Category: Teaching

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Requirements for Students Studying GIS Software Systems

By Shahabuddin Amerudin

Geographic Information System (GIS) software systems are a vital tool for professionals who need to visualize and analyze complex spatial data. As such, the demand for GIS professionals has increased in recent years, with a wide range of industries utilizing these systems. However, to succeed in this field, students studying GIS software systems must possess certain skills and competencies.

Basic Computer Skills

GIS software systems are computer-based, and therefore, a student studying GIS software systems should have a good grasp of computer hardware, software, and operating systems. They should be able to navigate the computer interface, troubleshoot common technical issues, and perform basic maintenance. Additionally, students should have experience with the basic computer tools used in data analysis, such as spreadsheets and databases.

Data Analysis and Management

GIS involves managing, analyzing, and manipulating large amounts of spatial data. Therefore, students should be comfortable with data analysis tools and techniques such as data classification, statistical analysis, and data visualization. They should be able to perform spatial analysis using various GIS software tools and interpret the results effectively. Additionally, students should have experience in data management and be able to integrate, organize, and maintain complex data sets.

Spatial Thinking

One of the most important requirements for students studying GIS software systems is the ability to think spatially. They should be able to understand and analyze spatial relationships between different geographic features, such as distance, scale, and projection. Students should also have a solid understanding of geography, map reading, and spatial reasoning.

Programming

GIS software systems often require some programming knowledge, especially if you want to customize or automate certain processes. Familiarity with programming languages such as Python or R can be helpful. Students should have a good understanding of computer programming and be able to write, modify, and execute scripts to automate processes and customize GIS software systems.

Cartography

As a GIS professional, you may be responsible for creating maps and visualizations that effectively communicate complex spatial information. Therefore, students should be familiar with cartographic principles and have experience working with map design software. They should be able to design effective maps that convey spatial information to various audiences.

Communication Skills

Finally, GIS often involves working with interdisciplinary teams, including engineers, planners, and policy makers. Strong communication skills are essential for effectively collaborating with others and presenting complex information to a variety of stakeholders. Students should be able to communicate effectively in writing and orally, and they should be comfortable working in teams to achieve common goals.

Conclusion

In conclusion, students studying GIS software systems must possess several skills and competencies to be successful in this field. They should have a solid understanding of basic computer skills, data analysis, and management, spatial thinking, programming, cartography, and communication skills. While these requirements may seem daunting, students who possess these skills will have a competitive edge in the job market and be able to contribute to a wide range of industries that utilize GIS software systems.

Suggestion for Citation:
Amerudin, S. (2023). Requirements for Students Studying GIS Software Systems. [Online] Available at: https://people.utm.my/shahabuddin/?p=6161 (Accessed: 28 March 2023).
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Object-Oriented Technology: A Look Back at its Definition and Relevance in Current Programming Technology

By Shahabuddin Amerudin

The article titled “What Is Object-Oriented Technology Anyway?” by Berry (1996) explains what object-oriented (OO) technology is and its three basic forms: Object-Oriented User Interfaces (OOUI), Object-Oriented Programming Systems (OOPS), and Object-Oriented Data Base Management (OODBM). The author discusses the differences between these forms and how they relate to GIS (Geographic Information Systems).

The article provides a detailed explanation of OOUIs and how they use “icons” and “glyphs” to launch repetitive procedures. OOUIs are described as graphical user interfaces that make it easier for users to interact with computers by using point-and-click methods. The article also notes that OOUIs have become commonplace with the advent of Windows ’95.

The article then moves on to discuss OOPS and how it uses “widgets” in the development of computer code. The author mentions that Visual Basic and Visual C are examples of object-oriented programming systems. The article notes that OOPS provides an easier way to develop fully structured computer programs.

The article concludes by discussing the importance of the OOPS flowchart in prescriptive modeling. The article notes that as GIS moves from descriptive geo-query applications to prescriptive modeling, the communication of logic becomes increasingly important. The OOPS flowchart provides a mechanism for both communicating and interacting with model logic.

In terms of relevance to current programming technology, the article provides a historical perspective on the development of object-oriented technology. Although some of the specifics may have changed, the basic concepts of OOUIs and OOPS remain relevant today.

OOUIs are still used in modern software development, although they have become more sophisticated over time. For example, modern web applications often use graphical user interfaces to make it easier for users to interact with web pages. Similarly, modern mobile applications often use graphical user interfaces to make it easier for users to interact with their mobile devices.

The article is relevant to current programming technology, particularly with regards to object-oriented programming. Object-oriented programming is still widely used in modern programming languages like Java, Python, and C++. OOUI is still used today in user interface design, and modern operating systems like macOS and Windows continue to use icon-based interfaces. The article’s explanation of OOPS is also relevant to modern programming. Many modern programming environments like Visual Studio and Xcode use visual tools to create software. These environments allow programmers to drag and drop widgets to create code, similar to the flowcharting objects mentioned in the article.

However, the article’s discussion of OODBM is less relevant to modern programming technology. The author notes that OODBM uses objects to manage data in a database. While object-oriented databases still exist, they are not as widely used as relational databases like MySQL and PostgreSQL. The rise of NoSQL databases like MongoDB and Cassandra has also impacted the use of object-oriented databases.

In conclusion, the article “What Is Object-Oriented Technology Anyway?” provides a historical perspective on the development of object-oriented technology. Although the specifics may have changed, the basic concepts of OOUIs and OOPS remain relevant today and the article’s discussion of OODBM provides an interesting historical perspective on the evolution of database management technology. The article serves as a reminder that technology is constantly evolving, and developers must continue to adapt and learn new techniques to stay current.

Reference:
Berry, J.K. (1996). What Is Object-Oriented Technology Anyway? GeoWorld. [Online] Available at: http://www.innovativegis.com/basis/mapanalysis/Topic1/Topic1.htm (Accessed: 28 March 2023).

Suggestion for Citation:
Amerudin, S. (2023). Object-Oriented Technology: A Look Back at its Definition and Relevance in Current Programming Technology. [Online] Available at: https://people.utm.my/shahabuddin/?p=6151 (Accessed: 28 March 2023).
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The Evolution of GIS Software Development and its Changing Roles

By Shahabuddin Amerudin

The article, “GIS Software’s Changing Roles” was written by Berry (1998), and it describes the evolution of GIS software from its inception to the 1990s. This article will evaluate the article and compare the state of GIS software in 2000, 2010, and 2020.

In the late 1980s, GIS software was primarily used by academics, and the software was not yet practical for everyday use. GIS software was expensive and required specialized equipment, which limited its accessibility to a select group of professionals. However, in the 1990s, Windows-based mapping packages were introduced, making GIS more accessible to a broader audience. The democratization of GIS software in the 1990s marked a significant milestone in the development of GIS technology.

In 2000, GIS software had matured, and the software was capable of handling large datasets with ease. The 2000s marked a new era for GIS software development. Companies such as ESRI, Autodesk, and MapInfo became industry leaders in GIS software development. These companies developed a wide range of GIS software products for different applications, including environmental modeling, urban planning, and public safety.

During the 2000s, ESRI’s ArcGIS software emerged as the industry standard for GIS software. ArcGIS provided users with a comprehensive suite of tools for analyzing and managing spatial data. The software was user-friendly and enabled users to create custom applications using ArcGIS’s extensive API library. The introduction of ArcGIS Server in 2003 enabled GIS applications to be deployed on the web, making it possible for users to access GIS data from anywhere in the world.

In the 2010s, GIS software development continued to evolve, with a growing emphasis on open-source GIS software. Open-source GIS software, such as QGIS, provided users with a free alternative to commercial GIS software. Open-source GIS software became increasingly popular, particularly in developing countries, where the cost of commercial GIS software was a significant barrier to entry. The 2010s also saw the emergence of cloud-based GIS software, such as ArcGIS Online, which enabled users to access GIS data and tools from anywhere with an internet connection.

In 2020, GIS software development has continued to evolve, with a growing emphasis on machine learning and artificial intelligence. The integration of machine learning and AI has enabled GIS software to analyze spatial data more efficiently and accurately. For example, GIS software can now analyze satellite imagery to detect changes in land use patterns, identify crop health, and assess the risk of natural disasters. The integration of machine learning and AI has also made it possible to automate GIS tasks, reducing the time and cost of data analysis.

GIS software has come a long way since its inception in the 1970s. Today, GIS software is used in a wide range of applications, including environmental modeling, urban planning, public safety, and agriculture. GIS software has become more accessible and user-friendly, enabling users to create custom applications without requiring specialized expertise. The integration of machine learning and AI has further enhanced the capabilities of GIS software, making it possible to analyze spatial data more efficiently and accurately.

In conclusion, the article “GIS Software’s Changing Roles” provides an excellent overview of the evolution of GIS software from its inception to the 1990s. GIS software development has continued to evolve since the 1990s, with a growing emphasis on accessibility, user-friendliness, and integration with other software applications. The integration of machine learning and AI has further enhanced the capabilities of GIS software, enabling users to analyze spatial data more efficiently and accurately.

Reference:
Berry, J.K. (1998). GIS Software’s Changing Roles. GeoWorld. [Online] Available at: http://www.innovativegis.com/basis/mapanalysis/MA_Intro/MA_Intro.htm (Accessed: 27 March 2023).

Suggestion for Citation:
Amerudin, S. (2023). The Evolution of GIS Software Development and its Changing Roles. [Online] Available at: https://people.utm.my/shahabuddin/?p=6144 (Accessed: 27 March 2023).
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GIS Software’s Changing Roles: A Review

By Shahabuddin Amerudin

The article “GIS Software’s Changing Roles” by Berry (1998) discusses the changing roles of GIS software over the past few decades. In the 70s, GIS software development primarily occurred on campuses and was limited to academia, with products relegated to library shelves of theses. The article argues that this was because of the necessity of building a viable tool before it could be taken on the road to practical solutions. As such, early GIS software development focused on technology itself rather than its applications.

In the 1980s, however, modern computers emerged, bringing with them the hardware and software environments needed by GIS. The research-oriented software gave way to operational systems, and the suite of basic features of a modern GIS became available. Software development switched from specialized programs to extensive “toolboxes” and subsequently spawned a new breed of software specialists.

From an application developer’s perspective, this opened floodgates. From an end user’s perspective, however, a key element still was missing: the gigabytes of data demanded by practical applications. Once again, GIS applications were frustrated. This time, it wasn’t the programming environment as much as it was the lagging investment in the conversion from paper maps to their digital form.

Another less obvious impediment hindered progress. Large GIS shops established to collect, nurture, and process spatial data intimidated their potential customers. The required professional sacrifice at the GIS altar kept the herds of dormant users away. GIS was more often seen within an organization as an adversary competing for corporate support than as a new and powerful capability one could use to improve workflow and address complex issues in entirely new ways.

The 1990s saw both the data logjam burst and the GIS mystique erode. As Windows-based mapping packages appeared on individuals’ desks, awareness of the importance of spatial data and its potential applications flourished. Direct electronic access enabled users to visualize their data without a GIS expert as a co-pilot. For many, the thrill of “visualizing mapped data” rivaled that of their first weekend with the car after the learner’s permit.

So where are we now? Has the role of GIS developers been extinguished, or merely evolved once again? Like a Power Rangers transformer, software development has taken two forms that blend the 1970s and 80s roles. These states are the direct result of changes in software programming approaches in general and “object-oriented” programming in particular.

MapInfo’s MapX and ESRI’s MapObjects are tangible GIS examples of this new era. These packages are functional libraries that contain individual map processing operations. In many ways, they are similar to their GIS toolbox predecessors, except they conform to general programming standards of interoperability, thereby enabling them to be linked easily to the wealth of non-GIS programs.

Like using a Lego set, application developers can apply the “building blocks” to construct specific solutions, such as a real estate application that integrates a multiple listing geo-query with a pinch of spatial analysis, a dab of spreadsheet simulation, a splash of chart plotting, and a sprinkle of report generation. In this instance, GIS functionality simply becomes one of the ingredients of a solution, not the entire recipe.

Overall, the article suggests that GIS software has come a long way since its early days in the 70s. Although software development primarily occurred on campuses in the past, modern computers have brought the hardware and software environments needed by GIS. Software development has switched from specialized programs to extensive “toolboxes” and subsequently spawned a new breed of software specialists. However, a key challenge for GIS software has been the lack of gigabytes of data demanded by practical applications. Additionally, the large GIS shops established to collect, nurture, and process spatial data have intimidated potential customers. But with the rise of Windows-based mapping packages, awareness of the importance of spatial data and its potential applications has flourished.

Reference:
Berry, J.K. (1998). GIS Software’s Changing Roles. GeoWorld [Online] Available at: http://www.innovativegis.com/basis/mapanalysis/MA_Intro/MA_Intro.htm (Accessed: 27 March 2023).

A copy of the article: https://people.utm.my/shahabuddin/?p=6136

Suggestion for Citation:
Amerudin, S. (2023). GIS Software’s Changing Roles: A Review. [Online] Available at: https://people.utm.my/shahabuddin/?p=6138 (Accessed: 27 March 2023).
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GIS Software’s Changing Roles

Although GIS is just three decades old, the approach of its software has evolved as much as its capabilities and practical expressions.  In the 70’s software development primarily occurred on campuses and its products relegated to library shelves of theses.  These formative years provided the basic organization (both data and processing structures) we find in the modern GIS.  Raging debate centered on “vector vs. raster” formats and efficient algorithms for processing— techy-stuff with minimal resonance outside of the small (but growing) group of innovators.

For a myriad of reasons, this early effort focused on GIS technology itself rather than its applications.  First, and foremost, is the necessity of building a viable tool before it can be taken on the road to practical solutions.  As with most revolutionary technologies, the “chicken and the egg” parable doesn’t apply—the tool must come before the application.

This point was struck home during a recent visit to Disneyland.  The newest ride subjects you to a seemingly endless harangue about the future of travel while you wait in line for over an hour.  The curious part is that the departed Walt Disney himself is outlining the future through video clips from the 1950s.  The dream of futuristic travel (application) hasn’t changed much and the 1990s practical reality (tool), as embodied in the herky-jerky ride, is a long way from fulfilling the vision.

What impedes the realization of a technological dream is rarely a lack of vision, but the nuts and bolts needed in its construction.  In the case of GIS, the hardware and software environments of the 1970s constrained its use outside of academia.  Working with 256K memory and less than a megabyte of disk storage made a GIS engine perform at the level of an old skateboard.  However, the environments were sufficient to develop “working prototypes” and test their theoretical foundations. The innovators of this era were able to explore the conceptual terrain of representing “maps as numbers,” but their software products were woefully impractical.

With the 1980s came the renaissance of modern computers and with it the hardware and software environments needed by GIS.  The research-oriented software gave way to operational systems.  Admittedly, the price tags were high and high-end, specialized equipment often required, but the suite of basic features of a modern GIS became available.  Software development switched from specialized programs to extensive “toolboxes” and subsequently spawned a new breed of software specialists.

Working within a GIS macro language, such as ARCINFO’s Arc Macro Language (AML), customized applications could be addressed.  Emphasis moved from programming the “tool” within generis computer languages (e.g., FORTRAN and Pascal), to programming the “application” within a comprehensive GIS language.  Expertise broadened from geography and computers to an understanding of the context, factors and relationships of spatial problems.  Programming skills were extended to spatial reasoning skills—the ability to postulate problems, perceive patterns and interpret spatial relationships.

From an application developer’s perspective the floodgates had opened.  From an end user’s perspective, however, a key element still was missing—the gigabytes of data demanded by practical applications.  Once again GIS applications were frustrated.  This time it wasn’t the programming environment as much as it was the lagging investment in the conversion from paper maps to their digital form.

But another less obvious impediment hindered progress.  As the comic strip character Pogo might say, “…we have found the enemy and it’s us.”  By their very nature, the large GIS shops established to collect, nurture, and process spatial data intimidated their potential customers.  The required professional sacrifice at the GIS altar “down the hall and to the right” kept the herds of dormant users away.  GIS was more often seen within an organization as an adversary competing for corporate support (a.k.a., a money pit) than as a new and powerful capability one could use to improve workflow and address complex issues in entirely new ways.

The 1990s saw both the data logjam burst and the GIS mystique erode.  As Windows-based mapping packages appeared on individuals’ desks, awareness of the importance of spatial data and its potential applications flourished.  Direct electronic access enabled users to visualize their data without a GIS expert as a co-pilot.  For many the thrill of “visualizing mapped data” rivaled that of their first weekend with the car after the learner’s permit.

So where are we now?  Has the role of GIS developers been extinguished, or merely evolved once again?  Like a Power Rangers transformer, software development has taken two forms that blend the 1970s and 80s roles.  These states are the direct result of changes in software programming approaches in general, and “object-oriented” programming in particular.

MapInfo’s MapX and ESRI’s MapObjects are tangible GIS examples of this new era.  These packages are functional libraries that contain individual map processing operations.  In many ways they are similar to their GIS toolbox predecessors, except they conform to general programming standards of interoperability, thereby enabling them to be linked easily to the wealth of non-GIS programs.

Like using a Lego set, application developers can apply the “building blocks” to construct specific solutions, such as a real estate application that integrates a multiple listing geo-query with a pinch of spatial analysis, a dab of spreadsheet simulation, a splash of chart plotting and a sprinkle of report generation.  In this instance, GIS functionality simply becomes one of the ingredients of a solution, not the entire recipe.

In its early stages, GIS required “bootstrap” programming of each operation and was the domain of the computer specialist.  The arrival of the GIS toolbox and macro languages allowed an application specialist to develop software that tracked the spatial context of a problem.  Today we have computer specialists generating functional libraries and application specialists assembling the bits of software from a variety of sources to tailor comprehensive solutions.

The distinction between computer and application specialist isn’t so much their roles, as it is characteristics of the combined product.  From a user’s perspective the entire character of a GIS dramatically changes.  The look-and-feel evolves from a generic “map-centric view “to an “application-centric” one with a few tailored buttons that walk users through analysis steps that are germane to an application.  Instead of presenting users with a generalized set of map processing operations as a maze of buttons, toggles and pull-down menus, only the relevant ones are integrated into the software solution.  Seamless links to nonspatial programming “objects,” such as pre-processing and post-processing functions, are automatically made.

As the future of GIS unfolds, it will be viewed less as a distinct activity and more as a key element in a thought process.  No longer will users “break shrink-wrap” on stand-alone GIS systems.  They simply will use GIS capabilities within an application and likely unaware of the underlying functional libraries.  GIS technology will finally come into its own by becoming simply part of the fabric of software solutions.

Source:
Berry, J.K. (1998). GIS Software’s Changing Roles. GeoWorld [Online] Available at: http://www.innovativegis.com/basis/mapanalysis/MA_Intro/MA_Intro.htm (Accessed: 27 March 2023).

 
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How To Write a Literature Review for a Research Paper

This post provides a comprehensive guide on how to write a literature review for a scientific or academic research paper. The process can be divided into five essential steps that will ensure a successful literature review:

Step 1: Research of Two Kinds The author needs to consult the guidelines provided by an instructor or an academic/scientific publisher and read literature reviews found in published research papers as models. Once the requirements are established, research into the topic can proceed via keyword searches in databases and library catalogs. The author should include publications that support and run contrary to their perspective.

Step 2: Reading and Evaluating Sources Each publication identified as relevant should be read carefully and thoroughly. The author should pay attention to elements that are especially pertinent to the topic of their research paper. Accurate notes of bibliographical information, content important to the research, and the researcher’s critical thoughts should be recorded.

Step 3: Comparison and Synthesis Comparison and synthesis of the publications considered are vital to determining how to write a literature review that effectively supports the original research. As sources are compared, the author should consider the methods and findings, ideas and theories, contrary and confirmative arguments of other researchers in direct relation to the findings and implications of their current research. Major patterns and trends in the body of scholarship should be a special concern.

Step 4: Writing the Literature Review The primary purpose of a literature review within a research paper is to demonstrate how the current state of scholarship in the area necessitates the research presented in the paper. Maintaining a clear line of thought based on the current research can prevent unnecessary digressions into the detailed contents and arguments of sources. Citations and references in the exact style and format indicated by publisher or instructor guidelines must be provided for all the sources discussed in a literature review.

Step 5: Revising and Editing The first draft of a literature review should be read critically and both revised and edited as an important part of the entire research paper. Clarifying and streamlining the argument of the literature review to ensure that it successfully provides the support and rationale needed for the research presented in the paper are essential, but so too is attention to many seemingly small details.

Overall, the literature review is a necessary part of most research papers and is never easy to write. However, by following these five essential steps, the author can ensure that their literature review is effective, well-organized, and well-supported.

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Voice Interaction with Smartphones: An Overview

In recent years, the use of voice interaction with smartphones has become increasingly popular. With advances in technology, smartphones are now able to recognize and interpret human speech, allowing users to interact with their devices in a more natural and intuitive way. In this article, we will explore the basics of voice interaction with smartphones, including how it works, its benefits, and its applications.

How Voice Interaction with Smartphones Works

Voice interaction with smartphones involves the use of speech recognition technology to convert spoken words into digital text. This technology is powered by natural language processing (NLP), which is a branch of artificial intelligence (AI) that focuses on the interpretation and generation of human language. The process of converting spoken words into digital text involves several steps:

  1. Audio Capture: The first step in voice interaction with smartphones is the capture of audio data. This is typically done using a microphone on the smartphone.

  2. Preprocessing: Once the audio data is captured, it undergoes preprocessing to remove background noise and other interference. This ensures that the speech recognition engine can accurately interpret the speech.

  3. Speech Recognition: The speech recognition engine then analyzes the audio data and converts it into digital text. This involves breaking down the audio data into individual words and comparing them to a database of known words.

  4. Natural Language Processing: Once the speech is recognized, NLP algorithms are used to interpret the meaning of the words and phrases in context. This allows the smartphone to understand the intent of the user’s speech and respond accordingly.

  5. Response: Finally, the smartphone generates a response based on the user’s speech. This could be in the form of a text message, a search result, or an action performed by the smartphone.

Benefits of Voice Interaction with Smartphones

There are several benefits to using voice interaction with smartphones:

  1. Convenience: Voice interaction allows users to interact with their smartphones without the need to physically touch them. This is especially useful when driving or performing other activities where using a smartphone could be dangerous.

  2. Speed: Voice interaction is often faster than typing, allowing users to perform tasks more quickly.

  3. Accessibility: Voice interaction can be useful for people with disabilities or impairments that make it difficult to use a keyboard or touchscreen.

  4. Natural and Intuitive: Voice interaction is a natural and intuitive way to communicate, making it easier for users to express themselves and get the information they need.

Applications of Voice Interaction with Smartphones

Voice interaction with smartphones has a wide range of applications, including:

  1. Personal Assistant: Voice interaction can be used to perform tasks such as setting reminders, scheduling appointments, and making phone calls.

  2. Navigation: Voice interaction can be used to get directions and navigate to a destination, which is especially useful when driving.

  3. Search: Voice interaction can be used to perform searches on the internet or within the smartphone itself.

  4. Home Automation: Voice interaction can be used to control smart home devices such as lights, thermostats, and security systems.

  5. Gaming: Voice interaction can be used to control games and interact with other players.

Challenges of Voice Interaction with Smartphones

While voice interaction with smartphones has many benefits, there are also several challenges that must be overcome:

  1. Accuracy: Speech recognition technology is not perfect and can sometimes misinterpret speech, leading to errors in text conversion.

  2. Security: Voice interaction can be vulnerable to security threats, such as unauthorized access to personal information.

  3. Privacy: Voice interaction requires access to a user’s microphone, which can raise privacy concerns.

  4. Languages: Speech recognition technology is typically designed for specific languages, which can limit its usefulness in multilingual environments.

Developers who want to incorporate voice interaction into their smartphone applications can use various tools, such as SDKs, APIs, and libraries. These tools help developers to overcome the technical challenges of speech recognition and natural language processing, and integrate voice commands into their applications.

However, developers must consider the privacy and security concerns associated with voice interaction technology. Voice data is sensitive information that requires protection, and developers must implement secure protocols to ensure user data is not compromised.

In conclusion, voice interaction with smartphones has become a significant trend in the digital world. As technology advances, speech recognition and natural language processing have been integrated into smartphones and other devices, allowing users to interact with their devices using voice commands. This technology has made the user experience more convenient and efficient, allowing users to perform tasks hands-free while on the go. Voice interaction with smartphones is a rapidly growing trend that has revolutionized the way we interact with our devices. Developers who want to leverage this technology in their applications must consider the technical, privacy, and security challenges associated with voice interaction. With careful planning and implementation, voice interaction can enhance the user experience and provide many potential benefits for various industries.

 

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Assessing Prior Knowledge and Expectations of GIS Software Systems Among Undergraduate Students at Universiti Teknologi Malaysia

By Shahabuddin Amerudin

The purpose of this study was to evaluate the knowledge of GIS software systems among 3rd year undergraduate students at Universiti Teknologi Malaysia. The study aimed to assess their experience with geospatial software, including identifying and evaluating software options, their comfort level with programming languages used in GIS software development, their prior knowledge related to GIS software systems, and their career goals after graduation.

The data was collected through a survey of 30 respondents enrolled in the GIS Software System course for Semester 2 Session 2022/2023 at the Geoinformation Programme, Faculty of Built Environment and Surveying, as part of the Bachelor of Science in Geoinformatics with Honours program. The survey data was analyzed using descriptive statistics.

Overall, the survey provided valuable insights into the prior knowledge of GIS software systems among 3rd year undergraduate students at Universiti Teknologi Malaysia. The findings revealed that the majority of respondents have experience in identifying and evaluating software options, but face challenges related to a lack of knowledge on software options. Python is the most commonly used programming language, and most respondents are somewhat comfortable with programming languages commonly used in GIS software development. Additionally, over half of the respondents have developed GIS applications before, with QGIS Plugin Development being the most commonly used GIS application development tool.

The survey also highlighted the diversity of career goals among respondents, with many undecided about their goals after graduation. Some respondents have specific goals related to GIS, such as GIS Analyst or GIS Developer, while others have broader goals related to web app development, database integration, and data analysis and management. Finally, the survey revealed that respondents have different expectations for the course, ranging from learning how to develop GIS software systems to exploring new things in the GIS field.

Based on these findings, it is clear that there is a need for continued education and training in GIS software systems to prepare students for careers in this field. Additionally, educators should focus on providing students with more information on software options to help them better identify and evaluate options that meet their needs. Finally, there is a need for greater exposure to a wider range of GIS application development tools to provide students with more options for their future careers.

In conclusion, this survey provides important insights into the prior knowledge, experience, and career goals of 3rd year undergraduate students at Universiti Teknologi Malaysia in relation to GIS software systems. These findings can inform future education and training initiatives in this field and help prepare students for successful careers in GIS.

Citation:
Amerudin, S. (2023) Assessing Prior Knowledge and Expectations of GIS Software Systems Among Undergraduate Students at Universiti Teknologi Malaysia. Available at: https://people.utm.my/shahabuddin/?p=6110 (Accessed: 22 March 2023).

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Analysis of Respondent’s Learning Goals and Expectations for GIS Software Systems Course

By Shahabuddin Amerudin

The survey collected data from 30 students who are going to take GIS Software System course in Semester 2 Session 2022/2023 from Bachelor of Science in Geoinformatics with Honours at Geoinformation Programme, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia. In this dataset, respondents were asked what new things they want to learn and what their expectations are for the GIS course they will be taking. The following is a detailed analysis of their responses:

New Things to Learn

  1. Development of web apps: Several respondents expressed interest in learning how to develop GIS-based web applications. They want to gain knowledge of programming languages and tools required to create dynamic web pages with GIS components and functionalities.
  2. Software system knowledge: A few respondents want to expand their understanding of GIS software systems, including their architecture, design, and development process. They want to learn about different types of software, their advantages and disadvantages, and how to evaluate them based on project requirements.
  3. Spatial analysis: Some respondents expressed interest in learning spatial analysis techniques and tools, including spatial data modeling, spatial statistics, and geostatistics. They want to gain knowledge of methods and tools to visualize and interpret spatial data.
  4. Database integration: A few respondents want to learn how to integrate GIS software with databases, including how to import/export data, manage databases, and conduct queries.
  5. New software and tools: Some respondents expressed an interest in learning about new GIS software and tools and their capabilities. They want to know about the latest trends and innovations in GIS technology.
  6. Advanced GIS development: A few respondents want to expand their knowledge of GIS development, including how to develop plugins, customize existing tools, and create new functionalities.
  7. Programming: Several respondents expressed an interest in learning programming languages used in GIS development, including Python, C++, C#, and Java. They want to learn how to write code, modify existing code, and create new software tools.

Expectations for the Course

  1. Practical skills: Most respondents expect the course to provide them with practical skills in GIS development, including coding, software design, and development. They want to gain hands-on experience in using GIS software tools to develop applications, plugins, and other software components.
  2. Industry-relevant knowledge: Respondents expect the course to provide them with knowledge that is relevant to the GIS industry, including current trends, best practices, and emerging technologies. They want to gain knowledge of industry standards, regulations, and certifications, and how to apply them to GIS projects.
  3. Collaborative learning: Respondents expect the course to provide opportunities for collaborative learning, including group projects, team-based assignments, and peer-to-peer interactions. They want to learn from other students and instructors and gain insight into how GIS projects are managed and executed in real-world settings.
  4. Flexibility: Some respondents expect the course to be flexible in terms of scheduling and delivery mode. They want to have the option to attend classes online or in-person, and they want to be able to access course materials and assignments at their convenience.
  5. Comprehensive curriculum: Respondents expect the course to cover a broad range of GIS topics, including software development, spatial analysis, database integration, and project management. They want to gain a comprehensive understanding of GIS and its applications in various industries and domains.
  6. Quality instruction: Respondents expect the course to be taught by experienced and knowledgeable instructors who have a strong understanding of GIS technology and its applications. They want instructors who can provide practical advice, guidance, and feedback on their projects and assignments.
  7. Career advancement: Respondents expect the course to help them advance their careers in GIS, including gaining new skills and knowledge that can enhance their job performance and competitiveness. They want to gain practical skills that can be applied to real-world GIS projects and that can help them achieve their career goals.

In conclusion, the analysis of the survey responses on what new things respondents want to learn and their expectations for the GIS course revealed various interests and expectations. Respondents expressed an interest in developing web apps, expanding their software system knowledge, learning spatial analysis techniques, integrating GIS with databases, and gaining knowledge of new software and tools, among others. Additionally, respondents expected the course to provide them with practical skills in GIS development and industry-relevant knowledge.

This analysis highlights the importance of understanding the needs and expectations of students in GIS education. It can guide educators and institutions in developing curriculums and programs that meet the needs of students and prepare them for the industry. Additionally, it can help students identify their interests and expectations and choose courses and programs that align with their goals.

Citation:
Amerudin, S. (2023) Analysis of Respondent’s Learning Goals and Expectations for GIS Software Systems Course. Available at: https://people.utm.my/shahabuddin/?p=6107 (Accessed: 22 March 2023).

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Assessing Students’ GIS Knowledge and Software Experience: A Survey Study

By Shahabuddin Amerudin

The survey collected data from 30 students who are going to take GIS Software System course in Semester 2 Session 2022/2023 from Bachelor of Science in Geoinformatics with Honours at Geoinformation Programme, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia. The survey aimed to identify the students’ understanding of GIS and their prior experience with different types of geospatial software, non-spatial software, and primary sources of geospatial software.

Overall Understanding of GIS

The first question in the survey asked the students to rate their overall understanding of GIS. The response options ranged from beginner to intermediate to advanced. Based on the survey data, it appears that the majority of the students (60%) rated their overall understanding of GIS as intermediate, while the remaining students rated their understanding as beginner.

Types of Geospatial Software Used Before

The survey asked the students about their prior experience with different types of geospatial software, including desktop GIS, server GIS, internet GIS, web GIS, cloud GIS, and mobile GIS. The response options were either “Selected” or “Not Selected.” Based on the survey data, it appears that all students have used desktop GIS software before. In contrast, fewer students have experience with other types of geospatial software, such as server GIS (56.7%), internet GIS (50%), and mobile GIS (36.7%). It is worth noting that the number of students who have experience with cloud GIS (26.7%) and web GIS (30%) is lower than the other types of geospatial software.

Types of Non-Spatial Software Used Before

The survey also asked the students about their prior experience with non-spatial software that can be used in geospatial applications, including databases, web services, programming environments, and none. Based on the survey data, it appears that the majority of the students have used databases (90%) and web services (70%) before, while a smaller percentage of students have experience with programming environments (56.7%). Notably, no students reported having no experience with non-spatial software.

Familiarity with Primary Sources of Geospatial Software

The survey asked the students about their familiarity with the primary sources of geospatial software, including commercial and Free and Open-Source Software for GIS (FOSS4G) options. The response options ranged from “I am familiar with both commercial and FOSS4G options” to “I am not familiar with either option.” Based on the survey data, it appears that the majority of the students (70%) are familiar with both commercial and FOSS4G options, while a smaller percentage of students are only familiar with commercial options (20%) or FOSS4G options (6.7%). Only one student reported not being familiar with either option.

Major Functionalities Needed from Off-the-Shelf Software Based on Requirements

Finally, the survey asked the students about the major functionalities needed from off-the-shelf software based on requirements, including data management, analysis tools, visualization tools, web publishing, mobile support, customization capabilities, and others. The response options were either “Selected” or “Not Selected.” Based on the survey data, it appears that all students identified data management as a major functionality needed from off-the-shelf software. Other functionalities that were commonly selected by the students include visualization tools (86.7%), analysis tools (76.7%), and customization capabilities (76.7%). In contrast, fewer students identified mobile support (53.3%) and web publishing (60%) as major functionalities needed from off-the-shelf software.

Discussion

Overall, the results of this survey indicate that the respondents have a varied level of experience and knowledge with geospatial software and tools.

It is worth noting that the majority of the respondents have used desktop GIS software before, but have little to no experience with cloud GIS or custom applications. Additionally, the majority of respondents have used databases and web services, while few have experience with programming environments.

In terms of knowledge about the primary sources of geospatial software, most respondents are familiar with both commercial and FOSS4G options, while a smaller number are familiar with only commercial options or only FOSS4G options.

When asked about the major functionalities needed from off-the-shelf software based on requirements, the most commonly selected options were data management, analysis tools, visualization tools, and web publishing. Mobile support and customization capabilities were also deemed important, but less frequently than the other options.

Conclusion

In conclusion, the results of this survey suggest that the students who are going to take the GIS Software System course have a varied level of experience and knowledge with geospatial software and tools, and may benefit from further education and training in areas such as cloud GIS and custom applications. Additionally, the results suggest that the students have a good understanding of the primary sources of geospatial software, and have a clear idea of the major functionalities needed from off-the-shelf software based on requirements.

Citation:
Amerudin, S. (2023) Assessing Students’ GIS Knowledge and Software Experience: A Survey Study. Available at: https://people.utm.my/shahabuddin/?p=6103 (Accessed: 22 March 2023).


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Exploring the Relationship Between Programming Skills and Career Goals in GIS: A Survey Analysis

By Shahabuddin Amerudin


Are you interested in working with GIS, or Geographic Information Systems? If so, you may be wondering if there is a relationship between being comfortable with programming languages and your career goals. Well, let’s take a look at some data to see if we can find any patterns.


The data we have comes from a survey of students who are interested in GIS. The survey asked questions about the participants’ understanding of GIS, their comfort with programming languages commonly used in GIS software development, which programming languages they have used before, and whether they have developed any GIS applications before. The survey also asked about the participants’ career goals after graduation.


First, let’s look at the participants’ comfort with programming languages. The survey asked participants to rate their comfort level on a scale from “not comfortable” to “somewhat comfortable” to “very comfortable”. The most commonly used programming language among the participants was Python, and most students rated themselves as “somewhat comfortable” with it.

Next, let’s look at the participants’ career goals. The survey asked participants what their career goals were after graduation. The most common responses were “GIS analyst”, “GIS developer”, “GIS specialist”, and “undecided at the moment”.

Now, let’s see if there is a relationship between being comfortable with programming languages and career goals. Looking at the data, it appears that students who rated themselves as “somewhat comfortable” with programming languages like Python were more likely to have career goals as a GIS analyst or developer. In fact, many of the participants who rated themselves as “somewhat comfortable” with Python had already developed GIS applications before. 

On the other hand, students who rated themselves as “not comfortable” with programming languages were more likely to be undecided about their career goals or to have career goals as a GIS specialist. 

So what does this mean? Well, it seems that being comfortable with programming languages, especially Python, can be a helpful skill to have if you’re interested in a career as a GIS analyst or developer. Developing GIS applications can also be a valuable experience that can lead to job opportunities in these fields. 

Of course, there are other factors that can influence career goals and success in GIS, such as education, experience, and networking. But if you’re interested in GIS and want to improve your career prospects, learning programming languages like Python may be a good place to start. In conclusion, the data suggests that there is a relationship between being comfortable with programming languages and career goals in GIS. If you’re interested in this field, consider learning Python and developing GIS applications to increase your chances of success. 

Citation: Amerudin, S. (2023) Exploring the Relationship Between Programming Skills and Career Goals in GIS: A Survey Analysis. Available at: https://people.utm.my/shahabuddin/?p=6095 (Accessed: 22 March 2023).
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50 Geo-Savvy Names for GIS: Unleashing the Power of Spatial Intelligence!

  1. CartoChampian – This name suggests that the GIS product is a leading solution for cartography and mapping, emphasizing its superior quality and performance.
  2. CartoCompass – This name suggests a focus on using GIS technology to create accurate and reliable navigation tools.
  3. CartoCove – This name suggests a focus on creating detailed and accurate maps of coastal regions.
  4. CartoCraft: This name suggests that the GIS is a tool for creating precise and well-crafted maps.
  5. CartoCraze – This name suggests a passion for mapping and an obsession with creating accurate and detailed maps.
  6. EarthData – This name suggests a focus on collecting and analyzing data related to the Earth’s surface and environment.
  7. EarthEnthusiast – This name suggests a passion and enthusiasm for the Earth’s surface and environment.
  8. EarthExpert – This name suggests a deep understanding and expertise in the field of Earth science and geospatial data analysis.
  9. EarthExplorer: This name suggests that the GIS can help users explore and analyze the Earth’s surface.
  10. EarthMap: This name suggests that the GIS is a tool for creating and analyzing maps of the Earth’s surface.
  11. EarthScope – This name suggests a broad and comprehensive view of the Earth’s surface and environment using GIS technology.
  12. GeoConnect: This name suggests that the GIS can connect different geographical data sets and sources.
  13. GeoExplorer – This name suggests a passion for exploring and discovering new insights using GIS technology.
  14. GeoGenius: This name suggests that the GIS user is a genius when it comes to working with geographical data.
  15. GeoGladiator – This name suggests a fierce and competitive approach to geospatial data analysis and interpretation.
  16. GeoGuardian – This name suggests a focus on protecting and managing the Earth’s resources using geospatial data analysis.
  17. GeoGuide – This name suggests a willingness to provide guidance and direction to others using geospatial data analysis.
  18. GeoGuru – This name suggests an expert in the field of geospatial data analysis and interpretation.
  19. GeoInsider – This name suggests an expert level of knowledge and understanding of geospatial data analysis.
  20. GeoInsight: This name suggests that the GIS provides deep insight into geographical data.
  21. GeoLogic: This name suggests that the GIS uses logical and scientific methods to analyze geographical data.
  22. Geomatics: This name is a term that refers to the science of measuring and mapping geographical features and suggests that the GIS is a tool for geomatics professionals.
  23. GeoNavigator – This name suggests expertise in navigating and interpreting geospatial data to find insights and solutions.
  24. GeoSense: This name implies that the GIS has a high degree of sensitivity and accuracy when it comes to spatial data.
  25. GeoVantage – This name suggests a competitive advantage in the field of geography and geospatial data analysis.
  26. LocationLeader – This name suggests a leadership position in the field of location-based data analysis.
  27. LocationLegend – This name suggests a reputation as a legendary figure in the field of location-based data analysis.
  28. LocationLion – This name suggests a bold and powerful approach to location-based data analysis and interpretation.
  29. LocationLogic: This name suggests that the GIS provides logical and data-driven solutions for location-based problems.
  30. MapMagic – This name suggests a focus on using GIS technology to create magical and innovative maps.
  31. MapMania – This name suggests a love for creating and analyzing maps using GIS technology.
  32. MapMaster: This name implies that the GIS user is a master at creating and analyzing maps.
  33. MapMastermind – This name suggests a genius level of skill and knowledge in mapping and GIS technology.
  34. MapMate – This name suggests a friendly and approachable attitude towards GIS technology and data analysis.
  35. MapMaven: This name implies that the GIS user is a knowledgeable expert in map creation and analysis.
  36. MapMax: This name implies that the GIS can help users achieve maximum potential when it comes to creating and analyzing maps.
  37. MapMentor – This name suggests a willingness to guide and teach others about GIS technology and data analysis.
  38. MapMinds – This name suggests intelligence and proficiency in mapping technologies and data analysis.
  39. MapMuse – This name suggests a love for creating beautiful and artistic maps using GIS technology.
  40. SpatialSage – This name suggests a wise and knowledgeable approach to GIS technology and spatial analysis.
  41. SpatialSavvy: This name implies that the GIS user is skilled and knowledgeable when it comes to spatial data analysis.
  42. SpatialScope: This name implies that the GIS has a broad scope and can handle various spatial data.
  43. SpatialSlinger – This name suggests a quick and accurate approach to spatial analysis using GIS technology.
  44. SpatialSolutions – This name suggests a focus on providing solutions to spatial problems using GIS technology.
  45. SpatialStrategist – This name suggests expertise in spatial analysis and strategic decision-making using geographic data.
  46. TerraTactics – This name suggests expertise in using geospatial data to develop strategic plans for the Earth’s surface and environment.
  47. TerraTracer – This name suggests a focus on tracking and analyzing changes to the Earth’s surface and environment using GIS technology.
  48. TerraTrailblazer – This name suggests a pioneering spirit in the field of geospatial data analysis and interpretation.
  49. TerraTrek: This name suggests that the GIS can be used to explore and navigate the Earth’s surface.
  50. TerraVision: This name suggests that the GIS can provide a clear view of the Earth’s surface.
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72 Alternative Names for GIS

  1. Cartographic Data Analytics Platform (CDAP) – a platform for analyzing and visualizing cartographic data.
  2. Cartographic Data Management System (CDMS) – a system for managing and analyzing cartographic data.
  3. Cartographic Information Analytics System (CIAS) – a system for analyzing cartographic information.
  4. Cartographic Information System (CIS) – A system for creating, managing, and analyzing maps and other cartographic data.
  5. Digital Earth System (DES) – A system for creating a comprehensive digital representation of the earth.
  6. Earth Information System (EIS) – A system that provides access to a wide range of earth science data and information.
  7. Earth Observation Analytics System (EOAS) – a system for analyzing data collected from Earth observation satellites.
  8. Earth Observation Data Management System (EODMS) – a system for managing and analyzing data collected from Earth observation satellites.
  9. Earth Observation Data System (EODS) – Refers to the use of satellite and other remote sensing technologies to gather earth observation data.
  10. Earth Observation Intelligence System (EOIS) – a system that provides intelligence based on data collected from Earth observation satellites.
  11. Earth Observation System (EOS) – a system that collects and analyzes data from Earth observation satellites.
  12. Earth Science Information System (ESIS) – Refers to the use of geospatial data to study and understand the earth’s systems.
  13. Earth System Science Information System (ESSIS) – A system for accessing and analyzing earth science data.
  14. Environmental Information System (EIS) – A system for managing and analyzing environmental data.
  15. Geo-Analytical System (GAS) – a system for analyzing geospatial data.
  16. Geo-Information System – Similar to GIS, but emphasizes the information aspect of the technology.
  17. Geographic Data Analytics System (GDAS) – a system for analyzing geographic data.
  18. Geographic Data Management Platform (GDMP) – a platform for managing and analyzing geographic data.
  19. Geographic Data System (GDS) – Similar to GIS, but emphasizes the data aspect of the technology.
  20. Geographic Information Analysis System (GIA) – a system for analyzing and managing geographic information.
  21. Geographic Information Analytics Platform (GIAP) – a platform for analyzing and visualizing geographic information.
  22. Geographic Information Analytics System (GIAS) – A system for analyzing and interpreting geographic information.
  23. Geographic Information Management System (GIMS) – A system for managing and analyzing geographic information.
  24. Geographic Information System for Health (GIS-H) – A system for managing and analyzing health-related geographic data.
  25. Geographic Intelligence Platform (GIP) – a platform for providing intelligence based on geographic data.
  26. Geographic Knowledge System (GKS) – A system that provides knowledge about geographic features and phenomena.
  27. Geomatics Information System (GIS) – A system for acquiring, storing, analyzing, and displaying geospatial information.
  28. Geomatics System (GS) – Refers to the use of various technologies to acquire, manage, and analyze geospatial data.
  29. Geospatial Analytics Management System (GAMS) – a system for managing and analyzing geospatial analytics.
  30. Geospatial Analytics Platform (GAP) – Focuses on the use of analytics and data science techniques to analyze geospatial data.
  31. Geospatial Analytics Platform (GAP) – a platform for analyzing and visualizing geospatial data.
  32. Geospatial Asset Management System (GAMS) – Refers to the use of geospatial data to manage assets such as infrastructure, buildings, and utilities.
  33. Geospatial Business Intelligence System (GBIS) – A system that integrates geospatial data into business intelligence processes.
  34. Geospatial Data Intelligence System (GDIS) – a system that provides intelligence based on geospatial data.
  35. Geospatial Data Management System (GDMS) – A system for managing and organizing geospatial data.
  36. Geospatial Decision Analysis System (GDAS) – a system for analyzing geospatial data to support decision-making.
  37. Geospatial Decision Support Platform (GDSP) – a platform for supporting decision-making based on geospatial data.
  38. Geospatial Decision Support System (GDSS) – a system that supports decision-making based on geospatial data.
  39. Geospatial Information Analysis Platform (GIAP) – a platform for analyzing and visualizing geospatial information.
  40. Geospatial Information Analytics Management System (GIAMS) – a system for managing and analyzing geospatial information analytics.
  41. Geospatial Information Management System (GIMS) – A system for managing and analyzing geospatial information.
  42. Geospatial Information Service (GISer) – A service that provides access to geospatial information.
  43. Geospatial Information System (GIS) – Similar to GIS, but emphasizes the spatial aspect of the technology.
  44. Geospatial Infrastructure System (GInS) – Refers to the underlying infrastructure and technologies that enable the use of geospatial data.
  45. Geospatial Intelligence System (GSIS) – A system that provides intelligence through the analysis of geospatial data.
  46. Geospatial Modeling System (GMS) – A system for creating and analyzing geospatial models.
  47. Geospatial Service-oriented Architecture (GSOA) – A system architecture that uses geospatial services for building and integrating applications.
  48. Geospatial Visualization System (GVS) – Focuses on the creation and visualization of geospatial data.
  49. Land Management System (LMS) – A system for managing land and natural resources.
  50. Location-based Services System (LBS) – A system that provides services based on the user’s location.
  51. Location-based Analytics System (LBAS) – A system for analyzing and interpreting location-based data.
  52. Location-based Marketing System (LBMS) – A system that provides marketing services based on the user’s location.
  53. Location-based Intelligence System (LIS) – a system that provides intelligence based on location-based data.
  54. Location-based Analytics System (LAS) – a system for analyzing location-based data.
  55. Location-based Service Platform (LBSP) – Refers to the use of geospatial data to provide location-based services to users.
  56. Location Intelligence Analytics System (LIAS) – a technology platform that enables businesses to analyze and gain insights from spatial data to make informed decisions and improve operations.
  57. Location Intelligence System (LIS) – A system that provides insights into location-based data for business intelligence.
  58. Remote Sensing Information System (RSIS) – A system for acquiring, processing, and analyzing remote sensing data.
  59. Remote Sensing System (RSS) – Focuses on the use of remote sensing technologies to gather geospatial data.
  60. Spatial Analysis System (SAS) – Focuses on the use of analysis techniques to derive insights from spatial data.
  61. Spatial Data Analysis System (SDAS) – A system for analyzing and interpreting spatial data.
  62. Spatial Data Analytics Platform (SDAP) – a platform for analyzing and visualizing spatial data.
  63. Spatial Data Infrastructure System (SDIS) – A system for sharing and managing spatial data across organizations.
  64. Spatial Data Intelligence System (SDIS) – a system that provides intelligence based on spatial data.Spatial Data Management System (SDMS) – a system for managing and analyzing spatial data.
  65. Spatial Data Science System (SDSS) – A system for applying data science techniques to spatial data.
  66. Spatial Decision Support System (SDSS) – a system for supporting decision-making based on spatial data.
  67. Spatial Information Analytics System (SIAS) – a system for analyzing spatial information.
  68. Spatial Information Management System (SIMS) – a system for managing and analyzing spatial information.
  69. Spatial Intelligence System (SIS) – a system that provides intelligent spatial analysis and decision-making capabilities.
  70. Spatial Mapping System (SMS) – Focuses on the creation and visualization of spatial maps.
  71. Spatial Query System (SQS) – Refers to the use of queries and search techniques to retrieve spatial data.
  72. Spatially-enabled Business Intelligence System (SBIS) – A system that integrates spatial data into business intelligence processes.
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10 Alternative Terms for Geographical Information System (GIS)

  1. Cartographic Information System: A system that manages and presents geographic information using maps and other visual representations.

  2. Digital Mapping and Analysis System: A system that integrates digital mapping and analysis tools to support a variety of applications, such as urban planning, environmental management, and emergency response.

  3. Earth Data Analytics System: A system that integrates earth observation data, modeling, and analysis tools to study and understand earth systems and phenomena.

  4. Geo-Analytical Platform: A platform that combines geospatial data with analytics and modeling tools to support spatial analysis and decision making.

  5. Geographic Information Services: Services that provide access to geographic data, tools, and applications for a variety of users.

  6. Geospatial Decision Support System: A system that provides decision support tools and capabilities based on geospatial data and analysis.

  7. Geospatial Intelligence System: A system that integrates geospatial data, analysis, and visualization tools to support intelligence and security operations.

  8. Location Intelligence Platform: A platform that combines geospatial data with business intelligence and analytics tools to enable data-driven decision making for organizations.

  9. Map Intelligence Technology: Technology that enhances maps with additional layers of data and analysis to support decision making.

  10. Spatial Data Management System: A system that manages and maintains spatial data to ensure data quality, availability, and interoperability.
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136 Definitions of Geo Terminology

  1. Geo-Tagging: The process of adding location metadata to media such as photos, videos or websites.
  2. Geo-Targeting: The process of delivering content or advertisements to a specific audience based on their location.
  3. Geo-Tracking: The process of monitoring and recording the movement of objects or people using GPS or other location-based technologies.
  4. Geo-Visualization: The process of displaying data on a map or in a spatial context to enhance understanding and analysis.
  5. Geo-Web: A term used to describe the geographic component of the World Wide Web, including services such as online mapping and location-based services.
  6. GeoAI: A branch of artificial intelligence that deals with spatial data and analysis, including machine learning and computer vision for spatial applications.
  7. GeoAnalytics – A type of analysis that uses geospatial data to understand patterns, relationships, and trends.
  8. GeoAware – Refers to being aware and knowledgeable about geospatial data and concepts.
  9. GeoAwareness – Refers to the awareness and understanding of geospatial concepts and data.
  10. Geocaching: An outdoor recreational activity in which participants use GPS or other location-based devices to hide and seek containers, called “geocaches” or “caches,” at specific locations marked by coordinates.
  11. Geoclimatology – the study of the relationship between climate and geographic location.
  12. Geocoding – the process of converting addresses or place names into geographic coordinates.
  13. Geodatabase: A database that is designed to store and manage spatial data, including features, attributes, and relationships.
  14. Geode – a hollow rock with crystals inside that are formed by minerals depositing over time.
  15. GeoDecision – Refers to making decisions based on geospatial data and analysis.
  16. Geodemography: The study of the spatial distribution of population characteristics, such as age, income, or education level.
  17. GeoDesign – The process of designing and planning using geospatial data.
  18. Geodesy: The study of the Earth’s shape, size, and gravity field.
  19. Geodetic – relating to the measurement and representation of the Earth’s surface.
  20. Geodiversity: The variety of geologic features and landscapes in a specific area or region.
  21. Geodome – a structure that is used for planetariums or other educational displays of the Earth and the universe.
  22. Geodynamics: The study of the Earth’s internal processes, including plate tectonics and mantle convection.
  23. Geoelectricity: The study of the electrical properties of the earth used for exploring the subsurface and understanding its distribution.
  24. Geoelectronics: The use of electronics and sensors to study and monitor the earth’s environment and geologic processes.
  25. GeoEngineering – Refers to the use of geospatial data and technology in engineering projects.
  26. Geoengineering: The use of technology to modify or manipulate the Earth’s environment.
  27. GeoExperience – The overall experience of working with and using geospatial data.
  28. Geofence: A virtual perimeter or boundary created around a real-world geographic area that is used for location-based services and marketing.
  29. Geofencing: A technology used to create virtual boundaries around a physical location, typically using GPS or cellular data, to trigger an action or notification when a device enters or exits the boundary.
  30. Geofilter: A graphic overlay that is applied to photos or videos based on the user’s geographic location in social media applications.
  31. GeoForecasting – Refers to the use of geospatial data in forecasting future events and trends.
  32. Geoglyph: A large-scale design or figure made on the ground, often using stones or earth, that is visible from above and has cultural or religious significance.
  33. Geohazard: A natural or human-made hazard that is related to the physical geography or geology of a particular area, such as earthquakes, landslides, or floods.
  34. GeoHealth – Refers to the use of geospatial data in health-related research and analysis.
  35. Geohydrology – the study of the interaction between groundwater and geologic formations.
  36. Geoid: A hypothetical surface that would coincide with the mean sea level of the earth’s oceans, if they were not affected by tides or currents.
  37. GeoInnovation – Refers to using geospatial data and technology to drive innovation and create new solutions.
  38. GeoInsight – Refers to gaining valuable insights from geospatial data.
  39. GeoIntel – Refers to the use of geospatial intelligence in decision-making processes.
  40. Geolinguistics: The study of the relationship between language and geography, including dialects, accents, and language use patterns in different regions.
  41. Geolocation: The process of determining the physical location of an object or person using GPS, cellular data, Wi-Fi signals or other location-based technologies.
  42. Geolocator: A device or software that is used to determine the location of an object, such as a GPS tracker.
  43. Geomagnetic: Relating to the magnetic fields of the earth, which are used in navigation and orientation.
  44. GeoManagement – Refers to the management of geospatial data and processes.
  45. GeoMapping – The process of creating maps that display geospatial data.
  46. Geomarketing: The use of geographic information and analysis to identify and target specific consumer groups or markets.
  47. Geomatics – the scientific study of the Earth’s geospatial data, including surveying, mapping, and remote sensing.
  48. Geomechanics: The study of the mechanical behavior of geological materials, including rocks, soils, and other materials under stress and strain.
  49. Geomembrane: A synthetic material used as a barrier or lining in geotechnical
  50. Geometadata: Information that describes the spatial characteristics of geographic data, such as its format, scale, projection, and accuracy.
  51. GeoMonitoring – The ongoing process of observing and tracking changes in geospatial data.
  52. Geomorphology: The study of the formation and evolution of landforms, including mountains, valleys, rivers, and other natural features.
  53. Geonavigation: The use of geographic data and navigation tools to navigate and explore the natural environment, including land, sea, and air.
  54. GeoPlanner – Refers to the use of geospatial data in the planning and design of projects.
  55. Geoponic – relating to the cultivation of plants in a geographically controlled environment.
  56. Geopositioning – the process of determining the position of a device or object in relation to a geographic reference system.
  57. GeoPrediction – Refers to predicting future events and trends based on geospatial data.
  58. Geoprocessing: The use of spatial analysis tools and techniques to analyze geospatial data, such as geographic information systems (GIS).
  59. Georeference – to provide a frame of reference for geospatial data.
  60. Georeferencing: The process of aligning digital data with real-world geographic locations.
  61. GeoRisk – Refers to assessing and managing risks based on geospatial data.
  62. Geoscience – the scientific study of the Earth’s physical structure, substance, and processes.
  63. GeoScience – The study of geospatial data and processes.
  64. GeoSensing – Refers to the use of sensors to collect geospatial data.
  65. Geosensing: The use of sensors to collect and analyze spatial data from the physical environment.
  66. Geosequestration – the process of storing carbon dioxide in geological formations to mitigate climate change.
  67. Geoserver: An open-source server that provides geospatial data and services, including maps, data layers, and geoprocessing functions.
  68. GeoSimulation – The process of simulating geospatial scenarios for analysis and planning purposes.
  69. Geosocial: A term that refers to the intersection between geography and social media, including location-based social networks and geotagging.
  70. Geospatial – relating to or denoting data that is associated with a particular location.
  71. Geospatial Analytics: The use of spatial data and statistical methods to analyze patterns, relationships, and trends in geographic data.
  72. Geospatial Information System (GIS): A system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data.
  73. Geospatial intelligence – information about human activity on the Earth’s surface that is derived from analysis of imagery and other geospatial data.
  74. Geospatial Intelligence (GEOINT): The analysis and interpretation of satellite imagery, aerial photography, and other geospatial data to support military, intelligence, and law enforcement activities.
  75. Geospatial Interoperability: The ability of different geospatial systems and technologies to work together and share data seamlessly.
  76. Geospatial Mapping: The process of creating maps and other visual representations of spatial data using various geospatial tools and techniques.
  77. Geospatial Metadata: Information that describes the content, quality, and other characteristics of geospatial data, allowing users to evaluate and use the data effectively.
  78. Geospatial Modelling: The use of mathematical and computational models to simulate and predict real-world phenomena in a geospatial context.
  79. Geospatial Navigation: The use of spatial data and location-based technologies to determine and navigate routes and directions.
  80. Geospatial Network Analysis: The process of analyzing and modeling the spatial relationships between objects or features in a network.
  81. Geospatial Networks: A network of interconnected spatial elements or features, such as roads, pipelines, or rivers.
  82. Geospatial Ontologies: A formal representation of the concepts and relationships in a specific geospatial domain, used to facilitate knowledge sharing and integration.
  83. Geospatial Optimization: The process of optimizing the use of geographic information and spatial data in decision making and problem-solving.
  84. Geospatial Planning: The use of geospatial data and analysis to inform and guide the development of plans and policies related to land use, infrastructure, and other spatial issues.
  85. Geospatial Positioning: The determination of precise geographic coordinates or positions using various location-based technologies and methods.
  86. Geospatial Predictive Modelling: The use of geospatial data and statistical models to make predictions and forecasts about future events or trends.
  87. Geospatial Programming: The development of software applications and tools that use geospatial data and analysis.
  88. Geospatial Query: The process of retrieving specific geospatial data or information from a database or other source using search criteria.
  89. Geospatial Reasoning: The ability to understand and reason about spatial relationships between objects or features using geospatial data.
  90. Geospatial Sampling: The process of selecting a subset of spatial data for analysis or modeling.
  91. Geospatial Science: The interdisciplinary study of geographic information, spatial data, and related technologies and applications.
  92. Geospatial Services: Online services that provide access to geospatial data, tools, and applications, often via web-based platforms.
  93. Geospatial Simulation: The use of computer models to simulate and predict the behavior of spatial systems or processes.
  94. Geospatial Standards: Technical specifications and guidelines for geospatial data, software, and systems to ensure interoperability and consistency.
  95. Geospatial Statistics: The application of statistical methods to geospatial data to analyze patterns, relationships, and trends.
  96. Geospatial Surveying: The use of geospatial tools and techniques to survey and map physical features and structures on the Earth’s surface.
  97. Geospatial Taxonomy: A hierarchical classification of geographic information and spatial data according to predefined categories and criteria.
  98. Geospatial Technology: A broad term that encompasses the use of technologies such as GPS, remote sensing, and GIS for geospatial data acquisition, analysis, and visualization.
  99. Geospatial Temporal Analysis: The analysis of spatial and temporal patterns and trends in geospatial data and information.
  100. Geospatial Topology: The study of the relationships and connectivity between spatial features and elements in a geospatial dataset.
  101. Geospatial Visualization: The use of visual representations, such as maps, charts, and graphs, to display and analyze geospatial data and information.
  102. Geospatial Web Services: Online services that provide access to geospatial data and tools using web-based protocols and standards.
  103. Geospatial Workflow: The sequence of tasks and processes involved in the collection, processing, and analysis of geospatial data and information.
  104. Geospatial XML: is a markup language used to store and exchange geospatial data in a standardized format.
  105. Geospatial: Relating to the physical location of objects or features on the earth’s surface, and the analysis of such data using geographic information systems (GIS).
  106. Geospatially Enabled Applications: Applications that incorporate geospatial data and analysis to provide enhanced functionality and user experience.
  107. Geospatially Integrated Data: Data that has been combined or linked with geospatial data to create new insights or knowledge.
  108. Geostatistics: The application of statistical methods to geospatial data to analyze patterns and relationships.
  109. GeoStrategy – Refers to the strategic use of geospatial data and analysis.
  110. Geosubstrate – the layer of rock or soil on which plants and animals live.
  111. Geosurvey: The process of collecting and analyzing geospatial data using various surveying techniques, including GPS, LiDAR, and photogrammetry.
  112. Geosynchronous Orbit: An orbit around the Earth that has a period of 24 hours and is synchronized with the rotation of the Earth, allowing a satellite to maintain a fixed position relative to the Earth’s surface.
  113. Geosynthetics: Synthetic materials used in geotechnical engineering applications to reinforce soil or provide a barrier against water or other materials.
  114. Geosystems – the study of the interaction between the Earth’s physical, biological, and human systems.
  115. Geotag: A digital tag or label that includes geographic information, such as latitude and longitude coordinates, associated with a particular object or resource.
  116. Geotagging: The process of adding geographic metadata, such as latitude and longitude coordinates, to digital media, including photos and videos.
  117. Geotarget: To deliver advertising or content to a specific audience based on their geographic location.
  118. Geotargeting: The use of geospatial data to deliver targeted content or advertising based on the user’s location.
  119. GeoTech – Refers to the use of technology to collect, analyze, and present geospatial data.
  120. Geotechnical: A field of engineering that deals with the study and design of structures and systems that interact with the ground, including foundations, slopes, and retaining walls.
  121. Geotectonics – the study of the movement and deformation of the Earth’s crust.
  122. Geotemporal: A term that refers to the intersection between geography and time, including the study of historical and contemporary spatial patterns and trends.
  123. Geotemporal: Relating to both geographic location and time, such as the analysis of how phenomena change over time in specific geographic locations.
  124. Geotextile: A permeable textile material used in civil engineering and landscape architecture to improve soil stability, drainage, and filtration.
  125. Geothermal Energy: Energy derived from the heat of the Earth’s interior, typically used to generate electricity or for heating and cooling buildings.
  126. Geothermal Gradient: The rate of increase in temperature with increasing depth below the Earth’s surface.
  127. Geothermal Heat Pump: A system that uses the constant temperature of the Earth to heat and cool buildings, reducing energy costs and greenhouse gas emissions.
  128. Geothermal: Relating to the heat energy that is generated and stored in the earth’s crust, and can be used to generate electricity or heat buildings.
  129. Geotourism: A form of sustainable tourism that emphasizes the natural and cultural heritage of a particular geographic area, including its landscapes, ecosystems, and communities.
  130. Geotropism – the growth or movement of an organism in response to gravity or the Earth’s magnetic field.
  131. GeoVis – Refers to the visualization of geospatial data.
  132. Geovisualization: The process of representing and exploring geographic data through visual means, such as maps, charts, and other graphical displays.
  133. Geoweb: The portion of the World Wide Web that is devoted to geographic information and services, including online mapping, location-based services, and geospatial data.
  134. Geoworkflow: A sequence of steps or tasks used to process and analyze geospatial data, typically using geographic information systems (GIS).
  135. Geowriting: The practice of writing about geographic topics, including maps, landscapes, and spatial relationships.
  136. Geozoning: The process of dividing a geographic area into zones or districts based on specific criteria.
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Exploring the Applications of AI and ML in Geospatial Technology

Geospatial technology has rapidly evolved over the years, and today, it plays an essential role in various fields, including environmental science, geography, urban planning, agriculture, and many more. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), geospatial analysis has become even more powerful, efficient, and accurate. In this article, we will explore how AI and ML can be used in geospatial for undergraduate students.

Before we dive deeper, let’s first understand what AI and ML are. AI refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. ML is a subset of AI that enables machines to learn and improve from experience without being explicitly programmed.

Now, let’s see how AI and ML can be used in geospatial.

  1. Land cover mapping

Land cover mapping is the process of determining the type and extent of land cover in a particular area. Traditionally, this process involves the use of satellite imagery, which can be time-consuming and tedious. With AI and ML, however, land cover mapping has become more efficient and accurate. AI algorithms can analyze large amounts of satellite imagery data and identify different types of land cover, such as forests, grasslands, and urban areas. ML algorithms can also learn from this data and improve their accuracy over time.

  1. Flood prediction and monitoring

Floods can cause significant damage to property and human life. Predicting and monitoring floods can be challenging, but AI and ML can help. By analyzing historical flood data, weather patterns, and other relevant factors, AI algorithms can predict the likelihood of a flood occurring in a particular area. ML algorithms can also learn from this data and improve their accuracy over time. Furthermore, geospatial technology can be used to monitor floods in real-time, providing timely information to emergency responders and the public.

  1. Precision agriculture

Precision agriculture involves using data and technology to optimize crop yields and reduce waste. Geospatial technology plays a significant role in precision agriculture, and AI and ML can enhance its effectiveness further. AI algorithms can analyze satellite imagery and other data to determine the health of crops, identify pests and diseases, and predict yields. ML algorithms can also learn from this data and improve their accuracy over time. With this information, farmers can make informed decisions about when to plant, fertilize, and harvest their crops.

  1. Traffic management

Traffic management is another area where geospatial technology and AI/ML can be used to great effect. By analyzing traffic patterns, road networks, and other relevant data, AI algorithms can optimize traffic flow, reduce congestion, and improve safety. ML algorithms can also learn from this data and improve their accuracy over time. Furthermore, geospatial technology can be used to monitor traffic in real-time, providing timely information to drivers and transportation authorities.

In conclusion, AI and ML have tremendous potential in geospatial technology, and undergraduate students interested in this field should learn about these technologies. By understanding how AI and ML can be used in geospatial, students can develop innovative solutions to real-world problems and contribute to the advancement of this exciting field.

 

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GIS and Blockchain Integration: Enhancing Spatial Data Management and Real-World Applications

Introduction

Geographic Information System (GIS) has been a widely used technology in various fields, including urban planning, environmental monitoring, disaster management, and natural resource management. It provides a framework for storing, managing, and analyzing spatial or geographic data. On the other hand, Blockchain is a decentralized, distributed ledger technology that provides a secure and tamper-proof way of recording and storing data. It is widely used in financial transactions, supply chain management, and identity verification. Combining the power of GIS and Blockchain can enhance the power of spatial data, real-world applications, and synergistic functionality. In this article, we will discuss the integration of GIS and Blockchain technology, its benefits, and real-world applications.

GIS and Blockchain Integration

The integration of GIS and Blockchain technology provides a secure and transparent platform for spatial data management, sharing, and analysis. Blockchain technology provides a tamper-proof and decentralized way of storing and sharing spatial data, while GIS provides the tools for analyzing and visualizing spatial data. By combining the two technologies, we can create a powerful platform for managing and sharing spatial data that is secure, transparent, and decentralized.

Benefits of GIS and Blockchain Integration

The integration of GIS and Blockchain technology has the potential to transform the way we manage and share spatial data. This integration offers several benefits, including enhanced data security, decentralized data management, improved data sharing, and the ability to use spatial data as a digital asset through tokenization. 

  1. Enhanced Data Security:

One of the significant benefits of GIS and Blockchain integration is enhanced data security. Blockchain technology provides a secure and tamper-proof way of storing and sharing spatial data, making it difficult for hackers to alter or manipulate the data. The decentralized nature of Blockchain also eliminates the risk of data loss due to a single point of failure.

  1. Decentralized Data Management:

GIS and Blockchain integration enables decentralized data management, which means that multiple parties can access and update the data without relying on a centralized authority. This eliminates the need for intermediaries and reduces the risk of data manipulation and fraud.

  1. Improved Data Sharing:

GIS and Blockchain integration enables secure and transparent data sharing among different parties, which improves collaboration and decision-making. Blockchain technology provides a tamper-proof and transparent way of sharing data, while GIS provides the tools for analyzing and visualizing spatial data.

  1. Smart Contracts:

GIS and Blockchain integration can be used to automate the exchange of spatial data, ensuring that all parties involved comply with the terms of the agreement. Smart contracts can be used to automate the exchange of data, ensuring that all parties involved comply with the terms of the agreement. This improves the efficiency of data exchange and reduces the risk of errors and disputes.

  1. Tokenization:

GIS and Blockchain integration enables spatial data to be tokenized, which means that it can be used as a digital asset that can be bought, sold, and traded. This enables the creation of new business models and revenue streams.

Real-world Applications of GIS and Blockchain Integration

The integration of GIS and Blockchain technology has opened up new possibilities for real-world applications. From land registration to disaster management and urban planning, the synergy between these two technologies has enhanced spatial data management and provided a secure and transparent platform for decision-making. 

  1. Land Registration:

GIS and Blockchain integration can be used to create a secure and transparent system for land registration. By using Blockchain technology, land registration can be made more secure, and ownership can be easily verified. This can help to reduce disputes over land ownership and improve the efficiency of land registration processes.

  1. Supply Chain Management:

GIS and Blockchain integration can be used to track the movement of goods along the supply chain, ensuring transparency and reducing the risk of fraud. Blockchain technology provides a tamper-proof and transparent way of tracking the movement of goods, while GIS provides the tools for visualizing the location and movement of goods.

  1. Disaster Management:

GIS and Blockchain integration can be used to manage disaster response and recovery efforts. By providing real-time information on the location and severity of the disaster, GIS can help to coordinate emergency response efforts. Blockchain technology can be used to track the distribution of aid and resources, ensuring that they reach the people who need them the most.

  1. Environmental Monitoring:

GIS and Blockchain integration can be used to monitor and track environmental data, such as air and water quality. Blockchain technology can be used to securely store and share this data, while GIS can be used to analyze and visualize the data. This can help to identify patterns and trends, and to make informed decisions about environmental management and policy.

  1. Urban Planning:

GIS and Blockchain integration can be used to create a secure and transparent system for urban planning. By using Blockchain technology, urban planning can be made more transparent, and decisions can be easily verified. This can help to reduce corruption and improve the efficiency of urban planning processes.

Challenges of GIS and Blockchain Integration

The integration of GIS and Blockchain technology has the potential to transform the way we manage and share spatial data, but there are also challenges to integrating these two technologies. This section explores the challenges of GIS and Blockchain integration, including technical challenges, data compatibility, and legal and regulatory challenges. Addressing these challenges is crucial for enabling seamless integration between these two technologies and unlocking their full potential for enhancing spatial data management and real-world applications.

  1. Technical Challenges:

Integrating GIS and Blockchain technology can be technically challenging, as both technologies have different architectures and require specialized knowledge to implement. There is a need for skilled professionals who can develop and maintain the integration between these two technologies.

  1. Data Compatibility:

Another challenge of GIS and Blockchain integration is data compatibility. GIS data is usually stored in different formats and structures, and converting this data into a Blockchain-compatible format can be challenging. There is a need for standardization of data formats and structures to enable seamless integration between these two technologies.

  1. Legal and Regulatory Challenges:

Blockchain technology is still relatively new, and there is a lack of legal and regulatory frameworks governing its use. The integration of GIS and Blockchain technology raises legal and regulatory challenges, such as data privacy, ownership, and liability. There is a need for legal and regulatory frameworks that address these challenges.

Conclusion

The integration of GIS and Blockchain technology can enhance the power of spatial data, real-world applications, and synergistic functionality. It provides a secure and transparent platform for spatial data management, sharing, and analysis. GIS and Blockchain integration can be used in various fields, including land registration, supply chain management, disaster management, environmental monitoring, and urban planning. However, there are also challenges to integrating these two technologies, such as technical challenges, data compatibility, and legal and regulatory challenges. These challenges need to be addressed to enable seamless integration between GIS and Blockchain technology. Overall, the integration of GIS and Blockchain technology has the potential to transform the way we manage and share spatial data, and to create new opportunities for innovation and growth.

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How to Cite a Blog using the APA Referencing Style

The blog article title is in plain text, and the name of the blog is italicized.

Structure: Last, F. M. (Year, Month Date Published). Article title. Blog Name. URL

In-text citation

Amerudin (2023) made his argument quite clear stating…

Reference list

Amerudin, S. (2023, March 13). Geospatial Data Analytics and Decision Making. Shahabuddin Amerudin @ UTM. https://people.utm.my/shahabuddin/?p=6041

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How to Cite a Blog using the Harvard Referencing Style

Your references for this type of web page will include the following information: 

  • Author’s/Authors’ names
  • The year the site was published or last updated (in round brackets)
  • Title of the web page (in italics)
  • Available at: URL (Accessed: date) 

In-text citation

Amerudin (2023) made his argument quite clear stating…

Reference list

Amerudin, S. (2023) Geospatial Data Analytics and Decision Making. Available at: https://people.utm.my/shahabuddin/?p=6041 (Accessed: 14 March 2023).

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Insights from Students: Mid-Course Evaluation of GIS Training Camp 2

By Dr. Shahabuddin Amerudin

GIS Training Camp 2 (SBEG3542) is an important course for students pursuing the Bachelor Degree of Science in Geoinformatics with Honours program at the Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia. The course provides students with an opportunity to learn and apply Geographic Information System (GIS) concepts and techniques to a real-world project. This project allows students to apply their knowledge in a practical setting. As the course progresses for 3 weeks in the end of Semester 1, Session 2022/3023, it is important to evaluate its effectiveness in achieving its objectives and identify areas for improvement. In this article, we will review the feedback and suggestions from the students who have completed the first half of the course.

Out of the 50 students who were included in the course evaluation survey, only 40 students provided their response. The students were asked to rate their satisfaction with various aspects of the course using a scale of 1 to 5, with 1 being “very satisfied” and 5 being “very dissatisfied.” The survey questions focused on the overall satisfaction with the course, the pace of the course, the effectiveness of the course materials, the support provided by the instructors and supervisors, the helpfulness of the stakeholders, and the difficulty level of the GIS project assigned in the course.

The results of the survey revealed that the students were generally satisfied with the course, with an average rating of 2.5 for the overall satisfaction with the course. However, some students felt that the pace of the course was too fast, with an average rating of 3.1. The students also felt that the course materials were effective in helping them understand the concepts and techniques necessary for the GIS project, with an average rating of 2.3. The support provided by the instructors and supervisors was rated as helpful, with an average rating of 2.3 and 2.2, respectively. The stakeholders were also rated as helpful, with an average rating of 3.0. The students found the GIS project to be challenging, with an average rating of 3.2.

In addition to the survey responses, students also provided feedback and suggestions for improvement. One common suggestion was to extend the period of the GIS Training Camp 2 because students were facing time issues in completing the project. Some students suggested reducing the number of lectures and avoiding conducting the camp during the semester break. Others suggested providing more briefing on each part of the project to enhance understanding.

Several students also suggested that there should be a backup plan for collecting data in case students were unable to obtain the necessary data from stakeholders. Additionally, some students suggested that the labs should be open for longer hours, without any gaps. This would provide students with more time to work on their project and seek guidance from instructors and supervisors.

Another suggestion was to present one or two completed GIS projects from students as a reference for the project. This would help students to better understand the requirements of the project and avoid any misunderstandings.

Finally, some students suggested that the course coordinator and instructors should communicate more effectively with each other to ensure that all students receive consistent instructions and explanations about the GIS project.

In conclusion, the feedback and suggestions provided by the students offer valuable insights into the strengths and weaknesses of the GIS Training Camp 2 course. The course coordinator and instructors should take into account these suggestions and make necessary adjustments to improve the course. With continuous evaluation and improvement, the GIS Training Camp 2 course can provide students with a valuable learning experience and prepare them for successful careers in the field of Geoinformation.

Citation:
Amerudin. S (2023). Insights from Students: Mid-Course Evaluation of GIS Training Camp 2. Available at: https://people.utm.my/shahabuddin/?p=6047 (Accessed: 14 March 2023).