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Chemical Leak Management: Predictive Modelling Techniques using GIS

Image Credit: European Environment Agency

By Shahabuddin Amerudin

Introduction

In the intricate landscape of industrial operations, chemical leaks stand as critical challenges that require rapid and precise responses. The fusion of technology, data, and science has led to the emergence of advanced modeling techniques that enable accurate prediction of the distribution of hazardous chemicals during such incidents. This article delves deep into the methodology behind utilizing atmospheric dispersion models and Geographic Information Systems (GIS) to forecast the spread of dangerous substances during leaks. By unraveling this process, we illuminate the pivotal role that these techniques play in ensuring efficient response and mitigation strategies.

Predictive Modeling: An In-Depth Exploration of the Methodology

1. Data Collection and Compilation: The cornerstone of effective predictive modelling lies in robust data collection. This initial phase involves gathering a comprehensive dataset that includes vital factors like the properties of the chemical substance, the release rate and duration, meteorological data, topographical features, and real-time monitoring inputs if available.

2. Atmospheric Dispersion Model Selection: Central to predictive modeling is selecting an appropriate atmospheric dispersion model. Choices among models such as AERMOD, CALPUFF, and ISCST3 depend on factors like the chemical’s properties, the nature of the release, and the availability of pertinent data.

3. Input Data Preparation: Translating data into actionable insights entails inputting the collected information into the chosen model. This process involves configuring parameters related to chemical properties, emission source characteristics, meteorological conditions, and topographical attributes. This step sets the stage for accurate predictions.

4. Simulation and Prediction: Executing the dispersion model initiates simulations that simulate the behavior of the chemical as it disperses over time. The model calculates concentration levels at various locations downwind from the source, offering predictions on the plume’s dimensions, shape, and concentration gradients.

5. Real-Time Data Integration (If Applicable): The integration of real-time monitoring data, when available, enhances the model’s precision. This data includes up-to-the-minute details such as wind speed, direction, temperature, and chemical concentrations. Integrating real-time data ensures that the model adapts dynamically to evolving conditions.

6. GIS Integration: The amalgamation of Geographic Information Systems into the modeling process adds a spatial dimension. GIS elements, such as maps and spatial data, provide a visual representation of the dispersion patterns on a geographical canvas. This aids in comprehending potential impact areas and affected regions.

7. Visualization and Analysis: Visual representations in the form of maps, graphs, and other visualizations portray predicted dispersion patterns. Through thorough analysis, potential risk zones, vulnerable areas, and population centers within the projected impact area can be identified.

8. Decision-Making and Response Planning: Empowered with insights from the modeled outcomes, decision-makers can formulate tailored response plans. Strategies for evacuations, resource allocation, and communication can be crafted with precision, maximizing their effectiveness.

9. Continuous Monitoring and Updating: The inclusion of real-time monitoring ensures continuous refinement of the model’s predictions based on real-world data. This iterative process guarantees the model’s accuracy throughout the incident’s progression.

10. Post-Incident Analysis: Upon the resolution of the incident, a post-analysis phase compares the actual outcomes with the predicted dispersion patterns. This retrospective examination informs refinements for the model’s future applications, contributing to the enhancement of response strategies.

Conclusion

In the realm of chemical leak incidents, the deployment of predictive modelling through atmospheric dispersion models and GIS is a triumph of technology and data synergy. These methodologies empower authorities to make informed decisions that mitigate risks, ensure public safety, and minimize the ecological footprint. The amalgamation of science, technology, and spatial intelligence emerges as a formidable tool in mastering the intricacies of chemical leak management, safeguarding communities, and paving the way for a safer and more resilient future.

Suggestion for Citation:
Amerudin, S. (2023). Chemical Leak Management: Predictive Modelling Techniques using GIS. [Online] Available at: https://people.utm.my/shahabuddin/?p=6767 (Accessed: 25 August 2023).
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Chemical Leak at Idemitsu Plant in Pasir Gudang, Johor – Safety Measures and Hazards

Source: Social Media

By Shahabuddin Amerudin

Incident Alert: Chemical Leak at Idemitsu Plant in Pasir Gudang, Johor – Safety Measures and Hazards

Introduction

A recent chemical leak incident at the Idemitsu (M) Sdn Bhd plant in Pasir Gudang, Johor, Malaysia, has raised concerns about safety and potential hazards. This article provides a comprehensive overview of the incident, details about the hazardous chemical involved, its distribution, and essential safety precautions for the local community.

Incident Details

On August 23, 2023, at approximately 5:21:54 PM, an emergency call reporting a chemical leak was received by the State Operations Center (PGO) in Johor. The incident occurred at the Idemitsu plant, located at Plo 408, Jalan Pekeliling, Pasir Gudang, Johor. An immediate response involving emergency personnel and specialized units was initiated.

The Hazardous Chemical: Styrene Monomer (UN Number 2055)

Styrene Monomer is the chemical responsible for the leak. It is a volatile organic compound (VOC) and is commonly used in the production of plastics, resins, and other materials. However, it can pose potential health and environmental risks under certain conditions.

Hazards of Styrene Monomer

Styrene Monomer can be hazardous when released into the environment, particularly in concentrated forms. Some of the risks associated with exposure include:

  1. Health Effects: Inhalation of styrene vapor can lead to irritation of the eyes, nose, throat, and respiratory tract. Prolonged or high-level exposure can cause dizziness, headache, and in some cases, central nervous system effects.
  2. Carcinogenic Concerns: There have been concerns about the potential carcinogenicity of styrene. Long-term occupational exposure to high concentrations of styrene vapor has been associated with an increased risk of certain cancers, although the evidence is not definitive.
  3. Environmental Impact: Styrene is considered a volatile organic compound (VOC) and can contribute to air pollution. It can also potentially contaminate soil and water bodies if not managed properly.

Distribution of Styrene Vapor

The distribution of styrene vapor during a leak depends on several factors:

  1. Wind Conditions: Wind speed and direction play a significant role in how far and in which direction the vapor disperses. Higher wind speeds can carry the vapor over longer distances.
  2. Ventilation: Adequate ventilation can help disperse the vapor more quickly, reducing the potential for vapor buildup in enclosed spaces.
  3. Terrain and Obstacles: Physical features such as buildings, hills, and valleys can impact the direction and distance of vapor dispersion.

Safety Precautions

To ensure the safety of the community:

  1. Stay Informed: Rely on official updates from authorities and avoid spreading unverified information.
  2. Avoid the Area: If you are not directly involved in response efforts, stay away from the vicinity of the incident.
  3. Comply with Authorities: Follow instructions from emergency personnel and cooperate with their directives.
  4. Stay Indoors: If near the incident site and indoors, remain indoors, close windows, and seal gaps to minimize exposure.

Conclusion

The chemical leak incident involving Styrene Monomer at the Idemitsu plant in Pasir Gudang underscores the importance of prompt response, community cooperation, and safety precautions. Understanding the hazards associated with the chemical and taking necessary precautions can help mitigate potential risks and ensure the well-being of both residents and the environment.

Suggestion for Citation:
Amerudin, S. (2023). Chemical Leak at Idemitsu Plant in Pasir Gudang, Johor – Safety Measures and Hazards. [Online] Available at: https://people.utm.my/shahabuddin/?p=6764 (Accessed: 24 August 2023).
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Choosing Between Web-Based Applications and Native Mobile Apps

Source: https://www.linkedin.com/pulse/android-developer-vs-web-best-choice-haitam-ghalem/

By Shahabuddin Amerudin

In the dynamic landscape of digital development, the choice between adopting web-based applications and native mobile apps has emerged as a pivotal decision for businesses and developers alike. The path chosen significantly influences user experience, functionality, accessibility, and long-term success. In this article, we delve into the intricate nuances of this decision, exploring in depth the benefits and drawbacks of both web-based applications and native mobile apps.

Web-Based Applications: Unleashing the Power of Platform Independence

Web-based applications have gained traction due to their inherent cross-platform compatibility and seamless accessibility. These applications, accessible through web browsers, transcend device boundaries, making them a versatile option for businesses targeting a diverse user base. The benefits of web-based apps extend to various dimensions:

1. Platform Independence: The capability to operate on any device with a web browser bestows web apps with a considerable advantage. This broader accessibility translates to users on different devices, including desktops, laptops, tablets, and smartphones, accessing the application without discrimination.

2. No Installation Hassles: One of the most notable perks of web-based applications is their installation-free nature. Users can instantly engage with the application without the need to download and install a separate app, thus reducing friction and encouraging immediate usage.

3. Easy Updates and Maintenance: Web apps streamline the process of updates and maintenance. Developers can swiftly push out updates, ensuring users always experience the latest version. This eliminates concerns associated with users running outdated software.

4. Cost Efficiency and Development Speed: Building a single web application that serves multiple platforms can be more cost-effective than creating separate native apps for each platform. This factor significantly impacts development budgets and accelerates the time-to-market.

However, web-based applications do come with certain limitations that must be considered:

1. Offline Limitations: While offline capabilities can be integrated to some extent, most web apps require an internet connection to function optimally. In comparison, native apps might offer more comprehensive offline functionality.

2. Performance Trade-Offs: In certain cases, web apps may not perform as smoothly as native apps, especially when handling complex interactions and animations. Native apps, which are optimized for specific platforms, tend to offer better performance.

Native Mobile Apps: Maximizing User Experience and Functionality

Native mobile apps, designed for a particular platform (iOS, Android, etc.), are celebrated for their exceptional performance, immersive user experience, and deep integration with device features. Here are the strengths of native apps that have contributed to their popularity:

1. Enhanced Performance: Native apps are meticulously optimized for specific platforms, resulting in superior performance that translates into smooth interactions and responsiveness. This is especially crucial for applications with intricate functionalities.

2. Full Device Integration: Native apps have the privilege of harnessing the full spectrum of a device’s features, such as the camera, GPS, and push notifications. This level of integration leads to richer and more diverse functionality, ultimately enhancing user engagement.

3. Offline Capabilities and Seamless Access: Unlike web apps, native apps can be developed to offer extensive offline capabilities. This is a crucial advantage in scenarios where consistent connectivity cannot be guaranteed. Moreover, native apps provide a seamless experience as they can be accessed directly from the user’s device.

4. App Store Exposure and Discoverability: Publishing an app on popular app stores enhances its visibility and discoverability among potential users, expanding its reach and potential user base.

However, native apps are not without their challenges:

1. Development Complexity and Cost: Building and maintaining separate apps for different platforms can be resource-intensive in terms of both time and finances. The complexity of this process often elongates the development lifecycle.

2. Distribution and Approval Processes: Native apps need to go through app store approval processes for updates and new versions. This procedure can result in delays in rolling out crucial changes or introducing new features.

3. Fragmentation and Consistency: Developing for various platforms can lead to slight variations in functionality and design, potentially affecting the consistency of the user experience across different devices.

The Hybrid Approach: Blending Strengths for Optimal Performance

While the decision between web-based applications and native mobile apps is of paramount importance, it’s essential to recognize that a hybrid approach is a viable alternative. This strategy involves developing a responsive web app as the core platform and complementing it with specific native apps for enhanced functionality and access to device features. The hybrid approach seeks to capitalise on the strengths of both approaches, providing an optimised user experience and wider accessibility.

Striking the Right Balance for Success

In the ever-evolving realm of app development, the decision between adopting web-based applications or native mobile apps is anything but simple. It hinges on a thorough understanding of the specific needs of your target audience, the desired level of functionality, offline requirements, budget constraints, and available resources. Each option brings a unique set of strengths and weaknesses, and the final choice should be driven by your project’s goals and the preferences of your users and stakeholders.

The true art lies in striking the delicate balance between functionality and accessibility. By meticulously considering these factors, you can chart a course that aligns with your project’s vision and sets the stage for a successful app deployment—one that not only meets user expectations but also propels business growth in the digital era.

Suggestion for Citation:
Amerudin, S. (2023). Choosing Between Web-Based Applications and Native Mobile Apps. [Online] Available at: https://people.utm.my/shahabuddin/?p=6756 (Accessed: 23 August 2023).
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Mengatasi Masalah Pengurusan Tanah Perkuburan melalui Inovasi Teknologi

Laman Web Tanah Perkuburan Islam Kampung Melayu Kangkar Pulai
https://kppusara.kstutm.com/jenazahmap.php

Oleh Shahabuddin Amerudin

Dalam dunia yang semakin berkembang dan penuh dengan kemajuan teknologi, pengurusan tanah perkuburan menghadapi pelbagai cabaran yang semakin rumit. Masyarakat yang berkembang pesat memerlukan pendekatan baharu untuk mengurus dan memudahkan akses kepada tanah perkuburan. Permasalahan ini menuntut penyelesaian yang inovatif dan lebih efisien. Artikel ini akan mengetengahkan kepentingan Laman Web Tanah Perkuburan Islam Kampung Melayu Kangkar Pulai (https://kppusara.kstutm.com) sebagai satu contoh inovasi yang berjaya menangani cabaran dalam pengurusan tanah perkuburan dengan pendekatan yang lebih berkesan.

Cabaran dalam Pengurusan Tanah Perkuburan

Pertumbuhan penduduk yang pesat telah memberi tekanan kepada penggunaan tanah, termasuklah tanah perkuburan, menjadikannya satu keperluan penting untuk diurus dengan efisien. Salah satu cabaran utama adalah kesukaran waris mencari kubur ahli keluarga disebabkan saiz tanah perkuburan yang besar dan susunan yang rumit, yang sering kali memakan masa dan tenaga. Tambahan pula, dengan peningkatan jumlah kubur baru, masalah kesesakan dan kekeliruan dalam menentukan lokasi kubur yang tepat semakin ketara, terutama bagi waris yang mencari kubur ahli keluarga yang telah lama dikebumikan. Kehilangan rekod jenazah yang biasanya diurus secara manual menjadi ancaman serius, kerana rekod-rekod ini mudah rosak atau hilang, menyebabkan maklumat penting mengenai jenazah turut terjejas. Selain itu, perkembangan teknologi telah menuntut pengurusan data yang lebih sistematik dan cekap, kerana data yang tidak teratur boleh menyebabkan kekeliruan serta ketidakcekapan dalam pengurusan maklumat jenazah dan susunan kubur.

Solusi Inovatif Melalui Laman Web Tanah Perkuburan Kangkar Pulai

Laman Web Tanah Perkuburan Islam Kampung Melayu Kangkar Pulai diperkenalkan sebagai solusi inovatif untuk mengatasi cabaran pengurusan tanah perkuburan. Melalui teknologi berasaskan laman web, pengguna dapat melayari peta tanah perkuburan dan mencari lokasi kubur dengan mudah tanpa memerlukan aplikasi tambahan. Ciri interaktif peta yang disediakan memberikan pengalaman pencarian yang lebih lancar dan terperinci. Antara kelebihan utama laman web ini termasuklah sistem pencarian jenazah yang lebih mudah dan cepat, pengurusan data yang teratur, serta visualisasi peta kawasan perkuburan melalui imej ortofoto. Laman web ini telah diuji oleh penjaga kubur serta beberapa pengguna secara rawak sebelum ia dilancarkan kepada umum, yang membuktikan keberkesanannya dalam memudahkan waris mencari kubur ahli keluarga dan membantu pengurusan maklumat dengan lebih efisien.

Impak dan Kesimpulan

Penerapan teknologi inovatif seperti Laman Web Tanah Perkuburan Islam Kampung Melayu Kangkar Pulai ini menunjukkan bahawa teknologi mampu menyelesaikan cabaran-cabaran kompleks dalam pengurusan tanah perkuburan. Teknologi ini memudahkan proses pencarian kubur, mengurangkan kekeliruan dalam pengurusan data, dan yang paling penting, memastikan jenazah diberi penghormatan yang sewajarnya. Melalui projek ini, dapat dilihat bahawa teknologi berperanan penting dalam memperbaiki sektor pengurusan tanah perkuburan, selari dengan keperluan masyarakat yang semakin moden.

Suggestion for Citation:
Amerudin, S. (2023). Mengatasi Masalah Pengurusan Tanah Perkuburan melalui Inovasi Teknologi. [Online] Available at: https://people.utm.my/shahabuddin/?p=6753 (Accessed: 22 August 2023).
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Laman Web Tanah Perkuburan Islam Kangkar Pulai

anah Perkuburan Islam Kampung Melayu Kangkar Pulai
https://kppusara.kstutm.com
https://kppusara.kstutm.com

Dalam era teknologi yang semakin canggih, pengalaman interaktif memainkan peranan penting dalam mengubah perkara biasa menjadi sesuatu yang luar biasa. Itulah yang cuba dicapai oleh Laman Web Tanah Perkuburan Islam Kampung Melayu Kangkar Pulai, Johor Bahru. Dengan memanfaatkan teknologi terkini, laman web ini membolehkan pengguna menjelajahi kawasan perkuburan dengan cara yang lebih menarik dan terperinci.

Hanya dengan melayari laman web di URL: https://kppusara.kstutm.com, pengguna dapat merasai pengalaman baharu. Laman web ini menyediakan peta interaktif yang bukan sahaja memaparkan lokasi kubur tetapi juga membolehkan pengguna berinteraksi dengan peta tersebut. Anda boleh menjelajah peta dengan mudah untuk mencari kubur yang diingini. Sekiranya anda ingin mendapatkan maklumat lanjut tentang sesebuah kubur, semua maklumat tersebut tersedia di hujung jari.

Tanah Perkuburan Islam Kampung Melayu Kangkar Pulai bukan sekadar tempat persemadian, tetapi ia juga membawa tanggungjawab besar dalam penyelenggaraan dan pengurusannya. Oleh itu, aplikasi ini diwujudkan bagi membantu masyarakat menguruskan kubur dengan lebih baik, memastikan setiap jenazah diurus dengan penuh rasa hormat dan teratur. Tanah perkuburan ini mula digunakan sejak tahun 1979 dan kini dikelola oleh kariah Masjid Nur-Syahadah, dengan Tn. Hj. Mahmood bin Ahmad sebagai penjaga semasa.

Secara purata 26 jenazah dikebumikan di perkuburan ini setiap bulan, yang menunjukkan betapa pentingnya tanah perkuburan ini dalam kalangan penduduk setempat. Jumlah yang semakin meningkat ini juga menimbulkan cabaran dalam pengurusan ruang kubur, dengan jangkaan bahawa tanah perkuburan ini akan penuh dalam masa terdekat. Oleh itu, data yang dikumpul melalui aplikasi ini amat penting untuk tujuan perancangan dan pengurusan masa depan.

Sebelum kewujudan laman web ini, pengurusan rekod jenazah dilakukan secara manual menggunakan fail kertas, yang sering kali menghadapi masalah penyelenggaraan dan kebolehpercayaan maklumat. Namun, dengan kemunculan Aplikasi Laman Web Tanah Perkuburan Islam Kangkar Pulai, perubahan besar telah berlaku dalam cara pengurusan dan penyimpanan maklumat. Aplikasi ini menawarkan beberapa kelebihan utama yang memudahkan pengguna, antaranya adalah kemudahan pelayaran melalui web tanpa perlu memuat turun sebarang aplikasi tambahan, di mana pengguna hanya perlu melayari laman web untuk mendapatkan maklumat yang diperlukan.

Selain itu, aplikasi ini menyediakan peta interaktif yang membolehkan pengguna mencari lokasi kubur dengan lebih tepat dan mudah. Sistem pencarian yang pantas turut memudahkan pengguna mendapatkan maklumat jenazah dengan cepat dan efisien. Lebih penting lagi, pengurusan data dilakukan dengan lebih teratur melalui sistem yang diwujudkan, memastikan setiap rekod jenazah disimpan dengan rapi dan mudah diakses pada bila-bila masa.

Aplikasi ini telah diuji terlebih dahulu oleh penjaga kubur, Tn. Haji Mahmood Bin Ahmad, bagi memastikan ia berfungsi dengan baik. Menurut beliau, “Aplikasi ini sangat membantu dalam menguruskan rekod jenazah dan memudahkan pencarian kubur.”

Data yang dipaparkan dalam aplikasi ini meliputi tempoh antara tahun 2014 hingga 2019, dan akan terus dikemas kini secara berkala untuk memastikan ketepatan dan kemas kini maklumat. Ketepatan lokasi di peta bergantung kepada GPS pada peranti masing-masing, jadi perbezaan ketepatan dalam penentuan lokasi mungkin berlaku.

KPpusara adalah hasil usaha pelajar program Ijazah Sarjana Muda Sains Geoinformatik dari Universiti Teknologi Malaysia, menunjukkan bagaimana ilmu dan teknologi boleh membentuk masa depan dengan cara yang inovatif dan bermanfaat.

Jika anda berminat untuk membangunkan laman web serupa, anda boleh menghubungi Dr. Shahabuddin Amerudin di nombor telefon 0127629717 atau melalui emel di shahabuddin@utm.my. Mari bersama-sama mencorak masa depan yang lebih maju, interaktif, dan penuh penghormatan kepada mereka yang telah pergi.

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Tiny Joys, Grand Memories

“Enjoy the little things in life because one day you’ll look back and realize they were the big things” is a quote often attributed to Kurt Vonnegut. This quote emphasizes the importance of finding joy and meaning in the small, everyday moments and experiences that can be easily overlooked. It suggests that these seemingly insignificant moments hold a deeper significance and contribute to our overall happiness and fulfillment over time. As time passes, we may come to appreciate these moments more and recognize their impact on our lives. This sentiment encourages mindfulness, gratitude, and the recognition of the value of the present moment.

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Belajar lain kerja pulak lain

Keputusan untuk belajar kursus tertentu di universiti tetapi kemudian bekerja dalam bidang yang tidak berkaitan dan berhutang adalah situasi yang kompleks dan boleh mencetuskan pelbagai isu. Di bawah ini adalah beberapa aspek yang perlu dipertimbangkan:

Pilihan Kursus Universiti

Keputusan memilih kursus di universiti merupakan langkah awal penting dalam membentuk kerjaya. Sekiranya seseorang memilih kursus yang tidak sejajar dengan pekerjaan yang beliau diperolehi, ini boleh mencerminkan ketidakselarasan dalam matlamat dan minat seseorang. Penting untuk membuat kajian yang mendalam dan memilih kursus yang sesuai dengan aspirasi dan kebolehan.

Kerja Tidak Berkaitan

Mungkin ada sebab-sebab tertentu mengapa seseorang berakhir bekerja dalam bidang yang tidak berkaitan langsung dengan kursus universiti mereka. Ini boleh termasuk kekurangan peluang pekerjaan dalam bidang pilihan, tekanan ekonomi, atau perubahan minat selepas tamat pengajian. Walau bagaimanapun, perbezaan antara kursus dan pekerjaan ini boleh menyebabkan perasaan tidak puas dan kehilangan fokus dalam kerjaya.

Berhutang

Menanggung hutang adalah tanggungjawab kewangan yang serius. Sekiranya seseorang berhutang untuk membiayai pendidikan universiti atau lain-lain tujuan, ini boleh memberi tekanan tambahan kepada kehidupan kewangan mereka. Memahami kadar faedah, jangka masa, dan cara untuk mengurangkan hutang menjadi perkara penting bagi pengurusan kewangan yang berkesan.

Kesan Emosi dan Psikologi

Perasaan tidak sepadan antara kursus universiti dan pekerjaan boleh menyebabkan tekanan emosi dan psikologi. Kekecewaan dan tekanan boleh timbul apabila seseorang terperangkap dalam rutin kerja yang tidak diingini. Ini boleh memberi impak kepada prestasi kerja, motivasi, dan kebahagiaan umum.

Pertumbuhan dan Pembangunan

Walaupun bekerja dalam bidang yang tidak berkaitan dengan kursus universiti, pengalaman kerja tetap dapat memberi peluang untuk pembelajaran dan pertumbuhan. Kemahiran umum seperti pengurusan masa, komunikasi, dan kepimpinan boleh diasah dalam pelbagai persekitaran.

Rancangan Masa Depan

Penting untuk memikirkan tentang apa yang ingin dicapai dalam jangka masa panjang. Jika kursus universiti dan pekerjaan yang tidak berkaitan merupakan salah satu fasa peralihan atau langkah strategik untuk mencapai matlamat kerjaya tertentu, maka langkah tersebut mungkin tidaklah sia-sia.

Alternatif Pilihan

Seseorang boleh mempertimbangkan alternatif pilihan untuk mencari kerjaya yang lebih berkaitan dengan kursus universiti mereka, seperti kursus latihan tambahan, pekerjaan separuh masa, atau projek sampingan yang berkaitan. Ini boleh membantu mengurangkan jurang antara kursus dan pekerjaan.

Penting untuk mengambil langkah-langkah yang tepat bagi mengatasi keadaan ini. Ini boleh melibatkan perancangan semula matlamat kerjaya, mempertimbangkan peluang pembelajaran tambahan, atau mencari peluang dalam bidang yang lebih berkaitan dengan kursus universiti. Menguruskan hutang dengan bijak juga merupakan elemen penting dalam pengurusan kewangan seseorang.

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Kelip-Kelip, Kunang-Kunang & Fireflies

Oleh Shahabuddin Amerudin

Selain “kelip-kelip” dan “kunang-kunang”, bergantung pada bahasa dan daerah, serangga ini juga dikenali dengan berbagai nama lain. Berikut adalah beberapa contoh nama lain yang mungkin digunakan untuk merujuk kepada serangga dengan kemampuan bioluminesensi:

  1. Api-api: Nama ini sering digunakan di beberapa daerah di Indonesia dan Malaysia untuk merujuk kepada serangga bioluminesensi.
  2. Api-emas: Nama ini juga dipakai di beberapa daerah di Indonesia, terutama di daerah Jawa.
  3. Lampyridae: Ini adalah nama ilmiah untuk keluarga serangga yang mencakupi kunang-kunang.
  4. Fireflies: Ini adalah istilah dalam bahasa Inggeris untuk merujuk kepada kunang-kunang.
  5. Luciola: Nama genus yang juga merujuk kepada beberapa jenis kunang-kunang.
  6. Lightning bugs: Ini adalah istilah lain dalam bahasa Inggeris yang merujuk kepada kunang-kunang.
  7. Glow-worms: Ini adalah istilah dalam bahasa Inggris yang merujuk kepada kunang-kunang dalam tahap larva yang juga memiliki kemampuan bioluminesensi.
  8. Blinkies: Ini adalah istilah informal dalam bahasa Inggeris yang merujuk kepada serangga bioluminesensi.

Setiap bahasa dan budaya mungkin memiliki nama khas mereka sendiri untuk serangga dengan cahaya berkedip ini.

Kerlipan Kelip-Kelip

“Kerlipan kelip-kelip” adalah istilah yang menggambarkan fenomena alami cahaya berkedip-kedip, seperti bintang-bintang di langit malam yang terlihat seperti titik-titik cahaya yang berkedip. Istilah ini sering kali digunakan dalam konteks sastra, puisi, dan lagu untuk menggambarkan suasana malam yang tenang, penuh keindahan dan misteri. Kerlipan kelip-kelip dari bintang-bintang sering dianggap sebagai lambang romantisisme dan keindahan alam yang mengagumkan.

Dalam bahasa yang lebih teknis, fenomena ini disebabkan oleh cahaya bintang yang melewati atmosfer bumi dan mengalami refraksi (pembelokan cahaya) serta dispersi (pemisahan cahaya menjadi warna-warna spektrum). Ketika cahaya bintang-bintang ini melewati lapisan atmosfer yang berbeda-beda, terutama akibat turbulensi, cahaya terlihat berkedip-kedip.

Kunang-Kunang

Kunang-kunang adalah serangga kecil yang terkenal karena kemampuannya mengeluarkan cahaya berkedip, yang dikenal sebagai bioluminesensi. Serangga ini termasuk dalam keluarga Lampyridae dan terdapat di berbagai bagian dunia, terutama di daerah berhutan atau bersemak. Bioluminesensi pada kunang-kunang terjadi melalui reaksi kimia antara zat luciferin dan enzim luciferase di dalam tubuh serangga.

Kunang-kunang menggunakan cahaya yang mereka hasilkan untuk berkomunikasi. Betina kunang-kunang akan mengeluarkan kerlipan cahaya berpola tertentu, dan jantan akan merespons dengan cahaya yang sesuai. Komunikasi ini membantu dalam proses perjodohan dan reproduksi. Selain itu, bioluminesensi juga berfungsi sebagai pertahanan dengan membuat kunang-kunang tampak tidak enak dimakan oleh pemangsa yang merasa waspada terhadap cahaya yang berkedip.

Kesimpulannya, baik “kelip-kelip” maupun “kunang-kunang” memiliki daya tarik dan pesona sendiri-sendiri dalam dunia sastra, sains, dan alam. Kedua fenomena ini mengajak kita untuk merenungi keindahan alam, kekompleksiti reaksi kimia, dan aspek-aspek misteri yang tersembunyi di dalamnya.

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Unveiling the Power of Geospatial Artificial Intelligence (GeoAI) and its Applications

Source: https://buntinglabs.com/blog/what-is-geoai-and-how-you-can-use-it

By Shahabuddin Amerudin

Introduction

The term Geospatial Artificial Intelligence (GeoAI) lacks a universally agreed-upon definition. Initially, GeoAI referred to the utilisation of machine learning tools within Geographic Information Systems (GISs) to predict future scenarios by classifying data. This included disaster occurrence, human health epidemiology, and ecosystem evolution, aimed at bolstering community resilience through traditional geographic information in digital cartography (Esri, 2018). A broader interpretation considers GeoAI as processing Geospatial Big Data (GBD) encompassing various sources, such as digital cartography, remote-sensing-based multidimensional data, and georeferenced texts. The focal point is the geographic dimension (Janowicz et al., 2019. Thus, GeoAI merges AI techniques and data science with GBD to comprehend natural and social phenomena. A comprehensive definition views GeoAI as utilizing artificial intelligence methods like machine learning and deep learning to extract insights from spatial data and imagery (Hernandez, 2020). GeoAI serves as an emerging analytical framework, facilitating data-intensive geographic information science and environmental and social sensing, thereby understanding human mobility patterns and societal dynamics.

GeoAI’s Challenges and Research Topics

The distinctive geospatial dimension, conceptual diversity between “place” and “space,” varied spatial information formats, and diverse scales create challenges and opportunities for GeoAI (Bordogna and Fugazza, 2023). Addressing the unique geosemantics and analytical needs dictated by application goals poses new hurdles with AI integration. Research directions encompass topics like multi-resolution GBD fusion, multi-source data integration, geosummarization for enhanced data quality, and deep learning exploration in remote sensing imagery (CNN, RCNN, LSTM, GANs) (Janowicz et al., 2019). A crucial goal is bridging the gap between complex AI technologies like deep learning and transparent methods such as decision trees, clustering, and data mining. This convergence can promote explainable AI features, critical for safety-critical domains like healthcare and law enforcement.

GeoAI for Analyzing Geotagged User-Generated Content and Traces

This section delves into innovative approaches to classify and mine geotagged user-generated content and traces within social networks:

  • In the study “Spatio-Temporal Sentiment Mining of COVID-19 Arabic Social Media” by Elsaka et al. (2022), diverse AI techniques combine NLP and GeoAI to analyze geotagged Arabic tweets addressing the COVID-19 pandemic. Techniques for inferring geospatial data from non-geotagged tweets were developed, followed by sentiment analysis at various location resolutions and topic abstraction levels. Correlation-based analysis between Arabic tweets and official health data was also presented. Results indicated enhanced location-enabled tweets (from 2% to 46%) and identified correlations between topics like lockdowns, vaccines, and COVID-19 cases. The study underscores social media’s role as a valuable “social sensing” tool.
  • “Automatic Classification of Photos by Tourist Attractions Using Deep Learning Model and Image Feature Vector Clustering” by Kim and Kang (2022) exemplifies social sensing. The study automates the classification of tourist photos based on attractions using deep learning and image feature clustering. The method, applied to TripAdvisor photos, offers flexibility in extracting categories for each destination and robust classification performance with limited data.
  • “Detecting People on the Street and the Streetscape Physical Environment from Baidu Street View Images and Their Effects on Community-Level Street Crime in a Chinese City” by Yue et al. (2022) showcases social sensing’s potential. Utilizing Baidu Street View images, deep learning, and spatial statistical regression models, the study assesses street crime through user traces. This pioneering approach quantifies street inhabitants and streetscape features impacting crime, revealing the positive correlation between street population and crime assessments.

Discussion

The evolution of GeoAI has illuminated its pivotal role in unraveling complex spatial phenomena and providing valuable insights across diverse domains. The multifaceted definitions of GeoAI reflect its adaptability to a wide range of applications, from predicting disasters and tracking health trends to understanding human mobility patterns through social sensing. This adaptability, however, presents challenges related to the uniqueness of geospatial data, the heterogeneity of spatial information, and the need for transparent AI solutions.

One of the key takeaways from the exploration of GeoAI’s applications is its capacity to extract actionable insights from geotagged user-generated content and traces. The studies discussed shed light on the potency of combining advanced AI techniques with geospatial data to tackle real-world challenges. For instance, the analysis of Arabic tweets during the COVID-19 pandemic not only improved geotagging accuracy but also revealed correlations between sentiment and health outcomes. Similarly, the automatic classification of tourist photos based on attractions exemplified how GeoAI can contribute to enhancing the tourism experience through personalized recommendations.

Furthermore, the discussion around the use of GeoAI in assessing street crime via user traces demonstrates the potential of AI to leverage previously untapped data sources. By harnessing Baidu Street View images and deep learning, researchers were able to quantify the relationship between street population and crime assessments. This underscores the transformative potential of GeoAI in contributing to urban planning, crime prevention, and public safety.

Conclusion

In conclusion, Geospatial Artificial Intelligence (GeoAI) presents an exciting frontier for innovation and understanding across various domains. Its ability to analyze spatial data, extract patterns from geotagged content, and predict future scenarios is reshaping how we approach complex challenges. GeoAI’s versatility, as showcased through applications like sentiment analysis during the pandemic, tourist attraction classification, and crime assessment, underscores its potential to drive positive change in society.

However, the journey of GeoAI is not without obstacles. The diversity of geospatial data sources, the need for transparent and explainable AI models, and the integration of multi-source data pose challenges that require ongoing research and development. As GeoAI continues to advance, striking a balance between harnessing the power of complex AI techniques and ensuring interpretability and accountability becomes crucial.

Ultimately, GeoAI’s evolution will rely on collaborative efforts between AI experts, geospatial specialists, domain experts, and policymakers. By combining their expertise, we can navigate the intricate landscape of GeoAI, harnessing its potential to create a safer, more sustainable, and more informed world. Through continued exploration, research, and refinement, GeoAI is poised to revolutionize how we understand and interact with the intricate spatial dynamics of our planet.

References

Bordogna, G. and Fugazza, C. (2023). Artificial Intelligence for Multisource Geospatial Information. ISPRS Int. J. Geo-Inf., 12, 10. https://doi.org/10.3390/ijgi12010010

Elsaka, T., Afyouni, I., Hashem, I., and Al Aghbari, Z. (2022). Spatio-Temporal Sentiment Mining of COVID-19 Arabic Social Media. ISPRS Int. J. Geo-Inf., 11, 476.

Esri (2018). What is GeoAI? Available online: https://ecce.esri.ca/mac-blog/2018/04/23/what-is-geoai/ (accessed on 10 May 2023).

Hernandez, L. (2020). ELISEWebianar: GeoAI—Presentation: Geospatial Data and Artificial Intelligence—A Deep Dive into GeoAI. Available online: https://joinup.ec.europa.eu/collection/elise-european-location-interoperability-solutions-e-government/document/presentation-geospatial-data-and-artificial-intelligence-deep-dive-geoai (accessed on 10 May 2023).

Kim, J. and Kang, Y. (2022). Automatic Classification of Photos by Tourist Attractions Using Deep Learning Model and Image Feature Vector Clustering. ISPRS Int. J. Geo-Inf. 2022, 11, 245.

Janowicz, K., Gao, S., McKenzie, G., Hu, Y., and Bhaduri, B. (2019). GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. Int. J. Geogr. Inf. Sci., 34, 625–636.

Yue, H., Xie, H., Liu, L., and Chen, J. (2022). Detecting People on the Street and the Streetscape Physical Environment from Baidu Street View Images and Their Effects on Community-Level Street Crime in a Chinese City. ISPRS Int. J. Geo-Inf., 11, 151.

Suggestion for Citation:
Amerudin, S. (2023). Unveiling the Power of Geospatial Artificial Intelligence (GeoAI) and its Applications. [Online] Available at: https://people.utm.my/shahabuddin/?p=6716 (Accessed: 21 August 2023).
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Navigating the Expansive Horizon of Spatial Data Science

By Shahabuddin Amerudin

Abstract

In recent times, the realm of spatial data science has witnessed an unprecedented surge, propelled by the exponential growth of spatial data and its potential applications across diverse domains. This review article delves into the multifaceted world of spatial data science, spanning its foundational principles, practical applications, inherent challenges, and the evolving research trends that are shaping its trajectory. By exploring the intricate interplay of spatial data, complexities, and novel methodologies, this review aims to provide a holistic understanding of this dynamic and interdisciplinary field.

Unveiling the Essence of Spatial Data Science

The advent of the digital age has ushered in an era of unprecedented data generation and availability. In response to this data deluge, spatial data science has emerged as a multidisciplinary discipline, seamlessly integrating methodologies from computer science, statistics, mathematics, and various specialized domains. This holistic approach is harnessed to acquire, store, preprocess, and unearth previously obscured insights from spatial data. The lifecycle of spatial data science encompasses five vital stages, namely spatial data acquisition, storage and preprocessing, spatial data mining, validation of outcomes, and the interpretation within the specific domain. Across various sectors, ranging from national security and public health to transportation and public safety, the pivotal role of spatial data science in shaping informed decisions and policies is increasingly evident.

The Landscape of Challenges in Spatial Data Science

The interdisciplinary essence of spatial data science brings forth a spectrum of challenges that must be effectively navigated. Its core engagement with tangible objects and phenomena necessitates a profound grasp of the underlying physics or theories within the pertinent domain, resulting in results that are not only interpretable but also trustworthy. The complexities posed by diverse spatial data types—ranging from object data types (such as points, lines, and polygons) to field data types like remote sensing images and digital elevation models—exceed those found in non-spatial data science. Further complexity arises from the distinctive attributes of spatial data, including spatial autocorrelation and heterogeneity. Tobler’s first law of geography—asserting that “everything is related to everything else, but near things are more related than distant things”—pervades spatial phenomena and influences analyses. The transition from discrete data inputs to continuous spatial datasets introduces an added layer of intricacy, rendering conventional non-spatial methods less applicable.

Navigating Emerging Research Trajectories in Spatial Data Science

This review article spotlights the emerging frontiers steering the evolution of spatial data science research. A key trajectory revolves around the integration of spatial and temporal information in observational data, unlocking new dimensions of understanding spatiotemporal patterns, associations, tele-coupling, prediction, forecasting, partitioning, and summarization. Expanding the realm of exploration, spatial data science is making strides within spatial networks. Cutting-edge methodologies, such as network K function and network spatial autocorrelation, are being developed to tackle spatial network data challenges. Innovations extend to the resolution of intricate puzzles like the linear hotspot discovery problem within spatial networks. An exciting avenue unfurls with spatial prediction within spatial networks, utilizing the wealth of information from GPS trajectories and on-board diagnostics (OBD) data collected from vehicles. Pioneering work by Li et al. (2018, 2019 and 2023) introduces an energy-efficient path selection algorithm grounded in historical OBD data.

Charting the Course Forward

As spatial data science continues to evolve, its centrality in diverse sectors remains pivotal. The capacity to extract actionable insights from spatial data empowers decision-makers to reimagine how they perceive and address challenges across domains. Yet, the enduring interdisciplinary nature and intrinsic attributes of spatial data pose ongoing challenges that require thoughtful consideration. By embracing these challenges and capitalizing on emerging trends, spatial data science stands poised to redefine the manner in which spatial information is harnessed. This review endeavors to guide both researchers and practitioners in navigating the intricate terrain of spatial data science, offering insights into its foundation, applications, challenges, and future horizons.

References

Li, Y., Shekhar, S., Wang, P., Northrop, W.: Physics-guided Energy-efficient Path Selection: A Summary of Results. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL ’18, pp. 99–108. ACM, Seattle, WA, USA (2018). https://doi.org/10.1145/3274895.3274933

Li, Y., Kotwal, P., Wang, P., Shekhar, S., Northrop, W.: Trajectory-aware Lowest-cost Path Selection: A Summary of Results. In: Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD ’19, pp. 61–69. ACM, Vienna, Austria (2019). https://doi.org/10.1145/3340964.3340971

Li, Y., Xie, Y., Shekhar, S. (2023). Spatial Data Science. In: Rokach, L., Maimon, O., Shmueli, E. (eds) Machine Learning for Data Science Handbook. Springer, Cham. https://doi.org/10.1007/978-3-031-24628-9_18

Suggestion for Citation:
Amerudin, S. (2023). Navigating the Expansive Horizon of Spatial Data Science. [Online] Available at: https://people.utm.my/shahabuddin/?p=6707 (Accessed: 21 August 2023).
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The Dynamic Potential of Named Entity Recognition (NER) in Extracting and Analyzing Geospatial Data

Source: https://www.esri.com/arcgis-blog/products/api-python/analytics/deep-learning-models-in-arcgis-learn/

By Shahabuddin Amerudin

Named Entity Recognition (NER), an integral component of Natural Language Processing (NLP), plays a pivotal role in extracting meaningful information from unstructured text. This technique involves the identification and classification of specific entities within text, ranging from names of people and organizations to temporal expressions and geographic locations. The applications of NER are wide-ranging and impactful across diverse industries. In this comprehensive article, we will delve deeper into the mechanics of NER, explore its diverse applications, and focus on a specific use case: geospatial data extraction facilitated by the EntityRecognizer model.

The Mechanism Behind NER

At its core, NER operates through a two-step process. The initial step involves the identification of words or phrases in the text that represent entities, which can span categories like “Person,” “Organization,” “Time,” “Location,” and more. Following this, these identified entities are categorized into predefined classes, resulting in structured information extraction from seemingly chaotic text data. This process contributes to converting unstructured text into structured data that can be utilized for further analysis.

Diverse Applications of NER

The versatility of NER transcends industries, offering valuable insights and solutions. In the realm of finance, NER is employed to extract critical information about companies, stock market trends, and financial events from news articles and reports. In healthcare, NER aids in the identification of medical terms, diseases, and treatments, supporting research and patient care. Furthermore, NER finds application in social media sentiment analysis, legal document processing, and academic research, exemplifying its widespread impact.

Application in Geospatial Data Extraction

A notable application of NER lies in geospatial data extraction, a field where unstructured text often conceals valuable location-based insights. Traditional Geographic Information Systems (GIS) primarily rely on structured data, making the integration of unstructured text a challenge. The EntityRecognizer model, as part of arcgis.learn, disrupts this barrier by leveraging advancements in deep learning and NLP (Singh, 2020). This model transforms unstructured text, such as incident reports, into structured geospatial information like feature layers, enhancing spatial analysis capabilities.

Realising Geospatial Insights

Imagine a scenario where incident reports containing unstructured text describe crime occurrences. Extracting crucial geospatial details, such as the crime type, location, incident time, and reporting time, from these reports can be arduous. The fusion of NER and the EntityRecognizer model streamlines this process. By discerning relevant entities within the text, this approach yields actionable insights that can be organized into geospatial features. Consequently, spatial analysis becomes more efficient, empowering informed decision-making.

Source: https://www.esri.com/arcgis-blog/products/api-python/analytics/deep-learning-models-in-arcgis-learn/

Unlocking New Possibilities

The amalgamation of NER and Deep Learning techniques for geospatial data extraction opens novel avenues for harnessing information locked within unstructured text. Organizations can swiftly process vast quantities of textual data, transforming them into actionable insights. These insights encompass various facets, including deciphering crime trends, identifying points of interest, and conducting sentiment analysis in specific geographic areas. NER’s application in geospatial analysis magnifies the scope of actionable intelligence derived from textual data.

Conclusion

Named Entity Recognition transcends its label as a mere NLP tool to stand as a dynamic force in information extraction. Its proficiency in autonomously identifying and classifying entities within text extends across industries, redefining data utilization. When synergized with Deep Learning, epitomized by the EntityRecognizer model within arcgis.learn, NER unveils its potential in geospatial data extraction. This integration empowers organizations to glean geospatial insights from seemingly inscrutable text, propelling spatial analysis and facilitating astute decision-making. As we traverse the ever-evolving landscape of NER and emergent technologies, the possibilities for innovative solutions in text analysis and geospatial intelligence continue to flourish.

Further Reading

  • Named Entity Extraction Workflow with: https://developers.arcgis.com/python/guide/how-named-entity-recognition-works/
  • Information extraction from Madison city crime incident reports using Deep Learning: https://developers.arcgis.com/python/samples/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning/

Reference: Singh, R. (2020). Deep learning models in arcgis.learn. [Online] Available at: https://www.esri.com/arcgis-blog/products/api-python/analytics/deep-learning-models-in-arcgis-learn/ (Accessed: 19 August 2023).

Suggestion for Citation:
Amerudin, S. (2023). The Dynamic Potential of Named Entity Recognition (NER) in Extracting and Analyzing Geospatial Data. [Online] Available at: https://people.utm.my/shahabuddin/?p=6699 (Accessed: 20 August 2023).
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Unlocking Textual Insights: The Power and Applications of Named Entity Recognition (NER)

Source: https://www.analyticsvidhya.com/blog/2021/11/a-beginners-introduction-to-ner-named-entity-recognition/

By Shahabuddin Amerudin

Named Entity Recognition (NER), often referred to as entity chunking, extraction, or identification, is a vital process in the realm of Natural Language Processing (NLP). It revolves around the identification and classification of crucial information, known as entities, within text. These entities can be single words or phrases consistently referring to the same concept. Through NER, we can automatically categorize these entities into predetermined classes, such as “Person,” “Organization,” “Time,” “Location,” and more. This computational feat yields valuable insights from extensive textual data and finds its application across a plethora of scenarios.

The Mechanism Behind NER

NER models primarily operate through a two-step approach:

  1. Detecting Named Entities: This pivotal step involves the identification of words or phrases representing entities. For instance, consider the sentence “Google’s headquarters are situated in Mountain View.” Here, the entities “Google” and “Mountain View” are discerned.
  2. Categorizing Entities: Once pinpointed, these entities are then assigned to predefined categories, such as identifying “Google” as an “Organization” and “Mountain View” as a “Location.”

Categories of Recognised Entities

Typical entity categories encompass:

  • Person: Names of individuals like “Shah Deans” and “Zai Jane.”
  • Organization: References to companies or institutions, such as “Google” or “University of Nottingham.”
  • Time: Temporal indications like “2003,” “16:34,” or “2am.”
  • Location: Place names including “Forest Fields” and “Hyson Green.”
  • Work of Art: Titles of creative works like “Bohemian Rhapsody” or “The Eiffel Tower in Paris, France”

Importantly, these categories can be tailored to the task’s specific requirements or custom ontologies.

The Real-World Significance of NER

NER proves invaluable across a diverse array of contexts, including:

  • Human Resources: Condensing CVs for efficient hiring processes, categorizing employee inquiries.
  • Customer Support: Grouping user requests, complaints, and questions for quicker responses.
  • Search and Recommendation Engines: Elevating the speed and relevance of search results, much like Booking.com.
  • Content Classification: Profiling themes and subjects within blog posts and news articles.
  • Healthcare: Extracting crucial details from medical reports.
  • Academia: Summarizing research papers and making historical newspapers searchable.

Getting Started with NER

For those interested in harnessing NER’s capabilities for their projects or enterprises, a systematic approach is recommended (Marshall, 2019):

  1. Choose an NER Library: Opt for established open-source libraries like NLTK, SpaCy, or Stanford NER.
  2. Label Your Data: Assemble a dataset with annotated entities and relevant categories tailored to your task.
  3. Train Your Model: Employ the annotated dataset to train your NER model to proficiently recognize and categorize entities.
  4. Implement NER: Deploy the trained model to analyze and process text data, unveiling crucial information.

Conclusion

Named Entity Recognition stands as a formidable tool in NLP, facilitating automatic identification and categorization of specific entities in text. Its potential is far-reaching, from streamlining customer support to optimizing search engines and content classification. With accessible NER libraries and customizable labeled datasets, integrating NER into your projects is an achievable endeavor that promises enhanced insights and efficiency.

Reference: Marshall, C. (2019). What is named entity recognition (NER) and how can I use it? [Online] Available at: https://medium.com/mysuperai/what-is-named-entity-recognition-ner-and-how-can-i-use-it-2b68cf6f545d (Accessed: 19 August 2023).

Suggestion for Citation:
Amerudin, S. (2023). Unlocking Textual Insights: The Power and Applications of Named Entity Recognition (NER). [Online] Available at: https://people.utm.my/shahabuddin/?p=6696 (Accessed: 20 August 2023).
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Simplifying Automated Building Footprint Extraction with Deep Learning in GIS

Source: https://www.esri.com/arcgis-blog/products/api-python/analytics/deep-learning-models-in-arcgis-learn/


By Shahabuddin Amerudin

Abstract

This paper delves into the realm of geospatial data processing, highlighting the amalgamation of Python scripting and advanced deep learning techniques for object detection. The resulting synergy offers an avenue to streamline complex tasks within this domain. The focus of this work is on the automation of building footprint extraction from aerial imagery using these integrated methodologies.

Automated Building Footprint Extraction via Deep Learning Techniques

Consider a scenario where the conventional approach of manually delineating building footprints from newly acquired aerial imagery demands weeks of laborious effort. Conversely, a technologically empowered approach leverages Python scripting in conjunction with deep learning for object detection. This paradigm shift not only improves operational efficiency but also obviates the need for labor-intensive manual interventions.

Efficiency in Object Detection

Human cognitive abilities can rapidly identify objects within images, often accomplished within a mere 5 seconds. This cognitive phenomenon can be emulated computationally through object detection, a technique where computers discern and localize objects within images. Despite the requirement for substantial training data and meticulous labeling, this goal is attainable. Esri, a renowned GIS technology enterprise, introduces pre-trained deep learning models termed DLPKs (deep learning packets) available on the ArcGIS Online platform. These models excel in recognizing diverse elements, including building footprints, vehicles, pools, solar panels, and roads within aerial imagery.

Practical Implementation

Initiating this transformative process requires specific prerequisites. These include access to ArcGIS Pro supplemented with the Image Analyst Extension, as well as aerial imagery featuring approximately 6-inch resolution. The ensuing steps provide a comprehensive guide for harnessing the capabilities of pre-trained models:

  1. Acquisition of Deep Learning Library Installers: Retrieve and install the Deep Learning Library Installers from the dedicated GitHub repository (https://github.com/Esri/deep-learning-frameworks/blob/master/README.md).
  2. Selection of Appropriate DLPK: Explore ArcGIS Online’s living atlas to identify the relevant DLPK suited for the intended object extraction task, such as building footprint identification.
  3. Integration of Aerial Imagery: Launch the ArcGIS Pro Project and import the targeted aerial imagery.
  4. Execution of Object Detection: Access the Geoprocessing window and select “Detect Objects Using Deep Learning.”
  5. Configuration of Object Detection: Specify the relevant raster image as input, provide an output name, and reference the downloaded DLPK. The tool will automatically populate the required parameters.
  6. Initiation of Automated Extraction: Commence the process by activating the “Run” button, subsequently witnessing the automated delineation of building footprints.

Overcoming Challenges and Enhancing Results

While maintaining optimistic expectations, acknowledge that processing speed is influenced by geographical extent and building density. It is recommended to perform preliminary tests on smaller image segments prior to achieving desired outcomes. Additionally, note that resulting building footprints might exhibit curvature and lack geometric precision. To address this, the “Regularize Building Footprints” Geoprocessing tool can rectify curvature issues by enforcing right-angle conformity (Fisher, 2021).

An optimization technique involves employing Model Builder to partition extensive raster images into manageable squares, thereby enhancing performance by processing a reduced dataset. Concluding this workflow, the merging of inferred building footprints into a cohesive layer is straightforward.

Performance Advantages and Future Prospects

The presented approach demonstrates operational efficiency, optimally utilizing computational hardware and system resources. Personal experience suggests the feasibility of background processing for an entire county over several days, concurrently managing other computer tasks (Fisher, 2021).

For those seeking in-depth engagement, the ArcGIS Pretrained Models documentation (https://doc.arcgis.com/en/pretrained-models/latest/get-started/intro.htm) offers a comprehensive resource for delving into the intricacies of these pre-trained models and their potential applications.

Reference

Fisher, C. (2021). Artificial Intelligence in GIS or “GeoAI”. [Online] Available at: https://www.linkedin.com/pulse/artificial-intelligence-gis-geoai-chase-fisher/ (Accessed: 19 August 2023).

Suggestion for Citation:
Amerudin, S. (2023). Simplifying Automated Building Footprint Extraction with Deep Learning in GIS. [Online] Available at: https://people.utm.my/shahabuddin/?p=6690 (Accessed: 20 August 2023).
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Beyond Horizons: Mapping the Future with AR, VR, and Boundless Innovation – Part 2

Source: https://www.frontiersin.org/articles/10.3389/frvir.2023.1071355/full

By Shahabuddin Amerudin

In the bustling heart of Nusantara, GeoSmart Solutions had transformed into an innovation powerhouse. Ahmad, the visionary System Analyst, was leading a team that had not only revolutionised tree data collection but was now poised to redefine the very landscape of GIS technology. The success of “Geoscape Greens” had unleashed a wave of creativity, propelling the team into uncharted territory.

The integration of AR, VR, XR, and MR into the application marked a turning point. As they tinkered with the potential of these technologies, the team realized they were on the cusp of something extraordinary. The world of maps and data was evolving into a realm where reality and virtuality converged.

With meticulous dedication, the team crafted an AR experience that was nothing short of magical. Trees came to life as digital information danced before users’ eyes, offering an interactive gateway to knowledge. Parks transformed into living museums, with historical data overlaid on present-day landscapes, allowing users to witness the passage of time in captivating detail.

The VR component was equally groundbreaking. Users donned headsets and were transported to lush forests, arid deserts, and bustling cities. The immersive experience wasn’t just informative; it was transformative. Users understood the delicate balance of ecosystems, the impact of urbanization, and the urgency of conservation like never before.

As XR and MR concepts were applied, the application took on a life of its own. Users could now manipulate and analyze data in ways previously thought impossible. With a wave of their hand, they could dissect landscapes, reveal hidden patterns, and even simulate the consequences of policy decisions on city growth. The digital and physical worlds coalesced into a playground of exploration and insight.

Source: https://www.malaysiakini.com/advertorial/581909


Spurred by this momentum, the team realized their platform’s potential extended beyond trees. They embarked on an audacious journey, creating a suite of applications that defied conventions. “LandScope” transformed geographical data into abstract art, merging technology and aesthetics in a mesmerizing dance. “TimeWarp” allowed users to unravel history through layered maps, witnessing the evolution of cities and cultures.

Their innovations sparked an international ripple. Other firms and cities took note, seeking collaboration and guidance. Ahmad’s team became consultants, guiding others on the path to technological transformation. The walls of GeoSmart Solutions echoed with a symphony of ideas, as experts from various fields converged to unravel the mysteries of AR, VR, XR, and MR in GIS.

In the midst of this whirlwind, Ahmad found moments of quiet reflection. The journey had taken unexpected turns, leading the team far beyond their original vision. As accolades and invitations poured in, Ahmad recalled the early days—the excitement of brainstorming, the sleepless nights of coding, and the unbreakable bonds forged with teammates.

As the years passed, the applications grew more sophisticated, and the technology evolved with lightning speed. “EcoScape Explorer” could recreate ancient landscapes with astonishing accuracy. “CityVista Planner” merged VR with urban planning, enabling citizens to participate in shaping their cities. “HistoriMap” ventured beyond geography, resurrecting cultures and stories that had long been forgotten.

Yet amid the advancement, Ahmad’s team remained grounded. They remembered their roots, the passion that fueled their innovation, and the commitment to sustainability that had set them on this journey. The story of “Geoscape Greens” had evolved into a saga of progress, discovery, and the relentless pursuit of a better future.

The tale was no longer just about technology; it was about people—the visionaries who dared to dream, the analysts who turned dreams into reality, and the users whose lives were transformed by their creations. In a world where the lines between reality and virtuality blurred, the impact of Ahmad’s team stretched beyond the confines of Nusantara, echoing through the annals of GIS history.

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Geoscape Greens: A Journey of Innovation and Sustainability – Part 1

Source: https://climbinghi.com/tree-inventory-mapping/

By Shahabuddin Amerudin

In the vibrant city of Nusantara, nestled between towering skyscrapers and bustling streets, stood the headquarters of GeoSmart Solutions, a leading GIS firm. Among its talented employees was Ahmad, a brilliant System Analyst with a passion for technology, geography, and a penchant for turning ideas into reality.

Ahmad was entrusted with leading a remarkable team on an ambitious project: the development of an innovative GIS application that would redefine how tree information was collected, analyzed, and utilized. This application would seamlessly integrate a cloud-based web platform with both iOS and Android mobile devices, enabling users to capture intricate tree data with submeter precision using their smartphones.

The journey began with a brainstorming session that crackled with excitement. Ahmad and his team huddled in the sleek conference room, sketching out visions of a cutting-edge application that would blend powerful technology with environmental stewardship. The challenge was immense, but Ahmad’s leadership inspired the team to forge ahead.

To lay a solid foundation, the team chose to leverage open-source tools. They designed a captivating web-based map using Leaflet, ensuring a user-friendly interface and fluid navigation. The heart of the application, the online database powered by MySQL, was meticulously constructed to handle vast amounts of geospatial data collected from the field.

As lines of code transformed into functional features, the team unveiled a host of impressive functionalities. The dynamic visualization feature breathed life into the maps, allowing users to interact with the data in real time. A comprehensive dashboard provided an at-a-glance overview of critical metrics, aiding decision-makers in shaping urban green spaces effectively.

Spatial analysis tools were crafted with precision, empowering users to conduct intricate geospatial calculations. Clusters of trees, trends in vegetation growth, and patterns of disease outbreaks could be deciphered with a few clicks. The application was evolving into a veritable powerhouse of environmental insights.

The most thrilling phase, however, was the integration of AI for advanced analysis. Ahmad collaborated closely with data scientists to develop machine learning models that predicted not only tree growth and health but also the potential spread of diseases. The algorithms digested copious amounts of data, unveiling trends that human eyes might miss. The marriage of AI and GIS was poised to bring about a paradigm shift.

As months of intensive development came to a close, the team was consumed by both anxiety and exhilaration. Field trials were launched, putting the application’s accuracy and reliability to the test. The city’s parks, avenues, and botanical gardens transformed into testing grounds, with users capturing tree data with precision that was once deemed impossible.

And then, the day arrived—the day the GIS application was ready to be unveiled to the world. The launch event was a culmination of creativity, innovation, and sheer determination. The presentation of the application left the audience in awe, and excitement rippled through the room as the first downloads began.

Users across Nusantara embraced the application with fervour. Urban planners marveled at its potential to inform city development plans. Environmentalists saw an ally in their efforts to preserve green spaces. Scientists reveled in the wealth of data for research. The city’s trees, once overlooked, became stars of their own story.

As time passed, Ahmad looked back at the tumultuous yet triumphant journey. The application’s impact had exceeded all expectations, fostering a greener, more sustainable urban landscape. Ahmad’s team continued to refine the application, incorporating user feedback and pushing the boundaries of technology and GIS.

Through the collaboration of visionaries, analysts, data scientists, and users, the GIS application stood as a testament to the power of innovation. The story of Ahmad and his team echoed in the city’s parks, streets, and botanical gardens—a tale of how a single idea, when nurtured by dedication and expertise, could grow into something that changed the world.

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GeoAI: Merging Geospatial Data and AI for Enhanced Decision-Making

By Shahabuddin Amerudin

Geospatial Artificial Intelligence (GeoAI) is a specialized field that combines geospatial data, which includes geographic information such as location, coordinates, and spatial relationships, with artificial intelligence (AI) techniques to extract valuable insights, patterns, and predictions from spatially referenced data. In essence, GeoAI involves the application of AI algorithms and methodologies to geospatial data to solve complex problems and enhance decision-making in various domains.

Key Components of GeoAI

  1. Geospatial Data: GeoAI relies on various types of geospatial data, such as satellite imagery, GPS coordinates, maps, geographic databases, and sensor data. These data sources provide the spatial context necessary for understanding and analyzing patterns and phenomena.
  2. Artificial Intelligence Techniques: AI techniques employed in GeoAI include machine learning, deep learning, natural language processing, computer vision, and other AI subfields. These techniques help process and analyze geospatial data to extract meaningful information.
  3. Data Fusion: GeoAI often involves the integration of multiple data sources, which may include satellite imagery, sensor data, and demographic information. Data fusion techniques are used to combine these sources and generate more accurate and comprehensive insights.

Applications of GeoAI

  1. Urban Planning and Management: GeoAI can aid in urban planning by analyzing traffic patterns, identifying suitable locations for infrastructure development, and predicting urban growth trends. It can also assist in managing city resources more efficiently.
  2. Environmental Monitoring: GeoAI is crucial for monitoring and assessing environmental changes, such as deforestation, climate change impacts, and natural disasters. It helps in early detection, response planning, and mitigation strategies.
  3. Agriculture and Precision Farming: GeoAI can analyze satellite images and sensor data to provide insights into crop health, soil quality, and water availability. This information enables farmers to optimize crop yields and resource usage.
  4. Disaster Management: GeoAI aids in disaster preparedness and response by analyzing real-time data from various sources to assess the extent of damage, identify affected areas, and plan rescue and relief operations.
  5. Infrastructure Maintenance: It can predict maintenance needs for infrastructure like roads, bridges, and utility networks by analyzing usage patterns, wear and tear, and other relevant data.
  6. Natural Resource Management: GeoAI helps monitor and manage natural resources like forests, water bodies, and mineral deposits, assisting in sustainable resource utilization.
  7. Public Health: GeoAI can analyze disease spread patterns, healthcare facility locations, and demographic data to improve disease surveillance and healthcare resource allocation.

Tools and Software Platforms for GeoAI

There are several tools and software platforms available for working with GeoAI. These tools offer functionalities for processing, analyzing, visualizing, and deriving insights from geospatial data using AI techniques. Here are some commonly used tools and software in the GeoAI domain:

  1. GIS Software
    • ArcGIS: A widely used geographic information system (GIS) software suite that offers tools for geospatial analysis, mapping, and visualization.
    • QGIS: An open-source GIS software that provides similar capabilities to ArcGIS, making it a popular choice for users seeking cost-effective solutions.
  2. Remote Sensing and Image Analysis
    • ENVI: A software platform for remote sensing and image analysis, suitable for processing satellite and aerial imagery for various applications.
    • Google Earth Engine: A cloud-based platform for analyzing geospatial data, particularly satellite imagery, using Google’s computational resources.
  3. Machine Learning and Data Science
    • Python: A versatile programming language commonly used for data analysis and machine learning. Libraries like NumPy, pandas, scikit-learn, and TensorFlow can be used for GeoAI applications.
    • R: Another programming language often used for statistical analysis and data visualization, with packages like the sf package for geospatial data manipulation.
  4. Deep Learning Frameworks
    • TensorFlow: An open-source deep learning framework developed by Google, suitable for building and training neural networks for geospatial tasks like image analysis.
    • PyTorch: Another popular deep learning framework that provides flexibility and ease of use, suitable for various AI tasks including geospatial applications.
  5. Geospatial Data Libraries
    • Geopandas: A Python library that extends the capabilities of pandas to handle geospatial data, making it easier to manipulate, analyze, and visualize spatial data.
    • Rasterio: A library for reading and writing geospatial raster data, allowing manipulation of satellite and aerial imagery.
  6. Visualization Tools
    • Matplotlib: A popular Python library for creating static, interactive, and dynamic visualizations, useful for visualizing geospatial data and analysis results.
    • Folium: A Python library that enables the creation of interactive maps and visualizations using leaflet.js.
  7. Cloud Computing Platforms
    • Amazon AWS: Offers cloud-based solutions for geospatial data storage, processing, and analysis, with services like Amazon S3 and Amazon EC2.
    • Google Cloud Platform: Provides tools and services for working with geospatial data, including Google Earth Engine and BigQuery GIS.
  8. Specialized GeoAI Platforms
    • SpaceNet: A collaborative project that provides high-quality satellite imagery datasets for AI research and development in tasks such as building footprint detection and road network extraction.
    • Esri GeoAI: Offers tools and solutions specifically designed for combining GIS and AI techniques for spatial analysis and decision-making.

The choice of tools and software depends on the specific tasks, data sources, and expertise available. Many GeoAI practitioners use a combination of these tools to effectively handle geospatial data and apply AI techniques for meaningful insights.

Challenges and Considerations

  1. Data Quality: Geospatial data can vary in quality and resolution, which affects the accuracy of GeoAI models. Ensuring data quality is crucial for reliable insights.
  2. Interdisciplinary Expertise: GeoAI requires collaboration between AI experts, geospatial analysts, and domain specialists to effectively address complex challenges.
  3. Ethical Concerns: Privacy, security, and potential biases in data can pose ethical concerns, especially when dealing with location-based information.
  4. Computational Resources: Processing large volumes of geospatial data requires significant computational power, which can be a limiting factor.
  5. Regulations and Standards: Different regions might have varying regulations and standards for geospatial data collection, sharing, and usage, which need to be navigated.

GeoAI holds tremendous potential to revolutionize decision-making processes across various industries by providing actionable insights derived from spatial data. However, its successful implementation requires a combination of technical expertise, high-quality data, and a deep understanding of the specific domain in question.

Suggestion for Citation:
Amerudin, S. (2023). GeoAI: Merging Geospatial Data and AI for Enhanced Decision-Making. [Online] Available at: https://people.utm.my/shahabuddin/?p=6667 (Accessed: 18 August 2023).
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GeoAI: Unveiling Patterns and Shaping Futures at the Nexus of Geography and Artificial Intelligence

By Shahabuddin Amerudin

Introduction

In the contemporary era of technological advancements, the amalgamation of artificial intelligence (AI) with geography has ushered in a revolutionary field known as GeoAI. This interdisciplinary domain leverages the prowess of AI to decode intricate patterns concealed within geospatial data, enabling us to predict, analyze, and respond to a spectrum of events and phenomena. From predicting ecological shifts to deciphering human mobility trends, GeoAI stands as a beacon of innovation that reshapes our perception of the world. In this article, we delve deeper into the essence of GeoAI and its multifaceted applications, bringing to light its significance and impact.

Defining GeoAI: From Narrow to Expansive Horizons

GeoAI’s foundation rests on the seamless integration of machine learning, data science, and Geographic Information Systems (GIS), creating a synergy that enables the exploration of Earth’s intricacies. This dynamic field embraces a range of definitions, each reflective of its multifarious dimensions.

In a narrower context, GeoAI entails the application of machine learning toolkits within the framework of GISs to simulate potential future scenarios. Through techniques such as data classification and intelligent predictive analysis, this facet of GeoAI forecasts outcomes encompassing natural disasters, health epidemiology, and biodiversity evolution. By processing conventional geographic information represented through digital cartography, these insights bolster community resilience and facilitate informed decision-making.

Expanding the scope, GeoAI transcends into the realm of Geospatial Big Data (GBD), encompassing a myriad of heterogeneous forms and sources. This expansive view accommodates not only traditional digital cartography managed by GIS but also incorporates remote-sensing-derived multidimensional data, georeferenced texts, and complex geo-databases. The underlying emphasis remains steadfastly fixed on the spatial dimension, weaving together a holistic comprehension of our planet’s complexities.

GeoAI’s Integral Role in Revelation

GeoAI transcends the mere processing of data; its essence lies in unearthing hidden truths encapsulated within that data. By amalgamating AI methodologies with geographic information, GeoAI empowers us to unravel the mysteries inherent in both natural and social phenomena. Picture a scenario where AI algorithms meticulously analyze satellite images to forecast deforestation patterns, enabling authorities to enact proactive conservation measures. This vividly portrays the core of GeoAI: transforming raw data into actionable insights.

GeoAI: A Universally Applicable Paradigm

In its broader context, GeoAI functions as the nexus between AI methodologies and spatial data, employing a comprehensive toolkit including machine learning and deep learning techniques. This amalgamation facilitates the extraction of knowledge from spatial data and imagery, underpinning a groundbreaking spatial analytical framework. This framework is not confined solely to environmental studies; it encompasses the broader spectrum of “social sensing.” This entails harnessing the digital traces people leave behind as they engage with the Internet of Things (IoT) and generate content on social networks. GeoAI, thus, acts as a decoder of urban dynamics, illuminating human mobility trends and sociocultural phenomena through the analysis of these digital imprints.

The Uncharted Landscape of GeoAI: A Promising Future

In conclusion, as we navigate the frontiers of AI and geography, GeoAI emerges as a compelling terrain where the two disciplines converge and synergize. Its capacity to decipher complex patterns, predict future occurrences, and unveil concealed insights sets it apart as a transformative paradigm. From disaster preparedness to unraveling societal dynamics, GeoAI ushers in a future where information shapes action. For undergraduate students keen on exploring the intersection of technology, geography, and the power of data, GeoAI presents a captivating avenue of discovery. As the landscape of GeoAI continues to evolve, its potential to reshape our understanding of the world remains boundless, promising a future replete with innovation and insight.

Suggestion for Citation:
Amerudin, S. (2023). GeoAI: Unveiling Patterns and Shaping Futures at the Nexus of Geography and Artificial Intelligence. [Online] Available at: https://people.utm.my/shahabuddin/?p=6663 (Accessed: 18 August 2023).
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Analisis dan Komen Mengenai Laporan Perkembangan Ekonomi dan Kewangan Malaysia pada Suku Kedua 2023

Oleh Shahabuddin Amerudin

Sumber: https://www.bnm.gov.my/-/qb23q2_bm_pr


Pada tarikh 18 Ogos 2023, Bank Negara Malaysia telah menerbitkan laporan mengenai perkembangan ekonomi dan kewangan Malaysia pada suku kedua tahun 2023. Laporan ini memberikan gambaran yang mendalam mengenai prestasi ekonomi negara dalam tempoh yang diulas. Mari kita lihat secara kritikal dan memberikan komen mengenai beberapa aspek yang diberikan dalam laporan ini.

Pertumbuhan Ekonomi yang Sederhana

Menurut laporan, ekonomi Malaysia mengalami pertumbuhan sederhana sebanyak 2.9% pada suku kedua tahun 2023. Walaupun pertumbuhan ini lebih rendah berbanding suku pertama tahun yang sama (5.6%), ia masih dianggap memberangsangkan berdasarkan kepada cabaran daripada permintaan luaran yang lebih perlahan. Permintaan domestik terus menjadi pendorong utama pertumbuhan, disokong oleh sektor swasta dan pelaburan. Kecenderungan ini menunjukkan prestasi kestabilan dalam ekonomi Malaysia yang bergantung kepada pelaburan dan perbelanjaan domestik.

Pengawalan Inflasi yang Berjaya

Inflasi telah berjaya dikawal dengan baik pada suku kedua tahun ini, dengan kadar inflasi terutamanya pada tahap yang sederhana pada 2.8%. Ini merupakan pencapaian yang baik kerana inflasi yang terkawal akan mengekalkan daya beli rakyat dan menggalakkan pertumbuhan ekonomi yang mapan. Walau bagaimanapun, laporan juga menunjukkan bahawa inflasi dalam segmen tertentu seperti perkhidmatan tertentu masih tinggi berbanding purata jangka panjang, dan ini perlu diambil perhatian untuk memastikan tahap inflasi yang mapan.

Pengaruh Kadar Pertukaran Mata Wang

Perkembangan global tetap menjadi faktor dominan dalam mempengaruhi keadaan kewangan domestik Malaysia. Faktor seperti aliran pasaran kewangan global, harga komoditi global, dan permintaan semikonduktor global turut mempengaruhi pasaran kewangan dalam negara. Ringgit Malaysia telah merosot sebanyak 5.8% dalam suku kedua 2023 disebabkan oleh faktor-faktor ini. Namun, berita baik adalah bahawa ringgit telah mengalami peningkatan sebanyak 1.1% dalam suku ketiga, menunjukkan potensi pulihnya mata wang ke peringkat yang lebih stabil.

Keadaan Pembiayaan

Pertumbuhan kredit kepada sektor bukan kewangan swasta telah merosot sedikit kepada 3.8%, yang membawa kepada pemulihan pertumbuhan hutang korporat. Walau bagaimanapun, hutang korporat masih berkembang dengan baik pada kadar 4.9%, menunjukkan kemakmuran dalam sektor korporat. Hutang rakyat pula berkembang sebanyak 5.1%, terutamanya disebabkan oleh pinjaman bagi pembelian hartanah kediaman dan kenderaan.

Prospek Masa Hadapan

Laporan ini juga memberi pandangan mengenai prospek ekonomi Malaysia untuk tempoh yang akan datang. Dalam keadaan persekitaran global yang mencabar, pertumbuhan ekonomi Malaysia dijangka mencapai tahap rendah dalam julat 4.0% hingga 5.0% untuk tahun 2023. Walaupun begitu, pertumbuhan ini akan diperkukuhkan oleh permintaan domestik yang teguh, pelaksanaan projek-projek jangka panjang, dan kenaikan aktiviti pelancongan. Namun, risiko terhadap pertumbuhan ini terletak pada kadar pertumbuhan perlahan faktor global yang tidak dijangka.

Kesimpulan

Laporan ini memberikan wawasan yang menyeluruh mengenai perkembangan ekonomi dan kewangan Malaysia pada suku kedua tahun 2023. Prestasi yang sederhana dalam pertumbuhan ekonomi dan pengawalan inflasi yang baik menunjukkan kecekapan dan daya tahan dalam pentadbiran ekonomi negara. Namun, perhatian perlu diberikan kepada keadaan kewangan global dan impaknya terhadap mata wang serta pasaran kewangan tempatan. Dengan strategi yang tepat, Malaysia mampu menghadapi cabaran global dan terus melangkah ke hadapan dengan keyakinan.

Suggestion for Citation:
Amerudin, S. (2023). Analisis dan Komen Mengenai Laporan Perkembangan Ekonomi dan Kewangan Malaysia pada Suku Kedua 2023. [Online] Available at: https://people.utm.my/shahabuddin/?p=6658 (Accessed: 18 August 2023).
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Free Online Guide: Uploading a Website with PHP and MySQL Database

By Trending Youth

In this tutorial, the video creator will walk you through the steps of uploading your website with a database for free, without requiring any financial investment. The video creator has previously designed a signup-login website using PHP and MySQL, and now they will illustrate the process of effectively deploying it online.

By meticulously adhering to the instructions provided in the video, you can ensure that your website and its associated database are accessible to a global audience. Be assured that individuals with a stable internet connection can easily access your website from various devices such as smartphones and PCs.

For additional details and pertinent links pertaining to this tutorial, please visit: https://www.000webhost.com.

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Steps to Publish an HTML Website Online and Make it Accessible on the Internet

https://www.youtube.com/watch?v=p1QU3kLFPdg
By SuperSimpleDev

Learn how to put a website online on the Internet for free with GitHub Pages (using a free GitHub Pages domain name). Learn how to buy and set up a custom domain name (like “mywebsite.com”). Learn how to set up HTTPS SSL encryption for free.

Sample website you can practice with: https://github.com/SuperSimpleDev/git…

Namecheap: Use coupon NEWCOM598 to get a .com domain for $5.98 (33%+ OFF, new accounts only).

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If you purchase your first domain name through the link above (without using Honey) Namecheap will give this channel $1 – $2. Thank you!

DNS instructions for other domain registrars: https://supersimple.dev/internet/dns-…

DNS lookup tool (IPv4): https://mxtoolbox.com/DNSLookup.aspx

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Why we set up www subdomain: https://www.yes-www.org/why-use-www/

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GitHub Pages Docs: https://docs.github.com/en/pages/gett…

0:00 Intro

0:24 1. Put a website on the Internet

3:34 Upload our code to GitHub

7:02 How GitHub Pages works

8:24 Add an index.html

10:51 2. Set up a domain name

12:34 Get a new domain name

15:37 How the Internet Works

18:51 Set up DNS A Records

21:55 Find the IP addresses of GitHub Pages

24:00 Set up www subdomain with CNAME Record

26:07 Link our domain name in GitHub Pages

27:31 Set up HTTPS for free in GitHub Pages

29:05 Thanks for watching!