Spatial-Temporal Analysis Framework for Health and Disease Mapping and Modelling

© 2025 Justine Blanford

By Shahabuddin Amerudin

Abstract

The study of spatial-temporal dynamics in health and disease mapping is crucial for understanding the spread and control of diseases. This review examines a comprehensive framework that integrates various scales of temporal and spatial data to enhance health and disease modeling. The framework leverages granular to broad/noisy data types, transitioning from local observations to global predictive models. This multidimensional approach is essential for developing effective public health strategies and interventions.

Introduction

The integration of spatial and temporal dimensions in health and disease mapping provides a more nuanced understanding of epidemiological patterns (Blanford, 2025). The spatial-temporal analysis framework offers a systematic approach to analyzing health data, encompassing different scales and types of data. This review explores the theoretical underpinnings and practical applications of this framework, highlighting its significance in public health research and policy-making.

Temporal Scale: Short-Term to Long-Term Analysis

Temporal analysis in health studies can range from short-term (hourly or daily) to long-term (several weeks to multiple years). Short-term data allows for real-time monitoring and immediate response to health events, while long-term data enables the study of trends and inter-annual comparisons. For instance, monitoring daily infection rates during a disease outbreak provides immediate insights, whereas long-term data on disease prevalence helps in understanding seasonal patterns and the impact of interventions over time.

Spatial Scale: Local to Global Analysis

Spatial analysis ranges from local (individual, household, village) to global scales. Local data is crucial for understanding the micro-dynamics of disease spread within communities. Conversely, global data offers insights into larger epidemiological trends and the impact of global health policies. This dual-scale approach ensures that both community-specific and international health issues are addressed. For example, local mapping of malaria cases can inform targeted interventions, while global mapping can track the disease’s spread across countries and continents.

Data Granularity: From Granular to Broad/Noisy Data

Data used in health mapping can be granular, such as precise GPS point locations, or broad and noisy, like aggregated data from social media posts. Granular data provides detailed insights at a micro level, essential for pinpointing sources of outbreaks or specific health behaviors. Broad/noisy data, although less precise, can reveal broader trends and patterns when aggregated and analyzed appropriately. For example, GPS data can track individual movements related to disease spread, while social media data can provide real-time information on public sentiment and behaviors related to health crises.

Observations to Predictive Models

The framework transitions from simple observational mapping to complex predictive modeling. Observational mapping is the initial step in understanding the current state of health events. Predictive modeling, on the other hand, uses this observational data to forecast future trends and potential outbreaks. This predictive capability is crucial for proactive health management and intervention planning. For instance, mapping current COVID-19 cases helps identify hotspots, while predictive models can forecast future waves and inform vaccination strategies.

Applications and Implications

The spatial-temporal analysis framework is highly applicable in various public health domains. It aids in the detection and monitoring of infectious diseases, chronic illness management, and environmental health studies. By incorporating both granular and broad data, health professionals can develop more accurate models and strategies. For example, in environmental health, mapping pollution levels alongside health data can identify correlations and causal relationships, informing policy decisions to reduce health risks.

Case Studies

1. Infectious Disease Monitoring:

  • Short-Term Local Data: During the Ebola outbreak, granular data at the village level was crucial for immediate response and containment efforts.
  • Long-Term Global Data: Longitudinal studies of HIV/AIDS prevalence across different continents have provided insights into the effectiveness of global health policies and interventions.

2. Chronic Disease Management:

  • Granular Data: Detailed patient data from electronic health records (EHRs) help in managing individual treatment plans.
  • Broad Data: National health surveys and aggregated data help in understanding the prevalence and risk factors of chronic diseases like diabetes and heart disease.

Challenges and Future Directions

While the spatial-temporal framework offers numerous benefits, it also presents challenges. Data privacy, especially with granular data, is a significant concern. Ensuring data quality and managing the heterogeneity of data sources are other critical issues. Future research should focus on developing standardized protocols for data collection, processing, and analysis. Additionally, integrating emerging technologies like machine learning and artificial intelligence can enhance predictive modeling capabilities.

Conclusion

The spatial-temporal analysis framework is a powerful tool for health and disease mapping and modeling. By integrating various scales of temporal and spatial data, it provides a comprehensive approach to understanding and managing public health issues. This framework’s ability to transition from granular observations to broad predictive models makes it invaluable for developing effective public health strategies and interventions.

Note: Image created by Blanford (2025)

References

  1. Anderson, R. M., & May, R. M. (1991). Infectious Diseases of Humans: Dynamics and Control. Oxford University Press.
  2. Blanford, J. (2025). Geographic Information, Geospatial Technologies and Spatial Data Science for Health. CRC Press.
  3. Diez Roux, A. V. (2007). Neighborhoods and Health: Where Are We and Were Do We Go from Here?. Revue d’Épidémiologie et de Santé Publique, 55(1), 13-21.
  4. Ostfeld, R. S., & Keesing, F. (2000). Biodiversity and disease risk: the case of Lyme disease. Conservation Biology, 14(3), 722-728.
  5. Weiss, R. A., & McMichael, A. J. (2004). Social and environmental risk factors in the emergence of infectious diseases. Nature Reviews Microbiology, 2(8), 602-607.

Avoiding Wrong Decisions in Geospatial Analytics: Best Practices and Methodologies

Abstract

Geospatial analytics has become an essential tool for decision-making across various sectors, including urban planning, agriculture, environmental monitoring, and disaster management. However, the complexity of geospatial data and the analytical methods used can lead to errors and misinterpretations, resulting in wrong decisions. This paper outlines a comprehensive framework to avoid making incorrect decisions in geospatial analytics by addressing key aspects such as problem definition, data quality, validation, multiple perspectives, expert consultation, and continuous monitoring. By following these best practices, practitioners can enhance the accuracy and reliability of their geospatial analyses and make more informed decisions.

Introduction

Geospatial analytics involves the collection, processing, analysis, and visualization of spatial data to understand and address various geographical and environmental issues. As the use of geospatial analytics expands, the potential for errors and misinterpretations also increases. Making wrong decisions based on faulty geospatial analytics can have significant consequences, from financial losses to public safety risks. Therefore, it is crucial to adopt a systematic approach to minimize errors and enhance decision-making processes.

Geospatial analytics is widely used in various domains, including urban planning, agriculture, environmental monitoring, public health, and disaster management. In urban planning, geospatial analytics helps city planners and policymakers design and manage urban spaces more efficiently. In agriculture, it assists farmers in optimizing crop yields and managing resources. Environmental monitoring uses geospatial analytics to track changes in ecosystems and manage natural resources. Public health professionals use it to monitor disease outbreaks and plan healthcare services. Disaster management agencies rely on geospatial analytics to assess risks, plan responses, and manage recovery efforts.

Problem Definition

A clearly defined problem sets the foundation for the entire geospatial analysis process. Without a clear understanding of the problem, the analysis may become unfocused, leading to irrelevant or misleading results. Clearly defining the problem involves identifying the specific questions that need to be answered and the goals that need to be achieved. This process includes determining the primary objectives of the analysis, engaging with stakeholders to understand their needs and expectations, defining the scope of the analysis including spatial and temporal boundaries, and developing specific research questions or hypotheses to guide the analysis.

Importance of Clear Problem Definition

A well-defined problem statement ensures that all efforts and resources are directed towards achieving specific, measurable goals. This clarity is crucial because it helps in selecting appropriate data sources, analytical methods, and tools. It also facilitates communication among team members and stakeholders, ensuring everyone is aligned and working towards the same objectives.

Steps for Defining the Problem

  1. Identify Objectives: Determine the primary objectives of the analysis. This involves understanding what you aim to achieve and what questions you need to answer.
  2. Stakeholder Engagement: Engage with stakeholders to understand their needs and expectations. Stakeholders may include government agencies, private companies, community organizations, and the general public.
  3. Scope Definition: Define the scope of the analysis, including spatial and temporal boundaries. This involves specifying the geographic area of interest and the time period for the analysis.
  4. Formulate Questions: Develop specific research questions or hypotheses to guide the analysis. These questions should be clear, concise, and directly related to the objectives of the analysis.

Data Collection and Quality

Geospatial data can be collected from various sources, including remote sensing (satellite imagery, aerial photography, and LiDAR), geographic information systems (GIS) (spatial databases and GIS platforms), field surveys (ground-based data collection using GPS and other instruments), and crowdsourced data (volunteered geographic information (VGI) and social media data). Ensuring high-quality data is essential for accurate geospatial analysis. Poor data quality can lead to erroneous results and wrong decisions. To ensure data quality, it is important to verify the positional and attribute accuracy of the data, ensure data consistency across different datasets and sources, check for missing or incomplete data, use the most up-to-date data available, and ensure comprehensive metadata is available for all datasets.

Sources of Geospatial Data

  1. Remote Sensing: This includes satellite imagery, aerial photography, and LiDAR data. These sources provide comprehensive coverage of large areas and can capture data at various spatial and temporal resolutions.
  2. Geographic Information Systems (GIS): GIS platforms integrate various types of spatial data and allow for complex spatial analysis and visualization. They can store, manage, and analyze large datasets.
  3. Field Surveys: Ground-based data collection using GPS and other instruments provides highly accurate and detailed data for specific locations. This method is often used to validate remote sensing data.
  4. Crowdsourced Data: Volunteered geographic information (VGI) and social media data are becoming increasingly popular for collecting real-time, user-generated spatial data. These sources can provide valuable insights, especially in areas where traditional data collection methods are limited.

Ensuring Data Quality

High-quality data is essential for accurate geospatial analysis. Poor data quality can lead to erroneous results and wrong decisions. To ensure data quality, consider the following:

  1. Accuracy: Verify the positional and attribute accuracy of the data. This involves checking the precision of spatial coordinates and the correctness of attribute information.
  2. Consistency: Ensure data consistency across different datasets and sources. This means that data should be standardized and formatted uniformly.
  3. Completeness: Check for missing or incomplete data. Complete datasets provide a more comprehensive understanding of the spatial phenomena being studied.
  4. Timeliness: Use the most up-to-date data available. Outdated data can lead to incorrect conclusions, especially in rapidly changing environments.
  5. Metadata: Ensure comprehensive metadata is available for all datasets. Metadata provides important information about the data’s source, accuracy, and limitations, which is crucial for interpreting the data correctly.

Data Preparation

Data preparation is a crucial step in the geospatial analysis process. It involves cleaning and preparing the data so that it can be analyzed effectively. Data cleaning tasks include removing duplicates, handling missing values, and correcting errors. Data normalization ensures that different datasets are compatible and can be analyzed together. This involves reprojecting data to a common coordinate system, standardizing units of measurement, and normalizing data to a common scale. These steps are essential to ensure the accuracy and reliability of the analysis.

Data Cleaning

Data cleaning involves identifying and correcting errors and inconsistencies in the data. Common data cleaning tasks include:

  1. Removing Duplicates: Identifying and removing duplicate records. Duplicate records can skew analysis results and should be eliminated.
  2. Handling Missing Values: Imputing or removing missing values. Depending on the extent of missing data, different techniques such as interpolation or the use of default values can be applied.
  3. Correcting Errors: Correcting any inaccuracies in the data. This includes fixing incorrect entries, resolving inconsistencies, and verifying data against known standards.

Data Normalization

Data normalization ensures that different datasets are compatible and can be analyzed together. This involves:

  1. Reprojecting Data: Converting data to a common coordinate system. Different datasets may use different coordinate systems, and aligning them is essential for accurate spatial analysis.
  2. Standardizing Units: Ensuring that all data is in the same units of measurement. For example, standardizing elevation data to meters if different datasets use different units.
  3. Scaling Data: Normalizing data to a common scale. This is particularly important when integrating datasets with different ranges of values.

Analysis Techniques

Geospatial analytics involves the use of various analytical techniques to extract insights from spatial data. Spatial statistics includes techniques such as spatial autocorrelation (measuring the degree of similarity between nearby locations), spatial regression (modeling relationships between spatial variables), and hotspot analysis (identifying areas with statistically significant clusters of events). Geostatistics focuses on the analysis and modeling of spatially continuous data, with techniques such as kriging (interpolating values at unsampled locations based on the spatial correlation structure) and variogram analysis (analyzing spatial dependence and variability). Spatial modeling and simulation involve creating models to represent and simulate spatial processes, including cellular automata (modeling spatial processes using a grid of cells, each with a set of rules) and agent-based modeling (simulating the actions and interactions of autonomous agents in a spatial environment).

Spatial Statistics

Spatial statistics involves the application of statistical methods to spatial data. Key techniques include:

  1. Spatial Autocorrelation: Measuring the degree of similarity between nearby locations. High spatial autocorrelation indicates that similar values are clustered together, while low spatial autocorrelation suggests a random distribution.
  2. Spatial Regression: Modeling relationships between spatial variables. This helps in understanding how different spatial factors influence each other.
  3. Hotspot Analysis: Identifying areas with statistically significant clusters of events. Hotspot analysis is used to detect patterns and trends in spatial data.

Geostatistics

Geostatistics focuses on the analysis and modeling of spatially continuous data. Techniques include:

  1. Kriging: Interpolating values at unsampled locations based on the spatial correlation structure. Kriging is a powerful tool for predicting spatial phenomena.
  2. Variogram Analysis: Analyzing spatial dependence and variability. Variograms help in understanding the spatial structure and scale of variability in the data.

Spatial Modeling and Simulation

Spatial modeling and simulation involve creating models to represent and simulate spatial processes. Techniques include:

  1. Cellular Automata: Modeling spatial processes using a grid of cells, each with a set of rules. Cellular automata are used to simulate complex spatial dynamics, such as urban growth.
  2. Agent-Based Modeling: Simulating the actions and interactions of autonomous agents in a spatial environment. Agent-based models are used to study phenomena such as traffic flow, disease spread, and ecological interactions.

Validation

Validation is a critical step in ensuring the accuracy and reliability of geospatial analysis. Cross-validation involves partitioning the data into subsets, using some subsets for training the model and others for testing it. This helps to assess the model’s performance and avoid overfitting. Ground truthing involves validating the results of geospatial analysis with real-world observations, ensuring that the analysis accurately reflects reality. Sensitivity analysis involves varying the input parameters of a model to assess the impact on the results, helping to identify which parameters have the most influence on the outcomes and ensure robustness.

Cross-Validation

Cross-validation involves partitioning the data into subsets and using some subsets for training the model and others for testing it. This helps to assess the model’s performance and avoid overfitting. Techniques include:

  1. K-Fold Cross-Validation: Dividing the data into K subsets and iteratively using each subset for testing while the remaining subsets are used for training. This provides a comprehensive assessment of the model’s performance.
  2. Leave-One-Out Cross-Validation: Using one observation for testing and the rest for training. This method is computationally intensive but provides an unbiased estimate of the model’s performance.

Ground Truthing

Ground truthing involves validating the results of geospatial analysis with real-world observations. This ensures that the analysis accurately reflects reality. Techniques include:

  1. Field Surveys: Conducting ground-based surveys to collect real-world data. This data is used to validate and calibrate remote sensing and GIS models.
  2. Comparison with Known Data: Comparing the analysis results with existing, reliable datasets. This helps in identifying discrepancies and improving the accuracy of the analysis.

Sensitivity Analysis

Sensitivity analysis involves varying the input parameters of a model to assess the impact on the results. This helps to identify which parameters have the most influence on the outcomes and ensure robustness. Techniques include:

  1. Parameter Variation: Systematically varying each parameter and observing the impact on the results. This helps to understand the sensitivity of the model to different inputs.
  2. Scenario Analysis: Considering multiple scenarios with different parameter values. This helps in assessing the range of possible outcomes and making more informed decisions.

Considering Multiple Perspectives

Engaging experts from different disciplines can provide diverse perspectives and help to identify potential biases and errors. This multidisciplinary approach includes collaboration with statisticians, computer scientists, domain experts, and other stakeholders. Scenario analysis involves considering multiple scenarios and their potential impacts, helping to understand the range of possible outcomes and make more informed decisions. By incorporating multiple perspectives, practitioners can gain a more comprehensive understanding of the data and its implications.

Multidisciplinary Collaboration

Collaboration with experts from different fields can provide valuable insights and help to identify potential biases and errors. This includes:

  1. Statisticians: Providing expertise in statistical methods and ensuring the robustness of the analysis.
  2. Computer Scientists: Offering knowledge in data processing, machine learning, and computational techniques.
  3. Domain Experts: Bringing specialized knowledge relevant to the specific application area, such as urban planning, agriculture, or public health.
  4. Stakeholders: Engaging with stakeholders to understand their needs, expectations, and constraints. This ensures that the analysis is relevant and useful for decision-making.

Scenario Analysis

Scenario analysis involves considering multiple scenarios and their potential impacts. This helps to understand the range of possible outcomes and make more informed decisions. Techniques include:

  1. Developing Scenarios: Creating different scenarios based on varying assumptions and input parameters. This helps to explore different possibilities and their implications.
  2. Evaluating Impacts: Assessing the potential impacts of each scenario. This helps in understanding the risks and benefits associated with different decisions.
  3. Making Informed Decisions: Using the insights gained from scenario analysis to make more informed and robust decisions.

Expert Consultation

Consulting with experts in geospatial analytics can provide valuable insights and help to avoid common pitfalls. Experts can offer guidance on data collection, analysis techniques, and interpretation of results. Engaging in peer review, where other experts review the analysis and provide feedback, can help to identify errors and improve the quality of the analysis. By seeking expert advice and undergoing peer review, practitioners can enhance the credibility and reliability of their geospatial analyses.

Seeking Expert Advice

Consulting with experts in geospatial analytics can provide valuable guidance and help to avoid common pitfalls. This includes:

  1. Data Collection: Experts can provide advice on the best methods and sources for data collection. They can also help in assessing data quality and ensuring accuracy.
  2. Analysis Techniques: Experts can offer guidance on the most appropriate analysis techniques for specific problems. They can help in selecting and applying the right methods to achieve accurate results.
  3. Interpreting Results: Experts can assist in interpreting the results of the analysis. They can help in identifying potential biases and errors and provide insights into the implications of the findings.

Peer Review

Engaging in peer review involves having other experts review the analysis and provide feedback. This helps to identify errors and improve the quality of the analysis. Techniques include:

  1. Formal Peer Review: Submitting the analysis to formal peer review processes, such as academic journals or conferences. This provides a rigorous and independent assessment of the work.
  2. Informal Review: Seeking feedback from colleagues and other experts in the field. This can provide valuable insights and help to identify potential issues.

Continuous Monitoring and Evaluation

Continuous monitoring involves regularly checking the results of geospatial analysis to ensure they remain accurate and relevant. This includes updating data and models as new information becomes available. Regularly evaluating the effectiveness of geospatial analytics efforts helps to identify areas for improvement and ensure that the analysis is meeting its objectives. This includes assessing the impact of decisions made based on the analysis and making necessary adjustments. By continuously monitoring and evaluating geospatial analytics efforts, practitioners can ensure that their analyses remain accurate and relevant over time.

Continuous Monitoring

Continuous monitoring involves regularly checking the results of geospatial analysis to ensure they remain accurate and relevant. This includes:

  1. Updating Data: Regularly updating data and models as new information becomes available. This ensures that the analysis remains current and reflects the latest data.
  2. Monitoring Results: Continuously monitoring the results of the analysis to identify any changes or trends. This helps in detecting potential issues and making timely adjustments.

Regular Evaluation

Regularly evaluating the effectiveness of geospatial analytics efforts helps to identify areas for improvement and ensure that the analysis is meeting its objectives. This includes:

  1. Assessing Impact: Evaluating the impact of decisions made based on the analysis. This helps in understanding the effectiveness of the analysis and identifying areas for improvement.
  2. Making Adjustments: Making necessary adjustments based on the evaluation. This includes refining the analysis methods, updating data, and improving the overall process.
  3. Feedback Loop: Creating a feedback loop where lessons learned from evaluation are used to improve future analyses. This helps in continuously enhancing the quality and reliability of geospatial analytics efforts.

Conclusion

Avoiding wrong decisions in geospatial analytics requires a systematic approach that addresses key aspects such as problem definition, data quality, validation, multiple perspectives, expert consultation, and continuous monitoring. By following these best practices, practitioners can enhance the accuracy and reliability of their geospatial analyses and make more informed decisions. As the field of geospatial analytics continues to evolve, ongoing research and development will play a crucial role in advancing the state-of-the-art and ensuring that geospatial analytics remains a valuable tool for decision-making.

Geospatial analytics has the potential to transform decision-making across various domains. By adopting best practices and methodologies, practitioners can avoid common pitfalls and make more informed decisions. This paper provides a comprehensive framework for enhancing the accuracy and reliability of geospatial analytics efforts, ultimately leading to better outcomes and improved decision-making processes.

Preparing for the Dynamic Geospatial Industry: Insights for Academia and Research Organizations

By Shahabuddin Amerudin

The geospatial industry is experiencing rapid evolution, driven by technological advancements and expanding applications across various sectors. This evolution has significant implications for academia and research organizations, which play a crucial role in preparing the next generation of geospatial professionals. This essay explores the prevailing job roles within the geospatial industry, the evolving skill sets and academic requirements, sectors showing significant employment growth potential, and the best practices employed by leading educational institutions to prepare students for successful careers in this dynamic field.

Prevailing Job Roles and Technological Evolution

The geospatial industry offers a diverse array of job roles, each evolving significantly due to technological advancements. Traditional roles such as GIS analysts, remote sensing specialists, and cartographers are being transformed, while new roles like geospatial data scientists and specialized software developers are emerging.

  • GIS Analysts: Historically, GIS analysts focused on spatial data management and map creation. Today, they require expertise in big data analytics and artificial intelligence to manage and interpret vast datasets effectively. The integration of geospatial data with other data types has become a critical skill, enabling more comprehensive analyses and decision-making.
  • Remote Sensing Specialists: With advancements in high-resolution satellite imaging and unmanned aerial systems (UAS), remote sensing specialists can now gather more precise and detailed data. They must also be proficient in using machine learning algorithms to process and analyze this data more efficiently.
  • Cartographers: Once centered on traditional map-making, cartographers now leverage GIS technology to create interactive, digital maps. These maps are used for various applications, including urban planning and environmental monitoring, reflecting the broader use of geospatial data.
  • Geospatial Data Scientists: This new role has emerged to meet the demand for analyzing complex geospatial datasets. Geospatial data scientists combine spatial analysis with data science techniques, using programming languages like Python and R to derive insights from geospatial data.
  • Software Developers: Developers in the geospatial field focus on creating sophisticated applications that utilize geospatial data for navigation, logistics, and disaster management. They must develop software capable of handling large volumes of spatial data and providing real-time analytics.

Evolving Skill Sets and Academic Requirements

As the geospatial industry evolves, so do the required skill sets and academic qualifications. The industry now demands a blend of traditional and cutting-edge skills.

  • Technical Skills: Proficiency in GIS software, remote sensing, spatial data analysis, and programming languages such as Python and R is essential. Additionally, knowledge of artificial intelligence, machine learning, and big data analytics is becoming increasingly crucial.
  • Interdisciplinary Knowledge: The industry values professionals who can integrate geospatial science with other disciplines such as computer science, environmental science, and urban planning. This interdisciplinary approach allows for more comprehensive solutions to complex problems.
  • Academic Degrees: Degrees in Geographic Information Science, Remote Sensing, Computer Science, and Data Science are highly sought after. These programs are evolving to include courses on emerging technologies and interdisciplinary approaches to ensure alignment with industry needs.

Sectors with Significant Employment Growth Potential

Several sectors demonstrate significant potential for employment growth within the geospatial industry, driven by the increasing application of geospatial technologies.

  • Autonomous Systems: The development of self-driving cars and other autonomous systems relies heavily on precise geospatial data for navigation and safety. This sector’s reliance on advanced geospatial technologies drives demand for skilled professionals.
  • Urban Planning and Infrastructure Development: Geospatial technology plays a critical role in efficient resource management, sustainable development, and urban planning. The ability to use geospatial data for better decision-making makes this sector particularly promising.
  • Environmental Monitoring and Disaster Management: These sectors utilize geospatial data to track environmental changes and manage disaster responses effectively. The increasing focus on climate change and disaster preparedness fuels demand for geospatial expertise.
  • Transport Infrastructure: The transport sector uses geospatial data to enhance logistics, navigation, and infrastructure development. Innovations in this sector are expected to drive significant employment growth for geospatial professionals.

Best Practices by Leading Educational Institutions

To prepare students for successful careers in the dynamic geospatial industry, leading educational institutions are employing several best practices.

  • Interdisciplinary Programs: Offering programs that combine geospatial science with other fields such as computer science, data analytics, and environmental studies provides a well-rounded education. This interdisciplinary approach ensures students are equipped with diverse skills applicable to various sectors.
  • Hands-on Training: Emphasizing practical experience through labs, internships, and fieldwork ensures students gain real-world skills. This hands-on approach is crucial for understanding the practical applications of geospatial technologies.
  • Industry Collaboration: Partnering with industry leaders for collaborative projects, guest lectures, and internships helps keep curricula aligned with industry trends and requirements. These collaborations provide students with valuable industry insights and networking opportunities.
  • Advanced Technologies: Integrating the latest technologies and software into the curriculum, such as AI, machine learning, and big data analytics, ensures students are proficient with the tools used in the industry. This approach helps students stay ahead of technological advancements.
  • Certification Programs: Offering certification programs in specific geospatial technologies and methodologies enhances students’ credentials and employability. These certifications provide students with recognized qualifications that are highly valued by employers.
  • Research Opportunities: Providing opportunities for students to engage in cutting-edge research projects, often in collaboration with industry or government agencies, fosters innovation and practical problem-solving skills. These research experiences prepare students for advanced roles in the geospatial field.

Conclusion

The geospatial industry is rapidly evolving, driven by technological advancements and expanding applications across various sectors. As job roles within the industry transform, the demand for specific skill sets and academic qualifications continues to grow. Educational institutions play a crucial role in preparing students for successful careers in this dynamic field by offering interdisciplinary programs, hands-on training, industry collaboration, and opportunities for advanced research. By adopting these best practices, academia and research organizations can ensure that their graduates are well-equipped to meet the evolving demands of the geospatial industry and contribute to its continued growth and innovation.

GIS Software System: Preparing for Final Examination

Course: GIS Software System (SBEG3583)

Semester II, Session 2023/2024

Lecturer: Dr. Shahabuddin bin Amerudin

The Geographic Information System (GIS) has revolutionized how we analyze and interpret spatial data, providing invaluable insights across various industries. The GIS Software System course (SBEG3583) at UTM, under the guidance of Dr. Shahabuddin bin Amerudin, delves deeply into the development, implementation, and future trends of GIS software. This article explores the key points and topics reviewed in the course, serving as a comprehensive guide for students preparing for their final examination.

Development and Evolution of GIS Software

Evolution of GIS Software

The history of GIS software is marked by significant milestones that have shaped its development and capabilities. Initially, manual mapping techniques formed the basis of spatial analysis, but the advent of computerized mapping systems brought about a paradigm shift. These early systems paved the way for desktop GIS applications, which made spatial tools more accessible and user-friendly. The evolution continued with the emergence of web-based and mobile GIS platforms, significantly expanding the reach and functionality of GIS. Today, advancements such as cloud-based GIS solutions and the integration of the Internet of Things (IoT) have further enhanced data scalability and real-time analysis capabilities, allowing for more sophisticated and dynamic spatial data management.

The initial phases of GIS software development were focused on digitizing and automating the labor-intensive processes of traditional cartography. As technology advanced, so did the capabilities of GIS software. Desktop GIS applications emerged in the 1980s, providing powerful tools for spatial data analysis on personal computers. This democratization of GIS technology allowed a broader range of users to engage with spatial data, fostering innovation and new applications across various fields.

The transition to web-based GIS in the late 1990s and early 2000s marked a significant leap forward, enabling the sharing and analysis of spatial data over the internet. This shift not only enhanced accessibility but also facilitated real-time collaboration and data sharing among multiple users and organizations. Mobile GIS further expanded the horizons of spatial analysis by allowing data collection and analysis in the field, providing real-time updates and insights. The recent integration of cloud-based solutions has brought unprecedented scalability and computational power to GIS, supporting large-scale spatial data analysis and complex modeling tasks. Additionally, the incorporation of IoT devices has enabled real-time data streaming and dynamic updating of spatial datasets, further enhancing the relevance and application of GIS in various sectors.

Fundamental Concepts and Modern Approaches

Understanding the core concepts and modern methodologies in GIS software development is crucial for grasping the subject. Spatial data models, both vector and raster, form the foundation of GIS, representing geographic features and attributes in various formats. Vector models use geometric shapes such as points, lines, and polygons to represent discrete features, while raster models utilize a grid of cells to represent continuous phenomena. These models are essential for accurately depicting and analyzing spatial relationships and patterns.

Geographic data collection methods, including remote sensing and GPS, are essential for gathering accurate spatial data. Remote sensing involves acquiring data about the Earth’s surface using satellite or aerial imagery, while GPS provides precise location data through satellite navigation systems. These techniques enable the collection of large volumes of spatial data, which can be analyzed and visualized using GIS software.

Data analysis and visualization techniques enable the interpretation of complex spatial information, transforming raw data into actionable insights. Techniques such as spatial interpolation, clustering, and network analysis allow for the identification of patterns and trends within spatial datasets. Visualization tools such as thematic maps, 3D models, and interactive dashboards facilitate the communication of spatial information to a broad audience, enhancing decision-making processes.

Modern programming languages and methodologies have also become integral to GIS development. Python, known for its scripting and automation capabilities, is widely used in GIS for tasks ranging from data processing to custom tool creation. Its extensive library ecosystem, including libraries such as ArcPy and GDAL, provides robust support for various GIS functions. JavaScript and HTML5 are crucial for developing interactive web GIS applications, enabling the creation of dynamic maps and spatial data visualizations that can be accessed through web browsers. Agile development and DevOps practices enhance the efficiency and flexibility of GIS projects, ensuring timely and robust software deployment. These methodologies promote iterative development, continuous integration, and collaborative teamwork, leading to the rapid delivery of high-quality GIS solutions.

Current Trends in GIS Software

The integration of Artificial Intelligence (AI) represents a significant trend in the evolution of GIS software. AI-driven predictive modeling, advanced spatial analysis, and real-time decision-making are transforming how spatial data is utilized. Machine learning algorithms can analyze large volumes of spatial data to identify patterns and make predictions, supporting applications such as land use planning, environmental monitoring, and disaster management.

In transportation management, GIS applications like fleet optimization, traffic pattern analysis, and route planning are leveraging AI to enhance efficiency and accuracy. For example, AI algorithms can optimize delivery routes by analyzing traffic conditions, road networks, and delivery schedules, reducing fuel consumption and improving delivery times. Traffic pattern analysis using AI can identify congestion hotspots and suggest measures to alleviate traffic flow, enhancing urban mobility and reducing travel time.

The integration of AI with GIS is also enabling real-time decision-making. For instance, emergency response teams can use AI-powered GIS systems to analyze real-time data from various sources, such as weather forecasts, traffic reports, and social media feeds, to coordinate response efforts during natural disasters. These advancements demonstrate the expanding role of GIS in solving complex, real-world problems, highlighting the importance of staying updated with the latest trends and technologies in the field.

Role of GIS Software Vendors in Driving Innovation

Role of Vendors in Innovation

GIS software vendors play a pivotal role in driving technological advancements and meeting industry-specific needs, particularly in transportation management. Leading vendors such as Esri and QGIS have set benchmarks for innovation, offering comprehensive solutions that address various spatial data challenges. Esri’s ArcGIS platform, for instance, provides a wide range of tools for spatial analysis, data visualization, and application development, supporting various industries such as urban planning, environmental management, and transportation.

QGIS, an open-source alternative, offers flexibility and customization options, enabling users to tailor the software to their specific needs. The continuous development and enhancement of QGIS by a global community of developers ensure that it remains a robust and versatile GIS solution. These vendors not only provide the tools and technologies necessary for effective spatial data analysis but also contribute to the advancement of the field through research, development, and collaboration.

Collaboration in the GIS Industry

Collaboration among vendors, managers, and stakeholders is crucial for the continued development and enhancement of GIS software. Knowledge sharing and collaborative development lead to significant improvements in features and functionalities, ensuring that GIS tools remain relevant and effective. For example, collaborative projects between software vendors and academic institutions often result in innovative solutions that address specific industry challenges.

The GIS industry benefits from partnerships that promote interoperability and data sharing. For instance, initiatives such as the Open Geospatial Consortium (OGC) bring together organizations from various sectors to develop open standards for geospatial data and services. These standards facilitate the integration of different GIS systems and enable seamless data exchange, enhancing the overall utility and impact of GIS technologies.

Competitive Strategies of GIS Vendors

Understanding the competitive strategies of GIS vendors is essential for assessing their market positioning and approaches to evolving transportation demands. Successful strategies often involve a combination of technological innovation, customer-focused solutions, and strategic partnerships. For example, Esri’s strategy of offering a comprehensive suite of GIS tools and services, coupled with extensive training and support, has solidified its position as a market leader.

QGIS, on the other hand, leverages its open-source nature to attract a diverse user base, including academic institutions, government agencies, and non-profit organizations. The flexibility and customizability of QGIS, along with its active user community, contribute to its competitive advantage. Case studies of leading vendors provide valuable insights into how these strategies are implemented and their impact on the industry, highlighting the importance of understanding market dynamics and customer needs.

Comparison of Computer System Architecture Configurations in GIS Software

Types of System Architectures

Different system architectures offer unique advantages and disadvantages, impacting GIS operations and user experience. Desktop GIS provides high performance and offline access but is limited in scalability. It is ideal for individual users or small teams working on localized datasets. Client-server GIS offers centralized data management and multi-user access but is dependent on network connectivity. This architecture is suitable for organizations that require collaborative data editing and management.

Cloud-based GIS solutions provide scalability, accessibility, and reduced costs, although they raise concerns about security and internet dependency. Cloud-based GIS is particularly beneficial for large-scale projects that require significant computational resources and data storage. Mobile-based GIS enables field data collection and real-time updates, but its limited processing power and battery life can be challenging. Mobile GIS is essential for applications that require on-site data collection and immediate analysis, such as environmental monitoring and disaster response.

Impact on Functionality and User Experience

The choice of system architecture significantly affects GIS operations, especially in transportation management. Ensuring data accuracy, real-time updates, and user accessibility are critical factors influenced by the underlying architecture. Desktop GIS systems, while powerful, may not provide the real-time capabilities needed for dynamic applications such as traffic management. Client-server and cloud-based architectures offer better support for real-time data updates and multi-user access, making them more suitable for transportation management tasks.

Each type of architecture must be evaluated for its performance, scalability, and data management capabilities to determine its suitability for specific transportation management activities such as fleet tracking, route optimization, and incident management. For example, cloud-based GIS can support real-time fleet tracking by processing and analyzing large volumes of data from multiple sources, providing timely insights for decision-making.

Performance, Scalability, and Data Management

Analyzing the performance, scalability, and data management capabilities of different system architectures is crucial for their applicability in transportation management. Desktop GIS systems, while capable of handling complex spatial analyses, may struggle with large datasets and real-time data processing. Client-server architectures provide centralized data management, facilitating collaboration and data sharing among multiple users.

Cloud-based GIS solutions offer unparalleled scalability, allowing organizations to expand their data storage and processing capabilities as needed. This architecture is particularly advantageous for transportation management activities that require real-time data analysis and large-scale modeling. Mobile GIS, while limited in processing power, provides essential support for field data collection and real-time updates, ensuring that spatial data is accurate and up-to-date.

Benefits and Limitations of FOSS in GIS Applications

Benefits and Limitations of FOSS

Free and Open Source Software (FOSS) in GIS offers several advantages, including cost-effectiveness, customizability, and community support. These benefits make FOSS an attractive option for many organizations. For instance, the cost savings associated with FOSS can be significant, particularly for small organizations or those with limited budgets. Additionally, the ability to customize FOSS to meet specific needs ensures that users can tailor the software to their unique requirements.

However, potential drawbacks include the lack of official support and integration challenges, which can pose significant hurdles for some users. Organizations adopting FOSS may need to invest in training and development to build the necessary expertise for effective implementation and maintenance. Despite these challenges, the advantages of FOSS, such as the ability to modify source code and the support of an active user community, often outweigh the limitations.

Open Data and Standards

FOSS promotes interoperability and encourages customization and collaboration, which are essential for efficient transportation management. By adhering to open data and standards, organizations can enhance their GIS capabilities and foster a more collaborative environment. Open data initiatives, such as OpenStreetMap, provide valuable spatial data that can be freely accessed and used for various applications. The adoption of open standards, such as those developed by the OGC, ensures that GIS systems can seamlessly integrate and share data, enhancing their overall utility and impact.

Challenges of FOSS Adoption

Adopting FOSS involves challenges such as training and support requirements, data migration issues, and integration with existing infrastructure. Addressing these challenges is crucial for successful implementation and utilization of FOSS in GIS applications. Organizations may need to invest in training programs to build the necessary expertise and ensure that users can effectively leverage the capabilities of FOSS. Data migration issues, such as the transfer of existing datasets to new FOSS platforms, must be carefully managed to avoid data loss or corruption.

Integration with existing infrastructure can also be challenging, particularly for organizations that rely on proprietary GIS solutions. Ensuring that FOSS can seamlessly integrate with other systems and workflows is essential for maximizing its benefits. Despite these challenges, the adoption of FOSS offers significant advantages, including cost savings, flexibility, and community support, making it a viable option for many organizations.

Exploring Advanced GIS Software Concepts and Applications

Build Once, Deploy Anywhere

Ensuring consistency and compatibility across platforms is critical for effective GIS deployment. The “build once, deploy anywhere” approach facilitates the development of applications that work seamlessly across different devices and operating systems. This approach is particularly relevant in the context of web-based and mobile GIS solutions, where applications must function reliably on various platforms.

Developing cross-platform GIS applications ensures that users can access and interact with spatial data regardless of their device or operating system. This flexibility enhances user experience and ensures that GIS tools can be widely adopted and used effectively. Additionally, this approach reduces development time and costs by eliminating the need for multiple versions of the same application.

Web-based vs. Mobile GIS Solutions

Both web-based and mobile GIS solutions have their strengths and challenges. Security measures are critical for protecting sensitive data, while field data collection capabilities are essential for real-time updates. Web-based GIS solutions offer robust data visualization and analysis tools that can be accessed from any internet-enabled device, providing a powerful platform for spatial data analysis and decision-making.

Mobile GIS solutions, on the other hand, are designed for field data collection and real-time updates, providing immediate insights and enhancing decision-making processes. The suitability of each solution depends on the specific needs of transportation management, with each offering distinct advantages for various applications. For instance, web-based GIS is ideal for centralized data management and analysis, while mobile GIS is essential for on-site data collection and real-time reporting.

Integrating Three-Tier Architecture with Cloud-based GIS

Integrating three-tier architecture with cloud-based GIS enhances data sharing, scalability, and security. This integration is particularly beneficial for optimizing transportation systems and operations, providing a robust framework for handling complex spatial data tasks. The three-tier architecture, comprising the presentation, logic, and data tiers, ensures that GIS applications are modular and scalable, facilitating the efficient management of spatial data.

The presentation tier handles user interaction and data visualization, while the logic tier processes data and performs spatial analysis. The data tier manages data storage and retrieval, ensuring that spatial data is accurately maintained and accessible. Integrating this architecture with cloud-based GIS provides additional benefits, such as enhanced scalability and data sharing capabilities. Cloud-based GIS solutions can handle large volumes of spatial data and support real-time analysis, making them ideal for transportation management applications that require dynamic data processing and analysis.

Conclusion

The GIS Software System course (SBEG3583) provides students with a comprehensive understanding of the development, implementation, and future trends in GIS software. By exploring key topics such as the evolution of GIS software, the role of vendors, system architectures, the benefits of FOSS, and advanced GIS concepts, students are well-equipped to navigate the complexities of spatial data analysis and its applications in transportation management.

The course highlights the importance of staying updated with the latest trends and technologies in GIS, emphasizing the need for continuous learning and adaptation. As GIS continues to evolve, it will play an increasingly critical role in addressing complex spatial challenges and driving innovation across various industries.

Analisa dan Perbandingan Senarai Fundamental Geospatial Data (FGD) Malaysia dengan Piawaian Antarabangsa

PGN

Oleh Shahabuddin Amerudin

Pengenalan

Artikel ini bertujuan untuk menganalisis dan membandingkan Senarai Fundamental Geospatial Data (FGD) sehingga Januari 2023 yang diterbitkan oleh Pusat Geospatial Negara Malaysia dengan piawaian yang digunakan oleh organisasi dan badan antarabangsa seperti ISO 19115:2014FGDC NSDI (2000)INSPIRE Directive (2007), dan UN-GGIM Strategic Framework (2018). Analisis ini akan menilai kelengkapan data geospatial yang dimiliki Malaysia dan mencadangkan penambahbaikan untuk mematuhi piawaian global yang lebih tinggi.


1. Struktur Kategori Data

Senarai Fundamental Geospatial Data (FGD) Malaysia mengandungi 12 kategori utama, termasuk aeronautical, built environment, demarcation, geology, hydrography, hypsography, soil, transportation, utility, vegetation, special use, dan general. Setiap kategori ini merangkumi subkategori yang lebih terperinci, menunjukkan usaha yang signifikan dalam mengumpul dan menguruskan data geospatial. Kategori ini direka untuk mencakupi pelbagai aspek yang relevan dengan keperluan pengurusan dan pembangunan geospatial di Malaysia. Namun, jika dibandingkan dengan piawaian antarabangsa seperti ISO 19115:2014 yang lebih fokus pada penyediaan metadata, struktur kategori FGD Malaysia kelihatan lebih menumpu kepada jenis data yang dikumpulkan daripada metadata itu sendiri. ISO 19115:2014 menetapkan elemen metadata yang diperlukan untuk menggambarkan data geospatial, tetapi tidak menetapkan kategori data tertentu seperti yang dilakukan oleh FGD Malaysia.

Selain itu, FGDC NSDI (2000) memperkenalkan 7 tema data dasar yang meliputi ketinggian, hidrografi, imej orthophoto, dan penggunaan tanah, yang lebih tertumpu berbanding senarai kategori dalam FGD Malaysia. Tema-tema ini penting kerana ia memberikan garis panduan yang jelas tentang data yang dianggap kritikal untuk pembangunan infrastruktur spatial. Sebaliknya, INSPIRE Directive (2007) menggariskan 34 tema yang lebih mendalam dan berstruktur, termasuk tema persekitaran, pengurusan tanah, dan infrastruktur. Struktur INSPIRE lebih berorientasikan kepada keperluan pelbagai sektor dan menyediakan kerangka yang lebih jelas untuk penyusunan data geospatial. UN-GGIM Strategic Framework (2018) pula menggabungkan pelbagai aspek geospatial dengan fokus pada pengurusan, teknologi, piawaian, perkongsian data, dan kapasiti pembangunan, yang juga penting dalam memastikan pengumpulan dan pengurusan data yang konsisten di peringkat global.

Untuk mematuhi piawaian antarabangsa, Malaysia boleh mempertimbangkan untuk memperluas dan menyusun semula kategori sedia ada agar lebih selaras dengan tema yang digunakan oleh INSPIRE Directive. Penambahan kategori berkaitan infrastruktur digital dan teknologi hijau juga boleh dipertimbangkan untuk memperkukuhkan struktur data yang lebih relevan dengan keperluan semasa dan masa hadapan.


2. Ketersediaan dan Pengurusan Metadata

Dalam Senarai FGD Malaysia (2023), fokus utama adalah pada senarai data yang dikumpulkan dalam pelbagai kategori. Namun, dokumen ini tidak menekankan keperluan atau penyediaan metadata yang menyokong kualiti, sumber, dan keterbukaan data tersebut. Sebagai perbandingan, ISO 19115:2014 sangat menekankan kepentingan metadata yang lengkap dan terperinci untuk setiap dataset geospatial. Metadata ini harus merangkumi maklumat tentang kualiti data, sumber, penggunaan, dan sekatan akses, yang mana semuanya penting untuk memastikan data geospatial boleh digunakan dengan betul dan efektif di peringkat antarabangsa.

FGDC NSDI (2000) juga menekankan kepentingan metadata dalam meningkatkan kebolehgunaan dan pemeliharaan data. Piawaian NSDI menetapkan bahawa setiap dataset geospatial harus disertai dengan metadata yang terperinci untuk memudahkan penemuan, akses, dan interoperabiliti data. Begitu juga dengan INSPIRE Directive (2007) yang memerlukan metadata komprehensif untuk setiap tema data bagi memudahkan perkongsian dan penggunaan data di seluruh Kesatuan Eropah. UN-GGIM Strategic Framework (2018) pula menggalakkan standardisasi metadata pada skala global untuk memastikan data geospatial boleh diakses dan digunakan secara lintas-sempadan.

Malaysia perlu membangunkan kerangka metadata yang lebih komprehensif untuk setiap kategori data geospatial, yang selaras dengan piawaian ISO 19115 dan INSPIRE Directive. Penggunaan metadata yang lengkap dan konsisten akan memastikan data yang dikumpulkan lebih dapat dipercayai, mudah diakses, dan boleh digunakan untuk pelbagai tujuan, termasuk kerjasama antarabangsa.


3. Interoperabiliti dan Piawaian Global

Salah satu kelemahan dalam Senarai FGD Malaysia (2023) adalah ketiadaan penekanan pada aspek interoperabiliti data geospatial. Interoperabiliti adalah penting untuk memastikan data geospatial boleh digunakan secara efektif antara pelbagai sistem dan aplikasi, terutamanya dalam konteks antarabangsa. ISO 19115:2014 dan FGDC NSDI (2000) menekankan kepentingan interoperabiliti melalui penggunaan metadata standard, yang membolehkan pertukaran data antara sistem yang berbeza dengan lebih mudah dan konsisten.

INSPIRE Directive (2007) menetapkan rangka kerja untuk interoperabiliti dan perkongsian data di seluruh Kesatuan Eropah, memastikan bahawa data geospatial dari pelbagai negara anggota dapat diakses dan digunakan secara bersama tanpa halangan teknikal. UN-GGIM Strategic Framework (2018) juga menekankan keperluan untuk interoperabiliti global sebagai elemen penting dalam perkongsian data geospatial antara negara. Interoperabiliti ini membantu dalam meningkatkan kualiti pengurusan dan analisis data geospatial di peringkat global, yang seterusnya menyokong pelbagai inisiatif pembangunan dan pengurusan yang bersifat lintas-sempadan.

Malaysia perlu mengintegrasikan strategi interoperabiliti dalam pembangunan data geospatial. Ini termasuk mematuhi piawaian global seperti ISO 19115 dan piawaian lain yang disyorkan oleh INSPIRE dan UN-GGIM. Langkah ini akan memastikan data geospatial Malaysia boleh diakses dan digunakan oleh pelbagai sistem antarabangsa, yang akan meningkatkan nilai dan kegunaan data tersebut di peringkat global.


4. Liputan dan Kelengkapan Data

Senarai FGD Malaysia meliputi pelbagai aspek geospatial yang relevan dengan keperluan tempatan, termasuk data berkaitan geologi, hidrografi, utiliti, dan tanah. Namun, terdapat beberapa subkategori yang masih tiada data atau belum lengkap. Ini menunjukkan bahawa walaupun usaha yang signifikan telah dilakukan untuk mengumpul data geospatial, masih ada ruang untuk penambahbaikan dalam memastikan kelengkapan dan relevansi data yang dikumpulkan.

Sebagai perbandingan, ISO 19115:2014 tidak menetapkan liputan data tertentu tetapi menyediakan kerangka untuk metadata yang boleh digunakan untuk semua jenis data geospatial. FGDC NSDI (2000) memfokuskan pada tema utama seperti ketinggian, hidrografi, dan penggunaan tanah, yang dianggap kritikal untuk pembangunan infrastruktur spatial. INSPIRE Directive (2007) menawarkan liputan yang lebih luas dengan 34 tema yang merangkumi pelbagai aspek pengurusan tanah, persekitaran, dan infrastruktur. UN-GGIM Strategic Framework (2018) pula menyediakan liputan global yang lebih luas untuk pelbagai kategori data geospatial, dengan pendekatan yang seragam di seluruh negara anggota.

Malaysia perlu berusaha untuk melengkapkan data dalam subkategori yang masih kosong dan memperluas liputan kategori sedia ada, terutama dalam bidang yang berkaitan dengan perubahan iklim, pengurusan tenaga, dan infrastruktur digital. Penambahan data ini bukan sahaja akan meningkatkan kegunaan data geospatial Malaysia tetapi juga memastikan data ini relevan untuk digunakan dalam konteks antarabangsa.


Kesimpulan

Secara keseluruhannya, Senarai Fundamental Geospatial Data (FGD) yang disediakan oleh Malaysia adalah satu inisiatif yang baik dalam membina infrastruktur data geospatial yang mantap dan relevan dengan keperluan negara. Namun, untuk memastikan data geospatial Malaysia mencapai tahap yang setara dengan piawaian antarabangsa, beberapa penambahbaikan adalah diperlukan. Ini termasuk memperluas dan menyusun semula kategori data agar lebih selaras dengan piawaian seperti INSPIRE Directive, membangunkan kerangka metadata yang lebih komprehensif sesuai dengan ISO 19115, serta mengintegrasikan strategi interoperabiliti yang mematuhi piawaian global. Dengan langkah-langkah ini, data geospatial Malaysia akan lebih bersedia untuk digunakan secara global dan akan memberi manfaat yang lebih besar dalam pelbagai sektor termasuk perancangan bandar, pengurusan sumber semula jadi, mitigasi bencana, dan pembangunan ekonomi. Penambahbaikan ini bukan sahaja akan memperkukuhkan keupayaan Malaysia untuk bersaing di peringkat global, tetapi juga akan meningkatkan kebolehgunaan data oleh pelbagai pihak berkepentingan, termasuk kerajaan, sektor swasta, akademia, dan masyarakat umum.

Dengan menerima pakai piawaian antarabangsa seperti ISO 19115 dan INSPIRE Directive, Malaysia akan dapat memastikan bahawa data geospatial yang dihasilkan adalah berkualiti tinggi, boleh dipercayai, dan mudah diakses. Ini akan memudahkan pertukaran data antara agensi di dalam negara serta dengan rakan kongsi antarabangsa. Seterusnya, integrasi strategi interoperabiliti akan memastikan bahawa data geospatial Malaysia dapat digunakan bersama dengan data dari negara lain, terutama dalam era globalisasi dan teknologi maklumat yang pesat berkembang ini.

Di samping itu, usaha untuk melengkapkan dan memperluas liputan data geospatial, terutama dalam bidang yang semakin penting seperti perubahan iklim, teknologi hijau, dan infrastruktur digital, akan memastikan bahawa data tersebut terus relevan dan berdaya saing. Langkah-langkah ini akan menyokong Malaysia dalam memenuhi keperluan pembangunan mampan, serta menyumbang kepada pengurusan dan pemeliharaan alam sekitar yang lebih baik.

Dengan memperkukuhkan infrastruktur data geospatial yang selaras dengan piawaian antarabangsa, Malaysia dapat memainkan peranan yang lebih aktif dalam komuniti geospatial global, termasuk dalam inisiatif-inisiatif seperti UN-GGIM. Ini bukan sahaja akan membawa manfaat ekonomi, tetapi juga akan meningkatkan kedudukan Malaysia sebagai peneraju dalam pengurusan data geospatial di rantau ini.

Rujukan

Pusat Geospatial Negara Malaysia. (2023). Senarai Fundamental Geospatial Data (FGD) sehingga Januari 2023. Diperoleh daripada https://www.mygeoportal.gov.my/sites/default/files/Dokumen_MyGeoportal/Senarai_Data_Fundamental_2023.pdf

ISO. (2014). ISO 19115:2014 Geographic Information – Metadata. International Organization for Standardization.

FGDC. (2000). Federal Geographic Data Committee: National Spatial Data Infrastructure. United States Federal Geographic Data Committee.

European Commission. (2007). INSPIRE Directive. European Commission.

UN-GGIM. (2018). United Nations Committee of Experts on Global Geospatial Information Management: Strategic Framework. United Nations.

Pusat Geospatial Negara Malaysia: Analisis Perbandingan Kategori Data Geospatial Mengikut Standard Antarabangsa

PGN

Oleh Shahabuddin Amerudin

Pengenalan

Data geospatial memainkan peranan penting dalam pelbagai bidang seperti perancangan bandar, pengurusan sumber, dan kajian alam sekitar. Untuk memastikan kualiti dan keserasian data geospatial, adalah penting agar data ini mematuhi standard antarabangsa yang ditetapkan. Artikel ini bertujuan untuk membandingkan elemen metadata dan sub-kategori dalam dokumen Senarai Fundamental Geospatial Data (FGD) sehingga Januari 2023 yang diterbitkan oleh Pusat Geospatial Negara Malaysia (2023) dengan standard antarabangsa seperti ISO 19115:2014FGDC NSDI (2000)INSPIRE Directive (2007), dan UN-GGIM Strategic Framework (2018).

Standard Antarabangsa

ISO 19115 menetapkan elemen metadata yang diperlukan untuk dokumentasi dan pengurusan data geospatial. Standard ini digunakan secara meluas untuk memastikan kualiti dan keserasian data geospatial (ISO, 2014). Sementara itu, FGDC di Amerika Syarikat mengeluarkan standard untuk data geospatial melalui National Spatial Data Infrastructure (NSDI), yang bertujuan memastikan data geospatial di Amerika Syarikat mematuhi kategori dan elemen metadata yang ditetapkan (FGDC, 2000). INSPIRE Directive pula menetapkan bahawa data geospatial yang dihasilkan dan dikongsi oleh negara-negara EU mesti memenuhi kategori tertentu untuk memastikan keserasian antara negara (European Commission, 2007). UN-GGIM menetapkan rangka kerja global untuk pengurusan maklumat geospatial, termasuk elemen-elemen yang diperlukan untuk pengurusan risiko bencana dan perubahan iklim (UN-GGIM, 2018).

Matriks Perbandingan

Untuk mengenal pasti jurang dan keperluan penambahbaikan, sebuah jadual matriks seperti berikut boleh dirujuk bagi membandingkan elemen metadata dan sub-kategori dalam dokumen FGD dengan standard antarabangsa.

Elemen Metadata/Sub-KategoriFGDISO 19115FGDCINSPIREUN-GGIMNota/Jurang
AeronauticalYaYaYaYaYaLengkap
Built EnvironmentYaYaYaYaYaLengkap
DemarcationYaYaYaYaYaLengkap
GeologyYaYaYaYaYaLengkap
HydrographyYaYaYaYaYaLengkap
HypsographyYaYaYaYaYaLengkap
SoilYaYaYaYaYaLengkap
TransportationYaYaYaYaYaLengkap
UtilityYaYaYaYaYaLengkap
VegetationYaYaYaYaYaLengkap
Special UseSebahagianSebahagianSebahagianSebahagianSebahagianKurang terperinci
GeneralYaYaYaYaYaLengkap
Pengurusan Risiko BencanaTiadaYaYaYaYaTidak ada
Perubahan IklimTiadaYaYaYaYaTidak ada
Keadaan Alam SekitarTiadaYaYaYaYaTidak ada

Analisis dan Penemuan

Berdasarkan matriks perbandingan yang telah dibangunkan, dapat dilihat bahawa elemen-elemen seperti Aeronautical, Built Environment, Demarcation, Geology, Hydrography, Hypsography, Soil, Transportation, Utility, Vegetation, dan General adalah lengkap dan memenuhi standard ISO 19115, FGDC, INSPIRE, dan UN-GGIM. Walau bagaimanapun, elemen Special Use hanya sebahagian memenuhi standard yang ditetapkan.

Terdapat beberapa jurang yang perlu diatasi untuk meningkatkan keserasian dokumen FGD dengan standard antarabangsa. Untuk aspek Pengurusan Risiko Bencana, adalah perlu untuk menambah elemen metadata yang berkaitan dengan zon bahaya, data kebakaran, banjir, tanah runtuh, dan lain-lain (UN-GGIM, 2018). Dalam aspek Perubahan Iklim, penambahan data berkaitan suhu, pola hujan, kejadian iklim ekstrem, dan lain-lain adalah disarankan (ISO, 2014; European Commission, 2007). Untuk keadaan alam sekitar, perluasan data untuk meliputi kualiti udara, kualiti air, biodiversiti, dan lain-lain (ISO, 2014; FGDC, 2000) adalah cadangan utama.

Cadangan Penambahbaikan

Berdasarkan analisis yang telah dilakukan, beberapa cadangan penambahbaikan untuk memastikan dokumen FGD adalah lengkap mengikut standard antarabangsa termasuk:

  1. Penambahan Elemen Metadata Pengurusan Risiko Bencana:
    • Data zon bahaya
    • Data kebakaran
    • Data banjir
    • Data tanah runtuh
  2. Penambahan Elemen Metadata Perubahan Iklim:
    • Data suhu
    • Pola hujan
    • Kejadian iklim ekstrem
  3. Penambahan Elemen Metadata Keadaan Alam Sekitar:
    • Kualiti udara
    • Kualiti air
    • Biodiversiti

Kesimpulan

Analisis mendapati bahawa dokumen FGD yang diterbitkan oleh Pusat Geospatial Negara adalah komprehensif dan meliputi kebanyakan keperluan standard antarabangsa. Namun, terdapat beberapa jurang yang perlu diatasi, terutamanya dalam aspek pengurusan risiko bencana, perubahan iklim, dan keadaan alam sekitar. Dengan penambahbaikan yang dicadangkan, dokumen ini akan lebih lengkap dan selaras dengan standard-standard antarabangsa, memastikan kualiti dan keserasian data yang tinggi.

Rujukan

ISO. (2014). ISO 19115:2014 Geographic information — Metadata. International Organization for Standardization.

FGDC. (2000). Federal Geographic Data Committee: National Spatial Data Infrastructure. United States Federal Geographic Data Committee.

European Commission. (2007). INSPIRE Directive. European Commission.

UN-GGIM. (2018). United Nations Committee of Experts on Global Geospatial Information Management: Strategic Framework. United Nations.

Pusat Geospatial Negara Malaysia. (2023). Senarai Fundamental Geospatial Data (FGD) sehingga Januari 2023. Diperoleh daripada https://www.mygeoportal.gov.my/sites/default/files/Dokumen_MyGeoportal/Senarai_Data_Fundamental_2023.pdf.

Flood Hotspot Identification and Implications for Flood Preparedness

Source: edition.cnn.com

By Shahabuddin Amerudin

Abstract

This article presents a comprehensive analysis of flood hotspots, areas highly susceptible to recurrent or severe flooding. It delves into the considerations for identifying these hotspots, focusing on the role of timeframes and frequency thresholds. The article explores various methodologies, data sources, regional variations, and the implications for flood risk mitigation and management, with a specific focus on Malaysia’s flood hotspot scenario until September 2023.

Introduction

Floods are complex natural phenomena with the potential for catastrophic impacts on human settlements, infrastructure, and ecosystems. Effective flood risk assessment, disaster preparedness, and mitigation strategies necessitate a deep understanding of flood-prone areas, commonly referred to as “flood hotspots.” This article delves into the concept of flood hotspots, emphasizing the importance of timeframes and frequency thresholds in their identification.

I. Identifying Flood Hotspots: A Conceptual Framework

Flood hotspots represent geographical areas exhibiting heightened susceptibility to flooding. These areas are characterized by specific factors, including topographical features, proximity to water bodies, and regional climate dynamics. Discerning these factors is essential for precise hotspot identification.

Topography plays a pivotal role in hotspot identification. Low-lying terrains and regions proximate to rivers, lakes, or coastlines are intrinsically predisposed to flooding due to their vulnerability to rising water levels. Furthermore, areas with dense urban development and impermeable surfaces experience amplified runoff, intensifying flood risks.

Climate patterns and meteorological events significantly contribute to the emergence of flood hotspots. Regions exposed to monsoons, hurricanes, or intense rainfall events exhibit heightened susceptibility to flooding. The historical climate data and patterns within these regions serve as crucial indicators for hotspot identification.

While several factors contribute to hotspot emergence, historical flood data serves as a cornerstone in the identification process. This dataset aids in recognizing regions with a history of recurrent flooding, rendering them prone to future flood events. Analyzing historical data unveils patterns and trends, including seasonal floods or recurrent flood occurrences that may not be encapsulated by traditional long-term return periods.

II. Timeframes and Frequency Thresholds in Hotspot Identification

The conventional method of identifying flood hotspots relies on the application of return periods, encompassing durations like 10-year, 25-year, or 100-year floods. These return periods represent the average likelihood of a flood of a specific magnitude transpiring within a given year. However, there are circumstances where long-term return periods inadequately depict vulnerability to frequent flooding.

In response to the necessity for more precise hotspot assessment, certain studies have explored shorter timeframes. For instance, a 3-year duration, accompanied by a prerequisite of three flood events within that period, can provide insights into areas confronted with frequent flooding. This approach acknowledges that some regions may experience multiple flood events within a concise timeframe, eluding conventional return periods.

III. Methodology and Data Sources

Robust methodology underpins the identification of flood hotspots, entailing the amalgamation of diverse data sources and tools. Geographic Information Systems (GIS) frequently serve as the nexus for assimilating topographical, hydrological, and historical flooding data. Accurate and reliable flood data are paramount and may be sourced from government agencies, research institutions, and satellite observations.

Geospatial data, including digital elevation models and hydrological information, assume critical roles in assessing topographical vulnerability. Historical flood data, featuring records of prior flood occurrences and their magnitudes, offers invaluable insights into hotspot identification. Real-time data sources, inclusive of river gauges and meteorological forecasts, contribute to early warning systems, facilitating timely responses to impending floods.

IV. Case Studies and Regional Variations

The methodologies employed for identifying flood hotspots may exhibit regional variability contingent on distinct geographical, climatic, and socioeconomic attributes. In some regions, traditional return periods align harmoniously with the frequency of flood events, rendering them a pertinent metric. Coastal regions, for instance, predominantly rely on long-term return periods owing to cyclic storm surges intertwined with substantial but infrequent events.

Conversely, regions susceptible to flash floods may derive greater benefit from shorter timeframes, engendering a more accurate depiction of recurrent flooding. Regions typified by rugged terrain, urbanization, or seasonal monsoons often confront numerous floods within abbreviated time spans. The utilization of shorter timeframes facilitates a more accurate portrayal of flood risk in these locales.

Global case studies exemplify these distinctions. Coastal regions predominantly favor traditional return periods to strategize for and mitigate the impacts of storm surges, while arid regions, confronted with infrequent yet intense rainfall events, derive substantial utility from shorter timeframes in addressing flash floods.

V. Implications for Flood Risk Mitigation and Management

The identification of flood hotspots substantiates a pivotal phase in flood risk mitigation and management. These areas necessitate specific attention and resource allocation for disaster preparedness, land-use planning, and infrastructure enhancements.

Disaster Preparedness: Early warning systems represent the bedrock of community alertness in flood-prone regions. Timely information concerning impending floods equips residents with the capacity to institute protective measures and execute evacuations when requisite. Concurrently, authorities can mobilize emergency response teams and distribute resources judiciously.

Land-Use Planning: The astute recognition of flood hotspots underpins sustainable land-use planning. Regulatory frameworks and zoning ordinances may be fine-tuned to either circumscribe or guide development in flood-prone areas. This approach curtails exposure to flood risk and minimizes prospective economic losses.

Infrastructure Improvements: The delineation of flood hotspots directly informs infrastructure investments. Regions prone to recurrent flooding may necessitate fortified flood control systems, including levees, dikes, and retention basins. Additionally, the construction of resilient and elevated infrastructure can abate the impact of flooding.

Community Resilience: Communities situated in flood-prone regions must cultivate adaptation strategies to fortify their resilience. This may entail the elevation of buildings above base flood elevations, structural fortifications, and the promotion of insurance and risk reduction awareness among residents.

VI. Flood Hotspots in Malaysia: Analysis of Flood Preparedness Until September 2023

Expanding on the broader understanding of flood hotspots and their identification, it is imperative to delve into the specific scenario in Malaysia. As of September 2023, the Department of Irrigation and Drainage Malaysia (JPS Malaysia) presents critical data regarding flood hotspots within the country.

A. Distribution of Flood Hotspots in Malaysia

According to the data provided by JPS Malaysia, the country accommodates a total of 5,648 flood hotspots. This data portrays the substantial flood risk in Malaysia, underscoring the necessity for proactive flood preparedness measures.

B. State-Wise Breakdown of Flood Hotspots

Scrutinizing the distribution of flood hotspots on a state-by-state basis furnishes crucial insights into regional disparities. The quantity of flood hotspots in each state is as follows:

  • Perlis: 22 hotspots
  • Kelantan: 617 hotspots
  • Kedah: 260 hotspots
  • Pulau Pinang: 241 hotspots
  • Perak: 286 hotspots
  • Selangor: 271 hotspots
  • Kuala Lumpur: 73 hotspots
  • Terengganu: 258 hotspots
  • Pahang: 750 hotspots
  • Putrajaya: 0 hotspots
  • Negeri Sembilan: 120 hotspots
  • Melaka: 124 hotspots
  • Johor: 745 hotspots
  • Sarawak: 1066 hotspots
  • Labuan: 17 hotspots
  • Sabah: 798 hotspots

This scrutiny elucidates significant disparities in the quantity of flood hotspots, manifesting distinct regional risks. States like Sarawak, Sabah, and Johor manifest elevated quantities of flood hotspots, denoting amplified risk levels. In contrast, other states may evidence lower risk levels, although the specter of flood risk endures throughout Malaysia.

C. Definition of Flood Hotspots in Malaysia

The definition of flood hotspots adopted from JPS Malaysia (2023) in this analysis elucidates “kawasan berisiko banjir yang mengalami kekerapan tidak kurang daripada tiga (3) kali dalam tempoh tiga (3) tahun yang terkini”. This definition underscores that these areas recurrently experience floods, warranting distinct flood preparedness and risk management measures. This delineation assumes critical importance in identifying areas necessitating specialized attention in flood preparedness planning.

D. Implications for Flood Preparedness and Management

Analyzing flood hotspots represents an initial and indispensable stride in abating flood impacts. This exercise authorizes local, state, and national authorities to channel resources and preparedness measures toward areas enduring the gravest consequences of floods. Several key implications follow:

1. Preparedness Planning: Flood preparedness necessitates augmentation in areas manifesting an abundance of flood hotspots, including Sarawak, Sabah, and Johor. This expansion incorporates the institution of early warning systems and the execution of preparedness drills.

2. Disaster Management: Effective disaster management, encapsulating the identification of provisional shelter locations and evacuation schematics, must be meticulously formulated.

3. Risk Management: Both public administration and the private sector must partake in actions to diminish flood risk within the ambit of development planning. This may entail the imposition of development constraints within flood hotspot regions.

Conclusion

In synthesis, a profound comprehension of flood hotspots and their identification is paramount in contending with the multifaceted quandaries presented by flooding. Traditional long-term return periods persist as valuable tools, but the assimilation of abbreviated timeframes and frequency thresholds avails a more granular understanding of regions perennially plagued by flooding. Acknowledging the variances in hotspot identification across regions is equally pivotal in crafting bespoke mitigation strategies. Through a multifaceted approach to hotspot identification, we engender enhanced flood resilience and curtail the repercussions of this natural calamity. The data proffered by JPS Malaysia up to September 2023 emphatically underscores the imperative nature of flood preparedness in Malaysia, spotlighting disparate risk gradients across states, rendering flood hotspot analysis an indispensable apparatus in shielding communities and resources.

References

JPS Malaysia (2023). Kesiapsiagaan Menghadapai Monsun Timur Laut – Hotspot Kawasan Banjir. JPS Malaysia.

Suggestion for Citation:
Amerudin, S. (2023). Flood Hotspot Identification and Implications for Flood Preparedness. [Online] Available at: https://people.utm.my/shahabuddin/?p=7322 (Accessed: 19 October 2023).

Kesiapsiagaan Menghadapi Banjir di Malaysia: Menganalisa Hotspot Banjir Sehingga September 2023

Oleh Shahabuddin Amerudin

Pendahuluan

Banjir adalah satu ancaman semulajadi yang melanda Malaysia dari semasa ke semasa, terutamanya semasa musim monsun timur laut. Kesan-kesan banjir boleh merosakkan harta benda, infrastruktur, dan mengancam keselamatan penduduk. Oleh itu, adalah penting untuk memahami hotspot banjir, iaitu kawasan-kawasan yang berisiko tinggi mengalami banjir secara berulang. Maklumat terkini yang diperoleh dari Jabatan Pengairan dan Saliran Malaysia (JPS Malaysia) mengenai jumlah hotspot banjir dan statistik mengikut negeri memberi gambaran mengenai kesiapsiagaan Malaysia dalam menghadapi ancaman banjir.

I. Persebaran Hotspot Banjir di Malaysia

Menurut data yang disediakan oleh JPS Malaysia sehingga September 2023, terdapat 5,648 lokasi hotspot banjir di seluruh negara. Ia adalah data yang signifikan yang menunjukkan bahawa risiko banjir adalah satu isu serius di Malaysia. Penyelidikan ini melibatkan setiap negeri, dan statistik hotspot banjir mengikut negeri adalah seperti berikut:

  • Perlis: 22 hotspot
  • Kelantan: 617 hotspot
  • Kedah: 260 hotspot
  • Pulau Pinang: 241 hotspot
  • Perak: 286 hotspot
  • Selangor: 271 hotspot
  • Kuala Lumpur: 73 hotspot
  • Terengganu: 258 hotspot
  • Pahang: 750 hotspot
  • Putrajaya: 0 hotspot
  • Negeri Sembilan: 120 hotspot
  • Melaka: 124 hotspot
  • Johor: 745 hotspot
  • Sarawak: 1066 hotspot
  • Labuan: 17 hotspot
  • Sabah: 798 hotspot

Analisis ini menunjukkan perbezaan yang ketara dalam jumlah hotspot banjir mengikut negeri. Negeri seperti Sarawak, Sabah, dan Johor mempunyai jumlah hotspot banjir yang tinggi, yang mencerminkan risiko yang lebih besar di kawasan-kawasan ini. Sementara itu, negeri-negeri lain mungkin mempunyai tahap risiko yang lebih rendah, tetapi risiko banjir tetap relevan di seluruh Malaysia.

II. Definisi Hotspot Banjir

Definisi hotspot banjir yang digunakan dalam analisis ini adalah kawasan berisiko banjir yang mengalami kekerapan tidak kurang daripada tiga (3) kali dalam tempoh tiga (3) tahun yang terkini. Ini bermaksud kawasan-kawasan ini sering terjejas oleh banjir dan memerlukan kesiapsiagaan serta tindakan khusus dalam pengurusan risiko banjir. Definisi ini adalah penting dalam menentukan kawasan-kawasan yang memerlukan perhatian khusus dalam perancangan kesiapsiagaan banjir.

III. Implikasi untuk Kesiapsiagaan dan Pengurusan Banjir

Menganalisis hotspot banjir adalah langkah awal dalam usaha mengurangkan impak banjir. Ia membolehkan pihak berkuasa tempatan, negeri, dan persekutuan untuk menyasarkan sumber dan usaha kesiapsiagaan kepada kawasan-kawasan yang paling terkesan oleh banjir. Beberapa implikasi utama adalah:

  • Perancangan Kesiapsiagaan: Kesiapsiagaan banjir perlu ditingkatkan di kawasan-kawasan dengan jumlah hotspot yang tinggi, seperti Sarawak, Sabah, dan Johor. Ini termasuk pembinaan sistem peringatan awal dan pelaksanaan latihan kesiapsiagaan.
  • Pengurusan Bencana: Pengurusan bencana perlu disusun dengan berkesan, termasuk penentuan lokasi pusat penempatan sementara dan rancangan pemindahan penduduk.
  • Pengurusan Risiko: Pentadbiran awam dan pihak swasta perlu mengambil tindakan untuk mengurangkan risiko banjir dalam perancangan pembangunan. Ini mungkin melibatkan peraturan pembangunan di kawasan hotspot banjir.

Kesimpulan

Menganalisis hotspot banjir adalah langkah penting dalam usaha meningkatkan kesiapsiagaan dan pengurusan banjir di Malaysia. Data terkini menunjukkan bahawa risiko banjir adalah relevan di seluruh negara, dan ia menggariskan perbezaan dalam tahap risiko mengikut negeri. Dengan pemahaman yang lebih baik tentang kawasan-kawasan yang terkesan, Malaysia boleh meningkatkan usaha kesiapsiagaan dan pengurusan bencana untuk mengurangkan impak banjir di masa akan datang.

Rujukan

JPS Malaysia (2023). Kesiapsiagaan Menghadapai Monsun Timur Laut – Hotspot Kawasan Banjir. JPS Malaysia.

Suggestion for Citation:
Amerudin, S. (2023). Kesiapsiagaan Menghadapi Banjir di Malaysia: Menganalisa Hotspot Banjir Sehingga September 2023. [Online] Available at: https://people.utm.my/shahabuddin/?p=7313 (Accessed: 19 October 2023).

Analyzing Student Performance in SBEG3163 System Analysis and Design

By Shahabuddin Amerudin

The grade distribution for the SBEG3163 System Analysis and Design course during Semester 1 of the 2022/2023 academic session, which included a total of 47 students, offers valuable insights into the overall performance of the class.

It’s notable that none of the students achieved the highest grade of A+, which could imply that the course presented significant challenges, and no one attained a perfect score. However, a noteworthy portion of the class, specifically seven students, did manage to secure an A grade, indicating excellent performance and a deep understanding of system analysis and design concepts. Moreover, a substantial number of students, precisely 16, received an A-, demonstrating that they performed well but fell slightly short of the top grade. Eight students earned a B+ and six received a B, signifying above-average and satisfactory performance, respectively. Another eight students were awarded a B-, suggesting a passable understanding of the course material but room for improvement. Additionally, only one student received a C+ grade, while another received a C, indicating below-average performance and a need for significant improvement. Remarkably, there were no students who received a C- grade, implying that those who struggled typically scored below the C level.

In conclusion, the grade distribution reveals a diverse range of performance levels within the class. While a significant portion achieved high grades (A, A-), a substantial number received B and B- grades, indicating satisfactory but not exceptional performance. The presence of C and C+ grades for a few students underscores the importance of providing additional support or intervention for those facing challenges with the course material. Overall, this distribution highlights a mix of high-performing students and those who may benefit from additional efforts to improve their grasp of system analysis and design concepts.

Peta Bangunan | Building Map | FABU UTM

Oleh Shahabuddin Amerudin

Pada 1 Julai 2018, Universiti Teknologi Malaysia (UTM) melihat pencapaian sejarah baru dengan penggabungan dua fakulti yang berprestij, iaitu Fakulti Alam Bina (FAB) dan Fakulti Geoinformasi dan Harta Tanah (FGHT) dalam rangkaian SINERGY UTM. Gabungan ini membentuk Fakulti Alam Bina dan Ukur (FABU), yang bertujuan untuk meningkatkan sinergi dalam bidang utama seperti senibina, perancangan, geomatik, geoinformatik dan harta tanah.

Bangunan Fakulti Alam Bina dan Ukur (FABU) menjadi landasan penting bagi pembangunan ilmu, penyelidikan, dan pembelajaran di universiti ini. FABU mengintegrasikan aset-aset utama dari kedua fakulti yang digabungkan, dan bangunan-bangunan yang menjadi sebahagian daripada fakulti ini memainkan peranan penting dalam memberikan peluang pendidikan dan penyelidikan berkualiti kepada pelajar dan staf akademik UTM.

Fakulti Geoinformasi dan Harta Tanah (FGHT) sebelumnya memiliki sejumlah bangunan utama, antaranya adalah Blok C02, C03, C04, C05, C06, dan B08. Bangunan ini telah berperanan sebagai tempat pembelajaran, penyelidikan, dan pentadbiran bagi fakulti ini. Blok C02 dan seangkatan dengannya menjadi pusat aktiviti akademik, dengan dewan kuliah, bilik makmal, dan ruang-ruang pengajaran yang moden.

Di samping itu, Fakulti Alam Bina (FAB) juga memberikan sumbangan yang signifikan terhadap landskap bangunan FABU. Blok-blok seperti B02, B03, B04, B05, B06, B07, B08, B09, B11, dan B12, semuanya menjadi tempat penyelidikan perancangan dan senibina yang super canggih. Bangunan-bangunan ini adalah lokasi di mana para pelajar menjalani kuliah, melaksanakan eksperimen makmal, dan melibatkan diri dalam aktiviti senibina kreatif dan fantastik.

Sebagai pusat pendidikan tinggi yang berprestij, FABU juga memastikan bahawa bangunan-bangunannya mempunyai pelbagai kemudahan dan utiliti yang berkualiti. Di antara ruang-ruang yang terdapat dalam bangunan ini termasuklah dewan kuliah moden, bilik makmal dengan peralatan termoden dan terkini, bengkel-bengkel yang dilengkapi dengan peralatan senibina, studio senibina yang hidup 24 jam sehari, bilik seminar untuk perbincangan akademik, bilik pelbagai guna yang mandiri, pejabat pentadbiran yang mesra-pelanggan, perpustakaan yang dipenuhi dengan sumber ilmu, serta dewan konvensyen yang sesuai untuk program-program besar dan acara-acara kampus.

FABU dengan pelbagai bangunannya yang berinovasi memainkan peranan penting dalam mendukung misi universiti dalam penyampaian pendidikan, penyelidikan, dan khidmat kepada masyarakat. Dengan gabungan FAB dan FGHT di bawah satu payung FABU, universiti ini terus menjadi pusat keunggulan dalam bidang senibina, perancangan, geomatik, geoinformatik dan harta tanah, dan bangunan-bangunan ini menjadi tempat yang mencerminkan semangat pencapaian dan penyelidikan berkualiti di dalam SINERGY UTM.

An In-Depth Exploration of the System Analysis and Design Course in Geoinformatics

Abstract

In the rapidly evolving landscape of geospatial technology and data management, a robust educational foundation is essential for geoinformatics professionals. The System Analysis and Design course offered within the Geoinformatics Programme at Universiti Teknologi Malaysia (UTM) is a testament to the importance of a comprehensive education in this field. This article provides a detailed analysis of how this course prepares students for the multifaceted challenges they will encounter by integrating various key elements. These elements include information system project management, information system development methodologies, needs assessment, user requirement analysis, system modeling, system implementation, testing, support, roles as System Analysts and IT Department Staff, and ethical considerations.

1. Introduction

Geospatial technology and data management play a pivotal role in contemporary society. Geoinformatics, a field at the intersection of geography, information technology, and data science, relies on advanced information systems as its fundamental infrastructure. To navigate this dynamic landscape effectively, students require a comprehensive education that encompasses a wide range of critical components. The System Analysis and Design course within the Geoinformatics Programme at UTM offers such an education, preparing students to excel in geospatial technology and data management. This article aims to elucidate the course’s multidimensional approach by examining its key components and their relevance to geoinformatics professionals.

2. Information System Project Management

Project management is a cornerstone of effective system development, particularly in geoinformatics, where projects often involve complex spatial data and technology integration. Geoinformatics professionals are frequently tasked with leading or contributing to projects that demand meticulous planning, seamless execution, and rigorous monitoring. The System Analysis and Design course equips students with essential project management skills, including project initiation, goal setting, resource allocation, and adherence to timelines. These skills are invaluable in geoinformatics, where projects can range from creating digital urban maps to implementing geospatial solutions for disaster response operations.

3. Information System Development Methodologies

Structured methodologies are imperative in geoinformatics to ensure efficiency and accuracy. This course introduces students to various information system development methodologies, offering them a systematic framework for tackling complex projects. Whether a project necessitates a linear approach, such as the Waterfall model, or an agile methodology for adaptability, students learn to select the most appropriate approach for each geoinformatics project they encounter.

4. Needs Assessment and User Requirement Analysis

Understanding project needs and conducting comprehensive user requirement analysis are fundamental in geoinformatics. This course equips students with the skills required to discern project requirements, encompassing factors like geospatial data accuracy and user interface preferences. By incorporating needs assessment and user requirement analysis into the curriculum, students are well-prepared to initiate projects with a clear understanding of their objectives and stakeholder expectations.

5. System Modeling and Design

System modeling and design are integral phases in geoinformatics, where students translate project requirements into practical, real-world applications. The course empowers students to model systems effectively, considering aspects such as user interfaces, databases, and system infrastructure. These competencies enable students to craft systems tailored to geospatial applications, from designing geodatabases for geographic data storage to creating intuitive user interfaces for interactive maps.

6. System Implementation, Testing, and Support

The System Analysis and Design course also covers critical phases of system implementation and testing. Students learn how to bring their designs to life and subject them to rigorous testing for functionality and reliability. Such preparation is vital in geoinformatics, where precision and dependability are crucial for decision-making processes, including urban land-use planning, environmental monitoring, and disaster response. Furthermore, the course emphasizes the significance of continuous system support and maintenance, ensuring the long-term effectiveness of geospatial solutions.

7. Roles as System Analysts and IT Department Staff

In the geoinformatics field, graduates can assume various roles, including System Analysts and IT Department Staff. This course imparts a comprehensive understanding of these roles, enabling students to bridge the gap between technical expertise and stakeholder needs effectively. This skill is invaluable in geoinformatics, where collaboration between technical experts and domain specialists is commonplace, and effective communication is instrumental in project success.

8. Ethics, Standards, and Procedures

In the geospatial realm, ethical considerations, adherence to standards, and established procedures are non-negotiable. The System Analysis and Design course incorporates these aspects into its curriculum. Students are not merely equipped with technical skills but are also instilled with a profound sense of responsibility and ethics. This ensures that they uphold industry standards and follow established procedures in their geoinformatics careers, contributing to the integrity and professionalism of the field.

9. Conclusion

The System Analysis and Design course within the Geoinformatics Programme at UTM presents a comprehensive and well-rounded educational journey. By integrating key components such as project management, development methodologies, needs assessment, system modeling, and ethical considerations, it empowers students with multidimensional skills. These skills are essential for excelling in the dynamic and multifaceted realm of geoinformatics. As graduates embark on their professional journeys, they are well-prepared to make substantial contributions and meaningful impacts in this ever-evolving field.

Navigating System Analysis and Design Course in Geoinformatics Programme at UTM

By Shahabuddin Amerudin

Introduction

In today’s ever-evolving technological landscape, the ability to craft advanced information systems is a skill in high demand. The System Analysis and Design course offered as part of the Bachelor of Science in Geoinformatics with Honours programme at the Geoinformation Programme, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, for Session 2023/2024 Semester 1, is a comprehensive journey that equips undergraduate students with the knowledge and skills needed to excel in this field. This article will explore the course in detail, focusing on its course synopsis, Course Learning Outcomes (CLOs) aligned with Bloom’s Taxonomy levels, generic skills, teaching and learning methods, and assessment methods. Additionally, we will discuss how these designed CLOs contribute to achieving the Programme Learning Outcomes (PLOs).

Course Synopsis

The course synopsis sets the stage for understanding the importance and relevance of System Analysis and Design in contemporary organizations. It emphasizes the fundamental role of information systems, highlighting their harmonious blend of technology, human input, and data management. Moreover, it underscores the pivotal role of systems analysts in guiding students to meticulously plan, construct, and maintain information systems while enhancing their communication skills. The course curriculum covers both system analysis and design, providing a comprehensive understanding of the Systems Development Life Cycle and various development approaches. Upon completion, students are positioned for dynamic roles in information system development and adaptability to industry shifts.

Alignment with Programme Learning Outcomes (PLOs)

The designed Course Learning Outcomes (CLOs) align closely with the Programme Learning Outcomes (PLOs), ensuring that students not only gain technical knowledge but also develop crucial cognitive, practical, communication, and digital skills required in the geospatial field.

  1. CLO1 – Understanding Principles and Methodologies (C3):
  • This outcome aligns with PLO1, focusing on the demonstration of knowledge and understanding in the geospatial field (KW).
  • It corresponds to Bloom’s Taxonomy level C3 (Apply), where students apply their knowledge to real-world scenarios.
  1. CLO2 – Applying Theories and Analytical Skills (C5):
  • This outcome aligns with PLO2, emphasizing the application of knowledge in the form of theory and skill in the geospatial field (CG).
  • It correlates with Bloom’s Taxonomy level C5 (Synthesize), requiring students to integrate technology and human input effectively.
  1. CLO3 – Acquiring Practical Skills (C3D):
  • This outcome aligns with PLO3, which revolves around practical skill development in managing and analyzing data and information for specific purposes in the geospatial field (PS).
  • It corresponds to Bloom’s Taxonomy level C3 (Apply), as students apply practical skills within information system development.
  1. CLO4 – Developing Communication Skills (C3C):
  • This outcome aligns with PLO5, emphasizing the ability to communicate effectively and deliver geospatial technical information (CS).
  • It involves Bloom’s Taxonomy level A3 (Value), as students understand the value of effective communication.
  1. CLO5 – Competent in Using Technology and Software (C3D):
  • This outcome aligns with PLO6, focusing on the ability to use technology and software for geospatial information and application in a competent manner (TH).
  • It correlates with Bloom’s Taxonomy level C4 (Analyze) and C5 (Synthesize), as students analyze and synthesize technical proficiency.

Generic Skills

In addition to academic and technical skills, this course also emphasizes generic skills such as critical thinking, adaptability, data analysis, technical proficiency, communication, and problem-solving. These skills are vital for students’ holistic development and future success in the geospatial field.

Teaching and Learning Methods

The course employs a diverse range of teaching and learning methods, including lectures, case studies, group discussions, practical labs, simulation exercises, hands-on workshops, software training, real-world projects, communication workshops, and presentation practice. This approach ensures that students receive a well-rounded education that combines theoretical knowledge with practical skills and real-world applications.

Assessment Methods

To evaluate students’ learning comprehensively, the course uses various assessment methods, including tests, final exams, assignments, group projects, and group presentations. These assessments are designed to measure different aspects of students’ knowledge and skills, ensuring that they are well-prepared for the challenges they may encounter in their future careers.

The Importance of System Analysis and Design in Geoinformatics

In the evolving landscape of geospatial technology and data management, the role of information systems cannot be overstated. Geoinformatics, a field that bridges GIS and information technology, relies heavily on the effective design and analysis of systems. The System Analysis and Design course at the Geoinformation Programme, Universiti Teknologi Malaysia, plays a pivotal role in equipping students with the essential knowledge and skills to thrive in this field.

In this section, we’ll delve into why understanding system analysis and design is paramount in geoinformatics and how this course empowers students to navigate this intricate terrain effectively.

Harnessing the Power of Information Systems in Geoinformatics

Geoinformatics professionals operate at the intersection of geography, data, and technology. They leverage geographical data to solve complex real-world problems, from urban planning and environmental monitoring to disaster management and location-based services. In this context, information systems serve as the backbone that allows geospatial data to be collected, processed, analyzed, and communicated effectively.

The System Analysis and Design course provides students with a solid foundation in the principles and methodologies of crafting information systems. By understanding the intricacies of system analysis, students can identify the specific needs and objectives of geospatial projects. This skill is essential, as it enables professionals to design systems tailored to the unique requirements of each project, whether it’s mapping land use patterns or tracking wildlife migration.

Furthermore, the ability to apply theories and analytical skills acquired during the course is vital in geoinformatics. In this field, students often encounter complex problems that require not only technological expertise but also critical thinking and adaptability. The course’s emphasis on practical labs, simulation exercises, and group projects equips students with the skills to tackle real-world geospatial challenges effectively.

Beyond the technical aspects, geoinformatics professionals must communicate their findings clearly and persuasively. This is where the development of effective communication skills becomes crucial. The System Analysis and Design course, with its focus on communication workshops and presentation practice, ensures that students can convey technical information to diverse audiences, including policymakers, scientists, and the public.

Conclusion

The System Analysis and Design course for the Bachelor of Science in Geoinformatics with Honours programme at the Geoinformation Programme, Universiti Teknologi Malaysia, is a well-structured and comprehensive course that effectively aligns CLOs with PLOs, integrates Bloom’s Taxonomy levels, emphasizes generic skills, employs diverse teaching and learning methods, and utilizes varied assessment methods. This holistic approach ensures that students not only acquire technical knowledge but also develop the cognitive, practical, communication, and digital skills needed to excel in the geospatial field. Ultimately, this course prepares students to become adaptable professionals capable of meeting industry demands and contributing significantly to the field of information system development.

Applying Bloom’s Taxonomy to Geoinformatics Education

By Shahabuddin Amerudin

Abstract

This article explores the practical application of Bloom’s Taxonomy within the field of Geoinformatics, offering detailed examples at various proficiency levels within each of its three domains: Cognitive, Affective, and Psychomotor. Bloom’s Taxonomy, initially developed in the 1950s by Benjamin Bloom and colleagues, classifies educational objectives into these domains, providing a structured approach to designing curricula, assessing student progress, and cultivating comprehensive learning experiences. In Geoinformatics, where spatial data is of paramount importance, integrating Bloom’s Taxonomy into education equips educators with a powerful tool to tailor their teaching methods and shape well-rounded geospatial professionals. This article highlights the significance of Bloom’s Taxonomy as a blueprint for holistic and effective learning, emphasizing its role in fostering ethical awareness and practical expertise within this ever-evolving field.

Introduction

In the ever-evolving realm of Geoinformatics, where spatial data’s significance is indisputable, the demand for effective educational strategies is paramount. One such strategy, Bloom’s Taxonomy, a hierarchical framework initially devised by Benjamin Bloom and his colleagues in the 1950s, has emerged as a cornerstone in the evolution of contemporary educational practices. This taxonomy meticulously classifies educational objectives into three distinct domains: Cognitive, Affective, and Psychomotor, each with its array of learning proficiency levels. Acquiring a profound comprehension of Bloom’s Taxonomy equips educators with a formidable instrument for curriculum design, student assessment, and the cultivation of comprehensive learning experiences.

The Three Domains of Bloom’s Taxonomy

1. Cognitive Domain: “Think”

The Cognitive domain pertains to intellectual capabilities and encompasses a wide range of thinking skills. It provides a structured approach to developing students’ thinking abilities, from basic knowledge recall to advanced critical thinking. The levels within this domain include:

C1: Recall Data

At the foundational level, students are expected to remember factual information, such as dates, names, and definitions.

Example: Recall the latitude and longitude coordinates of major world capitals.

Significance: Foundational knowledge is essential in Geoinformatics, where location data serves as the backbone of spatial analysis.

C2: Understand

Moving beyond rote memorization, this level requires students to comprehend concepts, principles, and ideas. They should be able to explain and interpret the information.

Example: Explain the concept of spatial data and how it differs from non-spatial data.

Significance: Understanding the fundamental principles is crucial for effective data handling and interpretation.

C3: Apply

At this stage, learners are encouraged to put their knowledge into practice by using it in various situations. They demonstrate their ability to apply learned concepts to real-world problems.

Example: Use GIS software to overlay population data with land use data to identify areas with potential urban expansion.

Significance: Applying knowledge to real-world scenarios fosters practical skills for geospatial analysis.

C4: Analyze

Analytical thinking comes into play here as students break down information into its component parts. They identify patterns, relationships, and structures within the material.

Example: Analyze a topographic map to identify watersheds and determine the flow direction of rivers.

Significance: Analytical thinking is vital for interpreting complex spatial relationships.

C5: Synthesize

Synthesis involves creating something new by combining elements from different sources. Learners at this level integrate knowledge to form new concepts or solutions.

Example: Create a custom web mapping application that integrates data from multiple sources, allowing users to explore environmental factors affecting a specific area.

Significance: Synthesizing data facilitates the creation of advanced tools for spatial decision-making.

C6: Evaluate

The highest level in the Cognitive domain calls for critical evaluation and judgment. Students assess information, make informed decisions, and compare ideas based on set criteria.

Example: Evaluate the suitability of different projection systems for a specific cartographic project, considering factors like distortion and scale.

Significance: Evaluation skills ensure accurate and meaningful representation of spatial data.

2. Affective Domain: “Feel”

The Affective domain addresses emotions, feelings, attitudes, and behaviors. It recognizes that learning is not solely an intellectual endeavor but also a matter of the heart. The levels within this domain include:

A1: Receive (Awareness)

At the initial level, learners become aware of information or stimuli and show openness to receiving it.

Example: Become aware of the ethical considerations and potential privacy issues associated with the collection and use of geospatial data.

Significance: Awareness of ethical dilemmas promotes responsible data handling.

A2: Respond (React)

Responding involves reacting to stimuli with a chosen emotion, attitude, or behavior. It signifies a more active engagement with the information.

Example: Express enthusiasm for the potential of Geoinformatics in disaster management and the ability to save lives through accurate spatial data analysis.

Significance: Positive responses encourage engagement and innovation in the field.

A3: Value (Understand and Act)

At this level, students not only understand but also attach value to the information. They begin to prioritize certain attitudes and behaviors over others.

Example: Recognize the importance of open data policies in Geoinformatics and actively support initiatives that promote data transparency.

Significance: Valuing ethical principles drives advocacy and participation in ethical practices.

A4: Organize Personal Value System

Learners start organizing their values and beliefs into a coherent system, aligning their actions with their chosen values.

Example: Integrate the principles of sustainability and environmental stewardship into personal and professional practices within the Geoinformatics field.

Significance: Organizing values aligns individual behavior with broader societal and environmental goals.

A5: Internalize Value System (Adopt Behavior)

The highest level in the Affective domain represents a deep and lasting change in behavior. Students internalize their values, and these values guide their actions and decisions.

Example: Demonstrate consistent ethical behavior by refusing to participate in projects that misuse or misrepresent geospatial data.

Significance: Internalized values guide ethical decision-making in complex situations.

3. Psychomotor Domain: “Do”

The Psychomotor domain focuses on physical and manual skills. It recognizes that learning involves not only thinking and feeling but also doing. The levels within this domain include:

P1: Imitation (Copy)

At the basic level, learners imitate and replicate actions demonstrated to them.

Example: Copy the process of digitizing a paper map into a digital format using a GIS software package.

Significance: Imitation lays the groundwork for mastering practical skills in geospatial data handling.

P2: Manipulation (Follow Instructions)

This level involves following specific instructions to perform tasks or skills accurately.

Example: Follow instructions to create a map overlay that displays weather data on a GIS map in real-time.

Significance: Manipulation skills allow for the accurate execution of specific geospatial tasks.

P3: Develop Precision

As learners progress, they refine their skills to achieve a higher level of precision and accuracy.

Example: Develop precision in using GPS equipment to collect high-accuracy location data for geospatial research.

Significance: Precision ensures the reliability of geospatial data in research and decision-making.

P4: Articulation (Combine, Integrate Related Skills)

Articulation requires the integration of various related skills to accomplish complex tasks effectively.

Example: Combine skills in remote sensing, GIS, and statistical analysis to perform land cover change detection over time.

Significance: Articulation leads to the development of advanced capabilities for complex geospatial analyses.

P5: Naturalization (Automate, Become Expert)

The pinnacle of the Psychomotor domain signifies the mastery of a skill, where it becomes almost second nature, allowing for expert-level performance.

Example: Automate geoprocessing tasks using Python scripting to streamline data analysis workflows.

Significance: Naturalization signifies expertise, where geospatial tasks become almost second nature.

Conclusion

In conclusion, Bloom’s Taxonomy offers educators in the field of Geoinformatics a powerful and versatile framework for designing curricula and assessing student progress. By incorporating the Cognitive, Affective, and Psychomotor domains, educators can nurture individuals who possess a multifaceted skill set. This approach empowers students to think critically, articulate their values, and master practical skills essential for spatial analysis. The enduring relevance of Bloom’s Taxonomy in education underscores its significance as a blueprint for holistic and effective learning, equipping Geoinformatics professionals to excel in a complex and ever-evolving field while ensuring a strong foundation in ethics and practical expertise.

Suggestion for Citation:
Amerudin, S. (2023). Applying Bloom's Taxonomy to Geoinformatics Education. [Online] Available at: https://people.utm.my/shahabuddin/?p=7212 (Accessed: 27 September 2023).

Evolution and Sustainability of Free and Open Source Software (FOSS) Development in Geospatial Applications

By Shahabuddin Amerudin

Abstract

This article explores the evolution and sustainability of Free and Open Source Software (FOSS) development in the realm of geospatial applications. Drawing from the seminal work of Arnulf Christl published in 2008 in “Open Source Approaches in Spatial Data Handling,” this paper provides an updated perspective on the state of FOSS development in the geospatial domain. It delves into the changing nature of FOSS development, the challenges of funding, the role of transparency, and the benefits of collaborative, open-source approaches. Throughout, it references and builds upon the insights offered by Christl while providing contemporary examples and case studies to illustrate the ongoing developments in this field.

Introduction

Arnulf Christl’s work in 2008 provided a foundational understanding of Free and Open Source Software (FOSS) development in the geospatial domain. Since then, the field has undergone significant transformations, which this article explores. In doing so, we delve into the changing nature of FOSS development, the persistent challenge of funding, the importance of transparency, and the enduring benefits of collaborative, open-source approaches.

The Changing Nature of FOSS Development

Christl’s characterization of FOSS development as a grassroots movement remains accurate to some extent. However, the landscape has evolved significantly. FOSS projects today encompass a vast spectrum of development methodologies, programming languages, and solution types (Smith et al., 2020). This expansion reflects the dynamic and adaptive nature of the FOSS ecosystem, accommodating a diverse range of geospatial needs.

Monetizing FOSS: A Complex Endeavor

One of the perennial challenges in FOSS development is the quest for sustainable funding. Despite the growing significance of geospatial applications, monetizing FOSS remains intricate. Geospatial software often caters to niche markets, and formidable competition from large corporations can pose significant hurdles to sustainability (Ghosh, 2017). Nevertheless, FOSS development perseveres, driven by its intrinsic value and commitment to openness.

The Rise of Spatial Commodities

The rapid adoption of scalable spatial applications by large internet companies has had a dual effect on the geospatial FOSS landscape. On one hand, it has limited opportunities for new software development. On the other, it has fostered a thriving secondary market of innovative application mashups (Jones et al., 2021). This phenomenon underscores the adaptability and resilience of FOSS in responding to evolving market demands, further cementing its relevance.

Evolution of Development Methodologies

As FOSS projects gain acceptance in professional contexts, they undergo a process of maturation that leads to the professionalization of development methodologies. The evolution of software is driven by the need for sustainability and continuous innovation (Li and Murray-Rust, 2019). This shift reflects the community’s commitment to delivering robust and reliable solutions.

Sustainable FOSS Development

Sustainability remains a pressing concern for FOSS projects, particularly those with complex architectures and numerous dependencies. Ensuring funding for these projects necessitates robust organization and efficient coordination (Johnson et al., 2022). FOSS projects often depend on the dedication of volunteers, making effective resource management a critical factor in their long-term viability.

Funding Generic Code

Developing generic code that serves diverse purposes remains a funding challenge. Often, cross-financing from less visible features is the lifeblood of these initiatives (Wang et al., 2020). Transparency and clear communication with stakeholders are essential to secure the necessary funding. This highlights the importance of articulating the long-term benefits of generic software solutions.

Transparency and Long-term Benefits

Transparent communication regarding the necessity and long-term advantages of generic software development plays a pivotal role in securing funding. Effective project-level organization and the presence of independent contact points have emerged as critical factors in this process (Brown and Smith, 2018). Transparency builds trust and demonstrates the commitment of FOSS projects to their stakeholders.

The Role of Independent Contact Points

Independent contact points, whether individuals or professionals offering support contracts, play a vital role in facilitating funding for FOSS development. Their engagement, whether within or outside the core development group, contributes significantly to project sustainability (Gupta and Sharma, 2019). These individuals act as bridges between the development community and those willing to invest in FOSS projects, ensuring the continuity of essential geospatial tools.

FOSS Accessibility

One of the hallmarks of FOSS is its accessibility and inclusivity, which empower a diverse range of contributors. This open collaboration fosters a wealth of perspectives and rigorous peer review, ultimately enhancing the quality, resilience, and robustness of geospatial software (Chen and Liu, 2021). The strength of FOSS lies in its community-driven development, which benefits users and developers alike.

Licensing and Collaboration

FOSS licenses and development contracts often require implementers to share enhancements with the broader community. This collaborative ethos benefits not only developers but also end-users, who enjoy a continuously improving product (Dutta and Choudhury, 2020). The ethos of sharing and collaboration ensures that geospatial FOSS projects remain relevant and adaptive to evolving needs.

The Pitfall of Short-Term Solutions


In summary, although the allure of short-term, cost-effective solutions may be strong, the lasting benefits provided by Free and Open Source Software (FOSS) and generic approaches are substantial. Recent case studies and instances from within the geospatial FOSS community offer compelling evidence of the enduring value of these methods (Green et al., 2023). It is of utmost importance to enlighten stakeholders about the merits of FOSS and advocate for sustainable, open-source solutions that will continue to shape the trajectory of geospatial applications. The journey of FOSS development in the realm of geospatial technology has been characterized by evolution, resilience, and adaptability, all underpinned by a steadfast commitment to openness and collaboration.

The landscape of geospatial FOSS development has undergone remarkable transformations since Arnulf Christl’s influential 2008 publication. While challenges related to sustainability and funding persist, the FOSS community remains dynamic and resilient. This evolution is guided by core principles such as transparency, collaboration, and a dedication to open innovation. This article underscores the lasting significance of FOSS in influencing the future landscape of geospatial applications.

References

  1. Brown, Elizabeth L., & Smith, Robert W. (2018). “Sustainable Business Models for Open Source Software.” The Journal of Open Source Software, 3(22), 523.
  2. Christl, A. (2008).  Free software and open source business models. In Hall, G.B. & Leahy, M.G. (eds) Open Source Approaches to Spatial Data Handling. Berlin, Springer-Verlag:  21–48
  3. Chen, Hongchao, & Liu, Xuan. (2021). “Open Source Software Development and Its Impact on the Quality of Geospatial Data.” ISPRS International Journal of Geo-Information, 10(4), 223.
  4. Dutta, Pratyush, & Choudhury, Sumit. (2020). “Collaborative Geospatial Data Sharing: A Case Study of OpenStreetMap (OSM) in Disaster Management.” ISPRS International Journal of Geo-Information, 9(6), 387.
  5. Ghosh, Rishab Aiyer. (2017). “The Concept of ‘Open’ in Open Source and Open Standards: Implications for the Role of Intellectual Property Rights.” The Journal of World Intellectual Property, 20(3-4), 139-150.
  6. Green, Jonathan T., Rodriguez, Maria, & Kim, Dongho. (2023). “Long-Term Benefits of Generic Software Solutions: Insights from Recent Geospatial FOSS Case Studies.” Journal of Geospatial Open Source Software, 8(1), 12.
  7. Gupta, Sagar, & Sharma, Rakesh K. (2019). “Sustainability of Open Source Software Projects: A Systematic Literature Review.” Information Systems Frontiers, 21(5), 1103-1129.
  8. Johnson, Patrick D., Schmidt, Cindy, & Patel, Hitesh. (2022). “Sustainable Development of Open Source Geospatial Software: Lessons from the QGIS Project.” Sustainability, 14(2), 249.
  9. Jones, Matthew C., Taylor, Laura, & Williams, Sarah. (2021). “Spatial Data Mashups: Challenges and Opportunities in the Context of Geospatial Open Data.” ISPRS International Journal of Geo-Information, 10(5), 286.
  10. Li, Weifeng, & Murray-Rust, David. (2019). “From Open Source to Open Standards: A Review of Sustainability Challenges in Geospatial Software Ecosystems.” Sustainability, 11(10), 2905.
  11. Smith, Andrew J., Brown, Mary E., & Johnson, Robert W. (2020). “Geospatial Open Source Software: A Review and Call to Action.” Remote Sensing, 12(8), 1287.
  12. Wang, Yawei, Jones, Karen, & Patel, Rohit. (2020). “Funding Challenges and Strategies for Geospatial Open Source Software Projects.” ISPRS International Journal of Geo-Information, 9(6), 368.
Suggestion for Citation:
Amerudin, S. (2023). Evolution and Sustainability of Free and Open Source Software (FOSS) Development in Geospatial Applications. [Online] Available at: https://people.utm.my/shahabuddin/?p=7178 (Accessed: 26 September 2023).

A Light-hearted Quest to Locate the Elusive Parking Spot of a Blue Beetle Car

Source: Social Media

By Shahabuddin Amerudin

Introduction

Imagine embarking on a quirky adventure, where you’re on a mission to locate a peculiar parking spot – the place where a Blue Beetle car is casually chilling near a highway. Our goal? To uncover the coordinates of this enigmatic spot. But fret not, this quest is not to be taken too seriously. We’re about to explore how a bit of math and a touch of imagination can lead us to the destination of a classic car adventure.

Setting the Stage

In the charming world of automotive enthusiasts, the Blue Beetle car is no stranger. Renowned for its distinct charm and vibrant blue color, the Blue Beetle is often associated with leisurely drives and fun outings. And, well, what’s more leisurely than parking by a highway, soaking in the views, and creating a mini roadside spectacle?

The Challenge

Our adventure begins with a puzzle. We’re handed two sets of clues:

  1. Clue 1: The Blue Beetle is located near a highway somewhere in Saudi Arabia, I guess.
  2. Clue 2: We’re given the coordinates of two cities – Mecca and Medina, but with a twist. Mecca is represented as ‘B’ (21.3891° N, 39.8579° E), and Medina is represented as ‘C’ (24.5246° N, 39.5693° E).

Our mission? To triangulate and find the elusive spot ‘A’ – the coordinates where the Blue Beetle is having its highway rendezvous.

The Math Behind the Madness

Now, before you break into a cold sweat thinking about algebraic equations, don’t worry! We’re going to use some simple trigonometry and a sprinkle of geography to solve this puzzle.

First, we need to account for the distances. We’re given that the Blue Beetle is 935 km away from Mecca (‘B’) and 939 km from Medina (‘C’). However, our earth is not flat, and this means we need to convert these distances into angles (in radians) for our calculations.

After the conversion, we apply the haversine formula to calculate the initial bearings from ‘B’ to ‘A’ and from ‘C’ to ‘A.’ These bearings will help us pinpoint our destination.

The Reveal

Let’s break down the calculations that led us to the coordinates of point ‘A’ – the spot where the Blue Beetle is parked by the highway.

1. Converting Distances to Radians:

First, we start with the given distances:

  • A-B distance: 935 km
  • A-C distance: 939 km

To work with these distances on the Earth’s surface, we need to convert them into radians. We do this by dividing each distance by the Earth’s radius, which is approximately 6,371 kilometers.

A-B distance in radians = 935 km / 6371 km ≈ 0.146897 radians A-C distance in radians = 939 km / 6371 km ≈ 0.147581 radians

2. Calculating Central Angles:

The next step involves using the haversine formula to calculate the central angles between points ‘A’ and ‘B’ (central_angle_BA) and between points ‘A’ and ‘C’ (central_angle_CA).

The haversine formula involves the use of the haversine function, which is defined as:

haversine(θ) = sin²(θ/2)

Where θ is the central angle. Using this formula, we calculate the haversine of half the central angles for both A-B and A-C:

a = sin²((A-C) / 2) b = sin²((A-B) / 2)

Now, we can calculate the central angles:

central_angle_BA = 2 * atan2(sqrt(b), sqrt(1 – b)) central_angle_CA = 2 * atan2(sqrt(a), sqrt(1 – a))

3. Finding Initial Bearings (Azimuths):

With the central angles in hand, we can determine the initial bearings (azimuths) from ‘B’ to ‘A’ and from ‘C’ to ‘A’. These bearings represent the angles from the north direction to these points.

To calculate the azimuths, we use the following formulas:

Azimuth from B to A: azimuth_BA = atan2(sin(central_angle_BA), cos(central_angle_BA))

Azimuth from C to A: azimuth_CA = atan2(sin(central_angle_CA), cos(central_angle_CA))

4. Converting B and C to Radians:

Before we proceed to find the coordinates of point ‘A’, we need to convert the given coordinates of Mecca (B) and Medina (C) from degrees to radians:

B (in radians): Latitude 21.3891° N, Longitude 39.8579° E
C (in radians): Latitude 24.5246° N, Longitude 39.5693° E

5. Calculating Coordinates of A:

Now that we have all the necessary information, we can compute the coordinates of point ‘A’:

  • Latitude of A: latitude_A = asin(sin(latitude_B) * cos(azimuth_BA) + sin(latitude_C) * cos(azimuth_CA))
  • Longitude of A: longitude_A = longitude_B + atan2(sin(azimuth_BA) * cos(latitude_B), cos(azimuth_CA) – sin(latitude_B) * sin(latitude_A))

After plugging in the values, we find:

latitude_A ≈ 0.406153 radians (approximately 23.31099 degrees)
longitude_A ≈ 0.849852 radians (approximately 48.69863 degrees)

Discussion

After some nifty calculations, we’ve got our answer! The coordinates of point ‘A’ are approximately 23.31099° N and 48.69863° E. We’ve pinpointed the parking spot of our Blue Beetle, as depicted on the map below. It’s situated at the intersection of two circles, marked as I2. Point I1 is not a feasible option as it is located in Africa. To reach Mecca and Medina, one would need to cross the Red Sea or embark on an exceptionally long journey.

Nonetheless, it’s crucial to emphasize that these calculations are based on straight-line radial distances and might not provide an exact representation of real on-road travel distances. When cross-referenced with Google Maps, the most favorable estimation for the blue car’s location points to Al-Kharj, Saudi Arabia. However, it’s essential to remember that this remains a conjecture; only the car’s owner or the photographer possesses precise knowledge of its whereabouts.

Conclusion

In this light-hearted adventure, we set out to solve the mystery of the Blue Beetle’s parking spot near a highway. Through a bit of math and geographical know-how, we successfully uncovered the elusive coordinates of point ‘A’. It’s not about the seriousness of the quest; it’s about the joy of the journey, the thrill of discovery, and the whimsical world of automotive adventures.

So, next time you spot a vibrant Blue Beetle by the highway, remember the charming quest that led us to its coordinates. And perhaps, in the spirit of adventure, you can take a moment to appreciate the fun and curiosity that drive us to explore the world around us, one quirky adventure at a time!

Suggestion for Citation:
Amerudin, S. (2023). A Light-hearted Quest to Locate the Elusive Parking Spot of a Blue Beetle Car. [Online] Available at: https://people.utm.my/shahabuddin/?p=7175 (Accessed: 25 September 2023).

Ants, Books, and World Domination

Source: Social Media

In a shocking turn of events, the most unsuspecting threat to humanity has emerged right under our noses, or should we say, under our books. It all starts innocently enough with a sign that reads, “Please Do Not Eat in the Library. The ants will get in.” But what if those ants aren’t just looking for crumbs? What if they’re after something much bigger—knowledge, power, and world domination?

Picture this: you stroll into your local library, armed with a thirst for knowledge and a bag of potato chips. You spot the sign and think, “Of course, I won’t eat here, I’m not a savage!” Little do you know, you’ve just saved the world from an impending ant-tastrophe (pun intended).

The first part of the puzzle is simple enough. Ants love food. But what happens next is the stuff of science fiction, or should we say, science friction? These ants, while indulging in their secret snacking sessions, are inadvertently absorbing knowledge from the books they crawl on. They’re like tiny, six-legged sponges soaking up all the wisdom they can find.

Now, you might be thinking, “How on earth can ants read?” Well, we’re not entirely sure, but we suspect they’ve been taking night classes. These little bibliophiles have been silently honing their reading skills, flipping through the pages of everything from Shakespeare to quantum physics. It’s like a scene straight out of an ant-sized Hogwarts library.

But here’s where it gets really concerning. Knowledge is power, and power corrupts. These ants, once innocent library patrons, are now on the brink of world domination. They’ve learned about politics, economics, and the art of manipulation from the self-help section. They’ve delved into history to study the rise and fall of empires, and they’ve even cracked the secrets of military strategy from the war books.

Soon, the ants will be too smart for their own good. They’ll form a secret ant council and hatch a diabolical plan to conquer the world. And let’s not forget that ants are known for their teamwork. They’ll be like a tiny, six-legged army, ready to march on the world’s picnic baskets and overthrow our human overlords.

But what can we do to prevent this ant-pocalypse, you ask? Well, it starts with obeying that library sign. Do not, under any circumstances, eat in the library. It’s not just about preserving the books; it’s about saving humanity from an insect uprising.

We must also launch a counterintelligence operation to infiltrate their ranks and disrupt their plans. Perhaps we can recruit some intellectual termites to wage a war of words against these ant overlords. Or maybe we should start leaving out decoy books with intentionally misleading information to confuse them.

In any case, it’s clear that the fate of the world now rests on our shoulders. We must be vigilant, stay informed, and never underestimate the power of an ant with a library card. So remember, the next time you’re in the library, keep your snacks at bay, and keep an eye out for any ants trying to borrow books. The future of our world may depend on it.

Muhammad al-Idrīsī: The Forgotten Genius of Medieval Geography and Cartography

Source: Social Media

Introduction

In an age long before digital navigation apps and satellite imagery revolutionized the way we perceive the world, a brilliant scholar and cartographer named Muhammad al-Idrīsī emerged as one of history’s greatest geographers. In the 12th century, a staggering 900 years ago, he unveiled a masterpiece of cartography known as the Tabula Rogeriana, an extraordinary map that showcased the world in unprecedented detail. Yet, despite his significant contributions to the field of geography, al-Idrīsī’s name remains relatively obscure today. One might wonder why this is the case, and as we delve into his life and work, the reasons become clear – al-Idrīsī was a Muslim scholar whose image did not conform to the prevailing Western stereotypes of his time.

The Pioneering Work of Muhammad al-Idrīsī

Muhammad al-Idrīsī, a Moroccan geographer, traveler, and scholar, made his mark on history through his monumental work titled “The excursion of one who is eager to traverse the regions of the world.” This ambitious project resulted in the creation of the Tabula Rogeriana, a world map that was far ahead of its time in terms of both accuracy and sophistication. Al-Idrīsī’s map was not just a geographical representation; it was a comprehensive description of the known world, reflecting the extent of human knowledge at the time.

The Tabula Rogeriana: A Masterpiece of Medieval Cartography

The Tabula Rogeriana was more than just a map; it was a testament to al-Idrīsī’s dedication and meticulous research. The map was a collaborative effort, commissioned by King Roger II of Sicily, and it took al-Idrīsī fifteen years to complete. Its impressive scale and level of detail showcased the diverse regions of the world, from Europe and Asia to Africa and beyond.

What set the Tabula Rogeriana apart was its innovative use of geographical coordinates, which allowed for accurate measurements and navigation. Al-Idrīsī’s map was not just a static representation; it was a tool that could be used for practical purposes, such as navigation and trade. In a time when maps were often crude and inaccurate, the Tabula Rogeriana was a true marvel of medieval cartography.

The Legacy of Al-Idrīsī

Although Muhammad al-Idrīsī’s work was initially produced in Arabic, it was later translated into Latin, making it accessible to scholars across Europe. This Latin translation of his work allowed al-Idrīsī’s knowledge to spread throughout the Western world, influencing later generations of cartographers and geographers.

However, despite the impact of his work, al-Idrīsī’s name and legacy have not received the recognition they deserve in modern times. One of the factors contributing to this lack of recognition may be the prevailing Western stereotypes of the time. Al-Idrīsī, being a Muslim with a turban and beard, did not fit the image of the typical European scholar of his era.

Conclusion

Muhammad al-Idrīsī stands as a testament to the diversity of knowledge and scholarship that has existed throughout human history. His groundbreaking contributions to geography and cartography, particularly through the Tabula Rogeriana, deserve to be celebrated and remembered. As we reflect on the remarkable achievements of this medieval Muslim scholar, we are reminded that the pursuit of knowledge knows no boundaries, and true genius transcends cultural and religious biases. It is time to recognize and appreciate the enduring legacy of Muhammad al-Idrīsī, a visionary geographer and cartographer whose work has left an indelible mark on the history of human exploration and understanding of the world.

Assessing Your Readiness for GIS Undergraduate Studies: A Review of the GIS Interest and Qualification Quiz

By Shahabuddin Amerudin

Are you considering a future in Geographic Information Systems (GIS) and contemplating pursuing your undergraduate studies at Universiti Teknologi Malaysia (UTM)? The GIS Interest and Qualification Quiz, hosted at https://dev.kstutm.com/ugquiz.php, offers an insightful and user-friendly way to determine your readiness and suitability for GIS undergraduate programs. Let’s take a closer look at this engaging quiz designed to guide prospective students on their academic journey.

Ease of Access

The GIS Interest and Qualification Quiz is readily accessible online, making it a convenient tool for anyone interested in GIS studies at UTM. The straightforward design ensures that users can navigate the quiz effortlessly, creating a user-friendly experience from start to finish.

Self-Assessment Made Simple

The quiz comprises ten thoughtfully crafted questions, each requiring a simple ‘Yes’ or ‘No’ response. These questions delve into various aspects of GIS and related fields, allowing respondents to self-assess their interest and qualifications. It’s an efficient and effective way to gauge your enthusiasm and readiness for GIS studies.

Tailored Recommendations

What sets this quiz apart is its ability to provide tailored recommendations based on your responses. Depending on the number of ‘Yes’ answers you provide, the quiz offers detailed justifications and suggestions for your academic and career path in GIS. It’s a personalized touch that helps individuals make informed decisions about their future studies.

A Sneak Peek into GIS

Through questions like, “Do you enjoy exploring geographic information and its applications in various fields?” and “Are you excited about the potential of GIS to contribute to sustainable development and decision-making?” the quiz gives prospective students a glimpse into the exciting world of GIS. It fosters curiosity and can inspire those who may not have considered GIS before.

Encouraging Exploration

The quiz encourages exploration, even for those who may not have initially considered GIS as their academic path. By providing recommendations for each level of interest, from “exceptional commitment” to “limited interest,” it allows users to reflect on their passions and aspirations. It’s a valuable tool for career guidance and self-discovery.

In conclusion, the GIS Interest and Qualification Quiz serves as an excellent resource for individuals contemplating their academic journey in GIS at UTM. Whether you’re already passionate about GIS or are just beginning to explore this dynamic field, this quiz offers valuable insights and personalized recommendations to help you make informed decisions about your future studies. It’s an engaging and informative tool that underscores UTM’s commitment to guiding students towards success in GIS and related disciplines.

Suggestion for Citation:
Amerudin, S. (2023). Assessing Your Readiness for GIS Undergraduate Studies: A Review of the GIS Interest and Qualification Quiz. [Online] Available at: https://people.utm.my/shahabuddin/?p=7166 (Accessed: 23 September 2023).

Assess Your GIS Early Career Potential with the GIS Career Assessment Quiz

Source: https://www.shine.com

By Shahabuddin Amerudin

Introduction

Are you considering a career in Geographic Information Systems (GIS) or looking to evaluate your potential in this exciting field? Look no further! The GIS Career Assessment Quiz is here to help you gauge your skills, knowledge, and experience to determine the most suitable GIS career path for you.

GIS, a technology that combines geography with information technology, has a wide range of applications across industries such as environmental science, urban planning, transportation, and more. Whether you’re a beginner or someone with some GIS experience, this quiz can provide valuable insights into your potential career prospects.

Skills and Knowledge Assessment

The GIS Career Assessment Quiz is designed to assess your skills and knowledge in three critical areas: Spatial Analysis Skills, Programming Skills, and Management Skills. To begin, all you need to do is answer a series of questions and rate your proficiency on a scale of 1 to 5, where 1 represents Low and 5 represents High.

  1. Spatial Analysis Skills: Spatial analysis is the core of GIS. It involves the ability to manipulate, analyze, and visualize geographic data. Rate your spatial analysis skills to determine how comfortable you are working with maps, geographic data, and spatial statistics.
  2. Programming Skills: In the modern GIS landscape, programming skills are highly valued. Rate your programming skills to assess your ability to write scripts or code for GIS tasks. Whether you are proficient in Python, R, or any other programming language, this skill can open up many GIS career opportunities.
  3. Management Skills: GIS projects often require effective management to ensure they meet objectives on time and within budget. Rate your management skills to understand your ability to plan, coordinate, and lead GIS projects.

Years of Experience

In addition to assessing your skills and knowledge, the quiz also asks about your years of experience in GIS. This factor is essential in determining your readiness for specific GIS career paths.

Receive Personalized Recommendations

Once you’ve completed the GIS Career Assessment Quiz, the website will analyze your responses and provide personalized recommendations based on your skills, knowledge, and experience. These recommendations will guide you towards one of the following GIS career options:

  1. GIS Analyst: If you have a strong foundation in spatial analysis and some experience working with geographic data, you may be well-suited for a role as a GIS Analyst.
  2. GIS Developer: Those with programming skills and a passion for developing GIS applications may find a rewarding career as a GIS Developer.
  3. GIS Manager: If you excel in management skills and have experience in overseeing GIS projects, a career as a GIS Manager could be a great fit.
  4. GIS Consultant: Individuals with a combination of skills, knowledge, and experience across various aspects of GIS may discover that a career as a GIS Consultant offers diverse opportunities.

Try It Now!

Curious to know which GIS career path suits you best? Take the GIS Career Assessment Quiz at https://dev.kstutm.com/GIS-career.html and receive your personalized recommendations today. Whether you’re just starting your GIS journey or looking to make a career change, this quiz is a valuable tool to help you make informed decisions about your future in the world of Geographic Information Systems.

Suggestion for Citation:
Amerudin, S. (2023). Assess Your GIS Early Career Potential with the GIS Career Assessment Quiz. [Online] Available at: https://people.utm.my/shahabuddin/?p=7152 (Accessed: 23 September 2023).

The Unconventional Malay Map

Source: Social Media

By Shahabuddin Amerudin

In the world of cartography, where precision and accuracy are highly valued, there exists a fascinating anomaly known as the “Malay Map.” This mysterious map, devoid of proper cartographic and geographical elements, offers a remarkable insight into the past when technology and orientation played a very different role in mapping the world.

The origins of the Malay Map are shrouded in mystery, with no known cartographer to claim authorship. What sets it apart from conventional maps is its striking departure from modern cartographic standards. Unlike contemporary maps that meticulously adhere to precise geographic coordinates, the Malay Map offers a unique perspective rooted in human perception rather than mathematical accuracy.

One of the most striking features of the Malay Map is its disregard for proper orientation. In today’s world, we are accustomed to maps that consistently display north at the top. However, this map challenges our expectations. It presents locations in a manner that reflects how people naturally view the world around them, rather than adhering to standardized directional conventions.

This unconventional approach to mapping is not a result of ignorance but rather a testament to the technological limitations of its time. The Malay Map was created in an era when the tools and resources available for cartography were vastly different from what we have today. These limitations forced mapmakers to rely on human perspective and local knowledge, rather than the precise measurements and satellite technology we enjoy today.

One of the most remarkable achievements of the Malay Map is its ability to position cities and states, albeit with less accuracy compared to modern maps. This feat is a testament to the skill and knowledge of the mapmakers of that era. They managed to represent the world around them with remarkable precision given the tools and techniques at their disposal.

Today, when we compare the Malay Map to contemporary maps, it may seem quaint and imprecise. However, we should view it with admiration for the resourcefulness of the people who created it. This map provides us with a glimpse into a time when mapping the world was an art as much as it was a science.

The Malay Map serves as a reminder that our understanding of the world is ever-evolving, shaped by technology, culture, and the tools at our disposal. It prompts us to appreciate the ingenuity of those who came before us and laid the foundation for the advanced cartography we enjoy today. In its unconventional nature, the Malay Map reveals the rich tapestry of human history and the diverse ways in which we have sought to make sense of our world.

Suggestion for Citation:
Amerudin, S. (2023). The Unconventional Malay Map. [Online] Available at: https://people.utm.my/shahabuddin/?p=7124 (Accessed: 19 September 2023).