Category: Teaching

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Pembangunan Sistem Pengurusan Tanah Perkuburan Berasaskan GIS

https://kppusara.kstutm.com/jenazahmap.php?query=budin

Oleh Shahabuddin Amerudin

Pembangunan Laman Web Tanah Perkuburan Islam Kampung Melayu Kangkar Pulai dijalankan melalui pendekatan kitar hayat pembangunan sistem (SDLC), yang terdiri daripada lima fasa: perancangan, analisis, reka bentuk, implementasi, dan penyelenggaraan.

Fasa Perancangan

Pada peringkat perancangan, keperluan utama laman web ini adalah untuk memudahkan pengurusan tanah perkuburan yang cekap dan menyediakan akses mudah kepada pengguna untuk mencari kubur. Kewujudan peta interaktif yang disokong oleh teknologi GIS menjadi keutamaan dalam sistem ini. Kajian keperluan melibatkan pemahaman terhadap konsep Sistem Maklumat Geografi (GIS), sistem koordinat seperti WGS84, penggunaan peta, dan keperluan untuk menghubungkan data spatial dengan data atribut kubur.

Fasa Analisis

Dalam fasa ini, kajian mendalam dilakukan untuk memahami cabaran-cabaran yang wujud dalam pengurusan rekod jenazah secara manual, di mana penyimpanan fail kertas sering menyebabkan kehilangan rekod dan kesukaran mencari lokasi kubur. Rekod jenazah diperolehi dari pihak pengurus kubur di dalam bentuk helaian kertas. Kajian ini juga merangkumi pengumpulan data GPS (latitude dan longitude) untuk kubur-kubur sedia ada, serta mengenal pasti kaedah integrasi data spatial dengan atribut seperti nama jenazah, tarikh kematian, dan lokasi.

Fasa Reka Bentuk

Peringkat reka bentuk memberi fokus kepada susunan sistem yang intuitif dan mesra pengguna. Laman web ini dibangunkan menggunakan PHP untuk pengendalian logik aplikasi, HTML dan JavaScript untuk antaramuka pengguna, serta MySQL untuk pengurusan pangkalan data. Leaflet.js, sebuah perpustakaan JavaScript untuk peta interaktif, dipilih bagi menguruskan visualisasi data spatial. Peta ortofoto bagi kawasan perkuburan diperoleh melalui pengimejan drone, yang memberikan visualisasi yang lebih jelas berbanding peta dari Google Maps atau OpenStreetMap.

Dalam aspek pengurusan data, setiap kubur dihubungkan dengan data atribut menggunakan sistem koordinat yang konsisten seperti WGS84. Setiap titik kubur pada peta interaktif disambungkan dengan pangkalan data yang menyimpan maklumat terperinci tentang jenazah. Proses ini melibatkan pembangunan query SQL yang kompleks untuk menyokong sistem carian pantas dan pengurusan data yang teratur.

Fasa Implementasi

Pada peringkat implementasi, pembangunan sistem dilakukan secara berperingkat, melibatkan kod pengaturcaraan dalam PHP, HTML, dan JavaScript. MySQL digunakan untuk membina pangkalan data yang menyimpan semua rekod jenazah secara digital. Data spatial dipersembahkan dengan menggunakan Leaflet.js, membolehkan pengguna berinteraksi dengan peta perkuburan untuk mencari lokasi kubur. Fungsi carian di laman web memanfaatkan query SQL, yang memudahkan akses kepada maklumat jenazah berdasarkan kata kunci yang dimasukkan oleh pengguna.

Peta interaktif diselaraskan dengan peta ortofoto yang diambil menggunakan drone, memberikan visual yang jelas tentang kawasan perkuburan, serta memudahkan penjaga kubur dan waris mencari lokasi jenazah. Setiap lokasi GPS kubur yang dipaparkan pada peta interaktif dapat dikaitkan dengan maklumat dalam pangkalan data, seperti nama dan tarikh pengebumian, melalui proses linking antara data spatial dan atribut jenazah.

Fasa Penyelenggaraan

Fasa penyelenggaraan melibatkan kemas kini berterusan pada pangkalan data dan penambahbaikan sistem untuk memastikan prestasi laman web yang optimum. Data baharu dimasukkan secara berkala, manakala peta ortofoto juga dikemas kini jika terdapat perubahan pada kawasan perkuburan. Selain itu, sebarang masalah atau bug yang ditemui dalam sistem akan diperbaiki untuk memastikan laman web terus beroperasi dengan lancar. Penggunaan backup dan recovery systems untuk pangkalan data juga dipastikan bagi mengelakkan kehilangan maklumat penting.

Secara keseluruhan, pembangunan Laman Web Tanah Perkuburan Islam Kampung Melayu Kangkar Pulai ini (https://kppusara.kstutm.com) merupakan satu langkah inovatif dalam menggabungkan teknologi GIS dan sistem maklumat dalam pengurusan jenazah. Laman web ini bukan sahaja menyelesaikan masalah rekod manual tetapi juga meningkatkan kecekapan dalam pencarian lokasi kubur, memberikan kemudahan kepada waris serta pengurusan tanah perkuburan.

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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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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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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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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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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

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Implikasi dan Pertimbangan Kenaikan Gred Markah 4 dan 9 dalam Sistem Penilaian

Oleh Shahabuddin Amerudin

Markah dan Gred Baru

Keputusan untuk menggalakkan pensyarah supaya menaikkan gred markah kursus yang mempunyai angka 4 dan 9 dihujungnya adalah suatu isu yang mempunyai pelbagai sudut pandangan dan pertimbangan. Sebagai contoh, markah keseluruhan pelajar sepanjang semester, iaitu selepas dicampurkan dengan skor kuiz, ujian, tugasan, projek, dan peperiksaan akhir, jika markahnya adalah 64, maka perlu dinaikkan kepada 65. Begitu juga, jika markah pelajar adalah 69, maka perlu dinaikkan kepada 70. Ini akan menjadikan gred markah seolah-olah tidak lagi mempunyai angka 4 dan 9 di dalamnya. Di bawah ini adalah beberapa aspek yang perlu dipertimbangkan dalam situasi ini:

Kelebihan

  1. Motivasi Positif: Tindakan ini mungkin dapat memberikan motivasi positif kepada pelajar kerana mereka akan melihat peningkatan sedikit dalam gred walaupun markah mereka hanya sedikit kurang dari angka seterusnya.
  2. Psikologi Pelajar: Penghapusan angka 4 dan 9 dapat mengurangkan tekanan psikologi di kalangan pelajar yang seringkali mengaitkan angka ini dengan kegagalan atau prestasi rendah.
  3. Peningkatan Prestasi Pelajar: Sekiranya tindakan ini berjaya memotivasikan pelajar untuk lebih berusaha, ia mungkin dapat membantu meningkatkan prestasi pelajar dalam jangka panjang.

Kekurangan

  1. Kecekapan dan Keadilan: Menaikkan gred tanpa mengambil kira prestasi sebenar pelajar boleh menjejaskan kecekapan dan keadilan sistem penilaian. Pelajar yang sepatutnya mendapat gred rendah mungkin akan menerima gred yang tidak sepadan dengan prestasi mereka.
  2. Pengurangan Standard: Penghapusan angka 4 dan 9 boleh mengurangkan standard akademik universiti. Ini mungkin memberi gambaran yang tidak tepat tentang prestasi pelajar dan menyulitkan pembandingan prestasi antara pelajar.
  3. Kehilangan Pelajaran: Pelajar mungkin tidak belajar untuk menghadapi kegagalan atau mengatasi kesukaran jika mereka tahu gred mereka akan dinaikkan secara automatik.
  4. Kurangnya Pengiktirafan Prestasi: Pelajar yang benar-benar berusaha untuk mencapai markah lebih tinggi mungkin tidak akan mendapat pengiktirafan yang setimpal kerana markah mereka akan dinaikkan secara seragam.

Pertimbangan Alternatif

  1. Peningkatan Kaedah Penilaian: Lebih baik untuk mempertimbangkan untuk meningkatkan kaedah penilaian yang lebih berfokus pada pemahaman dan kebolehan pelajar dalam menguasai kandungan. Ini boleh membantu mengukuhkan prestasi pelajar secara beransur-ansur tanpa mengorbankan standard akademik.
  2. Penyediaan Bantuan Tambahan: Universiti boleh mempertimbangkan untuk menyediakan bantuan tambahan seperti kelas tambahan, sokongan akademik, atau sesi mentor bagi pelajar yang menghadapi kesulitan dalam prestasi akademik.
  3. Pemahaman Psikologi Pelajar: Lebih penting untuk memahami mengapa pelajar mengaitkan angka 4 dan 9 dengan prestasi rendah. Ini mungkin berkaitan dengan tekanan sosial atau kurangnya keyakinan diri. Dengan memahami akar masalah ini, universiti dapat memberikan sokongan yang lebih tepat kepada pelajar.

Keseluruhannya, keputusan untuk menggalakkan kenaikan gred markah 4 dan 9 mempunyai implikasi yang meluas dalam sistem pendidikan. Penting untuk mempertimbangkan kesan jangka panjang dan potensi perubahan dalam polisi penilaian serta impak terhadap kualiti pendidikan yang disampaikan.

Suggestion for Citation:
Amerudin, S. (2023). Implikasi dan Pertimbangan Kenaikan Gred Markah 4 dan 9 dalam Sistem Penilaian. [Online] Available at: https://people.utm.my/shahabuddin/?p=6641 (Accessed: 16 August 2023).
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Cabaran Sikap Pelajar Universiti Terhadap Kemerosotan Pencapaian Akademik di Era Digital

Oleh Shahabuddin Amerudin

Dalam era digital yang penuh dengan kemudahan dan akses kepada maklumat, sikap pelajar universiti terhadap pencapaian akademik telah menunjukkan corak yang mencabar. Walaupun pelajar kini mempunyai pelbagai kemudahan dan sokongan untuk berjaya dalam pengajian mereka, namun masih terdapat pelbagai isu yang perlu diberi perhatian bagi mengatasi kemerosotan pencapaian akademik pelajar terbabit. Artikel ini akan membincangkan beberapa isu utama yang telah dinyatakan dalam konteks sikap pelajar universiti pada masa kini terhadap pencapaian akademik mereka.

Salah satu isu yang ketara adalah kurangnya usaha dan keengganan pelajar dalam melaksanakan tugasan yang diberikan oleh pensyarah. Sikap ini seringkali merumitkan proses pembelajaran dan menghambat perkembangan akademik pelajar. Penerimaan terhadap tugas sebagai tanggungjawab yang harus diambil serius masih belum ditanamkan dalam diri sebahagian besar pelajar. Fenomena ini turut disebabkan oleh kecenderungan untuk menangguhkan kerja hingga akhirnya tugasan tersebut tidak dapat disiapkan dengan baik.

Selain itu, sikap tidak kisah dan sering tidak hadir ke kuliah tanpa alasan yang kukuh turut membayangkan kecenderungan kurang serius dalam pendidikan. Ketidakhadiran ini mengganggu proses pembelajaran dan menghalang pelajar daripada memahami dengan baik topik yang diajar. Disamping itu, kelemahan dalam pengurusan masa menyumbang kepada ketidakmampuan pelajar menghadiri kuliah dan menyiapkan tugasan dengan baik.

Isu pengurusan masa menjadi perbualan yang sering kedengaran dalam kalangan pelajar. Walaupun universiti telah mengingatkan berkali-kali tentang kepentingan pengurusan masa, masih terdapat segelintir pelajar yang acuh tak acuh dan tidak mengambil berat akan nasihat tersebut. Terdapat kes-kes pelajar yang menghadapi kesukaran untuk menyeimbangkan antara pelbagai komitmen seperti kuliah, tugasan, dan kegiatan sosial, yang mengakibatkan pencapaian akademik merosot.

Fenomena mencari bahan tugasan di Internet juga menjadi satu isu yang patut dibincangkan. Walaupun teknologi memberikan peluang untuk mendapatkan maklumat dengan mudah, pelajar sering terjebak dalam memilih bahan yang tidak relevan atau tidak sah. Hal ini merosakkan kualiti kerja yang dihasilkan dan menghalang pemahaman mendalam terhadap topik yang dipelajari.

Sistem portal pendidikan yang memudahkan penghantaran tugasan di dalam bentuk digital juga telah disia-siakan oleh sebahagian pelajar. Keadaan ini membuktikan bahawa walaupun peluang sudah sedia ada, pelajar masih belum mampu menguruskan masa dan komitmen dengan baik. Berbanding dengan pelajar di era sebelum digital yang perlu menggunakan mesin taip atau pun mencetak dengan printer apabila menghasilkan laporan.

Selain itu, masalah beban kerja yang terlalu banyak turut menyumbang kepada kemerosotan pencapaian akademik. Pelajar yang mengambil terlalu banyak kursus atau terlibat dalam pelbagai projek universiti, kokurikulum dan sosial berlebihan cenderung untuk terjebak dalam tekanan dan kesukaran menyiapkan tugasan.

Di samping itu, keterlibatan dalam projek kumpulan juga boleh merumitkan pencapaian akademik. Pelajar yang terlalu banyak berfokus kepada projek kumpulan boleh menghadapi kesukaran dalam menumpukan masa dan tenaga kepada tugas individu.

Dalam usaha untuk mengatasi isu-isu ini, universiti perlu memberi penekanan kepada aspek pengurusan masa dalam kurikulum pendidikan mereka. Sistem sokongan akademik perlu diperkukuhkan dengan menyediakan kaedah untuk membantu pelajar menguruskan masa dan komitmen dengan baik. Selain itu, kesedaran tentang kepentingan penghayatan tanggungjawab dan usaha peribadi dalam pendidikan harus ditanamkan dalam minda pelajar.

Pendidikan berkualiti adalah usaha bersama antara pensyarah, pelajar, dan pihak pentadbiran universiti. Dengan mengatasi isu-isu sikap pelajar yang merosot dalam era digital ini, kita dapat meningkatkan kualiti pendidikan dan membantu pelajar mencapai pencapaian akademik yang lebih baik.

Suggestion for Citation:
Amerudin, S. (2023). Cabaran Sikap Pelajar Universiti Terhadap Kemerosotan Pencapaian Akademik di Era Digital. [Online] Available at: https://people.utm.my/shahabuddin/?p=6633 (Accessed: 16 August 2023).
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Kemerosotan Pencapaian Akademik dan Sikap Pelajar Universiti Pasca Era COVID-19 dan Perintah Kawalan Pergerakan (PKP)

Oleh Shahabuddin Amerudin

Pendahuluan

Pandemik COVID-19 telah membawa transformasi yang signifikan dalam sistem pendidikan, terutama di peringkat universiti. Era pasca-COVID-19 dan tempoh Perintah Kawalan Pergerakan (PKP) telah memberi kesan mendalam kepada sikap pelajar universiti terhadap pencapaian akademik mereka. Fenomena di mana pelajar kurang berusaha dalam melaksanakan tugasan, kurang hadir ke kuliah, dan suka menangguhkan kerja mengakibatkan kemerosotan pencapaian akademik yang mencemaskan. Terdapat beberapa faktor yang dapat dihubungkan dengan perkembangan ini. Artikel ini membincangkan fenomena kemerosotan dalam pencapaian akademik dan sikap yang kurang berusaha pelajar selepas era COVID-19 dan PKP.

Faktor-Faktor yang Mempengaruhi Sikap Pelajar

1. Masalah Kesejahteraan Emosi

Pandemik dan PKP telah mencipta gelombang tekanan emosi di kalangan pelajar, memberikan impak yang signifikan terhadap sikap dan pencapaian akademik mereka. Isolasi sosial, ketidakpastian mengenai kesihatan, dan kebimbangan tentang keselamatan diri dan keluarga telah mencipta perasaan keterasingan dan kebimbangan yang mendalam. Kehilangan interaksi langsung dengan rakan sebaya dan suasana pembelajaran universiti boleh merosakkan aspek sosial dan emosi pelajar, mengurangkan motivasi mereka untuk terlibat sepenuhnya dalam proses pembelajaran.

Keadaan emosi yang tidak stabil ini tidak hanya mempengaruhi kesejahteraan pelajar, tetapi juga kognitif dan pencapaian akademik mereka. Kehilangan tumpuan, kelesuan mental, dan kekurangan motivasi adalah beberapa hasil daripada ketidakseimbangan emosi. Inilah yang mengganggu proses pengumpulan maklumat, pemahaman konsep, dan kemampuan untuk menyelesaikan tugasan dengan efektif. Pelajar yang terlibat dalam peperiksaan dan tugasan tanpa perasaan yang seimbang boleh menghasilkan hasil yang lebih lemah dan tidak memuaskan.

2. Kurangnya Struktur dan Disiplin

Pembelajaran dalam talian, sementara menawarkan kelebihan seperti fleksibiliti, juga membawa cabaran dalam hal pengurusan diri dan pengurusan masa. Pelajar sekarang perlu mengambil alih tanggungjawab yang lebih besar dalam membangunkan jadual pembelajaran mereka, membuat keputusan mengenai waktu kerja dan rehat, serta menguruskan pengumpulan tugasan. Bagi mereka yang kurang terlatih dalam kemahiran pengurusan diri ini, pembelajaran dalam talian boleh menjadi cabaran yang besar.

Tanpa struktur dan disiplin yang kukuh, pelajar mungkin mengalami kesukaran dalam menjaga rutin pembelajaran yang teratur. Mereka cenderung menangguhkan tugasan atau mengambil sikap santai dalam hal-hal akademik. Kurangnya jadual yang teratur boleh mengakibatkan terabai dalam memenuhi tenggat masa penting, dan ini berpotensi merosakkan kualiti hasil kerja mereka.

3. Gangguan Maklumat Digital

Dalam era digital, akses kepada hiburan dan maklumat adalah lebih mudah daripada sebelumnya. Namun, ini juga membawa risiko gangguan yang besar dalam konteks pembelajaran. Pelajar yang terlibat dalam pembelajaran dalam talian boleh terdedah kepada pelbagai gangguan seperti media sosial, permainan dalam talian, dan hiburan digital lain. Kecenderungan untuk menangguhkan kerja dan menumpukan perhatian kepada aktiviti yang tidak berkaitan dengan pembelajaran boleh menjejaskan produktiviti dan fokus mereka.

Gangguan maklumat digital ini juga boleh mempengaruhi kualiti pemahaman dan penyerapan bahan pembelajaran. Pelajar mungkin cenderung untuk membaca dengan lewat atau tergesa-gesa melalui bahan pembelajaran, yang berpotensi merosakkan kefahaman mereka terhadap konsep-konsep kritikal.

Strategi untuk Mengatasi Kemerosotan Pencapaian Akademik dan Sikap Negatif

Kemerosotan dalam pencapaian akademik dan sikap negatif pelajar pasca era COVID-19 dan PKP adalah isu yang memerlukan tindakan bersepadu dan berkesan dari pihak universiti dan pensyarah. Melalui pelaksanaan strategi-strategi yang sesuai, masalah ini dapat diatasi dengan lebih berjaya:

1. Sokongan Emosi

Universiti perlu menyedarkan pentingnya kesejahteraan mental dalam pencapaian akademik. Menyediakan akses kepada perkhidmatan kaunseling yang profesional dan sumber sokongan emosi boleh membantu pelajar mengatasi tekanan emosi. Kaunseling individu atau sesi kumpulan boleh memberi peluang kepada pelajar untuk membincangkan kebimbangan dan tekanan yang mereka alami, serta mendapatkan nasihat tentang cara menguruskan emosi dalam suasana pembelajaran yang mencabar.

2. Pembelajaran Berinteraksi

Penggunaan kaedah pembelajaran dalam talian yang berinteraksi dapat mengekalkan minat dan motivasi pelajar. Pensyarah perlu memanfaatkan platform pembelajaran dalam talian yang membolehkan interaksi langsung antara pelajar dan pensyarah, seperti perbincangan dalam talian, kajian kes, dan sesi soal jawab. Ini dapat membantu mengatasi perasaan kesunyian dan isolasi yang mungkin dialami oleh pelajar dan memberikan rasa keterlibatan yang lebih mendalam dalam pembelajaran.

3. Menggalakkan Interaksi Sosial

Walaupun dalam talian, peluang untuk interaksi sosial harus dipelihara. Mencipta platform untuk perbincangan berkelompok dan projek kolaboratif dalam talian membolehkan pelajar bekerjasama dengan rakan sebaya dan mengatasi rasa kesunyian. Inisiatif ini mendorong perkongsian idea dan pembelajaran kolektif, yang dapat memperkukuhkan pemahaman konsep dan memberi pelajar pengalaman interaksi yang bermanfaat.

4. Pemberian Sokongan Pelajaran

Pensyarah boleh menyediakan sokongan tambahan dalam bentuk sesi tutorial dan bahan pembelajaran tambahan. Ini membantu pelajar yang menghadapi kesukaran dalam memahami bahan pembelajaran dan memberikan platform bagi mereka untuk bertanya soalan dan mendapatkan penjelasan yang lebih terperinci. Sesi tutorial juga membangunkan hubungan lebih dekat antara pensyarah dan pelajar, mendorong pelajar untuk terlibat dan mengambil tanggungjawab dalam pembelajaran.

5. Mengkomunikasikan Nilai Pendidikan

Universiti perlu mengkomunikasikan nilai pendidikan kepada pelajar dan mengaitkannya dengan peluang pekerjaan di masa depan. Pengenalan kepada peranan pendidikan dalam pembentukan minda dan pemahaman dunia dapat memberikan dorongan kepada pelajar untuk menghargai proses pembelajaran dan mengaitkannya dengan matlamat jangka panjang.

6. Mengajar Pengurusan Masa

Universiti dapat menyediakan panduan mengenai pengurusan masa yang baik dan efektif. Ini termasuk amalan-amalan terbaik dalam merancang jadual pembelajaran, menguruskan tenggat masa, dan memberi keutamaan kepada tugas-tugas penting. Pelatihan ini membantu pelajar membangunkan kemahiran penting yang akan bermanfaat tidak hanya dalam konteks akademik, tetapi juga dalam kehidupan masa depan.

Kesimpulan

Pandemik COVID-19 dan PKP telah memberi impak yang signifikan kepada sikap dan pencapaian akademik pelajar universiti. Kemerosotan dalam pencapaian akademik dan sikap negatif yang diperhatikan memerlukan tindakan proaktif dari universiti, pensyarah, dan pelajar sendiri. Dengan mengambil kira faktor-faktor yang mempengaruhi dan strategi untuk mengatasinya, kita dapat memastikan bahawa kualiti pendidikan tinggi tetap terjamin dalam era pasca pandemik ini.

Suggestion for Citation:
Amerudin, S. (2023). Kemerosotan Pencapaian Akademik dan Sikap Pelajar Universiti Pasca Era COVID-19 dan Perintah Kawalan Pergerakan (PKP). [Online] Available at: https://people.utm.my/shahabuddin/?p=6633 (Accessed: 16 August 2023).
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Developing Web Map-Based Applications

By Shahabuddin Amerudin

Introduction

Web map-based applications have transformed how we interact with geographic information, enabling us to explore, analyze, and visualize data on interactive maps. The development of such applications involves a unique set of challenges and considerations, ranging from selecting mapping libraries to optimizing performance for diverse devices. This article delves into the technical intricacies of creating web map-based applications, discussing mapping libraries, geospatial data integration, user experience, and optimization techniques.

Choosing Between the Libraries

Selecting the right mapping library is crucial for building effective web map-based applications. Two of the most prominent options are Leaflet and Google Maps API.

1. Leaflet

Leaflet is a popular open-source JavaScript library for building interactive maps. Its simplicity and flexibility have made it a go-to choice for developers working on web map-based applications. Here’s a closer look at its features and advantages:

  • Lightweight and Fast: Leaflet is designed to be lightweight, making it ideal for projects where performance is crucial. Its modular nature allows developers to include only the components they need, optimizing load times.
  • Customizable Map Styles: Leaflet provides various map tile providers that offer different map styles, such as street maps, satellite imagery, and topographic maps. Developers can easily switch between these styles or even use their custom map tiles.
  • Markers and Popups: Adding markers and popups to the map is straightforward with Leaflet. Markers can be used to indicate specific locations on the map, while popups can display additional information when users interact with these markers.
  • Third-Party Plugins: Leaflet has a vibrant ecosystem of third-party plugins that extend its functionality. These plugins cover a wide range of features, such as heatmaps, clustering, routing, and more. This allows developers to enhance their maps with advanced capabilities without reinventing the wheel.
  • Integration with Data Sources: Leaflet can integrate with various data sources, including GeoJSON files, web services, and APIs. This enables developers to overlay geographic data onto their maps and create compelling visualizations.

2. Google Maps API

Google Maps API is a comprehensive set of tools and services provided by Google for integrating maps and geospatial data into web applications. While powerful, it does come with some complexities:

  • Geospatial Capabilities: Google Maps API offers robust geospatial capabilities, including street view, geocoding (converting addresses to geographic coordinates), and routing. It’s particularly useful for applications that require accurate geolocation services.
  • Extensive Documentation: Google provides thorough documentation, guides, and tutorials for developers working with their API. This resource-rich environment can be extremely helpful for those new to geospatial development.
  • Embedding Maps: With Google Maps API, developers can embed interactive maps into their applications, allowing users to explore locations, zoom in and out, and even switch between map styles like terrain, satellite, and street view.
  • Custom Layers: Developers can create custom map layers using Google Maps API. This enables the overlay of additional information on top of the base map, such as weather data or traffic conditions.
  • API Key Requirement: To use Google Maps API, developers need to obtain an API key, which adds a layer of security and allows Google to track usage. While not overly complex, this additional step can be a consideration during the development process.

Choosing between Leaflet and Google Maps API depends on your project’s requirements, your team’s familiarity with each library, and your desired level of customization. If you’re looking for a lightweight and easily customizable solution, Leaflet might be the better option. On the other hand, if you need powerful geospatial capabilities, extensive documentation, and seamless integration with Google’s services, Google Maps API could be the way to go.

Both libraries have thriving communities, so finding support, tutorials, and plugins won’t be an issue. Evaluate your project’s specific needs and your team’s expertise to make an informed decision that aligns with your application’s goals and technical requirements.

Geospatial Data Integration

Geospatial data integration is a cornerstone of web map-based applications, allowing developers to visualize and interact with location-based information. GeoJSON, a widely used format for encoding geographical data structures, plays a pivotal role in this process.

GeoJSON Overview: GeoJSON is a lightweight and human-readable format that represents geographic data in JavaScript Object Notation (JSON) format. It supports various geometry types, including Point, LineString, Polygon, MultiPoint, MultiLineString, and MultiPolygon. Each geometry type corresponds to specific geographical features, such as individual points, lines, or complex polygons.

Integration with Mapping Libraries: Mapping libraries like Leaflet and Google Maps API allow developers to integrate GeoJSON data seamlessly. By creating GeoJSON-encoded data objects and feeding them into the libraries, developers can render geographic features on the map. For instance, to display a set of points representing cities on a map, developers can provide a GeoJSON structure containing these points’ coordinates and associated data.

Custom Styling and Interactivity: One of the benefits of GeoJSON integration is the ability to apply custom styling and interactivity to the map features. Developers can define different marker symbols, colors, and popups for each data point, enhancing the user experience and conveying information effectively.

Dynamic Data Sources: In addition to static GeoJSON files, web map-based applications can also integrate dynamic data sources through APIs. For instance, a real estate application could retrieve property listings in real-time from an API and display them on the map as clickable markers, linking to detailed property information.

Real-Time Data Integration: Integrating real-time data adds a layer of dynamic information to web map-based applications, enhancing their relevance and usefulness. Here are a couple of examples:

  1. Weather Data Integration: Real-time weather data can be integrated to provide users with current conditions, forecasts, and other meteorological information. OpenWeatherMap’s API, for instance, allows developers to fetch weather data for specific locations and display it on the map. This is particularly useful for travel applications, outdoor event planning, or any scenario where weather conditions impact user decisions.
  2. Traffic Data Integration: Real-time traffic data can enhance applications that involve route planning, navigation, or urban mobility. Services like HERE Traffic offer APIs that provide traffic congestion information, incidents, and suggested alternate routes. Developers can overlay this data on the map, helping users make informed decisions about their routes.

Enhancing User Experience: Integrating real-time data not only provides valuable information to users but also enriches the interactive experience. For instance, showing live traffic conditions on a map allows users to avoid congestion and find the fastest route. Similarly, displaying real-time weather information helps users plan their activities and journeys accordingly.

Considerations: When integrating real-time data, consider factors such as API availability, data freshness, and potential usage limits. Make sure to choose reputable sources that provide reliable and up-to-date data for a seamless user experience.

Geospatial data integration, particularly through formats like GeoJSON, and the incorporation of real-time data significantly enhance the value and functionality of web map-based applications. Whether you’re displaying static geographical features or dynamically updating information like weather or traffic conditions, careful integration and thoughtful presentation of data can create engaging and informative user experiences.

User Experience and Interactivity

User experience is paramount in web map-based applications. Interactivity plays a crucial role in engaging users and conveying information effectively. Here are some considerations:

1. User-Friendly Interface

An intuitive and user-friendly interface is essential for keeping users engaged with your web map-based application. Here’s how to design an interface that enhances user experience:

  • Clear Navigation: Ensure that users can easily navigate the map and access different features. Use familiar icons for zooming, panning, and toggling map layers.
  • Consistent Design: Maintain a consistent design language throughout the application. Use colors, typography, and layout that align with your brand and offer a cohesive visual experience.
  • Responsive Design: Ensure that the application is responsive and works well on various devices, including smartphones, tablets, and desktops. A responsive design adapts the layout and elements to different screen sizes, providing a seamless experience for users.

2. Markers and Popups

Markers and popups are essential tools for conveying information and enhancing interactivity in web map-based applications:

  • Markers: Use markers to pinpoint specific locations, points of interest, or important areas on the map. For example, in a tourism application, markers can indicate tourist attractions, hotels, and restaurants.
  • Popups: When users click on a marker, display a popup that provides additional information. This information could include details about the location, images, descriptions, and links. For instance, clicking on a restaurant marker could open a popup with the restaurant’s name, cuisine type, and a link to its website.

3. User Input and Customisation

Empowering users to customize their map experience enhances engagement and makes the application more user-centric:

  • Search Bars and Filters: Incorporate search bars or filters that allow users to refine the displayed data based on their preferences. For example, in a real estate application, users could use filters to narrow down properties by price range, number of bedrooms, or location.
  • Geocoding Services: Integrate geocoding services to convert user-provided addresses or location names into geographic coordinates. This feature helps users quickly find and visualize specific locations on the map.
  • Customization Options: Provide users with options to customize map elements such as map styles, colors, and overlays. This customization allows users to tailor the map to their preferences and needs.

Examples:

  • Travel Planner Application: Imagine a travel planner application that enables users to explore different travel destinations. The interface offers intuitive zoom and pan controls, making it easy for users to navigate the map. When users click on markers representing landmarks, popups display detailed information about each landmark, including historical facts, images, and opening hours.
  • Real Estate Finder: In a real estate application, users can search for properties by entering an address or a city. Geocoding services convert their input into geographic coordinates, placing a marker on the map at the specified location. Users can then apply filters to narrow down properties by price, number of bedrooms, and property type. Clicking on a property marker opens a popup with property details, photos, and contact information.

User experience and interactivity are pivotal aspects of web map-based applications. A user-friendly interface, markers, popups, user input elements, and customization options collectively enhance the application’s usability and engagement. By designing an intuitive interface, providing informative markers and popups, and enabling users to interact with and personalize the map, you create a compelling experience that keeps users engaged and empowers them to explore geographic data with ease.

Performance Optimization

Optimizing performance is crucial to ensure that your web map-based application runs smoothly across various devices and network conditions.

1. Data Caching

Caching is a strategy that involves storing frequently accessed data in a temporary storage location to reduce the need to fetch it from external sources repeatedly. In web map-based applications, caching map tiles and geospatial data is crucial for enhancing performance:

How It Works

  • When a user accesses the application, the map tiles and geospatial data are initially fetched from the server.
  • These fetched resources are then stored in the user’s browser cache.
  • If the user revisits the application or explores different areas of the map, the cached resources can be loaded directly from the browser cache, reducing load times.

Benefits

  • Caching minimizes the number of requests to external servers, reducing latency and improving responsiveness.
  • It ensures a smoother user experience, especially in scenarios where users navigate the map frequently.

2. Minification and Compression

Minification involves removing unnecessary characters and white spaces from code files (such as JavaScript and CSS), while compression reduces file sizes by encoding them in a more efficient manner. Both techniques contribute to faster loading times:

How It Works

  • Minification removes comments, white spaces, and unused code from files, reducing their size without affecting functionality.
  • Compression uses algorithms to encode files in a way that requires fewer bytes to transmit and store.

Benefits

  • Minification and compression significantly reduce the amount of data that needs to be downloaded by users.
  • Smaller file sizes lead to faster loading times, particularly on networks with limited bandwidth.

3. Responsive Design

Responsive design is the practice of designing web applications to adapt seamlessly to different screen sizes and devices, ensuring a consistent experience for users regardless of how they access the application:

How It Works

  • The layout, fonts, images, and other elements of the application are designed to respond and adjust based on the screen size.
  • Media queries are used in CSS to apply specific styles for different screen widths, ensuring that the application remains usable and visually appealing on various devices.

Benefits

  • A responsive design eliminates the need for users to zoom in or scroll horizontally, improving the overall usability of the application.
  • It ensures that the application functions well on smartphones, tablets, laptops, and desktops, enhancing accessibility and user satisfaction.

4. Lazy Loading

Lazy loading is a technique that delays the loading of certain resources until they are actually needed, improving initial loading times and conserving bandwidth:

How It Works

  • In web map-based applications, layers and assets that are not immediately visible when the application loads can be loaded lazily.
  • As the user interacts with the map and navigates to different areas, additional layers and assets are loaded on demand.

Benefits

  • Lazy loading reduces the initial load time of the application, allowing users to access the basic functionality quickly.
  • It optimizes resource usage, as only the resources required for the current view are fetched, conserving bandwidth.

Examples

  • Travel Guide Application: A responsive travel guide application displays an interactive map of a city’s landmarks. The application’s layout adapts based on the user’s device, ensuring a seamless experience on smartphones, tablets, and desktops. The map layers and assets are loaded lazily, ensuring that the application loads quickly, even on slower connections. Additionally, the map tiles and geospatial data are cached in the user’s browser, enhancing performance when the user explores different parts of the city.
  • Real-Time Traffic Application: In a real-time traffic application, markers indicate traffic incidents on the map. The application uses minification and compression techniques to reduce the size of JavaScript and CSS files, resulting in faster loading times. As users navigate the map to find alternative routes, the application dynamically fetches and displays additional traffic data while optimising performance through lazy loading.

Performance optimization is vital for delivering a smooth and responsive experience in web map-based applications. By employing techniques such as data caching, minification, compression, responsive design, and lazy loading, developers can create applications that load quickly, work well across different devices, and offer an enjoyable user experience, even in varying network conditions. These optimization techniques contribute to higher user engagement and satisfaction, ensuring that users can interact with and explore geographic data seamlessly.

Conclusion

Developing web map-based applications requires a deep understanding of mapping libraries, geospatial data integration, user experience design, and performance optimization. By selecting the appropriate mapping library, integrating geospatial data effectively, prioritizing user experience, and optimizing performance, developers can create captivating and efficient applications that empower users to explore the world through interactive maps. The world of web map-based applications is expanding rapidly, offering developers new opportunities to innovate and provide valuable spatial insights to users across various domains.

Suggestion for Citation:
Amerudin, S. (2023). Developing Web Map-Based Applications. [Online] Available at: https://people.utm.my/shahabuddin/?p=6629 (Accessed: 15 August 2023).
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Choosing Between Physical Servers and Cloud Services for Web-Based Application Development

By Shahabuddin Amerudin

Introduction

In the rapidly evolving landscape of web-based application development, the decision between deploying applications on physical servers or utilizing cloud services has become a pivotal choice. Both options present their own set of advantages and challenges, ranging from hardware infrastructure and software considerations to costs and management complexities. This article aims to delve into the technical aspects of this decision-making process, analyzing server specifications, cloud services, associated costs, and the level of expertise required for effective management.

Physical Servers: Building Your Own Infrastructure

Owning a physical server grants you complete control over your infrastructure. You have the freedom to select hardware components tailored to your application’s demands, ensuring optimal performance and resource allocation. For instance, consider a Dell PowerEdge R640 equipped with dual Intel Xeon Silver 4210 CPUs, 64GB of RAM, and 1TB of SSD storage. Such a configuration empowers developers to fine-tune the hardware environment, which can be advantageous for resource-intensive applications.

However, this level of control comes at a price. The initial investment includes hardware costs ranging from $3,000 to $4,000. Furthermore, ongoing expenses for electricity, cooling, maintenance, and potential upgrades should be factored into the equation. On top of financial considerations, managing physical servers requires an in-depth understanding of hardware setup, operating system installation, network configuration, security implementation, web server setup (such as Apache or Nginx), database management (MySQL or PostgreSQL), and continuous software updates.

Cloud Services: Flexibility and Scalability

Cloud services have revolutionized the way applications are developed and deployed. Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) offer virtualized resources on a pay-as-you-go model, eliminating the need for substantial upfront investments. Let’s take a closer look at a couple of examples.

Using AWS’s Elastic Compute Cloud (EC2), developers can choose instance types such as t3.medium, which features 2 vCPUs, 4GB of RAM, and 50GB of SSD storage. This choice provides a scalable environment that can easily adapt to varying workloads. The hourly cost is around $0.0416, translating to a monthly expense of $30 to $40. To manage an EC2 instance, one needs a basic understanding of cloud services, network configuration, operating system installation, and monitoring through the AWS Management Console.

Azure’s App Service offers a Platform as a Service (PaaS) environment, simplifying the deployment process. With a standard-tier instance boasting 1 core, 1.75GB of RAM, and 50GB of storage, monthly costs range from $70 to $90. Users should be familiar with PaaS concepts, application deployment methods, configuring settings, and monitoring application performance.

Similarly, GCP’s Compute Engine presents a n1-standard-2 instance with 2 vCPUs, 7.5GB of RAM, and 100GB of SSD storage. At approximately $0.0864 per hour, the monthly cost falls within the $60 to $70 range. Managing a Compute Engine instance entails understanding VM instances, networking, software installation, and snapshot management.

Conclusion

The choice between physical servers and cloud services for web-based application development is multifaceted. While physical servers offer unparalleled control, they demand significant financial investments and a comprehensive skill set. On the other hand, cloud services provide flexibility, scalability, and streamlined management, albeit with associated costs and varying levels of technical expertise required.

Ultimately, the decision hinges on factors such as budget, scalability requirements, application complexity, and the team’s proficiency. Evaluating your specific project needs against the pros and cons of each option will lead you to a solution that aligns with your development goals and operational capabilities. Whichever path you choose, the constant evolution of technology ensures that web-based applications will continue to thrive, delivering innovative solutions to users around the world.

Suggestion for Citation:
Amerudin, S. (2023). Choosing Between Physical Servers and Cloud Services for Web-Based Application Development. [Online] Available at: https://people.utm.my/shahabuddin/?p=6627 (Accessed: 15 August 2023).
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Exploring Firewall Bypass Techniques: Strategies and Countermeasures

Introduction

In today’s interconnected world, network security is paramount. Firewalls serve as the frontline defense against cyber threats, safeguarding sensitive data and digital assets from unauthorized access and malicious attacks. However, determined hackers employ a variety of sophisticated techniques to bypass firewalls and compromise networks. This article delves into the methods hackers employ to breach firewall defenses, as well as strategies and countermeasures to fortify network security.

Common Firewall Bypass Techniques

  1. Exploiting Vulnerabilities: Hackers actively search for vulnerabilities within the target’s software and systems. By identifying and exploiting these vulnerabilities, they can execute arbitrary code, effectively bypassing firewall protections.
  2. Malware and Malicious Software: The deployment of malware, such as viruses, worms, Trojans, and ransomware, allows hackers to establish a foothold within a network. These malware agents can communicate with external servers, thus sidestepping firewall rules.
  3. Phishing and Social Engineering: Hackers leverage social engineering tactics to manipulate employees into divulging sensitive information. Obtaining legitimate access credentials allows hackers to circumvent firewall alerts.
  4. IP Spoofing: IP spoofing involves manipulating the source IP address of network packets to deceive packet-filtering firewalls. This technique conceals the true origin of the traffic, facilitating unauthorized access.
  5. Application Layer Attacks: Targeting application vulnerabilities that may not be thoroughly inspected by firewalls enables hackers to infiltrate a network. Exploiting these weaknesses grants them unauthorized access.
  6. Firewall Misconfigurations: Weak firewall configurations or mismanagement can expose network gaps, providing hackers an entry point into the system.
  7. Zero-Day Exploits: Hackers exploit vulnerabilities that vendors and the public are unaware of. By targeting these zero-day vulnerabilities, attackers can breach systems before patches are released.
  8. Tunneling and Encryption: Encrypted tunnels and protocols allow hackers to evade firewall analysis, as encrypted traffic may not undergo full inspection. This technique conceals malicious activity.
  9. Backdoors and Remote Access Trojans (RATs): Hackers deploy backdoors and RATs to create covert communication channels. These channels allow hackers to bypass firewalls and remotely control compromised systems.
  10. Brute Force Attacks: Persistent attempts at gaining access through repetitive login attempts with varying credentials can grant hackers unauthorized entry.
  11. Insider Threats: Compromised employee accounts or malicious insiders can exploit their privileged access to bypass firewalls and compromise network security.

Strengthening Firewall Defenses: Strategies and Countermeasures

  1. Multi-Layered Security Approach: Implement a comprehensive security strategy, integrating firewalls with intrusion detection/prevention systems, endpoint protection, and regular security updates.
  2. Regular Vulnerability Assessments: Conduct routine vulnerability assessments and penetration testing to identify and rectify software vulnerabilities that hackers might exploit.
  3. Security Awareness Training: Educate employees about social engineering risks and phishing tactics to minimize the chances of hackers obtaining legitimate credentials.
  4. Strong Authentication and Access Controls: Implement strong authentication methods such as multi-factor authentication (MFA) and enforce strict access controls to limit unauthorized access.
  5. Firewall Configuration Audit: Regularly review and audit firewall configurations to ensure proper rule management and adherence to security best practices.
  6. Intrusion Detection and Prevention Systems (IDS/IPS): Implement IDS and IPS systems to monitor network traffic, detect anomalies, and proactively respond to potential threats.
  7. Application Layer Inspection: Deploy firewalls that offer application layer inspection to identify and block traffic exploiting vulnerabilities in specific applications.
  8. Threat Intelligence Integration: Integrate threat intelligence feeds to keep firewalls updated with the latest threat information, enhancing their ability to detect emerging risks.
  9. Regular Updates and Patch Management: Keep firewalls and all network software up to date to address known vulnerabilities and protect against attacks that exploit them.
  10. Incident Response Plan: Develop a robust incident response plan that outlines steps to be taken in the event of a security breach, ensuring a swift and coordinated response.

Conclusion

Firewalls remain a critical component of network security, but the tactics employed by hackers to bypass these defenses are constantly evolving. By understanding the techniques hackers use and implementing a multi-faceted security strategy, organizations can significantly reduce the risk of breaches and unauthorized access. Regular assessment, training, vigilant monitoring, and proactive response are essential in the ongoing battle to safeguard sensitive data and maintain the integrity of networks in the face of ever-evolving cyber threats.

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HD GNSS – An Introduction

By Shahabuddin Amerudin

HD GNSS, or High-Definition Global Navigation Satellite System, refers to advanced positioning and navigation technology that enhances the accuracy and precision of satellite-based location services. It is an evolution of traditional GNSS systems like GPS, GLONASS, Galileo, and BeiDou, designed to provide more accurate and reliable positioning information.

The concept of improving the accuracy of Global Navigation Satellite Systems (GNSS) has been an ongoing endeavor since the inception of GNSS technology itself. Here’s a brief overview of the evolution and context surrounding HD GNSS:

Early GNSS Development: The development of GNSS technology began with the launch of the first satellite-based navigation system, the U.S. Navy’s Transit system, in the 1960s. This system aimed to provide accurate positioning for military and maritime applications. Over the years, other GNSS systems, such as GPS (Global Positioning System), GLONASS (Global Navigation Satellite System), and more recently, Galileo and BeiDou, were launched to provide global positioning services.

Focus on Accuracy: While the early GNSS systems were primarily developed for military and navigation purposes, the civilian use of GNSS expanded rapidly. As various industries began relying on GNSS for positioning and navigation, the need for higher accuracy became apparent. The drive to enhance accuracy led to the development of techniques like Differential GPS (DGPS) and Real-Time Kinematic (RTK), which aimed to improve the accuracy of GNSS positioning.

Multi-Frequency and Multi-Constellation: The concept of using multiple frequencies and constellations to improve accuracy gained traction as more GNSS constellations were deployed. Multiple frequencies allowed for better error correction, and the integration of signals from multiple constellations increased satellite availability, especially in challenging environments.

Modern HD GNSS: The term “HD GNSS” gained prominence as a way to describe the advanced positioning capabilities that became possible with the evolution of GNSS technology. With the advent of multi-frequency, multi-constellation receivers and real-time correction services, positioning accuracy reached centimeter-level precision. HD GNSS solutions catered to a wide range of applications, from surveying and mapping to autonomous vehicles and precision agriculture.

Continual Advancements: The history of HD GNSS is closely tied to the ongoing advancements in satellite technology, receiver design, and data processing algorithms. Until today, researchers and engineers continued to explore ways to enhance GNSS accuracy further, potentially integrating new technologies such as quantum positioning systems and improved augmentation services.

HD GNSS incorporates various techniques and technologies to improve positioning accuracy, especially in challenging environments such as urban canyons, dense foliage, and areas with limited satellite visibility. Some key features and technologies associated with HD GNSS include:

  1. Multi-Frequency: HD GNSS receivers track multiple frequencies from different satellite constellations, such as L1, L2, L5, and others. This allows the receiver to mitigate errors caused by ionospheric delays and provides more accurate position solutions.
  2. Multi-Constellation: HD GNSS receivers utilize signals from multiple GNSS constellations, such as GPS, GLONASS, Galileo, and BeiDou. This diversification of satellite sources enhances satellite availability and improves accuracy.
  3. Real-Time Correction Services: HD GNSS often involves real-time correction services that provide accurate positioning corrections to the receiver. These services, such as RTK (Real-Time Kinematic) and PPP (Precise Point Positioning), enhance accuracy to centimeter or even millimeter levels.
  4. Advanced Algorithms: HD GNSS receivers employ advanced algorithms to process satellite signals and correct errors introduced by factors like multipath interference, signal obstructions, and atmospheric disturbances.
  5. Antenna Design: The design of the GNSS antenna plays a crucial role in HD GNSS accuracy. Antennas are designed to minimize interference, reduce multipath effects, and optimize signal reception.
  6. High-Performance Chips: Modern HD GNSS receivers use high-performance chipsets that are capable of processing multiple signals and performing advanced calculations quickly and accurately.
  7. Precise Timing Applications: HD GNSS is not only used for position determination but also for applications that require highly accurate timing synchronization, such as telecommunications, financial transactions, and scientific research.

HD GNSS technology finds applications in various industries, including surveying, mapping, construction, agriculture, autonomous vehicles, maritime navigation, and more. It enables professionals and systems to achieve higher levels of accuracy, enabling more precise decision-making and improved operational efficiency.

Suggestion for Citation:
Amerudin, S. (2023). HD GNSS - An Introduction. [Online] Available at: https://people.utm.my/shahabuddin/?p=6622 (Accessed: 14 August 2023).