan sistem informasi yang sesuai dengan kebutuhan. Dimana data dapat diproses dan diolah sehingga menghasilkan suatu informasi guna mendukung keputusan yang baik. Persaingan yang semakin ketat akibat perkembangan pesat bidang teknologi serta pergeseran demografi dan kondisi ekonomi yang tidak menentu juga dapat memicu perubahan di lingkungan bisnis ke arah ketidakpastian dan kompleks. Daya saing sebagai dasar keunggulan ditentukan oleh kemampuan berkembang dan memahami perubahan perilaku atau organisasi (sistem) yang melibatkan kombinasi pemikiran, proses serta pemenafaatan secara efektif teknologi dan manusia guna menghasilkan sesuatu yang berbeda atau lebih baik. Sistem Informasi Penyewaan Kaset (CD) pada Rental Kaset Akin Tanjung Pendam Kabupaten Belitung ini merupakan sistem informasi penyewaan kaset yang dirancang dengan menggunakan metodologi berorientasi objek dengan alat bantu UML (Unified Modelling Language), pengamatan langsung dan wawancara dengan pihak yang terkait. Kata kunci: penyewaan, teknologi, aplikasi, sistem informasi, UML, metodologi berorientasi objek.

Innovative Performance Measurement: an Integrative Perspective of Stakeholder's View

Abstract Business Process Management (BPM) has been increasingly focused as an holistic approach to manage organizations for better organizational effectiveness. BPM involves the use of innovative performance measurement systems to follow up, coordinate, control and improve processes and overall business efficacy and efficiency. In this paper we propose a global holistic perspective of integrated information, combining the view of all stakeholders and both qualitative and quantitative information, as a basic prerequisite for quality of information for better decision making. The paper includes findings from an empirical case study of measuring Parkinson's Disease Neurosurgery process, including stakeholder's view with an integrative perspective.

Automated, Data-Driven Performance Regime for Operations Management, Planning, and Control

As public transit agencies install new technology systems, these agencies are gaining increasing amounts of data. These data have the potential to change how transit agencies operate by generating better information for decision making. Deriving value from these data and applying it to improve service requires changing the institutional processes that developed when agencies had little reliable information about their systems and customers. This research used the Massachusetts Bay Transportation Authority as a case study. The research assessed how the agency measured performance and then redesigned and advanced the agency's daily performance reports for rapid transit through a collaborative and iterative process with the operations control center staff. These reports were used to identify poor performance, to implement pilot projects to address the causes of poor performance, and to evaluate the effects of these pilots. Through the case study, this research found that service controllers’ trust and interpretation of performance information determined the impact the information had on operations. The results showed that new data would be most effective in producing service improvements if measurements accurately reflected human experience and were developed in conjunction with their intended users. Developing small pilot projects during this collaborative process would also enable new performance information and results in sustainable service improvements.

Relative weight evaluation of the factors inducing social media service use

As an influence of social media service (SMS) is extending enormously and SMS becomes an important marketing way and a field of modern industry beyond a method to transfer informations. Under these circumstances that the influence of SMS is getting bigger bigger, which factors make users participate and work in the social media service and how relatively important these factors are between them need to be examined. But SMS are different from the existing information systems. Most information systems are task-oriented, that is, the systems aim to provide users with useful information for better decision making. Therefore, a different approach is required. In this research, we examine and select the main factors which affect users' social media service use, and evaluate relative importance degrees of each factor on the basis of relationships between the factors with Analytic Network Process (ANP).

How satellite rainfall estimate errors may impact rainfed cereal yield simulation in West Africa

Rainfall monitoring via satellite sensors is particularly relevant for the agricultural sector of West Africa. Indeed, food shortages in this region are often caused by rainfall deficits and an early access to data available for the entire region can help to provide credible and timely information for better decision making. This study assesses the accuracy of state-of-the-art satellite rainfall retrievals for agriculture applications in two sites in Niger and Benin. Although these satellite data are widely used instead of rain gauge data for such applications, we found that, in a crop-modelling framework, their use can introduce large biases in crop yield simulations. Biases differ strongly among the four cultivars considered in both sites and are not simple extrapolation of each satellite product cumulative rainfall amount biases. In particular, we found that if an accurate estimation of the annual cumulative rainfall amount is important for yield simulations of pearl millet ‘Souna 3’ and ‘Somno’ cultivars in Niger, a realistic distribution of rainfall is also very important for predicting pearl millet ‘Somno’ and ‘HK’ yields in Niger as well as maize yields in Benin. Overall the satellite products tested, 3B42v6 appears to be the most suitable satellite product for our specific agricultural application since it minimizes both biases in rainfall distribution and in annual cumulative rainfall amount. For each crop and in both regions, biases in crop yield prediction are the highest when using non-calibrated satellite rainfall products (PERSIANN, 3B42RT, CMORPH and GSMAP).

Analysis of Mast Management Distribution and Telecommunication Service Using Geospatial Technique

Information management has become a very important topic in recent times and technological advancement depends more and more on the amount of information that is available in real time. As the popular saying goes information is power. This is why investment in the information networking of an economy cannot be over flogged. Great nations of today are characterized by the amount of information they harness and how well and efficient this information is being dispersed to its citizens. GIS (Geographic Information System) is of great importance in accomplishing modern day task of information management. This project is focused on the direct application GIS is proffering solutions to telecommunications masts distribution, the spread and their locations. For this study, two major telecoms companies in Lagos were chosen for consideration and analysis in other to appreciate the lapses and the short comings of the present spread and the need for a spatial adjustment. Also given the ongoing Federal Government proposed policy on mast stations networking by all telecom companies, we shall achieve maximum output, reduced cost of service and increase in the efficiency of the telecoms companies to all the customers. Geographic information System (GIS) as a tool enables telecommunication professionals to integrate maps and information for better decision making, from planning and maintenance of infrastructure to administering of mobile telephone coverage, managing existing customers and finding of new ones. In all these GIS Users rely on location based data to proffer solutions. The methodology employed for this project is data acquisition, data storage, data manipulation and data presentation. Analyses of the results obtained shows the spatial distribution of the masts around Lagos state and highlights the different logical paths the GIS can be queried to achieve the required results. This study has provided a Geospatial analysis of Mast Management distribution and Telecommunication Service in Lagos State.